Perceptual Calibration for Immersive Display Environments

PubMed Central

Ponto, Kevin; Gleicher, Michael; Radwin, Robert G.; Shin, Hyun Joon

2013-01-01

The perception of objects, depth, and distance has been repeatedly shown to be divergent between virtual and physical environments. We hypothesize that many of these discrepancies stem from incorrect geometric viewing parameters, specifically that physical measurements of eye position are insufficiently precise to provide proper viewing parameters. In this paper, we introduce a perceptual calibration procedure derived from geometric models. While most research has used geometric models to predict perceptual errors, we instead use these models inversely to determine perceptually correct viewing parameters. We study the advantages of these new psychophysically determined viewing parameters compared to the commonly used measured viewing parameters in an experiment with 20 subjects. The perceptually calibrated viewing parameters for the subjects generally produced new virtual eye positions that were wider and deeper than standard practices would estimate. Our study shows that perceptually calibrated viewing parameters can significantly improve depth acuity, distance estimation, and the perception of shape. PMID:23428454

Parameter estimation for compact binary coalescence signals with the first generation gravitational-wave detector network

NASA Astrophysics Data System (ADS)

Aasi, J.; Abadie, J.; Abbott, B. P.; Abbott, R.; Abbott, T. D.; Abernathy, M.; Accadia, T.; Acernese, F.; Adams, C.; Adams, T.; Addesso, P.; Adhikari, R.; Affeldt, C.; Agathos, M.; Agatsuma, K.; Ajith, P.; Allen, B.; Allocca, A.; Amador Ceron, E.; Amariutei, D.; Anderson, S. B.; Anderson, W. G.; Arai, K.; Araya, M. C.; Ast, S.; Aston, S. M.; Astone, P.; Atkinson, D.; Aufmuth, P.; Aulbert, C.; Aylott, B. E.; Babak, S.; Baker, P.; Ballardin, G.; Ballmer, S.; Bao, Y.; Barayoga, J. C. B.; Barker, D.; Barone, F.; Barr, B.; Barsotti, L.; Barsuglia, M.; Barton, M. A.; Bartos, I.; Bassiri, R.; Bastarrika, M.; Basti, A.; Batch, J.; Bauchrowitz, J.; Bauer, Th. S.; Bebronne, M.; Beck, D.; Behnke, B.; Bejger, M.; Beker, M. G.; Bell, A. S.; Bell, C.; Belopolski, I.; Benacquista, M.; Berliner, J. M.; Bertolini, A.; Betzwieser, J.; Beveridge, N.; Beyersdorf, P. T.; Bhadbade, T.; Bilenko, I. A.; Billingsley, G.; Birch, J.; Biswas, R.; Bitossi, M.; Bizouard, M. A.; Black, E.; Blackburn, J. K.; Blackburn, L.; Blair, D.; Bland, B.; Blom, M.; Bock, O.; Bodiya, T. P.; Bogan, C.; Bond, C.; Bondarescu, R.; Bondu, F.; Bonelli, L.; Bonnand, R.; Bork, R.; Born, M.; Boschi, V.; Bose, S.; Bosi, L.; Bouhou, B.; Braccini, S.; Bradaschia, C.; Brady, P. R.; Braginsky, V. B.; Branchesi, M.; Brau, J. E.; Breyer, J.; Briant, T.; Bridges, D. O.; Brillet, A.; Brinkmann, M.; Brisson, V.; Britzger, M.; Brooks, A. F.; Brown, D. A.; Bulik, T.; Bulten, H. J.; Buonanno, A.; Burguet–Castell, J.; Buskulic, D.; Buy, C.; Byer, R. L.; Cadonati, L.; Cagnoli, G.; Calloni, E.; Camp, J. B.; Campsie, P.; Cannon, K.; Canuel, B.; Cao, J.; Capano, C. D.; Carbognani, F.; Carbone, L.; Caride, S.; Caudill, S.; Cavaglià, M.; Cavalier, F.; Cavalieri, R.; Cella, G.; Cepeda, C.; Cesarini, E.; Chalermsongsak, T.; Charlton, P.; Chassande-Mottin, E.; Chen, W.; Chen, X.; Chen, Y.; Chincarini, A.; Chiummo, A.; Cho, H. S.; Chow, J.; Christensen, N.; Chua, S. S. Y.; Chung, C. T. Y.; Chung, S.; Ciani, G.; Clara, F.; Clark, D. E.; Clark, J. A.; Clayton, J. H.; Cleva, F.; Coccia, E.; Cohadon, P.-F.; Colacino, C. N.; Colla, A.; Colombini, M.; Conte, A.; Conte, R.; Cook, D.; Corbitt, T. R.; Cordier, M.; Cornish, N.; Corsi, A.; Costa, C. A.; Coughlin, M.; Coulon, J.-P.; Couvares, P.; Coward, D. M.; Cowart, M.; Coyne, D. C.; Creighton, J. D. E.; Creighton, T. D.; Cruise, A. M.; Cumming, A.; Cunningham, L.; Cuoco, E.; Cutler, R. M.; Dahl, K.; Damjanic, M.; Danilishin, S. L.; D'Antonio, S.; Danzmann, K.; Dattilo, V.; Daudert, B.; Daveloza, H.; Davier, M.; Daw, E. J.; Dayanga, T.; De Rosa, R.; DeBra, D.; Debreczeni, G.; Degallaix, J.; Del Pozzo, W.; Dent, T.; Dergachev, V.; DeRosa, R.; Dhurandhar, S.; Di Fiore, L.; Di Lieto, A.; Di Palma, I.; Di Paolo Emilio, M.; Di Virgilio, A.; Díaz, M.; Dietz, A.; Donovan, F.; Dooley, K. L.; Doravari, S.; Dorsher, S.; Drago, M.; Drever, R. W. P.; Driggers, J. C.; Du, Z.; Dumas, J.-C.; Dwyer, S.; Eberle, T.; Edgar, M.; Edwards, M.; Effler, A.; Ehrens, P.; Endrőczi, G.; Engel, R.; Etzel, T.; Evans, K.; Evans, M.; Evans, T.; Factourovich, M.; Fafone, V.; Fairhurst, S.; Farr, B. F.; Farr, W. M.; Favata, M.; Fazi, D.; Fehrmann, H.; Feldbaum, D.; Feroz, F.; Ferrante, I.; Ferrini, F.; Fidecaro, F.; Finn, L. S.; Fiori, I.; Fisher, R. P.; Flaminio, R.; Foley, S.; Forsi, E.; Forte, L. A.; Fotopoulos, N.; Fournier, J.-D.; Franc, J.; Franco, S.; Frasca, S.; Frasconi, F.; Frede, M.; Frei, M. A.; Frei, Z.; Freise, A.; Frey, R.; Fricke, T. T.; Friedrich, D.; Fritschel, P.; Frolov, V. V.; Fujimoto, M.-K.; Fulda, P. J.; Fyffe, M.; Gair, J.; Galimberti, M.; Gammaitoni, L.; Garcia, J.; Garufi, F.; Gáspár, M. E.; Gelencser, G.; Gemme, G.; Genin, E.; Gennai, A.; Gergely, L. Á.; Ghosh, S.; Giaime, J. A.; Giampanis, S.; Giardina, K. D.; Giazotto, A.; Gil-Casanova, S.; Gill, C.; Gleason, J.; Goetz, E.; González, G.; Gorodetsky, M. L.; Goßler, S.; Gouaty, R.; Graef, C.; Graff, P. B.; Granata, M.; Grant, A.; Gray, C.; Greenhalgh, R. J. S.; Gretarsson, A. M.; Griffo, C.; Grote, H.; Grover, K.; Grunewald, S.; Guidi, G. M.; Guido, C.; Gupta, R.; Gustafson, E. K.; Gustafson, R.; Hallam, J. M.; Hammer, D.; Hammond, G.; Hanks, J.; Hanna, C.; Hanson, J.; Harms, J.; Harry, G. M.; Harry, I. W.; Harstad, E. D.; Hartman, M. T.; Haster, C.-J.; Haughian, K.; Hayama, K.; Hayau, J.-F.; Heefner, J.; Heidmann, A.; Heintze, M. C.; Heitmann, H.; Hello, P.; Hemming, G.; Hendry, M. A.; Heng, I. S.; Heptonstall, A. W.; Herrera, V.; Heurs, M.; Hewitson, M.; Hild, S.; Hoak, D.; Hodge, K. A.; Holt, K.; Holtrop, M.; Hong, T.; Hooper, S.; Hough, J.; Howell, E. J.; Hughey, B.; Husa, S.; Huttner, S. H.; Huynh-Dinh, T.; Ingram, D. R.; Inta, R.; Isogai, T.; Ivanov, A.; Izumi, K.; Jacobson, M.; James, E.; Jang, Y. J.; Jaranowski, P.; Jesse, E.; Johnson, W. W.; Jones, D. I.; Jones, R.; Jonker, R. J. G.; Ju, L.; Kalmus, P.; Kalogera, V.; Kandhasamy, S.; Kang, G.; Kanner, J. B.; Kasprzack, M.; Kasturi, R.; Katsavounidis, E.; Katzman, W.; Kaufer, H.; Kaufman, K.; Kawabe, K.; Kawamura, S.; Kawazoe, F.; Keitel, D.; Kelley, D.; Kells, W.; Keppel, D. G.; Keresztes, Z.; Khalaidovski, A.; Khalili, F. Y.; Khazanov, E. A.; Kim, B. K.; Kim, C.; Kim, H.; Kim, K.; Kim, N.; Kim, Y. M.; King, P. J.; Kinzel, D. L.; Kissel, J. S.; Klimenko, S.; Kline, J.; Kokeyama, K.; Kondrashov, V.; Koranda, S.; Korth, W. Z.; Kowalska, I.; Kozak, D.; Kringel, V.; Krishnan, B.; Królak, A.; Kuehn, G.; Kumar, P.; Kumar, R.; Kurdyumov, R.; Kwee, P.; Lam, P. K.; Landry, M.; Langley, A.; Lantz, B.; Lastzka, N.; Lawrie, C.; Lazzarini, A.; Le Roux, A.; Leaci, P.; Lee, C. H.; Lee, H. K.; Lee, H. M.; Leong, J. R.; Leonor, I.; Leroy, N.; Letendre, N.; Lhuillier, V.; Li, J.; Li, T. G. F.; Lindquist, P. E.; Litvine, V.; Liu, Y.; Liu, Z.; Lockerbie, N. A.; Lodhia, D.; Logue, J.; Lorenzini, M.; Loriette, V.; Lormand, M.; Losurdo, G.; Lough, J.; Lubinski, M.; Lück, H.; Lundgren, A. P.; Macarthur, J.; Macdonald, E.; Machenschalk, B.; MacInnis, M.; Macleod, D. M.; Mageswaran, M.; Mailand, K.; Majorana, E.; Maksimovic, I.; Malvezzi, V.; Man, N.; Mandel, I.; Mandic, V.; Mantovani, M.; Marchesoni, F.; Marion, F.; Márka, S.; Márka, Z.; Markosyan, A.; Maros, E.; Marque, J.; Martelli, F.; Martin, I. W.; Martin, R. M.; Marx, J. N.; Mason, K.; Masserot, A.; Matichard, F.; Matone, L.; Matzner, R. A.; Mavalvala, N.; Mazzolo, G.; McCarthy, R.; McClelland, D. E.; McGuire, S. C.; McIntyre, G.; McIver, J.; Meadors, G. D.; Mehmet, M.; Meier, T.; Melatos, A.; Melissinos, A. C.; Mendell, G.; Menéndez, D. F.; Mercer, R. A.; Meshkov, S.; Messenger, C.; Meyer, M. S.; Miao, H.; Michel, C.; Milano, L.; Miller, J.; Minenkov, Y.; Mingarelli, C. M. F.; Mitrofanov, V. P.; Mitselmakher, G.; Mittleman, R.; Moe, B.; Mohan, M.; Mohapatra, S. R. P.; Moraru, D.; Moreno, G.; Morgado, N.; Morgia, A.; Mori, T.; Morriss, S. R.; Mosca, S.; Mossavi, K.; Mours, B.; Mow–Lowry, C. M.; Mueller, C. L.; Mueller, G.; Mukherjee, S.; Mullavey, A.; Müller-Ebhardt, H.; Munch, J.; Murphy, D.; Murray, P. G.; Mytidis, A.; Nash, T.; Naticchioni, L.; Necula, V.; Nelson, J.; Neri, I.; Newton, G.; Nguyen, T.; Nishizawa, A.; Nitz, A.; Nocera, F.; Nolting, D.; Normandin, M. E.; Nuttall, L.; Ochsner, E.; O'Dell, J.; Oelker, E.; Ogin, G. H.; Oh, J. J.; Oh, S. H.; Oldenberg, R. G.; O'Reilly, B.; O'Shaughnessy, R.; Osthelder, C.; Ott, C. D.; Ottaway, D. J.; Ottens, R. S.; Overmier, H.; Owen, B. J.; Page, A.; Palladino, L.; Palomba, C.; Pan, Y.; Pankow, C.; Paoletti, F.; Paoletti, R.; Papa, M. A.; Parisi, M.; Pasqualetti, A.; Passaquieti, R.; Passuello, D.; Pedraza, M.; Penn, S.; Perreca, A.; Persichetti, G.; Phelps, M.; Pichot, M.; Pickenpack, M.; Piergiovanni, F.; Pierro, V.; Pihlaja, M.; Pinard, L.; Pinto, I. M.; Pitkin, M.; Pletsch, H. J.; Plissi, M. V.; Poggiani, R.; Pöld, J.; Postiglione, F.; Poux, C.; Prato, M.; Predoi, V.; Prestegard, T.; Price, L. R.; Prijatelj, M.; Principe, M.; Privitera, S.; Prodi, G. A.; Prokhorov, L. G.; Puncken, O.; Punturo, M.; Puppo, P.; Quetschke, V.; Quitzow-James, R.; Raab, F. J.; Rabeling, D. S.; Rácz, I.; Radkins, H.; Raffai, P.; Rakhmanov, M.; Ramet, C.; Rankins, B.; Rapagnani, P.; Raymond, V.; Re, V.; Reed, C. M.; Reed, T.; Regimbau, T.; Reid, S.; Reitze, D. H.; Ricci, F.; Riesen, R.; Riles, K.; Roberts, M.; Robertson, N. A.; Robinet, F.; Robinson, C.; Robinson, E. L.; Rocchi, A.; Roddy, S.; Rodriguez, C.; Rodruck, M.; Rolland, L.; Rollins, J. G.; Romano, R.; Romie, J. H.; Rosińska, D.; Röver, C.; Rowan, S.; Rüdiger, A.; Ruggi, P.; Ryan, K.; Salemi, F.; Sammut, L.; Sandberg, V.; Sankar, S.; Sannibale, V.; Santamaría, L.; Santiago-Prieto, I.; Santostasi, G.; Saracco, E.; Sassolas, B.; Sathyaprakash, B. S.; Saulson, P. R.; Savage, R. L.; Schilling, R.; Schnabel, R.; Schofield, R. M. S.; Schulz, B.; Schutz, B. F.; Schwinberg, P.; Scott, J.; Scott, S. M.; Seifert, F.; Sellers, D.; Sentenac, D.; Sergeev, A.; Shaddock, D. A.; Shaltev, M.; Shapiro, B.; Shawhan, P.; Shoemaker, D. H.; Sidery, T. L.; Siemens, X.; Sigg, D.; Simakov, D.; Singer, A.; Singer, L.; Sintes, A. M.; Skelton, G. R.; Slagmolen, B. J. J.; Slutsky, J.; Smith, J. R.; Smith, M. R.; Smith, R. J. E.; Smith-Lefebvre, N. D.; Somiya, K.; Sorazu, B.; Speirits, F. C.; Sperandio, L.; Stefszky, M.; Steinert, E.; Steinlechner, J.; Steinlechner, S.; Steplewski, S.; Stochino, A.; Stone, R.; Strain, K. A.; Strigin, S. E.; Stroeer, A. S.; Sturani, R.; Stuver, A. L.; Summerscales, T. Z.; Sung, M.; Susmithan, S.; Sutton, P. J.; Swinkels, B.; Szeifert, G.; Tacca, M.; Taffarello, L.; Talukder, D.; Tanner, D. B.; Tarabrin, S. P.; Taylor, R.; ter Braack, A. P. M.; Thomas, P.; Thorne, K. A.; Thorne, K. S.; Thrane, E.; Thüring, A.; Titsler, C.; Tokmakov, K. V.; Tomlinson, C.; Toncelli, A.; Tonelli, M.; Torre, O.; Torres, C. V.; Torrie, C. I.; Tournefier, E.; Travasso, F.; Traylor, G.; Tse, M.; Ugolini, D.; Vahlbruch, H.; Vajente, G.; van den Brand, J. F. J.; Van Den Broeck, C.; van der Putten, S.; van Veggel, A. A.; Vass, S.; Vasuth, M.; Vaulin, R.; Vavoulidis, M.; Vecchio, A.; Vedovato, G.; Veitch, J.; Veitch, P. J.; Venkateswara, K.; Verkindt, D.; Vetrano, F.; Viceré, A.; Villar, A. E.; Vinet, J.-Y.; Vitale, S.; Vocca, H.; Vorvick, C.; Vyatchanin, S. P.; Wade, A.; Wade, L.; Wade, M.; Waldman, S. J.; Wallace, L.; Wan, Y.; Wang, M.; Wang, X.; Wanner, A.; Ward, R. L.; Was, M.; Weinert, M.; Weinstein, A. J.; Weiss, R.; Welborn, T.; Wen, L.; Wessels, P.; West, M.; Westphal, T.; Wette, K.; Whelan, J. T.; Whitcomb, S. E.; White, D. J.; Whiting, B. F.; Wiesner, K.; Wilkinson, C.; Willems, P. A.; Williams, L.; Williams, R.; Willke, B.; Wimmer, M.; Winkelmann, L.; Winkler, W.; Wipf, C. C.; Wiseman, A. G.; Wittel, H.; Woan, G.; Wooley, R.; Worden, J.; Yablon, J.; Yakushin, I.; Yamamoto, H.; Yamamoto, K.; Yancey, C. C.; Yang, H.; Yeaton-Massey, D.; Yoshida, S.; Yvert, M.; Zadrożny, A.; Zanolin, M.; Zendri, J.-P.; Zhang, F.; Zhang, L.; Zhao, C.; Zotov, N.; Zucker, M. E.; Zweizig, J.

2013-09-01

Compact binary systems with neutron stars or black holes are one of the most promising sources for ground-based gravitational-wave detectors. Gravitational radiation encodes rich information about source physics; thus parameter estimation and model selection are crucial analysis steps for any detection candidate events. Detailed models of the anticipated waveforms enable inference on several parameters, such as component masses, spins, sky location and distance, that are essential for new astrophysical studies of these sources. However, accurate measurements of these parameters and discrimination of models describing the underlying physics are complicated by artifacts in the data, uncertainties in the waveform models and in the calibration of the detectors. Here we report such measurements on a selection of simulated signals added either in hardware or software to the data collected by the two LIGO instruments and the Virgo detector during their most recent joint science run, including a “blind injection” where the signal was not initially revealed to the collaboration. We exemplify the ability to extract information about the source physics on signals that cover the neutron-star and black-hole binary parameter space over the component mass range 1M⊙-25M⊙ and the full range of spin parameters. The cases reported in this study provide a snapshot of the status of parameter estimation in preparation for the operation of advanced detectors.

On the predictivity of pore-scale simulations: Estimating uncertainties with multilevel Monte Carlo

NASA Astrophysics Data System (ADS)

Icardi, Matteo; Boccardo, Gianluca; Tempone, Raúl

2016-09-01

A fast method with tunable accuracy is proposed to estimate errors and uncertainties in pore-scale and Digital Rock Physics (DRP) problems. The overall predictivity of these studies can be, in fact, hindered by many factors including sample heterogeneity, computational and imaging limitations, model inadequacy and not perfectly known physical parameters. The typical objective of pore-scale studies is the estimation of macroscopic effective parameters such as permeability, effective diffusivity and hydrodynamic dispersion. However, these are often non-deterministic quantities (i.e., results obtained for specific pore-scale sample and setup are not totally reproducible by another ;equivalent; sample and setup). The stochastic nature can arise due to the multi-scale heterogeneity, the computational and experimental limitations in considering large samples, and the complexity of the physical models. These approximations, in fact, introduce an error that, being dependent on a large number of complex factors, can be modeled as random. We propose a general simulation tool, based on multilevel Monte Carlo, that can reduce drastically the computational cost needed for computing accurate statistics of effective parameters and other quantities of interest, under any of these random errors. This is, to our knowledge, the first attempt to include Uncertainty Quantification (UQ) in pore-scale physics and simulation. The method can also provide estimates of the discretization error and it is tested on three-dimensional transport problems in heterogeneous materials, where the sampling procedure is done by generation algorithms able to reproduce realistic consolidated and unconsolidated random sphere and ellipsoid packings and arrangements. A totally automatic workflow is developed in an open-source code [1], that include rigid body physics and random packing algorithms, unstructured mesh discretization, finite volume solvers, extrapolation and post-processing techniques. The proposed method can be efficiently used in many porous media applications for problems such as stochastic homogenization/upscaling, propagation of uncertainty from microscopic fluid and rock properties to macro-scale parameters, robust estimation of Representative Elementary Volume size for arbitrary physics.

Field-Scale Evaluation of Infiltration Parameters From Soil Texture for Hydrologic Analysis

NASA Astrophysics Data System (ADS)

Springer, Everett P.; Cundy, Terrance W.

1987-02-01

Recent interest in predicting soil hydraulic properties from simple physical properties such as texture has major implications in the parameterization of physically based models of surface runoff. This study was undertaken to (1) compare, on a field scale, soil hydraulic parameters predicted from texture to those derived from field measurements and (2) compare simulated overland flow response using these two parameter sets. The parameters for the Green-Ampt infiltration equation were obtained from field measurements and using texture-based predictors for two agricultural fields, which were mapped as single soil units. Results of the analyses were that (1) the mean and variance of the field-based parameters were not preserved by the texture-based estimates, (2) spatial and cross correlations between parameters were induced by the texture-based estimation procedures, (3) the overland flow simulations using texture-based parameters were significantly different than those from field-based parameters, and (4) simulations using field-measured hydraulic conductivities and texture-based storage parameters were very close to simulations using only field-based parameters.

Acoustic parameters inversion and sediment properties in the Yellow River reservoir

NASA Astrophysics Data System (ADS)

Li, Chang-Zheng; Yang, Yong; Wang, Rui; Yan, Xiao-Fei

2018-03-01

The physical properties of silt in river reservoirs are important to river dynamics. Unfortunately, traditional techniques yield insufficient data. Based on porous media acoustic theory, we invert the acoustic parameters for the top river-bottom sediments. An explicit form of the acoustic reflection coefficient at the water-sediment interface is derived based on Biot's theory. The choice of parameters in the Biot model is discussed and the relation between acoustic and geological parameters is studied, including that between the reflection coefficient and porosity and the attenuation coefficient and permeability. The attenuation coefficient of the sound wave in the sediments is obtained by analyzing the shift of the signal frequency. The acoustic reflection coefficient at the water-sediment interface is extracted from the sonar signal. Thus, an inversion method of the physical parameters of the riverbottom surface sediments is proposed. The results of an experiment at the Sanmenxia reservoir suggest that the estimated grain size is close to the actual data. This demonstrates the ability of the proposed method to determine the physical parameters of sediments and estimate the grain size.

Monthly hydroclimatology of the continental United States

NASA Astrophysics Data System (ADS)

Petersen, Thomas; Devineni, Naresh; Sankarasubramanian, A.

2018-04-01

Physical/semi-empirical models that do not require any calibration are of paramount need for estimating hydrological fluxes for ungauged sites. We develop semi-empirical models for estimating the mean and variance of the monthly streamflow based on Taylor Series approximation of a lumped physically based water balance model. The proposed models require mean and variance of monthly precipitation and potential evapotranspiration, co-variability of precipitation and potential evapotranspiration and regionally calibrated catchment retention sensitivity, atmospheric moisture uptake sensitivity, groundwater-partitioning factor, and the maximum soil moisture holding capacity parameters. Estimates of mean and variance of monthly streamflow using the semi-empirical equations are compared with the observed estimates for 1373 catchments in the continental United States. Analyses show that the proposed models explain the spatial variability in monthly moments for basins in lower elevations. A regionalization of parameters for each water resources region show good agreement between observed moments and model estimated moments during January, February, March and April for mean and all months except May and June for variance. Thus, the proposed relationships could be employed for understanding and estimating the monthly hydroclimatology of ungauged basins using regional parameters.

Estimating Convection Parameters in the GFDL CM2.1 Model Using Ensemble Data Assimilation

NASA Astrophysics Data System (ADS)

Li, Shan; Zhang, Shaoqing; Liu, Zhengyu; Lu, Lv; Zhu, Jiang; Zhang, Xuefeng; Wu, Xinrong; Zhao, Ming; Vecchi, Gabriel A.; Zhang, Rong-Hua; Lin, Xiaopei

2018-04-01

Parametric uncertainty in convection parameterization is one major source of model errors that cause model climate drift. Convection parameter tuning has been widely studied in atmospheric models to help mitigate the problem. However, in a fully coupled general circulation model (CGCM), convection parameters which impact the ocean as well as the climate simulation may have different optimal values. This study explores the possibility of estimating convection parameters with an ensemble coupled data assimilation method in a CGCM. Impacts of the convection parameter estimation on climate analysis and forecast are analyzed. In a twin experiment framework, five convection parameters in the GFDL coupled model CM2.1 are estimated individually and simultaneously under both perfect and imperfect model regimes. Results show that the ensemble data assimilation method can help reduce the bias in convection parameters. With estimated convection parameters, the analyses and forecasts for both the atmosphere and the ocean are generally improved. It is also found that information in low latitudes is relatively more important for estimating convection parameters. This study further suggests that when important parameters in appropriate physical parameterizations are identified, incorporating their estimation into traditional ensemble data assimilation procedure could improve the final analysis and climate prediction.

Robust Alternatives to the Standard Deviation in Processing of Physics Experimental Data

NASA Astrophysics Data System (ADS)

Shulenin, V. P.

2016-10-01

Properties of robust estimations of the scale parameter are studied. It is noted that the median of absolute deviations and the modified estimation of the average Gini differences have asymptotically normal distributions and bounded influence functions, are B-robust estimations, and hence, unlike the estimation of the standard deviation, are protected from the presence of outliers in the sample. Results of comparison of estimations of the scale parameter are given for a Gaussian model with contamination. An adaptive variant of the modified estimation of the average Gini differences is considered.

Assessment of mild steel damage characteristics by physical methods

NASA Astrophysics Data System (ADS)

Botvina, L. R.; Soldatenkov, A. P.; Levin, V. P.; Tyutin, M. R.; Demina, Yu. A.; Petersen, T. B.; Dubov, A. A.; Semashko, N. A.

2016-01-01

The deformation and fracture localization characteristics are estimated by the methods of replicas, acoustic emission, metal magnetic memory, ultrasonic attenuation, microhardness, and electrical resistance. The relation between the estimated physical parameters on the one hand and the plastic zone size and the microcrack concentration in this zone, on the other, is considered.

Noise elimination algorithm for modal analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bao, X. X., E-mail: baoxingxian@upc.edu.cn; Li, C. L.; Xiong, C. B.

2015-07-27

Modal analysis is an ongoing interdisciplinary physical issue. Modal parameters estimation is applied to determine the dynamic characteristics of structures under vibration excitation. Modal analysis is more challenging for the measured vibration response signals are contaminated with noise. This study develops a mathematical algorithm of structured low rank approximation combined with the complex exponential method to estimate the modal parameters. Physical experiments using a steel cantilever beam with ten accelerometers mounted, excited by an impulse load, demonstrate that this method can significantly eliminate noise from measured signals and accurately identify the modal frequencies and damping ratios. This study provides amore » fundamental mechanism of noise elimination using structured low rank approximation in physical fields.« less

Impact of the time scale of model sensitivity response on coupled model parameter estimation

NASA Astrophysics Data System (ADS)

Liu, Chang; Zhang, Shaoqing; Li, Shan; Liu, Zhengyu

2017-11-01

That a model has sensitivity responses to parameter uncertainties is a key concept in implementing model parameter estimation using filtering theory and methodology. Depending on the nature of associated physics and characteristic variability of the fluid in a coupled system, the response time scales of a model to parameters can be different, from hourly to decadal. Unlike state estimation, where the update frequency is usually linked with observational frequency, the update frequency for parameter estimation must be associated with the time scale of the model sensitivity response to the parameter being estimated. Here, with a simple coupled model, the impact of model sensitivity response time scales on coupled model parameter estimation is studied. The model includes characteristic synoptic to decadal scales by coupling a long-term varying deep ocean with a slow-varying upper ocean forced by a chaotic atmosphere. Results show that, using the update frequency determined by the model sensitivity response time scale, both the reliability and quality of parameter estimation can be improved significantly, and thus the estimated parameters make the model more consistent with the observation. These simple model results provide a guideline for when real observations are used to optimize the parameters in a coupled general circulation model for improving climate analysis and prediction initialization.

Improving the precision of lake ecosystem metabolism estimates by identifying predictors of model uncertainty

USGS Publications Warehouse

Rose, Kevin C.; Winslow, Luke A.; Read, Jordan S.; Read, Emily K.; Solomon, Christopher T.; Adrian, Rita; Hanson, Paul C.

2014-01-01

Diel changes in dissolved oxygen are often used to estimate gross primary production (GPP) and ecosystem respiration (ER) in aquatic ecosystems. Despite the widespread use of this approach to understand ecosystem metabolism, we are only beginning to understand the degree and underlying causes of uncertainty for metabolism model parameter estimates. Here, we present a novel approach to improve the precision and accuracy of ecosystem metabolism estimates by identifying physical metrics that indicate when metabolism estimates are highly uncertain. Using datasets from seventeen instrumented GLEON (Global Lake Ecological Observatory Network) lakes, we discovered that many physical characteristics correlated with uncertainty, including PAR (photosynthetically active radiation, 400-700 nm), daily variance in Schmidt stability, and wind speed. Low PAR was a consistent predictor of high variance in GPP model parameters, but also corresponded with low ER model parameter variance. We identified a threshold (30% of clear sky PAR) below which GPP parameter variance increased rapidly and was significantly greater in nearly all lakes compared with variance on days with PAR levels above this threshold. The relationship between daily variance in Schmidt stability and GPP model parameter variance depended on trophic status, whereas daily variance in Schmidt stability was consistently positively related to ER model parameter variance. Wind speeds in the range of ~0.8-3 m s–1 were consistent predictors of high variance for both GPP and ER model parameters, with greater uncertainty in eutrophic lakes. Our findings can be used to reduce ecosystem metabolism model parameter uncertainty and identify potential sources of that uncertainty.

Numerical discretization-based estimation methods for ordinary differential equation models via penalized spline smoothing with applications in biomedical research.

PubMed

Wu, Hulin; Xue, Hongqi; Kumar, Arun

2012-06-01

Differential equations are extensively used for modeling dynamics of physical processes in many scientific fields such as engineering, physics, and biomedical sciences. Parameter estimation of differential equation models is a challenging problem because of high computational cost and high-dimensional parameter space. In this article, we propose a novel class of methods for estimating parameters in ordinary differential equation (ODE) models, which is motivated by HIV dynamics modeling. The new methods exploit the form of numerical discretization algorithms for an ODE solver to formulate estimating equations. First, a penalized-spline approach is employed to estimate the state variables and the estimated state variables are then plugged in a discretization formula of an ODE solver to obtain the ODE parameter estimates via a regression approach. We consider three different order of discretization methods, Euler's method, trapezoidal rule, and Runge-Kutta method. A higher-order numerical algorithm reduces numerical error in the approximation of the derivative, which produces a more accurate estimate, but its computational cost is higher. To balance the computational cost and estimation accuracy, we demonstrate, via simulation studies, that the trapezoidal discretization-based estimate is the best and is recommended for practical use. The asymptotic properties for the proposed numerical discretization-based estimators are established. Comparisons between the proposed methods and existing methods show a clear benefit of the proposed methods in regards to the trade-off between computational cost and estimation accuracy. We apply the proposed methods t an HIV study to further illustrate the usefulness of the proposed approaches. © 2012, The International Biometric Society.

A physical mechanism for the prediction of the sunspot number during solar cycle 21. [graphs (charts)

NASA Technical Reports Server (NTRS)

Schatten, K. H.; Scherrer, P. H.; Svalgaard, L.; Wilcox, J. M.

1978-01-01

On physical grounds it is suggested that the sun's polar field strength near a solar minimum is closely related to the following cycle's solar activity. Four methods of estimating the sun's polar magnetic field strength near solar minimum are employed to provide an estimate of cycle 21's yearly mean sunspot number at solar maximum of 140 plus or minus 20. This estimate is considered to be a first order attempt to predict the cycle's activity using one parameter of physical importance.

Identification of open quantum systems from observable time traces

DOE PAGES

Zhang, Jun; Sarovar, Mohan

2015-05-27

Estimating the parameters that dictate the dynamics of a quantum system is an important task for quantum information processing and quantum metrology, as well as fundamental physics. In our paper we develop a method for parameter estimation for Markovian open quantum systems using a temporal record of measurements on the system. Furthermore, the method is based on system realization theory and is a generalization of our previous work on identification of Hamiltonian parameters.

Estimation of Transport and Kinetic Parameters of Vanadium Redox Batteries Using Static Cells

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Seong Beom; Pratt, III, Harry D.; Anderson, Travis M.

Mathematical models of Redox Flow Batteries (RFBs) can be used to analyze cell performance, optimize battery operation, and control the energy storage system efficiently. Among many other models, physics-based electrochemical models are capable of predicting internal states of the battery, such as temperature, state-of-charge, and state-of-health. In the models, estimating parameters is an important step that can study, analyze, and validate the models using experimental data. A common practice is to determine these parameters either through conducting experiments or based on the information available in the literature. However, it is not easy to investigate all proper parameters for the modelsmore » through this way, and there are occasions when important information, such as diffusion coefficients and rate constants of ions, has not been studied. Also, the parameters needed for modeling charge-discharge are not always available. In this paper, an efficient way to estimate parameters of physics-based redox battery models will be proposed. Furthermore, this paper also demonstrates that the proposed approach can study and analyze aspects of capacity loss/fade, kinetics, and transport phenomena of the RFB system.« less

Estimation of Transport and Kinetic Parameters of Vanadium Redox Batteries Using Static Cells

DOE PAGES

Lee, Seong Beom; Pratt, III, Harry D.; Anderson, Travis M.; ...

2018-03-27

Mathematical models of Redox Flow Batteries (RFBs) can be used to analyze cell performance, optimize battery operation, and control the energy storage system efficiently. Among many other models, physics-based electrochemical models are capable of predicting internal states of the battery, such as temperature, state-of-charge, and state-of-health. In the models, estimating parameters is an important step that can study, analyze, and validate the models using experimental data. A common practice is to determine these parameters either through conducting experiments or based on the information available in the literature. However, it is not easy to investigate all proper parameters for the modelsmore » through this way, and there are occasions when important information, such as diffusion coefficients and rate constants of ions, has not been studied. Also, the parameters needed for modeling charge-discharge are not always available. In this paper, an efficient way to estimate parameters of physics-based redox battery models will be proposed. Furthermore, this paper also demonstrates that the proposed approach can study and analyze aspects of capacity loss/fade, kinetics, and transport phenomena of the RFB system.« less

Physics of ultrasonic wave propagation in bone and heart characterized using Bayesian parameter estimation

NASA Astrophysics Data System (ADS)

Anderson, Christian Carl

This Dissertation explores the physics underlying the propagation of ultrasonic waves in bone and in heart tissue through the use of Bayesian probability theory. Quantitative ultrasound is a noninvasive modality used for clinical detection, characterization, and evaluation of bone quality and cardiovascular disease. Approaches that extend the state of knowledge of the physics underpinning the interaction of ultrasound with inherently inhomogeneous and isotropic tissue have the potential to enhance its clinical utility. Simulations of fast and slow compressional wave propagation in cancellous bone were carried out to demonstrate the plausibility of a proposed explanation for the widely reported anomalous negative dispersion in cancellous bone. The results showed that negative dispersion could arise from analysis that proceeded under the assumption that the data consist of only a single ultrasonic wave, when in fact two overlapping and interfering waves are present. The confounding effect of overlapping fast and slow waves was addressed by applying Bayesian parameter estimation to simulated data, to experimental data acquired on bone-mimicking phantoms, and to data acquired in vitro on cancellous bone. The Bayesian approach successfully estimated the properties of the individual fast and slow waves even when they strongly overlapped in the acquired data. The Bayesian parameter estimation technique was further applied to an investigation of the anisotropy of ultrasonic properties in cancellous bone. The degree to which fast and slow waves overlap is partially determined by the angle of insonation of ultrasound relative to the predominant direction of trabecular orientation. In the past, studies of anisotropy have been limited by interference between fast and slow waves over a portion of the range of insonation angles. Bayesian analysis estimated attenuation, velocity, and amplitude parameters over the entire range of insonation angles, allowing a more complete characterization of anisotropy. A novel piecewise linear model for the cyclic variation of ultrasonic backscatter from myocardium was proposed. Models of cyclic variation for 100 type 2 diabetes patients and 43 normal control subjects were constructed using Bayesian parameter estimation. Parameters determined from the model, specifically rise time and slew rate, were found to be more reliable in differentiating between subject groups than the previously employed magnitude parameter.

Multiparameter elastic full waveform inversion with facies-based constraints

NASA Astrophysics Data System (ADS)

Zhang, Zhen-dong; Alkhalifah, Tariq; Naeini, Ehsan Zabihi; Sun, Bingbing

2018-06-01

Full waveform inversion (FWI) incorporates all the data characteristics to estimate the parameters described by the assumed physics of the subsurface. However, current efforts to utilize FWI beyond improved acoustic imaging, like in reservoir delineation, faces inherent challenges related to the limited resolution and the potential trade-off between the elastic model parameters. Some anisotropic parameters are insufficiently updated because of their minor contributions to the surface collected data. Adding rock physics constraints to the inversion helps mitigate such limited sensitivity, but current approaches to add such constraints are based on including them as a priori knowledge mostly valid around the well or as a global constraint for the whole area. Since similar rock formations inside the Earth admit consistent elastic properties and relative values of elasticity and anisotropy parameters (this enables us to define them as a seismic facies), utilizing such localized facies information in FWI can improve the resolution of inverted parameters. We propose a novel approach to use facies-based constraints in both isotropic and anisotropic elastic FWI. We invert for such facies using Bayesian theory and update them at each iteration of the inversion using both the inverted models and a priori information. We take the uncertainties of the estimated parameters (approximated by radiation patterns) into consideration and improve the quality of estimated facies maps. Four numerical examples corresponding to different acquisition, physical assumptions and model circumstances are used to verify the effectiveness of the proposed method.

Estimation of soil saturated hydraulic conductivity by artificial neural networks ensemble in smectitic soils

NASA Astrophysics Data System (ADS)

Sedaghat, A.; Bayat, H.; Safari Sinegani, A. A.

2016-03-01

The saturated hydraulic conductivity ( K s ) of the soil is one of the main soil physical properties. Indirect estimation of this parameter using pedo-transfer functions (PTFs) has received considerable attention. The Purpose of this study was to improve the estimation of K s using fractal parameters of particle and micro-aggregate size distributions in smectitic soils. In this study 260 disturbed and undisturbed soil samples were collected from Guilan province, the north of Iran. The fractal model of Bird and Perrier was used to compute the fractal parameters of particle and micro-aggregate size distributions. The PTFs were developed by artificial neural networks (ANNs) ensemble to estimate K s by using available soil data and fractal parameters. There were found significant correlations between K s and fractal parameters of particles and microaggregates. Estimation of K s was improved significantly by using fractal parameters of soil micro-aggregates as predictors. But using geometric mean and geometric standard deviation of particles diameter did not improve K s estimations significantly. Using fractal parameters of particles and micro-aggregates simultaneously, had the most effect in the estimation of K s . Generally, fractal parameters can be successfully used as input parameters to improve the estimation of K s in the PTFs in smectitic soils. As a result, ANNs ensemble successfully correlated the fractal parameters of particles and micro-aggregates to K s .

Dynamic modelling and parameter estimation of a hydraulic robot manipulator using a multi-objective genetic algorithm

NASA Astrophysics Data System (ADS)

Montazeri, A.; West, C.; Monk, S. D.; Taylor, C. J.

2017-04-01

This paper concerns the problem of dynamic modelling and parameter estimation for a seven degree of freedom hydraulic manipulator. The laboratory example is a dual-manipulator mobile robotic platform used for research into nuclear decommissioning. In contrast to earlier control model-orientated research using the same machine, the paper develops a nonlinear, mechanistic simulation model that can subsequently be used to investigate physically meaningful disturbances. The second contribution is to optimise the parameters of the new model, i.e. to determine reliable estimates of the physical parameters of a complex robotic arm which are not known in advance. To address the nonlinear and non-convex nature of the problem, the research relies on the multi-objectivisation of an output error single-performance index. The developed algorithm utilises a multi-objective genetic algorithm (GA) in order to find a proper solution. The performance of the model and the GA is evaluated using both simulated (i.e. with a known set of 'true' parameters) and experimental data. Both simulation and experimental results show that multi-objectivisation has improved convergence of the estimated parameters compared to the single-objective output error problem formulation. This is achieved by integrating the validation phase inside the algorithm implicitly and exploiting the inherent structure of the multi-objective GA for this specific system identification problem.

Parameter Estimation for Geoscience Applications Using a Measure-Theoretic Approach

NASA Astrophysics Data System (ADS)

Dawson, C.; Butler, T.; Mattis, S. A.; Graham, L.; Westerink, J. J.; Vesselinov, V. V.; Estep, D.

2016-12-01

Effective modeling of complex physical systems arising in the geosciences is dependent on knowing parameters which are often difficult or impossible to measure in situ. In this talk we focus on two such problems, estimating parameters for groundwater flow and contaminant transport, and estimating parameters within a coastal ocean model. The approach we will describe, proposed by collaborators D. Estep, T. Butler and others, is based on a novel stochastic inversion technique based on measure theory. In this approach, given a probability space on certain observable quantities of interest, one searches for the sets of highest probability in parameter space which give rise to these observables. When viewed as mappings between sets, the stochastic inversion problem is well-posed in certain settings, but there are computational challenges related to the set construction. We will focus the talk on estimating scalar parameters and fields in a contaminant transport setting, and in estimating bottom friction in a complicated near-shore coastal application.

Uncertainty of inhalation dose coefficients for representative physical and chemical forms of iodine-131

NASA Astrophysics Data System (ADS)

Harvey, Richard Paul, III

Releases of radioactive material have occurred at various Department of Energy (DOE) weapons facilities and facilities associated with the nuclear fuel cycle in the generation of electricity. Many different radionuclides have been released to the environment with resulting exposure of the population to these various sources of radioactivity. Radioiodine has been released from a number of these facilities and is a potential public health concern due to its physical and biological characteristics. Iodine exists as various isotopes, but our focus is on 131I due to its relatively long half-life, its prevalence in atmospheric releases and its contribution to offsite dose. The assumption of physical and chemical form is speculated to have a profound impact on the deposition of radioactive material within the respiratory tract. In the case of iodine, it has been shown that more than one type of physical and chemical form may be released to, or exist in, the environment; iodine can exist as a particle or as a gas. The gaseous species can be further segregated based on chemical form: elemental, inorganic, and organic iodides. Chemical compounds in each class are assumed to behave similarly with respect to biochemistry. Studies at Oak Ridge National Laboratories have demonstrated that 131I is released as a particulate, as well as in elemental, inorganic and organic chemical form. The internal dose estimate from 131I may be very different depending on the effect that chemical form has on fractional deposition, gas uptake, and clearance in the respiratory tract. There are many sources of uncertainty in the estimation of environmental dose including source term, airborne transport of radionuclides, and internal dosimetry. Knowledge of uncertainty in internal dosimetry is essential for estimating dose to members of the public and for determining total uncertainty in dose estimation. Important calculational steps in any lung model is regional estimation of deposition fractions and gas uptake of radionuclides in various regions of the lung. Variability in regional radionuclide deposition within lung compartments may significantly contribute to the overall uncertainty of the lung model. The uncertainty of lung deposition and biological clearance is dependent upon physiological and anatomical parameters of individuals as well as characteristic parameters of the particulate material. These parameters introduce uncertainty into internal dose estimates due to their inherent variability. Anatomical and physiological input parameters are age and gender dependent. This work has determined the uncertainty in internal dose estimates and the sensitive parameters involved in modeling particulate deposition and gas uptake of different physical and chemical forms of 131I with age and gender dependencies.

Dual Extended Kalman Filter for the Identification of Time-Varying Human Manual Control Behavior

NASA Technical Reports Server (NTRS)

Popovici, Alexandru; Zaal, Peter M. T.; Pool, Daan M.

2017-01-01

A Dual Extended Kalman Filter was implemented for the identification of time-varying human manual control behavior. Two filters that run concurrently were used, a state filter that estimates the equalization dynamics, and a parameter filter that estimates the neuromuscular parameters and time delay. Time-varying parameters were modeled as a random walk. The filter successfully estimated time-varying human control behavior in both simulated and experimental data. Simple guidelines are proposed for the tuning of the process and measurement covariance matrices and the initial parameter estimates. The tuning was performed on simulation data, and when applied on experimental data, only an increase in measurement process noise power was required in order for the filter to converge and estimate all parameters. A sensitivity analysis to initial parameter estimates showed that the filter is more sensitive to poor initial choices of neuromuscular parameters than equalization parameters, and bad choices for initial parameters can result in divergence, slow convergence, or parameter estimates that do not have a real physical interpretation. The promising results when applied to experimental data, together with its simple tuning and low dimension of the state-space, make the use of the Dual Extended Kalman Filter a viable option for identifying time-varying human control parameters in manual tracking tasks, which could be used in real-time human state monitoring and adaptive human-vehicle haptic interfaces.

An uncertainty analysis of the hydrogen source term for a station blackout accident in Sequoyah using MELCOR 1.8.5

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gauntt, Randall O.; Bixler, Nathan E.; Wagner, Kenneth Charles

2014-03-01

A methodology for using the MELCOR code with the Latin Hypercube Sampling method was developed to estimate uncertainty in various predicted quantities such as hydrogen generation or release of fission products under severe accident conditions. In this case, the emphasis was on estimating the range of hydrogen sources in station blackout conditions in the Sequoyah Ice Condenser plant, taking into account uncertainties in the modeled physics known to affect hydrogen generation. The method uses user-specified likelihood distributions for uncertain model parameters, which may include uncertainties of a stochastic nature, to produce a collection of code calculations, or realizations, characterizing themore » range of possible outcomes. Forty MELCOR code realizations of Sequoyah were conducted that included 10 uncertain parameters, producing a range of in-vessel hydrogen quantities. The range of total hydrogen produced was approximately 583kg 131kg. Sensitivity analyses revealed expected trends with respected to the parameters of greatest importance, however, considerable scatter in results when plotted against any of the uncertain parameters was observed, with no parameter manifesting dominant effects on hydrogen generation. It is concluded that, with respect to the physics parameters investigated, in order to further reduce predicted hydrogen uncertainty, it would be necessary to reduce all physics parameter uncertainties similarly, bearing in mind that some parameters are inherently uncertain within a range. It is suspected that some residual uncertainty associated with modeling complex, coupled and synergistic phenomena, is an inherent aspect of complex systems and cannot be reduced to point value estimates. The probabilistic analyses such as the one demonstrated in this work are important to properly characterize response of complex systems such as severe accident progression in nuclear power plants.« less

Calibrating Physical Parameters in House Models Using Aggregate AC Power Demand

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sun, Yannan; Stevens, Andrew J.; Lian, Jianming

For residential houses, the air conditioning (AC) units are one of the major resources that can provide significant flexibility in energy use for the purpose of demand response. To quantify the flexibility, the characteristics of all the houses need to be accurately estimated, so that certain house models can be used to predict the dynamics of the house temperatures in order to adjust the setpoints accordingly to provide demand response while maintaining the same comfort levels. In this paper, we propose an approach using the Reverse Monte Carlo modeling method and aggregate house models to calibrate the distribution parameters ofmore » the house models for a population of residential houses. Given the aggregate AC power demand for the population, the approach can successfully estimate the distribution parameters for the sensitive physical parameters based on our previous uncertainty quantification study, such as the mean of the floor areas of the houses.« less

HiRadProp: High-Frequency Modeling and Prediction of Tropospheric Radiopropagation Parameters from Ground-Based-Multi-Channel Radiometric Measurements between Ka and W Band

DTIC Science & Technology

2016-05-11

new physically -based prediction models for all-weather path attenuation estimation at Ka, V and W band from multi- channel microwave radiometric data...of new physically -based prediction models for all-weather path attenuation estimation at Ka, V and W band from multi- channel microwave radiometric...the medium behavior at these frequency bands from both a physical and a statistical point of view (e.g., [5]-[7]). However, these campaigns are

Estimating and Testing the Sources of Evoked Potentials in the Brain.

ERIC Educational Resources Information Center

Huizenga, Hilde M.; Molenaar, Peter C. M.

1994-01-01

The source of an event-related brain potential (ERP) is estimated from multivariate measures of ERP on the head under several mathematical and physical constraints on the parameters of the source model. Statistical aspects of estimation are discussed, and new tests are proposed. (SLD)

Two-dimensional advective transport in ground-water flow parameter estimation

USGS Publications Warehouse

Anderman, E.R.; Hill, M.C.; Poeter, E.P.

1996-01-01

Nonlinear regression is useful in ground-water flow parameter estimation, but problems of parameter insensitivity and correlation often exist given commonly available hydraulic-head and head-dependent flow (for example, stream and lake gain or loss) observations. To address this problem, advective-transport observations are added to the ground-water flow, parameter-estimation model MODFLOWP using particle-tracking methods. The resulting model is used to investigate the importance of advective-transport observations relative to head-dependent flow observations when either or both are used in conjunction with hydraulic-head observations in a simulation of the sewage-discharge plume at Otis Air Force Base, Cape Cod, Massachusetts, USA. The analysis procedure for evaluating the probable effect of new observations on the regression results consists of two steps: (1) parameter sensitivities and correlations calculated at initial parameter values are used to assess the model parameterization and expected relative contributions of different types of observations to the regression; and (2) optimal parameter values are estimated by nonlinear regression and evaluated. In the Cape Cod parameter-estimation model, advective-transport observations did not significantly increase the overall parameter sensitivity; however: (1) inclusion of advective-transport observations decreased parameter correlation enough for more unique parameter values to be estimated by the regression; (2) realistic uncertainties in advective-transport observations had a small effect on parameter estimates relative to the precision with which the parameters were estimated; and (3) the regression results and sensitivity analysis provided insight into the dynamics of the ground-water flow system, especially the importance of accurate boundary conditions. In this work, advective-transport observations improved the calibration of the model and the estimation of ground-water flow parameters, and use of regression and related techniques produced significant insight into the physical system.

Estimation of anisotropy parameters in organic-rich shale: Rock physics forward modeling approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Herawati, Ida, E-mail: ida.herawati@students.itb.ac.id; Winardhi, Sonny; Priyono, Awali

Anisotropy analysis becomes an important step in processing and interpretation of seismic data. One of the most important things in anisotropy analysis is anisotropy parameter estimation which can be estimated using well data, core data or seismic data. In seismic data, anisotropy parameter calculation is generally based on velocity moveout analysis. However, the accuracy depends on data quality, available offset, and velocity moveout picking. Anisotropy estimation using seismic data is needed to obtain wide coverage of particular layer anisotropy. In anisotropic reservoir, analysis of anisotropy parameters also helps us to better understand the reservoir characteristics. Anisotropy parameters, especially ε, aremore » related to rock property and lithology determination. Current research aims to estimate anisotropy parameter from seismic data and integrate well data with case study in potential shale gas reservoir. Due to complexity in organic-rich shale reservoir, extensive study from different disciplines is needed to understand the reservoir. Shale itself has intrinsic anisotropy caused by lamination of their formed minerals. In order to link rock physic with seismic response, it is necessary to build forward modeling in organic-rich shale. This paper focuses on studying relationship between reservoir properties such as clay content, porosity and total organic content with anisotropy. Organic content which defines prospectivity of shale gas can be considered as solid background or solid inclusion or both. From the forward modeling result, it is shown that organic matter presence increases anisotropy in shale. The relationships between total organic content and other seismic properties such as acoustic impedance and Vp/Vs are also presented.« less

Estimating parameters with pre-specified accuracies in distributed parameter systems using optimal experiment design

NASA Astrophysics Data System (ADS)

Potters, M. G.; Bombois, X.; Mansoori, M.; Hof, Paul M. J. Van den

2016-08-01

Estimation of physical parameters in dynamical systems driven by linear partial differential equations is an important problem. In this paper, we introduce the least costly experiment design framework for these systems. It enables parameter estimation with an accuracy that is specified by the experimenter prior to the identification experiment, while at the same time minimising the cost of the experiment. We show how to adapt the classical framework for these systems and take into account scaling and stability issues. We also introduce a progressive subdivision algorithm that further generalises the experiment design framework in the sense that it returns the lowest cost by finding the optimal input signal, and optimal sensor and actuator locations. Our methodology is then applied to a relevant problem in heat transfer studies: estimation of conductivity and diffusivity parameters in front-face experiments. We find good correspondence between numerical and theoretical results.

ESTIMATION OF PHYSIOCHEMICAL PROPERTIES OF ORGANIC COMPOUNDS BY SPARC

EPA Science Inventory

The computer program SPARC (SPARC Performs Automated Reasoning in Chemistry) has been under development for several years to estimate physical properties and chemical reactivity parameters of organic compounds strictly from molecular structure. SPARC uses computational algorithms...

A Theoretical Framework for Calibration in Computer Models: Parametrization, Estimation and Convergence Properties

DOE PAGES

Tuo, Rui; Jeff Wu, C. F.

2016-07-19

Calibration parameters in deterministic computer experiments are those attributes that cannot be measured or available in physical experiments. Here, an approach to estimate them by using data from physical experiments and computer simulations. A theoretical framework is given which allows us to study the issues of parameter identifiability and estimation. We define the L 2-consistency for calibration as a justification for calibration methods. It is shown that a simplified version of the original KO method leads to asymptotically L 2-inconsistent calibration. This L 2-inconsistency can be remedied by modifying the original estimation procedure. A novel calibration method, called the Lmore » 2 calibration, is proposed and proven to be L 2-consistent and enjoys optimal convergence rate. Furthermore a numerical example and some mathematical analysis are used to illustrate the source of the L 2-inconsistency problem.« less

Rapid earthquake hazard and loss assessment for Euro-Mediterranean region

NASA Astrophysics Data System (ADS)

Erdik, Mustafa; Sesetyan, Karin; Demircioglu, Mine; Hancilar, Ufuk; Zulfikar, Can; Cakti, Eser; Kamer, Yaver; Yenidogan, Cem; Tuzun, Cuneyt; Cagnan, Zehra; Harmandar, Ebru

2010-10-01

The almost-real time estimation of ground shaking and losses after a major earthquake in the Euro-Mediterranean region was performed in the framework of the Joint Research Activity 3 (JRA-3) component of the EU FP6 Project entitled "Network of Research Infra-structures for European Seismology, NERIES". This project consists of finding the most likely location of the earthquake source by estimating the fault rupture parameters on the basis of rapid inversion of data from on-line regional broadband stations. It also includes an estimation of the spatial distribution of selected site-specific ground motion parameters at engineering bedrock through region-specific ground motion prediction equations (GMPEs) or physical simulation of ground motion. By using the Earthquake Loss Estimation Routine (ELER) software, the multi-level methodology developed for real time estimation of losses is capable of incorporating regional variability and sources of uncertainty stemming from GMPEs, fault finiteness, site modifications, inventory of physical and social elements subjected to earthquake hazard and the associated vulnerability relationships.

The effect of heart motion on parameter bias in dynamic cardiac SPECT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ross, S.G.; Gullberg, G.T.; Huesman, R.H.

1996-12-31

Dynamic cardiac SPECT can be used to estimate kinetic rate parameters which describe the wash-in and wash-out of tracer activity between the blood and the myocardial tissue. These kinetic parameters can in turn be correlated to myocardial perfusion. There are, however, many physical aspects associated with dynamic SPECT which can introduce errors into the estimates. This paper describes a study which investigates the effect of heart motion on kinetic parameter estimates. Dynamic SPECT simulations are performed using a beating version of the MCAT phantom. The results demonstrate that cardiac motion has a significant effect on the blood, tissue, and backgroundmore » content of regions of interest. This in turn affects estimates of wash-in, while it has very little effect on estimates of wash-out. The effect of cardiac motion on parameter estimates appears not to be as great as effects introduced by photon noise and geometric collimator response. It is also shown that cardiac motion results in little extravascular contamination of the left ventricle blood region of interest.« less

SPIPS: Spectro-Photo-Interferometry of Pulsating Stars

NASA Astrophysics Data System (ADS)

Mérand, Antoine

2017-10-01

SPIPS (Spectro-Photo-Interferometry of Pulsating Stars) combines radial velocimetry, interferometry, and photometry to estimate physical parameters of pulsating stars, including presence of infrared excess, color excess, Teff, and ratio distance/p-factor. The global model-based parallax-of-pulsation method is implemented in Python. Derived parameters have a high level of confidence; statistical precision is improved (compared to other methods) due to the large number of data taken into account, accuracy is improved by using consistent physical modeling and reliability of the derived parameters is strengthened by redundancy in the data.

Distance biases in the estimation of the physical properties of Hi-GAL compact sources - I. Clump properties and the identification of high-mass star-forming candidates

NASA Astrophysics Data System (ADS)

Baldeschi, Adriano; Elia, D.; Molinari, S.; Pezzuto, S.; Schisano, E.; Gatti, M.; Serra, A.; Merello, M.; Benedettini, M.; Di Giorgio, A. M.; Liu, J. S.

2017-04-01

The degradation of spatial resolution in star-forming regions, observed at large distances (d ≳ 1 kpc) with Herschel, can lead to estimates of the physical parameters of the detected compact sources (clumps), which do not necessarily mirror the properties of the original population of cores. This paper aims at quantifying the bias introduced in the estimation of these parameters by the distance effect. To do so, we consider Herschel maps of nearby star-forming regions taken from the Herschel Gould Belt survey, and simulate the effect of increased distance to understand what amount of information is lost when a distant star-forming region is observed with Herschel resolution. In the maps displaced to different distances we extract compact sources, and we derive their physical parameters as if they were original Herschel infrared Galactic Plane Survey maps of the extracted source samples. In this way, we are able to discuss how the main physical properties change with distance. In particular, we discuss the ability of clumps to form massive stars: we estimate the fraction of distant sources that are classified as high-mass stars-forming objects due to their position in the mass versus radius diagram, that are only 'false positives'. We also give a threshold for high-mass star formation M>1282 (r/ [pc])^{1.42} M_{⊙}. In conclusion, this paper provides the astronomer dealing with Herschel maps of distant star-forming regions with a set of prescriptions to partially recover the character of the core population in unresolved clumps.

Developments in Sensitivity Methodologies and the Validation of Reactor Physics Calculations

DOE PAGES

Palmiotti, Giuseppe; Salvatores, Massimo

2012-01-01

The sensitivity methodologies have been a remarkable story when adopted in the reactor physics field. Sensitivity coefficients can be used for different objectives like uncertainty estimates, design optimization, determination of target accuracy requirements, adjustment of input parameters, and evaluations of the representativity of an experiment with respect to a reference design configuration. A review of the methods used is provided, and several examples illustrate the success of the methodology in reactor physics. A new application as the improvement of nuclear basic parameters using integral experiments is also described.

Integrated direct/indirect adaptive robust motion trajectory tracking control of pneumatic cylinders

NASA Astrophysics Data System (ADS)

Meng, Deyuan; Tao, Guoliang; Zhu, Xiaocong

2013-09-01

This paper studies the precision motion trajectory tracking control of a pneumatic cylinder driven by a proportional-directional control valve. An integrated direct/indirect adaptive robust controller is proposed. The controller employs a physical model based indirect-type parameter estimation to obtain reliable estimates of unknown model parameters, and utilises a robust control method with dynamic compensation type fast adaptation to attenuate the effects of parameter estimation errors, unmodelled dynamics and disturbances. Due to the use of projection mapping, the robust control law and the parameter adaption algorithm can be designed separately. Since the system model uncertainties are unmatched, the recursive backstepping technology is adopted to design the robust control law. Extensive comparative experimental results are presented to illustrate the effectiveness of the proposed controller and its performance robustness to parameter variations and sudden disturbances.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tuo, Rui; Jeff Wu, C. F.

Calibration parameters in deterministic computer experiments are those attributes that cannot be measured or available in physical experiments. Here, an approach to estimate them by using data from physical experiments and computer simulations. A theoretical framework is given which allows us to study the issues of parameter identifiability and estimation. We define the L 2-consistency for calibration as a justification for calibration methods. It is shown that a simplified version of the original KO method leads to asymptotically L 2-inconsistent calibration. This L 2-inconsistency can be remedied by modifying the original estimation procedure. A novel calibration method, called the Lmore » 2 calibration, is proposed and proven to be L 2-consistent and enjoys optimal convergence rate. Furthermore a numerical example and some mathematical analysis are used to illustrate the source of the L 2-inconsistency problem.« less

Undersampling power-law size distributions: effect on the assessment of extreme natural hazards

USGS Publications Warehouse

Geist, Eric L.; Parsons, Thomas E.

2014-01-01

The effect of undersampling on estimating the size of extreme natural hazards from historical data is examined. Tests using synthetic catalogs indicate that the tail of an empirical size distribution sampled from a pure Pareto probability distribution can range from having one-to-several unusually large events to appearing depleted, relative to the parent distribution. Both of these effects are artifacts caused by limited catalog length. It is more difficult to diagnose the artificially depleted empirical distributions, since one expects that a pure Pareto distribution is physically limited in some way. Using maximum likelihood methods and the method of moments, we estimate the power-law exponent and the corner size parameter of tapered Pareto distributions for several natural hazard examples: tsunamis, floods, and earthquakes. Each of these examples has varying catalog lengths and measurement thresholds, relative to the largest event sizes. In many cases where there are only several orders of magnitude between the measurement threshold and the largest events, joint two-parameter estimation techniques are necessary to account for estimation dependence between the power-law scaling exponent and the corner size parameter. Results indicate that whereas the corner size parameter of a tapered Pareto distribution can be estimated, its upper confidence bound cannot be determined and the estimate itself is often unstable with time. Correspondingly, one cannot statistically reject a pure Pareto null hypothesis using natural hazard catalog data. Although physical limits to the hazard source size and by attenuation mechanisms from source to site constrain the maximum hazard size, historical data alone often cannot reliably determine the corner size parameter. Probabilistic assessments incorporating theoretical constraints on source size and propagation effects are preferred over deterministic assessments of extreme natural hazards based on historic data.

Physical, Hydraulic, and Transport Properties of Sediments and Engineered Materials Associated with Hanford Immobilized Low-Activity Waste

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rockhold, Mark L.; Zhang, Z. F.; Meyer, Philip D.

2015-02-28

Current plans for treatment and disposal of immobilized low-activity waste (ILAW) from Hanford’s underground waste storage tanks include vitrification and storage of the glass waste form in a nearsurface disposal facility. This Integrated Disposal Facility (IDF) is located in the 200 East Area of the Hanford Central Plateau. Performance assessment (PA) of the IDF requires numerical modeling of subsurface flow and reactive transport processes over very long periods (thousands of years). The models used to predict facility performance require parameters describing various physical, hydraulic, and transport properties. This report provides updated estimates of physical, hydraulic, and transport properties and parametersmore » for both near- and far-field materials, intended for use in future IDF PA modeling efforts. Previous work on physical and hydraulic property characterization for earlier IDF PA analyses is reviewed and summarized. For near-field materials, portions of this document and parameter estimates are taken from an earlier data package. For far-field materials, a critical review is provided of methodologies used in previous data packages. Alternative methods are described and associated parameters are provided.« less

BayeSED: A General Approach to Fitting the Spectral Energy Distribution of Galaxies

NASA Astrophysics Data System (ADS)

Han, Yunkun; Han, Zhanwen

2014-11-01

We present a newly developed version of BayeSED, a general Bayesian approach to the spectral energy distribution (SED) fitting of galaxies. The new BayeSED code has been systematically tested on a mock sample of galaxies. The comparison between the estimated and input values of the parameters shows that BayeSED can recover the physical parameters of galaxies reasonably well. We then applied BayeSED to interpret the SEDs of a large Ks -selected sample of galaxies in the COSMOS/UltraVISTA field with stellar population synthesis models. Using the new BayeSED code, a Bayesian model comparison of stellar population synthesis models has been performed for the first time. We found that the 2003 model by Bruzual & Charlot, statistically speaking, has greater Bayesian evidence than the 2005 model by Maraston for the Ks -selected sample. In addition, while setting the stellar metallicity as a free parameter obviously increases the Bayesian evidence of both models, varying the initial mass function has a notable effect only on the Maraston model. Meanwhile, the physical parameters estimated with BayeSED are found to be generally consistent with those obtained using the popular grid-based FAST code, while the former parameters exhibit more natural distributions. Based on the estimated physical parameters of the galaxies in the sample, we qualitatively classified the galaxies in the sample into five populations that may represent galaxies at different evolution stages or in different environments. We conclude that BayeSED could be a reliable and powerful tool for investigating the formation and evolution of galaxies from the rich multi-wavelength observations currently available. A binary version of the BayeSED code parallelized with Message Passing Interface is publicly available at https://bitbucket.org/hanyk/bayesed.

BayeSED: A GENERAL APPROACH TO FITTING THE SPECTRAL ENERGY DISTRIBUTION OF GALAXIES

DOE Office of Scientific and Technical Information (OSTI.GOV)

Han, Yunkun; Han, Zhanwen, E-mail: hanyk@ynao.ac.cn, E-mail: zhanwenhan@ynao.ac.cn

2014-11-01

We present a newly developed version of BayeSED, a general Bayesian approach to the spectral energy distribution (SED) fitting of galaxies. The new BayeSED code has been systematically tested on a mock sample of galaxies. The comparison between the estimated and input values of the parameters shows that BayeSED can recover the physical parameters of galaxies reasonably well. We then applied BayeSED to interpret the SEDs of a large K{sub s} -selected sample of galaxies in the COSMOS/UltraVISTA field with stellar population synthesis models. Using the new BayeSED code, a Bayesian model comparison of stellar population synthesis models has beenmore » performed for the first time. We found that the 2003 model by Bruzual and Charlot, statistically speaking, has greater Bayesian evidence than the 2005 model by Maraston for the K{sub s} -selected sample. In addition, while setting the stellar metallicity as a free parameter obviously increases the Bayesian evidence of both models, varying the initial mass function has a notable effect only on the Maraston model. Meanwhile, the physical parameters estimated with BayeSED are found to be generally consistent with those obtained using the popular grid-based FAST code, while the former parameters exhibit more natural distributions. Based on the estimated physical parameters of the galaxies in the sample, we qualitatively classified the galaxies in the sample into five populations that may represent galaxies at different evolution stages or in different environments. We conclude that BayeSED could be a reliable and powerful tool for investigating the formation and evolution of galaxies from the rich multi-wavelength observations currently available. A binary version of the BayeSED code parallelized with Message Passing Interface is publicly available at https://bitbucket.org/hanyk/bayesed.« less

Order-of-magnitude physics of neutron stars. Estimating their properties from first principles

NASA Astrophysics Data System (ADS)

Reisenegger, Andreas; Zepeda, Felipe S.

2016-03-01

We use basic physics and simple mathematics accessible to advanced undergraduate students to estimate the main properties of neutron stars. We set the stage and introduce relevant concepts by discussing the properties of "everyday" matter on Earth, degenerate Fermi gases, white dwarfs, and scaling relations of stellar properties with polytropic equations of state. Then, we discuss various physical ingredients relevant for neutron stars and how they can be combined in order to obtain a couple of different simple estimates of their maximum mass, beyond which they would collapse, turning into black holes. Finally, we use the basic structural parameters of neutron stars to briefly discuss their rotational and electromagnetic properties.

Particle Dark Matter constraints: the effect of Galactic uncertainties

DOE Office of Scientific and Technical Information (OSTI.GOV)

Benito, Maria; Bernal, Nicolás; Iocco, Fabio

2017-02-01

Collider, space, and Earth based experiments are now able to probe several extensions of the Standard Model of particle physics which provide viable dark matter candidates. Direct and indirect dark matter searches rely on inputs of astrophysical nature, such as the local dark matter density or the shape of the dark matter density profile in the target in object. The determination of these quantities is highly affected by astrophysical uncertainties. The latter, especially those for our own Galaxy, are ill-known, and often not fully accounted for when analyzing the phenomenology of particle physics models. In this paper we present amore » systematic, quantitative estimate of how astrophysical uncertainties on Galactic quantities (such as the local galactocentric distance, circular velocity, or the morphology of the stellar disk and bulge) propagate to the determination of the phenomenology of particle physics models, thus eventually affecting the determination of new physics parameters. We present results in the context of two specific extensions of the Standard Model (the Singlet Scalar and the Inert Doublet) that we adopt as case studies for their simplicity in illustrating the magnitude and impact of such uncertainties on the parameter space of the particle physics model itself. Our findings point toward very relevant effects of current Galactic uncertainties on the determination of particle physics parameters, and urge a systematic estimate of such uncertainties in more complex scenarios, in order to achieve constraints on the determination of new physics that realistically include all known uncertainties.« less

Measurement of surface physical properties and radiation balance for KUREX-91 study

NASA Technical Reports Server (NTRS)

Walter-Shea, Elizabeth A.; Blad, Blaine L.; Mesarch, Mark A.; Hays, Cynthia J.

1992-01-01

Biophysical properties and radiation balance components were measured at the Streletskaya Steppe Reserve of the Russian Republic in July 1991. Steppe vegetation parameters characterized include leaf area index (LAI), leaf angle distribution, mean tilt angle, canopy height, leaf spectral properties, leaf water potential, fraction of absorbed photosynthetically active radiation (APAR), and incoming and outgoing shortwave and longwave radiation. Research results, biophysical parameters, radiation balance estimates, and sun-view geometry effects on estimating APAR are discussed. Incoming and outgoing radiation streams are estimated using bidirectional spectral reflectances and bidirectional thermal emittances. Good agreement between measured and modeled estimates of the radiation balance were obtained.

SED Modeling of 20 Massive Young Stellar Objects

NASA Astrophysics Data System (ADS)

Tanti, Kamal Kumar

In this paper, we present the spectral energy distributions (SEDs) modeling of twenty massive young stellar objects (MYSOs) and subsequently estimated different physical and structural/geometrical parameters for each of the twenty central YSO outflow candidates, along with their associated circumstellar disks and infalling envelopes. The SEDs for each of the MYSOs been reconstructed by using 2MASS, MSX, IRAS, IRAC & MIPS, SCUBA, WISE, SPIRE and IRAM data, with the help of a SED Fitting Tool, that uses a grid of 2D radiative transfer models. Using the detailed analysis of SEDs and subsequent estimation of physical and geometrical parameters for the central YSO sources along with its circumstellar disks and envelopes, the cumulative distribution of the stellar, disk and envelope parameters can be analyzed. This leads to a better understanding of massive star formation processes in their respective star forming regions in different molecular clouds.

The estimation of material and patch parameters in a PDE-based circular plate model

NASA Technical Reports Server (NTRS)

Banks, H. T.; Smith, Ralph C.; Brown, D. E.; Metcalf, Vern L.; Silcox, R. J.

1995-01-01

The estimation of material and patch parameters for a system involving a circular plate, to which piezoceramic patches are bonded, is considered. A partial differential equation (PDE) model for the thin circular plate is used with the passive and active contributions form the patches included in the internal and external bending moments. This model contains piecewise constant parameters describing the density, flexural rigidity, Poisson ratio, and Kelvin-Voigt damping for the system as well as patch constants and a coefficient for viscous air damping. Examples demonstrating the estimation of these parameters with experimental acceleration data and a variety of inputs to the experimental plate are presented. By using a physically-derived PDE model to describe the system, parameter sets consistent across experiments are obtained, even when phenomena such as damping due to electric circuits affect the system dynamics.

Detecting changes in ultrasound backscattered statistics by using Nakagami parameters: Comparisons of moment-based and maximum likelihood estimators.

PubMed

Lin, Jen-Jen; Cheng, Jung-Yu; Huang, Li-Fei; Lin, Ying-Hsiu; Wan, Yung-Liang; Tsui, Po-Hsiang

2017-05-01

The Nakagami distribution is an approximation useful to the statistics of ultrasound backscattered signals for tissue characterization. Various estimators may affect the Nakagami parameter in the detection of changes in backscattered statistics. In particular, the moment-based estimator (MBE) and maximum likelihood estimator (MLE) are two primary methods used to estimate the Nakagami parameters of ultrasound signals. This study explored the effects of the MBE and different MLE approximations on Nakagami parameter estimations. Ultrasound backscattered signals of different scatterer number densities were generated using a simulation model, and phantom experiments and measurements of human liver tissues were also conducted to acquire real backscattered echoes. Envelope signals were employed to estimate the Nakagami parameters by using the MBE, first- and second-order approximations of MLE (MLE 1 and MLE 2 , respectively), and Greenwood approximation (MLE gw ) for comparisons. The simulation results demonstrated that, compared with the MBE and MLE 1 , the MLE 2 and MLE gw enabled more stable parameter estimations with small sample sizes. Notably, the required data length of the envelope signal was 3.6 times the pulse length. The phantom and tissue measurement results also showed that the Nakagami parameters estimated using the MLE 2 and MLE gw could simultaneously differentiate various scatterer concentrations with lower standard deviations and reliably reflect physical meanings associated with the backscattered statistics. Therefore, the MLE 2 and MLE gw are suggested as estimators for the development of Nakagami-based methodologies for ultrasound tissue characterization. Copyright © 2017 Elsevier B.V. All rights reserved.

Measurement of pixel response functions of a fully depleted CCD

NASA Astrophysics Data System (ADS)

Kobayashi, Yukiyasu; Niwa, Yoshito; Yano, Taihei; Gouda, Naoteru; Hara, Takuji; Yamada, Yoshiyuki

2014-07-01

We describe the measurement of detailed and precise Pixel Response Functions (PRFs) of a fully depleted CCD. Measurements were performed under different physical conditions, such as different wavelength light sources or CCD operating temperatures. We determined the relations between these physical conditions and the forms of the PRF. We employ two types of PRFs: one is the model PRF (mPRF) that can represent the shape of a PRF with one characteristic parameter and the other is the simulated PRF (sPRF) that is the resultant PRF from simulating physical phenomena. By using measured, model, and simulated PRFs, we determined the relations between operational parameters and the PRFs. Using the obtained relations, we can now estimate a PRF under conditions that will be encountered during the course of Nano-JASMINE observations. These estimated PRFs will be utilized in the analysis of the Nano-JASMINE data.

A data-driven and physics-based single-pass retrieval of active-passive microwave covariation and vegetation parameters for the SMAP mission

NASA Astrophysics Data System (ADS)

Entekhabi, D.; Jagdhuber, T.; Das, N. N.; Baur, M.; Link, M.; Piles, M.; Akbar, R.; Konings, A. G.; Mccoll, K. A.; Alemohammad, S. H.; Montzka, C.; Kunstmann, H.

2016-12-01

The active-passive soil moisture retrieval algorithm of NASA's SMAP mission depends on robust statistical estimation of active-passive covariation (β) and vegetation structure (Γ) parameters in order to provide reliable global measurements of soil moisture on an intermediate level (9km) compared to the native resolution of the radiometer (36km) and radar (3km) instruments. These parameters apply to the SMAP radiometer-radar combination over the period of record that was cut short with the end of the SMAP radar transmission. They also apply to the current SMAP radiometer and Sentinel 1A/B radar combination for high-resolution surface soil moisture mapping. However, the performance of the statistically-based approach is directly dependent on the selection of a representative time frame in which these parameters can be estimated assuming dynamic soil moisture and stationary soil roughness and vegetation cover. Here, we propose a novel, data-driven and physics-based single-pass retrieval of active-passive microwave covariation and vegetation parameters for the SMAP mission. The algorithm does not depend on time series analyses and can be applied using minimum one pair of an active-passive acquisition. The algorithm stems from the physical link between microwave emission and scattering via conservation of energy. The formulation of the emission radiative transfer is combined with the Distorted Born Approximation of radar scattering for vegetated land surfaces. The two formulations are simultaneously solved for the covariation and vegetation structure parameters. Preliminary results from SMAP active-passive observations (April 13th to July 7th 2015) compare well with the time-series statistical approach and confirms the capability of this method to estimate these parameters. Moreover, the method is not restricted to a given frequency (applies to both L-band and C-band combinations for the radar) or incidence angle (all angles and not just the fixed 40° incidence). Therefore, the approach is applicable to the combination of SMAP and Sentinel-1A/B data for active-passive and high-resolution soil moisture estimation.

The association between quality of care and quality of life in long-stay nursing home residents with preserved cognition.

PubMed

Kim, Sun Jung; Park, Eun-Cheol; Kim, Sulgi; Nakagawa, Shunichi; Lung, John; Choi, Jong Bum; Ryu, Woo Sang; Min, Too Jae; Shin, Hyun Phil; Kim, Kyudam; Yoo, Ji Won

2014-03-01

To assess the overall quality of life of long-stay nursing home residents with preserved cognition, to examine whether the Centers for Medicare and Medicaid Service's Nursing Home Compare 5-star quality rating system reflects the overall quality of life of such residents, and to examine whether residents' demographics and clinical characteristics affect their quality of life. Quality of life was measured using the Participant Outcomes and Status Measures-Nursing Facility survey, which has 10 sections and 63 items. Total scores range from 20 (lowest possible quality of life) to 100 (highest). Long-stay nursing home residents with preserved cognition (n = 316) were interviewed. The average quality- of-life score was 71.4 (SD: 7.6; range: 45.1-93.0). Multilevel regression models revealed that quality of life was associated with physical impairment (parameter estimate = -0.728; P = .04) and depression (parameter estimate = -3.015; P = .01) but not Nursing Home Compare's overall star rating (parameter estimate = 0.683; P = .12) and not pain (parameter estimate = -0.705; P = .47). The 5-star quality rating system did not reflect the quality of life of long-stay nursing home residents with preserved cognition. Notably, pain was not associated with quality of life, but physical impairment and depression were. Copyright © 2014 American Medical Directors Association, Inc. Published by Elsevier Inc. All rights reserved.

Masses and ages of Delta Scuti stars in eclipsing binary systems

NASA Astrophysics Data System (ADS)

Tsvetkov, Ts. G.; Petrova, Ts. C.

1993-05-01

By using data mainly from Frolov et al. (1982) for four Delta Scuti stars in eclipsing binary systems, AB Cas, Y Cam, RS Cha, and AI Hya, their physical parameters, distances, and radial pulsation modes are determined. The evolutionary track systems of Iben (1967), Paczynski (1970), and Maeder and Meynet (1988) are interpolated in order to estimate evolutionary masses Me and ages t of these variables. Their pulsation masses MQ are estimated from the fitting formulae of Faulkner (1977) and Fitch (1981). Our estimates of evolutionary masses M(e) and pulsation masses M(Q) are close to the masses M determined by Frolov et al. from the star binarity. The only exception is AB Cas, for which there is no agreement between certain star parameters. Another, independent approach is also applied to the stars RS Cha and AI Hya: by using their photometric indices b - y and c(1) from the catalog of Lopez de Coca et al. (1990) and appropriate photometric calibrations, other sets of physical parameters, distances, modes, ages, and evolutionary and pulsation masses of both variables are obtained.

V2676 Oph: Estimating Physical Parameters of a Moderately Fast Nova

NASA Astrophysics Data System (ADS)

Raj, A.; Pavana, M.; Kamath, U. S.; Anupama, G. C.; Walter, F. M.

2018-03-01

Using our previously reported observations, we derive some physical parameters of the moderately fast nova V2676 Oph 2012 #1. The best-fit Cloudy model of the nebular spectrum obtained on 2015 May 8 shows a hot white dwarf source with TBB≍1.0×105 K having a luminosity of 1.0×1038 erg/s. Our abundance analysis shows that the ejecta are significantly enhanced relative to solar, He/H=2.14, O/H=2.37, S/H=6.62 and Ar/H=3.25. The ejecta mass is estimated to be 1.42×10-5 M⊙. The nova showed a pronounced dust formation phase after 90 d from discovery. The J-H and H-K colors were very large as compared to other molecule- and dust-forming novae in recent years. The dust temperature and mass at two epochs have been estimated from spectral energy distribution fits to infrared photometry.

PREDICTION OF THE VAPOR PRESSURE, BOILING POINT, HEAT OF VAPORIZATION AND DIFFUSION COEFFICIENT OF ORGANIC COMPOUNDS

EPA Science Inventory

The prototype computer program SPARC has been under development for several years to estimate physical properties and chemical reactivity parameters of organic compounds strictly from molecular structure. SPARC solute-solute physical process models have been developed and tested...

Spatial design and strength of spatial signal: Effects on covariance estimation

USGS Publications Warehouse

Irvine, Kathryn M.; Gitelman, Alix I.; Hoeting, Jennifer A.

2007-01-01

In a spatial regression context, scientists are often interested in a physical interpretation of components of the parametric covariance function. For example, spatial covariance parameter estimates in ecological settings have been interpreted to describe spatial heterogeneity or “patchiness” in a landscape that cannot be explained by measured covariates. In this article, we investigate the influence of the strength of spatial dependence on maximum likelihood (ML) and restricted maximum likelihood (REML) estimates of covariance parameters in an exponential-with-nugget model, and we also examine these influences under different sampling designs—specifically, lattice designs and more realistic random and cluster designs—at differing intensities of sampling (n=144 and 361). We find that neither ML nor REML estimates perform well when the range parameter and/or the nugget-to-sill ratio is large—ML tends to underestimate the autocorrelation function and REML produces highly variable estimates of the autocorrelation function. The best estimates of both the covariance parameters and the autocorrelation function come under the cluster sampling design and large sample sizes. As a motivating example, we consider a spatial model for stream sulfate concentration.

Catchment Tomography - Joint Estimation of Surface Roughness and Hydraulic Conductivity with the EnKF

NASA Astrophysics Data System (ADS)

Baatz, D.; Kurtz, W.; Hendricks Franssen, H. J.; Vereecken, H.; Kollet, S. J.

2017-12-01

Parameter estimation for physically based, distributed hydrological models becomes increasingly challenging with increasing model complexity. The number of parameters is usually large and the number of observations relatively small, which results in large uncertainties. A moving transmitter - receiver concept to estimate spatially distributed hydrological parameters is presented by catchment tomography. In this concept, precipitation, highly variable in time and space, serves as a moving transmitter. As response to precipitation, runoff and stream discharge are generated along different paths and time scales, depending on surface and subsurface flow properties. Stream water levels are thus an integrated signal of upstream parameters, measured by stream gauges which serve as the receivers. These stream water level observations are assimilated into a distributed hydrological model, which is forced with high resolution, radar based precipitation estimates. Applying a joint state-parameter update with the Ensemble Kalman Filter, the spatially distributed Manning's roughness coefficient and saturated hydraulic conductivity are estimated jointly. The sequential data assimilation continuously integrates new information into the parameter estimation problem, especially during precipitation events. Every precipitation event constrains the possible parameter space. In the approach, forward simulations are performed with ParFlow, a variable saturated subsurface and overland flow model. ParFlow is coupled to the Parallel Data Assimilation Framework for the data assimilation and the joint state-parameter update. In synthetic, 3-dimensional experiments including surface and subsurface flow, hydraulic conductivity and the Manning's coefficient are efficiently estimated with the catchment tomography approach. A joint update of the Manning's coefficient and hydraulic conductivity tends to improve the parameter estimation compared to a single parameter update, especially in cases of biased initial parameter ensembles. The computational experiments additionally show to which degree of spatial heterogeneity and to which degree of uncertainty of subsurface flow parameters the Manning's coefficient and hydraulic conductivity can be estimated efficiently.

Synthetic Earthquake Statistics From Physical Fault Models for the Lower Rhine Embayment

NASA Astrophysics Data System (ADS)

Brietzke, G. B.; Hainzl, S.; Zöller, G.

2012-04-01

As of today, seismic risk and hazard estimates mostly use pure empirical, stochastic models of earthquake fault systems tuned specifically to the vulnerable areas of interest. Although such models allow for reasonable risk estimates they fail to provide a link between the observed seismicity and the underlying physical processes. Solving a state-of-the-art fully dynamic description set of all relevant physical processes related to earthquake fault systems is likely not useful since it comes with a large number of degrees of freedom, poor constraints on its model parameters and a huge computational effort. Here, quasi-static and quasi-dynamic physical fault simulators provide a compromise between physical completeness and computational affordability and aim at providing a link between basic physical concepts and statistics of seismicity. Within the framework of quasi-static and quasi-dynamic earthquake simulators we investigate a model of the Lower Rhine Embayment (LRE) that is based upon seismological and geological data. We present and discuss statistics of the spatio-temporal behavior of generated synthetic earthquake catalogs with respect to simplification (e.g. simple two-fault cases) as well as to complication (e.g. hidden faults, geometric complexity, heterogeneities of constitutive parameters).

Regularized Semiparametric Estimation for Ordinary Differential Equations

PubMed Central

Li, Yun; Zhu, Ji; Wang, Naisyin

2015-01-01

Ordinary differential equations (ODEs) are widely used in modeling dynamic systems and have ample applications in the fields of physics, engineering, economics and biological sciences. The ODE parameters often possess physiological meanings and can help scientists gain better understanding of the system. One key interest is thus to well estimate these parameters. Ideally, constant parameters are preferred due to their easy interpretation. In reality, however, constant parameters can be too restrictive such that even after incorporating error terms, there could still be unknown sources of disturbance that lead to poor agreement between observed data and the estimated ODE system. In this paper, we address this issue and accommodate short-term interferences by allowing parameters to vary with time. We propose a new regularized estimation procedure on the time-varying parameters of an ODE system so that these parameters could change with time during transitions but remain constants within stable stages. We found, through simulation studies, that the proposed method performs well and tends to have less variation in comparison to the non-regularized approach. On the theoretical front, we derive finite-sample estimation error bounds for the proposed method. Applications of the proposed method to modeling the hare-lynx relationship and the measles incidence dynamic in Ontario, Canada lead to satisfactory and meaningful results. PMID:26392639

Applicability of Broad-Band Photometry for Determining the Properties of Stars and Interstellar Extinction

NASA Astrophysics Data System (ADS)

Sichevskij, S. G.

2018-01-01

The feasibility of the determination of the physical conditions in star's atmosphere and the parameters of interstellar extinction from broad-band photometric observations in the 300-3000 nm wavelength interval is studied using SDSS and 2MASS data. The photometric accuracy of these surveys is shown to be insufficient for achieving in practice the theoretical possibility of estimating the atmospheric parameters of stars based on ugriz and JHK s photometry exclusively because such determinations result in correlations between the temperature and extinction estimates. The uncertainty of interstellar extinction estimates can be reduced if prior data about the temperature are available. The surveys considered can nevertheless be potentially valuable sources of information about both stellar atmospheric parameters and the interstellar medium.

FPGA-based fused smart sensor for dynamic and vibration parameter extraction in industrial robot links.

PubMed

Rodriguez-Donate, Carlos; Morales-Velazquez, Luis; Osornio-Rios, Roque Alfredo; Herrera-Ruiz, Gilberto; de Jesus Romero-Troncoso, Rene

2010-01-01

Intelligent robotics demands the integration of smart sensors that allow the controller to efficiently measure physical quantities. Industrial manipulator robots require a constant monitoring of several parameters such as motion dynamics, inclination, and vibration. This work presents a novel smart sensor to estimate motion dynamics, inclination, and vibration parameters on industrial manipulator robot links based on two primary sensors: an encoder and a triaxial accelerometer. The proposed smart sensor implements a new methodology based on an oversampling technique, averaging decimation filters, FIR filters, finite differences and linear interpolation to estimate the interest parameters, which are computed online utilizing digital hardware signal processing based on field programmable gate arrays (FPGA).

FPGA-Based Fused Smart Sensor for Dynamic and Vibration Parameter Extraction in Industrial Robot Links

PubMed Central

Rodriguez-Donate, Carlos; Morales-Velazquez, Luis; Osornio-Rios, Roque Alfredo; Herrera-Ruiz, Gilberto; de Jesus Romero-Troncoso, Rene

2010-01-01

Intelligent robotics demands the integration of smart sensors that allow the controller to efficiently measure physical quantities. Industrial manipulator robots require a constant monitoring of several parameters such as motion dynamics, inclination, and vibration. This work presents a novel smart sensor to estimate motion dynamics, inclination, and vibration parameters on industrial manipulator robot links based on two primary sensors: an encoder and a triaxial accelerometer. The proposed smart sensor implements a new methodology based on an oversampling technique, averaging decimation filters, FIR filters, finite differences and linear interpolation to estimate the interest parameters, which are computed online utilizing digital hardware signal processing based on field programmable gate arrays (FPGA). PMID:22319345

Upper-extremity and mobility subdomains from the Patient-Reported Outcomes Measurement Information System (PROMIS) adult physical functioning item bank.

PubMed

Hays, Ron D; Spritzer, Karen L; Amtmann, Dagmar; Lai, Jin-Shei; Dewitt, Esi Morgan; Rothrock, Nan; Dewalt, Darren A; Riley, William T; Fries, James F; Krishnan, Eswar

2013-11-01

To create upper-extremity and mobility subdomain scores from the Patient-Reported Outcomes Measurement Information System (PROMIS) physical functioning adult item bank. Expert reviews were used to identify upper-extremity and mobility items from the PROMIS item bank. Psychometric analyses were conducted to assess empirical support for scoring upper-extremity and mobility subdomains. Data were collected from the U.S. general population and multiple disease groups via self-administered surveys. The sample (N=21,773) included 21,133 English-speaking adults who participated in the PROMIS wave 1 data collection and 640 Spanish-speaking Latino adults recruited separately. Not applicable. We used English- and Spanish-language data and existing PROMIS item parameters for the physical functioning item bank to estimate upper-extremity and mobility scores. In addition, we fit graded response models to calibrate the upper-extremity items and mobility items separately, compare separate to combined calibrations, and produce subdomain scores. After eliminating items because of local dependency, 16 items remained to assess upper extremity and 17 items to assess mobility. The estimated correlation between upper extremity and mobility was .59 using existing PROMIS physical functioning item parameters (r=.60 using parameters calibrated separately for upper-extremity and mobility items). Upper-extremity and mobility subdomains shared about 35% of the variance in common, and produced comparable scores whether calibrated separately or together. The identification of the subset of items tapping these 2 aspects of physical functioning and scored using the existing PROMIS parameters provides the option of scoring these subdomains in addition to the overall physical functioning score. Copyright © 2013 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Use of Landsat and environmental satellite data in evapotranspiration estimation from a wildland area

NASA Technical Reports Server (NTRS)

Khorram, S.; Smith, H. G.

1979-01-01

A remote sensing-aided procedure was applied to the watershed-wide estimation of water loss to the atmosphere (evapotranspiration, ET). The approach involved a spatially referenced databank based on both remotely sensed and ground-acquired information. Physical models for both estimation of ET and quantification of input parameters are specified, and results of the investigation are outlined.

Black Hole Mergers as Probes of Structure Formation

NASA Technical Reports Server (NTRS)

Alicea-Munoz, E.; Miller, M. Coleman

2008-01-01

Intense structure formation and reionization occur at high redshift, yet there is currently little observational information about this very important epoch. Observations of gravitational waves from massive black hole (MBH) mergers can provide us with important clues about the formation of structures in the early universe. Past efforts have been limited to calculating merger rates using different models in which many assumptions are made about the specific values of physical parameters of the mergers, resulting in merger rate estimates that span a very wide range (0.1 - 104 mergers/year). Here we develop a semi-analytical, phenomenological model of MBH mergers that includes plausible combinations of several physical parameters, which we then turn around to determine how well observations with the Laser Interferometer Space Antenna (LISA) will be able to enhance our understanding of the universe during the critical z 5 - 30 structure formation era. We do this by generating synthetic LISA observable data (total BH mass, BH mass ratio, redshift, merger rates), which are then analyzed using a Markov Chain Monte Carlo method. This allows us to constrain the physical parameters of the mergers. We find that our methodology works well at estimating merger parameters, consistently giving results within 1- of the input parameter values. We also discover that the number of merger events is a key discriminant among models. This helps our method be robust against observational uncertainties. Our approach, which at this stage constitutes a proof of principle, can be readily extended to physical models and to more general problems in cosmology and gravitational wave astrophysics.

Reduced Order Modeling in General Relativity

NASA Astrophysics Data System (ADS)

Tiglio, Manuel

2014-03-01

Reduced Order Modeling is an emerging yet fast developing filed in gravitational wave physics. The main goals are to enable fast modeling and parameter estimation of any detected signal, along with rapid matched filtering detecting. I will focus on the first two. Some accomplishments include being able to replace, with essentially no lost of physical accuracy, the original models with surrogate ones (which are not effective ones, that is, they do not simplify the physics but go on a very different track, exploiting the particulars of the waveform family under consideration and state of the art dimensional reduction techniques) which are very fast to evaluate. For example, for EOB models they are at least around 3 orders of magnitude faster than solving the original equations, with physically equivalent results. For numerical simulations the speedup is at least 11 orders of magnitude. For parameter estimation our current numbers are about bringing ~100 days for a single SPA inspiral binary neutron star Bayesian parameter estimation analysis to under a day. More recently, it has been shown that the full precessing problem for, say, 200 cycles, can be represented, through some new ideas, by a remarkably compact set of carefully chosen reduced basis waveforms (~10-100, depending on the accuracy requirements). I will highlight what I personally believe are the challenges to face next in this subarea of GW physics and where efforts should be directed. This talk will summarize work in collaboration with: Harbir Antil (GMU), Jonathan Blackman (Caltech), Priscila Canizares (IoA, Cambridge, UK), Sarah Caudill (UWM), Jonathan Gair (IoA. Cambridge. UK), Scott Field (UMD), Chad R. Galley (Caltech), Frank Herrmann (Germany), Han Hestahven (EPFL, Switzerland), Jason Kaye (Brown, Stanford & Courant). Evan Ochsner (UWM), Ricardo Nochetto (UMD), Vivien Raymond (LIGO, Caltech), Rory Smith (LIGO, Caltech) Bela Ssilagyi (Caltech) and MT (UMD & Caltech).

Earth-Moon system: Dynamics and parameter estimation

NASA Technical Reports Server (NTRS)

Breedlove, W. J., Jr.

1979-01-01

The following topics are discussed: (1) the Unified Model of Lunar Translation/Rotation (UMLTR); (2) the effect of figure-figure interactions on lunar physical librations; (3) the effect of translational-rotational coupling on the lunar orbit; and(4) an error analysis for estimating lunar inertias from LURE (Lunar Laser Ranging Experiment) data.

SPECIFYING PHYSIOLOGICAL PARAMETERS FOR THE KINETICS OF INHALED TOLUENE IN RATS PERFORMING THE VISUAL SIGNAL DETECTION TASK (SDT).

EPA Science Inventory

A physiologically-based pharmacokinetic (PBPK) model is being developed to estimate the dosimetry of toluene in rats inhaling the VOC under various experimental conditions. The effects of physical activity are currently being estimated utilizing a three-step process. First, we d...

Bayesian calibration for electrochemical thermal model of lithium-ion cells

NASA Astrophysics Data System (ADS)

Tagade, Piyush; Hariharan, Krishnan S.; Basu, Suman; Verma, Mohan Kumar Singh; Kolake, Subramanya Mayya; Song, Taewon; Oh, Dukjin; Yeo, Taejung; Doo, Seokgwang

2016-07-01

Pseudo-two dimensional electrochemical thermal (P2D-ECT) model contains many parameters that are difficult to evaluate experimentally. Estimation of these model parameters is challenging due to computational cost and the transient model. Due to lack of complete physical understanding, this issue gets aggravated at extreme conditions like low temperature (LT) operations. This paper presents a Bayesian calibration framework for estimation of the P2D-ECT model parameters. The framework uses a matrix variate Gaussian process representation to obtain a computationally tractable formulation for calibration of the transient model. Performance of the framework is investigated for calibration of the P2D-ECT model across a range of temperatures (333 Ksbnd 263 K) and operating protocols. In the absence of complete physical understanding, the framework also quantifies structural uncertainty in the calibrated model. This information is used by the framework to test validity of the new physical phenomena before incorporation in the model. This capability is demonstrated by introducing temperature dependence on Bruggeman's coefficient and lithium plating formation at LT. With the incorporation of new physics, the calibrated P2D-ECT model accurately predicts the cell voltage with high confidence. The accurate predictions are used to obtain new insights into the low temperature lithium ion cell behavior.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tuo, Rui; Wu, C. F. Jeff

Many computer models contain unknown parameters which need to be estimated using physical observations. Furthermore, the calibration method based on Gaussian process models may lead to unreasonable estimate for imperfect computer models. In this work, we extend their study to calibration problems with stochastic physical data. We propose a novel method, called the L 2 calibration, and show its semiparametric efficiency. The conventional method of the ordinary least squares is also studied. Theoretical analysis shows that it is consistent but not efficient. Here, numerical examples show that the proposed method outperforms the existing ones.

K-ε Turbulence Model Parameter Estimates Using an Approximate Self-similar Jet-in-Crossflow Solution

DOE PAGES

DeChant, Lawrence; Ray, Jaideep; Lefantzi, Sophia; ...

2017-06-09

The k-ε turbulence model has been described as perhaps “the most widely used complete turbulence model.” This family of heuristic Reynolds Averaged Navier-Stokes (RANS) turbulence closures is supported by a suite of model parameters that have been estimated by demanding the satisfaction of well-established canonical flows such as homogeneous shear flow, log-law behavior, etc. While this procedure does yield a set of so-called nominal parameters, it is abundantly clear that they do not provide a universally satisfactory turbulence model that is capable of simulating complex flows. Recent work on the Bayesian calibration of the k-ε model using jet-in-crossflow wind tunnelmore » data has yielded parameter estimates that are far more predictive than nominal parameter values. In this paper, we develop a self-similar asymptotic solution for axisymmetric jet-in-crossflow interactions and derive analytical estimates of the parameters that were inferred using Bayesian calibration. The self-similar method utilizes a near field approach to estimate the turbulence model parameters while retaining the classical far-field scaling to model flow field quantities. Our parameter values are seen to be far more predictive than the nominal values, as checked using RANS simulations and experimental measurements. They are also closer to the Bayesian estimates than the nominal parameters. A traditional simplified jet trajectory model is explicitly related to the turbulence model parameters and is shown to yield good agreement with measurement when utilizing the analytical derived turbulence model coefficients. Finally, the close agreement between the turbulence model coefficients obtained via Bayesian calibration and the analytically estimated coefficients derived in this paper is consistent with the contention that the Bayesian calibration approach is firmly rooted in the underlying physical description.« less

Tuning a physically-based model of the air-sea gas transfer velocity

NASA Astrophysics Data System (ADS)

Jeffery, C. D.; Robinson, I. S.; Woolf, D. K.

Air-sea gas transfer velocities are estimated for one year using a 1-D upper-ocean model (GOTM) and a modified version of the NOAA-COARE transfer velocity parameterization. Tuning parameters are evaluated with the aim of bringing the physically based NOAA-COARE parameterization in line with current estimates, based on simple wind-speed dependent models derived from bomb-radiocarbon inventories and deliberate tracer release experiments. We suggest that A = 1.3 and B = 1.0, for the sub-layer scaling parameter and the bubble mediated exchange, respectively, are consistent with the global average CO 2 transfer velocity k. Using these parameters and a simple 2nd order polynomial approximation, with respect to wind speed, we estimate a global annual average k for CO 2 of 16.4 ± 5.6 cm h -1 when using global mean winds of 6.89 m s -1 from the NCEP/NCAR Reanalysis 1 1954-2000. The tuned model can be used to predict the transfer velocity of any gas, with appropriate treatment of the dependence on molecular properties including the strong solubility dependence of bubble-mediated transfer. For example, an initial estimate of the global average transfer velocity of DMS (a relatively soluble gas) is only 11.9 cm h -1 whilst for less soluble methane the estimate is 18.0 cm h -1.

A predictive parameter estimation approach for the thermodynamically constrained averaging theory applied to diffusion in porous media

NASA Astrophysics Data System (ADS)

Valdes-Parada, F. J.; Ostvar, S.; Wood, B. D.; Miller, C. T.

2017-12-01

Modeling of hierarchical systems such as porous media can be performed by different approaches that bridge microscale physics to the macroscale. Among the several alternatives available in the literature, the thermodynamically constrained averaging theory (TCAT) has emerged as a robust modeling approach that provides macroscale models that are consistent across scales. For specific closure relation forms, TCAT models are expressed in terms of parameters that depend upon the physical system under study. These parameters are usually obtained from inverse modeling based upon either experimental data or direct numerical simulation at the pore scale. Other upscaling approaches, such as the method of volume averaging, involve an a priori scheme for parameter estimation for certain microscale and transport conditions. In this work, we show how such a predictive scheme can be implemented in TCAT by studying the simple problem of single-phase passive diffusion in rigid and homogeneous porous media. The components of the effective diffusivity tensor are predicted for several porous media by solving ancillary boundary-value problems in periodic unit cells. The results are validated through a comparison with data from direct numerical simulation. This extension of TCAT constitutes a useful advance for certain classes of problems amenable to this estimation approach.

Dynamic Parameters of the 2015 Nepal Gorkha Mw7.8 Earthquake Constrained by Multi-observations

NASA Astrophysics Data System (ADS)

Weng, H.; Yang, H.

2017-12-01

Dynamic rupture model can provide much detailed insights into rupture physics that is capable of assessing future seismic risk. Many studies have attempted to constrain the slip-weakening distance, an important parameter controlling friction behavior of rock, for several earthquakes based on dynamic models, kinematic models, and direct estimations from near-field ground motion. However, large uncertainties of the values of the slip-weakening distance still remain, mostly because of the intrinsic trade-offs between the slip-weakening distance and fault strength. Here we use a spontaneously dynamic rupture model to constrain the frictional parameters of the 25 April 2015 Mw7.8 Nepal earthquake, by combining with multiple seismic observations such as high-rate cGPS data, strong motion data, and kinematic source models. With numerous tests we find the trade-off patterns of final slip, rupture speed, static GPS ground displacements, and dynamic ground waveforms are quite different. Combining all the seismic constraints we can conclude a robust solution without a substantial trade-off of average slip-weakening distance, 0.6 m, in contrast to previous kinematical estimation of 5 m. To our best knowledge, this is the first time to robustly determine the slip-weakening distance on seismogenic fault from seismic observations. The well-constrained frictional parameters may be used for future dynamic models to assess seismic hazard, such as estimating the peak ground acceleration (PGA) etc. Similar approach could also be conducted for other great earthquakes, enabling broad estimations of the dynamic parameters in global perspectives that can better reveal the intrinsic physics of earthquakes.

A three-dimensional cohesive sediment transport model with data assimilation: Model development, sensitivity analysis and parameter estimation

NASA Astrophysics Data System (ADS)

Wang, Daosheng; Cao, Anzhou; Zhang, Jicai; Fan, Daidu; Liu, Yongzhi; Zhang, Yue

2018-06-01

Based on the theory of inverse problems, a three-dimensional sigma-coordinate cohesive sediment transport model with the adjoint data assimilation is developed. In this model, the physical processes of cohesive sediment transport, including deposition, erosion and advection-diffusion, are parameterized by corresponding model parameters. These parameters are usually poorly known and have traditionally been assigned empirically. By assimilating observations into the model, the model parameters can be estimated using the adjoint method; meanwhile, the data misfit between model results and observations can be decreased. The model developed in this work contains numerous parameters; therefore, it is necessary to investigate the parameter sensitivity of the model, which is assessed by calculating a relative sensitivity function and the gradient of the cost function with respect to each parameter. The results of parameter sensitivity analysis indicate that the model is sensitive to the initial conditions, inflow open boundary conditions, suspended sediment settling velocity and resuspension rate, while the model is insensitive to horizontal and vertical diffusivity coefficients. A detailed explanation of the pattern of sensitivity analysis is also given. In ideal twin experiments, constant parameters are estimated by assimilating 'pseudo' observations. The results show that the sensitive parameters are estimated more easily than the insensitive parameters. The conclusions of this work can provide guidance for the practical applications of this model to simulate sediment transport in the study area.

Smoothing-based compressed state Kalman filter for joint state-parameter estimation: Applications in reservoir characterization and CO2 storage monitoring

NASA Astrophysics Data System (ADS)

Li, Y. J.; Kokkinaki, Amalia; Darve, Eric F.; Kitanidis, Peter K.

2017-08-01

The operation of most engineered hydrogeological systems relies on simulating physical processes using numerical models with uncertain parameters and initial conditions. Predictions by such uncertain models can be greatly improved by Kalman-filter techniques that sequentially assimilate monitoring data. Each assimilation constitutes a nonlinear optimization, which is solved by linearizing an objective function about the model prediction and applying a linear correction to this prediction. However, if model parameters and initial conditions are uncertain, the optimization problem becomes strongly nonlinear and a linear correction may yield unphysical results. In this paper, we investigate the utility of one-step ahead smoothing, a variant of the traditional filtering process, to eliminate nonphysical results and reduce estimation artifacts caused by nonlinearities. We present the smoothing-based compressed state Kalman filter (sCSKF), an algorithm that combines one step ahead smoothing, in which current observations are used to correct the state and parameters one step back in time, with a nonensemble covariance compression scheme, that reduces the computational cost by efficiently exploring the high-dimensional state and parameter space. Numerical experiments show that when model parameters are uncertain and the states exhibit hyperbolic behavior with sharp fronts, as in CO2 storage applications, one-step ahead smoothing reduces overshooting errors and, by design, gives physically consistent state and parameter estimates. We compared sCSKF with commonly used data assimilation methods and showed that for the same computational cost, combining one step ahead smoothing and nonensemble compression is advantageous for real-time characterization and monitoring of large-scale hydrogeological systems with sharp moving fronts.

Quantitative Diagnosis of Continuous-Valued, Stead-State Systems

NASA Technical Reports Server (NTRS)

Rouquette, N.

1995-01-01

Quantitative diagnosis involves numerically estimating the values of unobservable parameters that best explain the observed parameter values. We consider quantitative diagnosis for continuous, lumped- parameter, steady-state physical systems because such models are easy to construct and the diagnosis problem is considerably simpler than that for corresponding dynamic models. To further tackle the difficulties of numerically inverting a simulation model to compute a diagnosis, we propose to decompose a physical system model in terms of feedback loops. This decomposition reduces the dimension of the problem and consequently decreases the diagnosis search space. We illustrate this approach on a model of thermal control system studied in earlier research.

Increasing precision of turbidity-based suspended sediment concentration and load estimates.

PubMed

Jastram, John D; Zipper, Carl E; Zelazny, Lucian W; Hyer, Kenneth E

2010-01-01

Turbidity is an effective tool for estimating and monitoring suspended sediments in aquatic systems. Turbidity can be measured in situ remotely and at fine temporal scales as a surrogate for suspended sediment concentration (SSC), providing opportunity for a more complete record of SSC than is possible with physical sampling approaches. However, there is variability in turbidity-based SSC estimates and in sediment loadings calculated from those estimates. This study investigated the potential to improve turbidity-based SSC, and by extension the resulting sediment loading estimates, by incorporating hydrologic variables that can be monitored remotely and continuously (typically 15-min intervals) into the SSC estimation procedure. On the Roanoke River in southwestern Virginia, hydrologic stage, turbidity, and other water-quality parameters were monitored with in situ instrumentation; suspended sediments were sampled manually during elevated turbidity events; samples were analyzed for SSC and physical properties including particle-size distribution and organic C content; and rainfall was quantified by geologic source area. The study identified physical properties of the suspended-sediment samples that contribute to SSC estimation variance and hydrologic variables that explained variability of those physical properties. Results indicated that the inclusion of any of the measured physical properties in turbidity-based SSC estimation models reduces unexplained variance. Further, the use of hydrologic variables to represent these physical properties, along with turbidity, resulted in a model, relying solely on data collected remotely and continuously, that estimated SSC with less variance than a conventional turbidity-based univariate model, allowing a more precise estimate of sediment loading, Modeling results are consistent with known mechanisms governing sediment transport in hydrologic systems.

Natriuretic Peptide and Clinical Evaluation in the Diagnosis of Heart Failure Hemodynamic Profile: Comparison with Tissue Doppler Echocardiography.

PubMed

Almeida Junior, Gustavo Luiz Gouvêa de; Clausell, Nadine; Garcia, Marcelo Iorio; Esporcatte, Roberto; Rangel, Fernando Oswaldo Dias; Rocha, Ricardo Mourilhe; Beck-da-Silva, Luis; Silva, Fabricio Braga da; Gorgulho, Paula de Castro Carvalho; Xavier, Sergio Salles

2018-03-01

Physical examination and B-type natriuretic peptide (BNP) have been used to estimate hemodynamics and tailor therapy of acute decompensated heart failure (ADHF) patients. However, correlation between these parameters and left ventricular filling pressures is controversial. This study was designed to evaluate the diagnostic accuracy of physical examination, chest radiography (CR) and BNP in estimating left atrial pressure (LAP) as assessed by tissue Doppler echocardiogram. Patients admitted with ADHF were prospectively assessed. Diagnostic characteristics of physical signs of heart failure, CR and BNP in predicting elevation (> 15 mm Hg) of LAP, alone or combined, were calculated. Spearman test was used to analyze the correlation between non-normal distribution variables. The level of significance was 5%. Forty-three patients were included, with mean age of 69.9 ± 11.1years, left ventricular ejection fraction of 25 ± 8.0%, and BNP of 1057 ± 1024.21 pg/mL. Individually, all clinical, CR or BNP parameters had a poor performance in predicting LAP ≥ 15 mm Hg. A clinical score of congestion had the poorest performance [area under the receiver operating characteristic curve (AUC) 0.53], followed by clinical score + CR (AUC 0.60), clinical score + CR + BNP > 400 pg/mL (AUC 0.62), and clinical score + CR + BNP > 1000 pg/mL (AUC 0.66). Physical examination, CR and BNP had a poor performance in predicting a LAP ≥ 15 mm Hg. Using these parameters alone or in combination may lead to inaccurate estimation of hemodynamics.

Physical and geometrical parameters of VCBS XIII: HIP 105947

NASA Astrophysics Data System (ADS)

Gumaan Masda, Suhail; Al-Wardat, Mashhoor Ahmed; Pathan, Jiyaulla Khan Moula Khan

2018-06-01

The best physical and geometrical parameters of the main sequence close visual binary system (CVBS), HIP 105947, are presented. These parameters have been constructed conclusively using Al-Wardat’s complex method for analyzing CVBSs, which is a method for constructing a synthetic spectral energy distribution (SED) for the entire binary system using individual SEDs for each component star. The model atmospheres are in its turn built using the Kurucz (ATLAS9) line-blanketed plane-parallel models. At the same time, the orbital parameters for the system are calculated using Tokovinin’s dynamical method for constructing the best orbits of an interferometric binary system. Moreover, the mass-sum of the components, as well as the Δθ and Δρ residuals for the system, is introduced. The combination of Al-Wardat’s and Tokovinin’s methods yields the best estimations of the physical and geometrical parameters. The positions of the components in the system on the evolutionary tracks and isochrones are plotted and the formation and evolution of the system are discussed.

Parameter estimation in a structural acoustic system with fully nonlinear coupling conditions

NASA Technical Reports Server (NTRS)

Banks, H. T.; Smith, Ralph C.

1994-01-01

A methodology for estimating physical parameters in a class of structural acoustic systems is presented. The general model under consideration consists of an interior cavity which is separated from an exterior noise source by an enclosing elastic structure. Piezoceramic patches are bonded to or embedded in the structure; these can be used both as actuators and sensors in applications ranging from the control of interior noise levels to the determination of structural flaws through nondestructive evaluation techniques. The presence and excitation of patches, however, changes the geometry and material properties of the structure as well as involves unknown patch parameters, thus necessitating the development of parameter estimation techniques which are applicable in this coupled setting. In developing a framework for approximation, parameter estimation and implementation, strong consideration is given to the fact that the input operator is unbonded due to the discrete nature of the patches. Moreover, the model is weakly nonlinear. As a result of the coupling mechanism between the structural vibrations and the interior acoustic dynamics. Within this context, an illustrating model is given, well-posedness and approximations results are discussed and an applicable parameter estimation methodology is presented. The scheme is then illustrated through several numerical examples with simulations modeling a variety of commonly used structural acoustic techniques for systems excitations and data collection.

A Bayesian network for modelling blood glucose concentration and exercise in type 1 diabetes.

PubMed

Ewings, Sean M; Sahu, Sujit K; Valletta, John J; Byrne, Christopher D; Chipperfield, Andrew J

2015-06-01

This article presents a new statistical approach to analysing the effects of everyday physical activity on blood glucose concentration in people with type 1 diabetes. A physiologically based model of blood glucose dynamics is developed to cope with frequently sampled data on food, insulin and habitual physical activity; the model is then converted to a Bayesian network to account for measurement error and variability in the physiological processes. A simulation study is conducted to determine the feasibility of using Markov chain Monte Carlo methods for simultaneous estimation of all model parameters and prediction of blood glucose concentration. Although there are problems with parameter identification in a minority of cases, most parameters can be estimated without bias. Predictive performance is unaffected by parameter misspecification and is insensitive to misleading prior distributions. This article highlights important practical and theoretical issues not previously addressed in the quest for an artificial pancreas as treatment for type 1 diabetes. The proposed methods represent a new paradigm for analysis of deterministic mathematical models of blood glucose concentration. © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Remedial options for creosote-contaminated sites

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, W.J.; Delshad, M.; Oolman, T.

2000-03-31

Free-phase DNAPL recovery operations are becoming increasingly prevalent at creosote-contaminated aquifer sites. This paper illustrates the potential of both classical and innovative recovery methods. The UTCHEM multiphase flow and transport numerical simulator was used to predict the migration of creosote DNAPL during a hypothetical spill event, during a long-term redistribution after the spill, and for a variety of subsequent free-phase DNAPL recovery operations. The physical parameters used for the DNAPL and the aquifer in the model are estimates for the DNAPL and the aquifer in the model are estimates for a specific creosote DNAPL site. Other simulations were also conductedmore » using physical parameters that are typical of a trichloroethene (TCE) DNAPL. Dramatic differences in DNAPL migration were observed between these simulations.« less

Quantifying the impact of the longitudinal dispersion coefficient parameter uncertainty on the physical transport processes in rivers

NASA Astrophysics Data System (ADS)

Camacho Suarez, V. V.; Shucksmith, J.; Schellart, A.

2016-12-01

Analytical and numerical models can be used to represent the advection-dispersion processes governing the transport of pollutants in rivers (Fan et al., 2015; Van Genuchten et al., 2013). Simplifications, assumptions and parameter estimations in these models result in various uncertainties within the modelling process and estimations of pollutant concentrations. In this study, we explore both: 1) the structural uncertainty due to the one dimensional simplification of the Advection Dispersion Equation (ADE) and 2) the parameter uncertainty due to the semi empirical estimation of the longitudinal dispersion coefficient. The relative significance of these uncertainties has not previously been examined. By analysing both the relative structural uncertainty of analytical solutions of the ADE, and the parameter uncertainty due to the longitudinal dispersion coefficient via a Monte Carlo analysis, an evaluation of the dominant uncertainties for a case study in the river Chillan, Chile is presented over a range of spatial scales.

Towards a smart non-invasive fluid loss measurement system.

PubMed

Suryadevara, N K; Mukhopadhyay, S C; Barrack, L

2015-04-01

In this article, a smart wireless sensing non-invasive system for estimating the amount of fluid loss, a person experiences while physical activity is presented. The system measures three external body parameters, Heart Rate, Galvanic Skin Response (GSR, or skin conductance), and Skin Temperature. These three parameters are entered into an empirically derived formula along with the user's body mass index, and estimation for the amount of fluid lost is determined. The core benefit of the developed system is the affluence usage in combining with smart home monitoring systems to care elderly people in ambient assisted living environments as well in automobiles to monitor the body parameters of a motorist.

Parameter Estimation for Viscoplastic Material Modeling

NASA Technical Reports Server (NTRS)

Saleeb, Atef F.; Gendy, Atef S.; Wilt, Thomas E.

1997-01-01

A key ingredient in the design of engineering components and structures under general thermomechanical loading is the use of mathematical constitutive models (e.g. in finite element analysis) capable of accurate representation of short and long term stress/deformation responses. In addition to the ever-increasing complexity of recent viscoplastic models of this type, they often also require a large number of material constants to describe a host of (anticipated) physical phenomena and complicated deformation mechanisms. In turn, the experimental characterization of these material parameters constitutes the major factor in the successful and effective utilization of any given constitutive model; i.e., the problem of constitutive parameter estimation from experimental measurements.

Quantum energy teleportation in a quantum Hall system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yusa, Go; Izumida, Wataru; Hotta, Masahiro

2011-09-15

We propose an experimental method for a quantum protocol termed quantum energy teleportation (QET), which allows energy transportation to a remote location without physical carriers. Using a quantum Hall system as a realistic model, we discuss the physical significance of QET and estimate the order of energy gain using reasonable experimental parameters.

Assessing first-order emulator inference for physical parameters in nonlinear mechanistic models

USGS Publications Warehouse

Hooten, Mevin B.; Leeds, William B.; Fiechter, Jerome; Wikle, Christopher K.

2011-01-01

We present an approach for estimating physical parameters in nonlinear models that relies on an approximation to the mechanistic model itself for computational efficiency. The proposed methodology is validated and applied in two different modeling scenarios: (a) Simulation and (b) lower trophic level ocean ecosystem model. The approach we develop relies on the ability to predict right singular vectors (resulting from a decomposition of computer model experimental output) based on the computer model input and an experimental set of parameters. Critically, we model the right singular vectors in terms of the model parameters via a nonlinear statistical model. Specifically, we focus our attention on first-order models of these right singular vectors rather than the second-order (covariance) structure.

Combined Uncertainty and A-Posteriori Error Bound Estimates for CFD Calculations: Theory and Implementation

NASA Technical Reports Server (NTRS)

Barth, Timothy J.

2014-01-01

Simulation codes often utilize finite-dimensional approximation resulting in numerical error. Some examples include, numerical methods utilizing grids and finite-dimensional basis functions, particle methods using a finite number of particles. These same simulation codes also often contain sources of uncertainty, for example, uncertain parameters and fields associated with the imposition of initial and boundary data,uncertain physical model parameters such as chemical reaction rates, mixture model parameters, material property parameters, etc.

Long Term Baseline Atmospheric Monitoring

ERIC Educational Resources Information Center

Goldman, Mark A.

1975-01-01

Describes a program designed to measure the normal concentrations of certain chemical and physical parameters of the atmosphere so that quantitative estimates can be made of local, regional, and global pollution. (GS)

Parameter Estimation for Real Filtered Sinusoids

DTIC Science & Technology

1997-09-01

Statistical Signal Processing: Detection, Estimation and Time Series Analysis. New York: Addison-Wesley, 1991. 74. Serway , Raymond A . Physics for...Dr. Yung Kee Yeo, Dean’s Representative Dr. Robert A . Calico, Jr., Dean Table of Contents Page List of Abbreviations...Contributions ....... ...................... 5-4 5.4 Summary ........ ............................. 5-6 Appendix A . Vector-Matrix Differentiation

CosmoSIS: A system for MC parameter estimation

DOE PAGES

Bridle, S.; Dodelson, S.; Jennings, E.; ...

2015-12-23

CosmoSIS is a modular system for cosmological parameter estimation, based on Markov Chain Monte Carlo and related techniques. It provides a series of samplers, which drive the exploration of the parameter space, and a series of modules, which calculate the likelihood of the observed data for a given physical model, determined by the location of a sample in the parameter space. While CosmoSIS ships with a set of modules that calculate quantities of interest to cosmologists, there is nothing about the framework itself, nor in the Markov Chain Monte Carlo technique, that is specific to cosmology. Thus CosmoSIS could bemore » used for parameter estimation problems in other fields, including HEP. This paper describes the features of CosmoSIS and show an example of its use outside of cosmology. Furthermore, it also discusses how collaborative development strategies differ between two different communities: that of HEP physicists, accustomed to working in large collaborations, and that of cosmologists, who have traditionally not worked in large groups.« less

Quantifying thermohaline circulations: seawater isotopic compositions and salinity as proxies of the ratio between advection time and evaporation time

NASA Astrophysics Data System (ADS)

Paldor, N.; Berman, H.; Lazar, B.

2017-12-01

Uncertainties in quantitative estimates of the thermohaline circulation in any particular basin are large, partly due to large uncertainties in quantifying excess evaporation over precipitation and surface velocities. A single nondimensional parameter, γ=(qx)/(hu) is proposed to characterize the "strength" of the thermohaline circulation by combining the physical parameters of surface velocity (u), evaporation rate (q), mixed layer depth (h) and trajectory length (x). Values of g can be estimated directly from cross-sections of salinity or seawater isotopic composition (δ18O and δD). Estimates of q in the Red Sea and the South-West Indian Ocean are 0.1 and 0.02, respectively, which implies that the thermohaline contribution to the circulation in the former is higher than in the latter. Once the value of g has been determined in a particular basin, either q or u can be estimated from known values of the remaining parameters. In the studied basins such estimates are consistent with previous studies.

The cosmological analysis of X-ray cluster surveys. III. 4D X-ray observable diagrams

NASA Astrophysics Data System (ADS)

Pierre, M.; Valotti, A.; Faccioli, L.; Clerc, N.; Gastaud, R.; Koulouridis, E.; Pacaud, F.

2017-11-01

Context. Despite compelling theoretical arguments, the use of clusters as cosmological probes is, in practice, frequently questioned because of the many uncertainties surrounding cluster-mass estimates. Aims: Our aim is to develop a fully self-consistent cosmological approach of X-ray cluster surveys, exclusively based on observable quantities rather than masses. This procedure is justified given the possibility to directly derive the cluster properties via ab initio modelling, either analytically or by using hydrodynamical simulations. In this third paper, we evaluate the method on cluster toy-catalogues. Methods: We model the population of detected clusters in the count-rate - hardness-ratio - angular size - redshift space and compare the corresponding four-dimensional diagram with theoretical predictions. The best cosmology+physics parameter configuration is determined using a simple minimisation procedure; errors on the parameters are estimated by averaging the results from ten independent survey realisations. The method allows a simultaneous fit of the cosmological parameters of the cluster evolutionary physics and of the selection effects. Results: When using information from the X-ray survey alone plus redshifts, this approach is shown to be as accurate as the modelling of the mass function for the cosmological parameters and to perform better for the cluster physics, for a similar level of assumptions on the scaling relations. It enables the identification of degenerate combinations of parameter values. Conclusions: Given the considerably shorter computer times involved for running the minimisation procedure in the observed parameter space, this method appears to clearly outperform traditional mass-based approaches when X-ray survey data alone are available.

Bayesian Markov Chain Monte Carlo inversion for weak anisotropy parameters and fracture weaknesses using azimuthal elastic impedance

NASA Astrophysics Data System (ADS)

Chen, Huaizhen; Pan, Xinpeng; Ji, Yuxin; Zhang, Guangzhi

2017-08-01

A system of aligned vertical fractures and fine horizontal shale layers combine to form equivalent orthorhombic media. Weak anisotropy parameters and fracture weaknesses play an important role in the description of orthorhombic anisotropy (OA). We propose a novel approach of utilizing seismic reflection amplitudes to estimate weak anisotropy parameters and fracture weaknesses from observed seismic data, based on azimuthal elastic impedance (EI). We first propose perturbation in stiffness matrix in terms of weak anisotropy parameters and fracture weaknesses, and using the perturbation and scattering function, we derive PP-wave reflection coefficient and azimuthal EI for the case of an interface separating two OA media. Then we demonstrate an approach to first use a model constrained damped least-squares algorithm to estimate azimuthal EI from partially incidence-phase-angle-stack seismic reflection data at different azimuths, and then extract weak anisotropy parameters and fracture weaknesses from the estimated azimuthal EI using a Bayesian Markov Chain Monte Carlo inversion method. In addition, a new procedure to construct rock physics effective model is presented to estimate weak anisotropy parameters and fracture weaknesses from well log interpretation results (minerals and their volumes, porosity, saturation, fracture density, etc.). Tests on synthetic and real data indicate that unknown parameters including elastic properties (P- and S-wave impedances and density), weak anisotropy parameters and fracture weaknesses can be estimated stably in the case of seismic data containing a moderate noise, and our approach can make a reasonable estimation of anisotropy in a fractured shale reservoir.

A comparison of two approaches to modelling snow cover dynamics at the Polish Polar Station at Hornsund

NASA Astrophysics Data System (ADS)

Luks, B.; Osuch, M.; Romanowicz, R. J.

2012-04-01

We compare two approaches to modelling snow cover dynamics at the Polish Polar Station at Hornsund. In the first approach we apply physically-based Utah Energy Balance Snow Accumulation and Melt Model (UEB) (Tarboton et al., 1995; Tarboton and Luce, 1996). The model uses a lumped representation of the snowpack with two primary state variables: snow water equivalence and energy. Its main driving inputs are: air temperature, precipitation, wind speed, humidity and radiation (estimated from the diurnal temperature range). Those variables are used for physically-based calculations of radiative, sensible, latent and advective heat exchanges with a 3 hours time step. The second method is an application of a statistically efficient lumped parameter time series approach to modelling the dynamics of snow cover , based on daily meteorological measurements from the same area. A dynamic Stochastic Transfer Function model is developed that follows the Data Based Mechanistic approach, where a stochastic data-based identification of model structure and an estimation of its parameters are followed by a physical interpretation. We focus on the analysis of uncertainty of both model outputs. In the time series approach, the applied techniques also provide estimates of the modeling errors and the uncertainty of the model parameters. In the first, physically-based approach the applied UEB model is deterministic. It assumes that the observations are without errors and that the model structure perfectly describes the processes within the snowpack. To take into account the model and observation errors, we applied a version of the Generalized Likelihood Uncertainty Estimation technique (GLUE). This technique also provide estimates of the modelling errors and the uncertainty of the model parameters. The observed snowpack water equivalent values are compared with those simulated with 95% confidence bounds. This work was supported by National Science Centre of Poland (grant no. 7879/B/P01/2011/40). Tarboton, D. G., T. G. Chowdhury and T. H. Jackson, 1995. A Spatially Distributed Energy Balance Snowmelt Model. In K. A. Tonnessen, M. W. Williams and M. Tranter (Ed.), Proceedings of a Boulder Symposium, July 3-14, IAHS Publ. no. 228, pp. 141-155. Tarboton, D. G. and C. H. Luce, 1996. Utah Energy Balance Snow Accumulation and Melt Model (UEB). Computer model technical description and users guide, Utah Water Research Laboratory and USDA Forest Service Intermountain Research Station (http://www.engineering.usu.edu/dtarb/). 64 pp.

A history of presatellite investigations of the earth's radiation budget

NASA Technical Reports Server (NTRS)

Hunt, G. E.; Kandel, R.; Mecherikunnel, A. T.

1986-01-01

The history of radiation budget studies from the early twentieth century to the advent of the space age is reviewed. By the beginning of the 1960's, accurate radiative models had been developed capable of estimating the global and zonally averaged components of the radiation budget, though great uncertainty in the derived parameters existed due to inaccuracy of the data describing the physical parameters used in the model, associated with clouds, the solar radiation, and the gaseous atmospheric absorbers. Over the century, the planetary albedo estimates had reduced from 89 to 30 percent.

The logic of comparative life history studies for estimating key parameters, with a focus on natural mortality rate

USGS Publications Warehouse

Hoenig, John M; Then, Amy Y.-H.; Babcock, Elizabeth A.; Hall, Norman G.; Hewitt, David A.; Hesp, Sybrand A.

2016-01-01

There are a number of key parameters in population dynamics that are difficult to estimate, such as natural mortality rate, intrinsic rate of population growth, and stock-recruitment relationships. Often, these parameters of a stock are, or can be, estimated indirectly on the basis of comparative life history studies. That is, the relationship between a difficult to estimate parameter and life history correlates is examined over a wide variety of species in order to develop predictive equations. The form of these equations may be derived from life history theory or simply be suggested by exploratory data analysis. Similarly, population characteristics such as potential yield can be estimated by making use of a relationship between the population parameter and bio-chemico–physical characteristics of the ecosystem. Surprisingly, little work has been done to evaluate how well these indirect estimators work and, in fact, there is little guidance on how to conduct comparative life history studies and how to evaluate them. We consider five issues arising in such studies: (i) the parameters of interest may be ill-defined idealizations of the real world, (ii) true values of the parameters are not known for any species, (iii) selecting data based on the quality of the estimates can introduce a host of problems, (iv) the estimates that are available for comparison constitute a non-random sample of species from an ill-defined population of species of interest, and (v) the hierarchical nature of the data (e.g. stocks within species within genera within families, etc., with multiple observations at each level) warrants consideration. We discuss how these issues can be handled and how they shape the kinds of questions that can be asked of a database of life history studies.

Simple Experiment for Studying the Properties of a Ferromagnetic Material.

ERIC Educational Resources Information Center

Sood, B. R.; And Others

1980-01-01

Describes an undergraduate physics experiment for studying Curie temperature and Curie constant of a ferromagnetic material. The exchange field (Weiss field) has been estimated by using these parameters. (HM)

Scale-Dependent Solute Dispersion in Variably Saturated Porous Media

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rockhold, Mark L.; Zhang, Z. F.; Bott, Yi-Ju

2016-03-29

This work was performed to support performance assessment (PA) calculations for the Integrated Disposal Facility (IDF) at the Hanford Site. PA calculations require defensible estimates of physical, hydraulic, and transport parameters to simulate subsurface water flow and contaminant transport in both the near- and far-field environments. Dispersivity is one of the required transport parameters.

Estimation of the viscosities of liquid binary alloys

NASA Astrophysics Data System (ADS)

Wu, Min; Su, Xiang-Yu

2018-01-01

As one of the most important physical and chemical properties, viscosity plays a critical role in physics and materials as a key parameter to quantitatively understanding the fluid transport process and reaction kinetics in metallurgical process design. Experimental and theoretical studies on liquid metals are problematic. Today, there are many empirical and semi-empirical models available with which to evaluate the viscosity of liquid metals and alloys. However, the parameter of mixed energy in these models is not easily determined, and most predictive models have been poorly applied. In the present study, a new thermodynamic parameter Δ G is proposed to predict liquid alloy viscosity. The prediction equation depends on basic physical and thermodynamic parameters, namely density, melting temperature, absolute atomic mass, electro-negativity, electron density, molar volume, Pauling radius, and mixing enthalpy. Our results show that the liquid alloy viscosity predicted using the proposed model is closely in line with the experimental values. In addition, if the component radius difference is greater than 0.03 nm at a certain temperature, the atomic size factor has a significant effect on the interaction of the binary liquid metal atoms. The proposed thermodynamic parameter Δ G also facilitates the study of other physical properties of liquid metals.

Efficient calibration for imperfect computer models

DOE PAGES

Tuo, Rui; Wu, C. F. Jeff

2015-12-01

Many computer models contain unknown parameters which need to be estimated using physical observations. Furthermore, the calibration method based on Gaussian process models may lead to unreasonable estimate for imperfect computer models. In this work, we extend their study to calibration problems with stochastic physical data. We propose a novel method, called the L 2 calibration, and show its semiparametric efficiency. The conventional method of the ordinary least squares is also studied. Theoretical analysis shows that it is consistent but not efficient. Here, numerical examples show that the proposed method outperforms the existing ones.

Parameter extraction using global particle swarm optimization approach and the influence of polymer processing temperature on the solar cell parameters

NASA Astrophysics Data System (ADS)

Kumar, S.; Singh, A.; Dhar, A.

2017-08-01

The accurate estimation of the photovoltaic parameters is fundamental to gain an insight of the physical processes occurring inside a photovoltaic device and thereby to optimize its design, fabrication processes, and quality. A simulative approach of accurately determining the device parameters is crucial for cell array and module simulation when applied in practical on-field applications. In this work, we have developed a global particle swarm optimization (GPSO) approach to estimate the different solar cell parameters viz., ideality factor (η), short circuit current (Isc), open circuit voltage (Voc), shunt resistant (Rsh), and series resistance (Rs) with wide a search range of over ±100 % for each model parameter. After validating the accurateness and global search power of the proposed approach with synthetic and noisy data, we applied the technique to the extract the PV parameters of ZnO/PCDTBT based hybrid solar cells (HSCs) prepared under different annealing conditions. Further, we examine the variation of extracted model parameters to unveil the physical processes occurring when different annealing temperatures are employed during the device fabrication and establish the role of improved charge transport in polymer films from independent FET measurements. The evolution of surface morphology, optical absorption, and chemical compositional behaviour of PCDTBT co-polymer films as a function of processing temperature has also been captured in the study and correlated with the findings from the PV parameters extracted using GPSO approach.

Users guide for EASI graphics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sasser, D.W.

1978-03-01

EASI (Estimate of Adversary Sequence Interruption) is an analytical technique for measuring the effectiveness of physical protection systems. EASI Graphics is a computer graphics extension of EASI which provides a capability for performing sensitivity and trade-off analyses of the parameters of a physical protection system. This document reports on the implementation of EASI Graphics and illustrates its application with some examples.

Development of property-transfer models for estimating the hydraulic properties of deep sediments at the Idaho National Engineering and Environmental Laboratory, Idaho

USGS Publications Warehouse

Winfield, Kari A.

2005-01-01

Because characterizing the unsaturated hydraulic properties of sediments over large areas or depths is costly and time consuming, development of models that predict these properties from more easily measured bulk-physical properties is desirable. At the Idaho National Engineering and Environmental Laboratory, the unsaturated zone is composed of thick basalt flow sequences interbedded with thinner sedimentary layers. Determining the unsaturated hydraulic properties of sedimentary layers is one step in understanding water flow and solute transport processes through this complex unsaturated system. Multiple linear regression was used to construct simple property-transfer models for estimating the water-retention curve and saturated hydraulic conductivity of deep sediments at the Idaho National Engineering and Environmental Laboratory. The regression models were developed from 109 core sample subsets with laboratory measurements of hydraulic and bulk-physical properties. The core samples were collected at depths of 9 to 175 meters at two facilities within the southwestern portion of the Idaho National Engineering and Environmental Laboratory-the Radioactive Waste Management Complex, and the Vadose Zone Research Park southwest of the Idaho Nuclear Technology and Engineering Center. Four regression models were developed using bulk-physical property measurements (bulk density, particle density, and particle size) as the potential explanatory variables. Three representations of the particle-size distribution were compared: (1) textural-class percentages (gravel, sand, silt, and clay), (2) geometric statistics (mean and standard deviation), and (3) graphical statistics (median and uniformity coefficient). The four response variables, estimated from linear combinations of the bulk-physical properties, included saturated hydraulic conductivity and three parameters that define the water-retention curve. For each core sample,values of each water-retention parameter were estimated from the appropriate regression equation and used to calculate an estimated water-retention curve. The degree to which the estimated curve approximated the measured curve was quantified using a goodness-of-fit indicator, the root-mean-square error. Comparison of the root-mean-square-error distributions for each alternative particle-size model showed that the estimated water-retention curves were insensitive to the way the particle-size distribution was represented. Bulk density, the median particle diameter, and the uniformity coefficient were chosen as input parameters for the final models. The property-transfer models developed in this study allow easy determination of hydraulic properties without need for their direct measurement. Additionally, the models provide the basis for development of theoretical models that rely on physical relationships between the pore-size distribution and the bulk-physical properties of the media. With this adaptation, the property-transfer models should have greater application throughout the Idaho National Engineering and Environmental Laboratory and other geographic locations.

A Parameter Identification Method for Helicopter Noise Source Identification and Physics-Based Semi-Empirical Modeling

NASA Technical Reports Server (NTRS)

Greenwood, Eric, II; Schmitz, Fredric H.

2010-01-01

A new physics-based parameter identification method for rotor harmonic noise sources is developed using an acoustic inverse simulation technique. This new method allows for the identification of individual rotor harmonic noise sources and allows them to be characterized in terms of their individual non-dimensional governing parameters. This new method is applied to both wind tunnel measurements and ground noise measurements of two-bladed rotors. The method is shown to match the parametric trends of main rotor Blade-Vortex Interaction (BVI) noise, allowing accurate estimates of BVI noise to be made for operating conditions based on a small number of measurements taken at different operating conditions.

Uncertainty analysis of signal deconvolution using a measured instrument response function

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hartouni, E. P.; Beeman, B.; Caggiano, J. A.

2016-10-05

A common analysis procedure minimizes the ln-likelihood that a set of experimental observables matches a parameterized model of the observation. The model includes a description of the underlying physical process as well as the instrument response function (IRF). Here, we investigate the National Ignition Facility (NIF) neutron time-of-flight (nTOF) spectrometers, the IRF is constructed from measurements and models. IRF measurements have a finite precision that can make significant contributions to the uncertainty estimate of the physical model’s parameters. Finally, we apply a Bayesian analysis to properly account for IRF uncertainties in calculating the ln-likelihood function used to find the optimummore » physical parameters.« less

Mesh refinement and numerical sensitivity analysis for parameter calibration of partial differential equations

NASA Astrophysics Data System (ADS)

Becker, Roland; Vexler, Boris

2005-06-01

We consider the calibration of parameters in physical models described by partial differential equations. This task is formulated as a constrained optimization problem with a cost functional of least squares type using information obtained from measurements. An important issue in the numerical solution of this type of problem is the control of the errors introduced, first, by discretization of the equations describing the physical model, and second, by measurement errors or other perturbations. Our strategy is as follows: we suppose that the user defines an interest functional I, which might depend on both the state variable and the parameters and which represents the goal of the computation. First, we propose an a posteriori error estimator which measures the error with respect to this functional. This error estimator is used in an adaptive algorithm to construct economic meshes by local mesh refinement. The proposed estimator requires the solution of an auxiliary linear equation. Second, we address the question of sensitivity. Applying similar techniques as before, we derive quantities which describe the influence of small changes in the measurements on the value of the interest functional. These numbers, which we call relative condition numbers, give additional information on the problem under consideration. They can be computed by means of the solution of the auxiliary problem determined before. Finally, we demonstrate our approach at hand of a parameter calibration problem for a model flow problem.

77 FR 4806 - Dominion Transmission, Inc.; Notice of Request Under Blanket Authorization

Federal Register 2010, 2011, 2012, 2013, 2014

2012-01-31

... the physical parameters, including total natural gas inventory, reservoir pressure, reservoir and... the construction and operation of wells TW-210 and TW-211. Dominion estimates that the proposed wells...

E-FAST-Exposure and Fate Assessment Screening Tool Version 2014

EPA Pesticide Factsheets

E-FAST estimates potential exposures to the general population and surface water concentrations based on releases from industrial operations and basic physical-chemical properties and fate parameters of the substance

Women use voice parameters to assess men's characteristics

PubMed Central

Bruckert, Laetitia; Liénard, Jean-Sylvain; Lacroix, André; Kreutzer, Michel; Leboucher, Gérard

2005-01-01

The purpose of this study was: (i) to provide additional evidence regarding the existence of human voice parameters, which could be reliable indicators of a speaker's physical characteristics and (ii) to examine the ability of listeners to judge voice pleasantness and a speaker's characteristics from speech samples. We recorded 26 men enunciating five vowels. Voices were played to 102 female judges who were asked to assess vocal attractiveness and speakers' age, height and weight. Statistical analyses were used to determine: (i) which physical component predicted which vocal component and (ii) which vocal component predicted which judgment. We found that men with low-frequency formants and small formant dispersion tended to be older, taller and tended to have a high level of testosterone. Female listeners were consistent in their pleasantness judgment and in their height, weight and age estimates. Pleasantness judgments were based mainly on intonation. Female listeners were able to correctly estimate age by using formant components. They were able to estimate weight but we could not explain which acoustic parameters they used. However, female listeners were not able to estimate height, possibly because they used intonation incorrectly. Our study confirms that in all mammal species examined thus far, including humans, formant components can provide a relatively accurate indication of a vocalizing individual's characteristics. Human listeners have the necessary information at their disposal; however, they do not necessarily use it. PMID:16519239

Characterization of classical static noise via qubit as probe

NASA Astrophysics Data System (ADS)

Javed, Muhammad; Khan, Salman; Ullah, Sayed Arif

2018-03-01

The dynamics of quantum Fisher information (QFI) of a single qubit coupled to classical static noise is investigated. The analytical relation for QFI fixes the optimal initial state of the qubit that maximizes it. An approximate limit for the time of coupling that leads to physically useful results is identified. Moreover, using the approach of quantum estimation theory and the analytical relation for QFI, the qubit is used as a probe to precisely estimate the disordered parameter of the environment. Relation for optimal interaction time with the environment is obtained, and condition for the optimal measurement of the noise parameter of the environment is given. It is shown that all values, in the mentioned range, of the noise parameter are estimable with equal precision. A comparison of our results with the previous studies in different classical environments is made.

PV cells electrical parameters measurement

NASA Astrophysics Data System (ADS)

Cibira, Gabriel

2017-12-01

When measuring optical parameters of a photovoltaic silicon cell, precise results bring good electrical parameters estimation, applying well-known physical-mathematical models. Nevertheless, considerable re-combination phenomena might occur in both surface and intrinsic thin layers within novel materials. Moreover, rear contact surface parameters may influence close-area re-combination phenomena, too. Therefore, the only precise electrical measurement approach is to prove assumed cell electrical parameters. Based on theoretical approach with respect to experiments, this paper analyses problems within measurement procedures and equipment used for electrical parameters acquisition within a photovoltaic silicon cell, as a case study. Statistical appraisal quality is contributed.

Suspension parameter estimation in the frequency domain using a matrix inversion approach

NASA Astrophysics Data System (ADS)

Thite, A. N.; Banvidi, S.; Ibicek, T.; Bennett, L.

2011-12-01

The dynamic lumped parameter models used to optimise the ride and handling of a vehicle require base values of the suspension parameters. These parameters are generally experimentally identified. The accuracy of identified parameters can depend on the measurement noise and the validity of the model used. The existing publications on suspension parameter identification are generally based on the time domain and use a limited degree of freedom. Further, the data used are either from a simulated 'experiment' or from a laboratory test on an idealised quarter or a half-car model. In this paper, a method is developed in the frequency domain which effectively accounts for the measurement noise. Additional dynamic constraining equations are incorporated and the proposed formulation results in a matrix inversion approach. The nonlinearities in damping are estimated, however, using a time-domain approach. Full-scale 4-post rig test data of a vehicle are used. The variations in the results are discussed using the modal resonant behaviour. Further, a method is implemented to show how the results can be improved when the matrix inverted is ill-conditioned. The case study shows a good agreement between the estimates based on the proposed frequency-domain approach and measurable physical parameters.

Urban air quality estimation study, phase 1

NASA Technical Reports Server (NTRS)

Diamante, J. M.; Englar, T. S., Jr.; Jazwinski, A. H.

1976-01-01

Possibilities are explored for applying estimation theory to the analysis, interpretation, and use of air quality measurements in conjunction with simulation models to provide a cost effective method of obtaining reliable air quality estimates for wide urban areas. The physical phenomenology of real atmospheric plumes from elevated localized sources is discussed. A fluctuating plume dispersion model is derived. Individual plume parameter formulations are developed along with associated a priori information. Individual measurement models are developed.

Target Classification of Canonical Scatterers Using Classical Estimation and Dictionary Based Techniques

DTIC Science & Technology

2012-03-22

shapes tested , when the objective parameter set was confined to a dictionary’s de - fined parameter space. These physical characteristics included...8 2.3 Hypothesis Testing and Detection Theory . . . . . . . . . . . . . . . 8 2.4 3-D SAR Scattering Models...basis pursuit de -noising (BPDN) algorithm is chosen to perform extraction due to inherent efficiency and error tolerance. Multiple shape dictionaries

Determination of Eros Physical Parameters for Near Earth Asteroid Rendezvous Orbit Phase Navigation

NASA Technical Reports Server (NTRS)

Miller, J. K.; Antreasian, P. J.; Georgini, J.; Owen, W. M.; Williams, B. G.; Yeomans, D. K.

1995-01-01

Navigation of the orbit phase of the Near Earth steroid Rendezvous (NEAR) mission will re,quire determination of certain physical parameters describing the size, shape, gravity field, attitude and inertial properties of Eros. Prior to launch, little was known about Eros except for its orbit which could be determined with high precision from ground based telescope observations. Radar bounce and light curve data provided a rough estimate of Eros shape and a fairly good estimate of the pole, prime meridian and spin rate. However, the determination of the NEAR spacecraft orbit requires a high precision model of Eros's physical parameters and the ground based data provides only marginal a priori information. Eros is the principal source of perturbations of the spacecraft's trajectory and the principal source of data for determining the orbit. The initial orbit determination strategy is therefore concerned with developing a precise model of Eros. The original plan for Eros orbital operations was to execute a series of rendezvous burns beginning on December 20,1998 and insert into a close Eros orbit in January 1999. As a result of an unplanned termination of the rendezvous burn on December 20, 1998, the NEAR spacecraft continued on its high velocity approach trajectory and passed within 3900 km of Eros on December 23, 1998. The planned rendezvous burn was delayed until January 3, 1999 which resulted in the spacecraft being placed on a trajectory that slowly returns to Eros with a subsequent delay of close Eros orbital operations until February 2001. The flyby of Eros provided a brief glimpse and allowed for a crude estimate of the pole, prime meridian and mass of Eros. More importantly for navigation, orbit determination software was executed in the landmark tracking mode to determine the spacecraft orbit and a preliminary shape and landmark data base has been obtained. The flyby also provided an opportunity to test orbit determination operational procedures that will be used in February of 2001. The initial attitude and spin rate of Eros, as well as estimates of reference landmark locations, are obtained from images of the asteroid. These initial estimates are used as a priori values for a more precise refinement of these parameters by the orbit determination software which combines optical measurements with Doppler tracking data to obtain solutions for the required parameters. As the spacecraft is maneuvered; closer to the asteroid, estimates of spacecraft state, asteroid attitude, solar pressure, landmark locations and Eros physical parameters including mass, moments of inertia and gravity harmonics are determined with increasing precision. The determination of the elements of the inertia tensor of the asteroid is critical to spacecraft orbit determination and prediction of the asteroid attitude. The moments of inertia about the principal axes are also of scientific interest since they provide some insight into the internal mass distribution. Determination of the principal axes moments of inertia will depend on observing free precession in the asteroid's attitude dynamics. Gravity harmonics are in themselves of interest to science. When compared with the asteroid shape, some insight may be obtained into Eros' internal structure. The location of the center of mass derived from the first degree harmonic coefficients give a direct indication of overall mass distribution. The second degree harmonic coefficients relate to the radial distribution of mass. Higher degree harmonics may be compared with surface features to gain additional insight into mass distribution. In this paper, estimates of Eros physical parameters obtained from the December 23,1998 flyby will be presented. This new knowledge will be applied to simplification of Eros orbital operations in February of 2001. The resulting revision to the orbit determination strategy will also be discussed.

Multiobjective sampling design for parameter estimation and model discrimination in groundwater solute transport

USGS Publications Warehouse

Knopman, Debra S.; Voss, Clifford I.

1989-01-01

Sampling design for site characterization studies of solute transport in porous media is formulated as a multiobjective problem. Optimal design of a sampling network is a sequential process in which the next phase of sampling is designed on the basis of all available physical knowledge of the system. Three objectives are considered: model discrimination, parameter estimation, and cost minimization. For the first two objectives, physically based measures of the value of information obtained from a set of observations are specified. In model discrimination, value of information of an observation point is measured in terms of the difference in solute concentration predicted by hypothesized models of transport. Points of greatest difference in predictions can contribute the most information to the discriminatory power of a sampling design. Sensitivity of solute concentration to a change in a parameter contributes information on the relative variance of a parameter estimate. Inclusion of points in a sampling design with high sensitivities to parameters tends to reduce variance in parameter estimates. Cost minimization accounts for both the capital cost of well installation and the operating costs of collection and analysis of field samples. Sensitivities, discrimination information, and well installation and sampling costs are used to form coefficients in the multiobjective problem in which the decision variables are binary (zero/one), each corresponding to the selection of an observation point in time and space. The solution to the multiobjective problem is a noninferior set of designs. To gain insight into effective design strategies, a one-dimensional solute transport problem is hypothesized. Then, an approximation of the noninferior set is found by enumerating 120 designs and evaluating objective functions for each of the designs. Trade-offs between pairs of objectives are demonstrated among the models. The value of an objective function for a given design is shown to correspond to the ability of a design to actually meet an objective.

Estimating Cosmic-Ray Spectral Parameters from Simulated Detector Responses with Detector Design Implications

NASA Technical Reports Server (NTRS)

Howell, L. W.

2001-01-01

A simple power law model consisting of a single spectral index (alpha-1) is believed to be an adequate description of the galactic cosmic-ray (GCR) proton flux at energies below 10(exp 13) eV, with a transition at knee energy (E(sub k)) to a steeper spectral index alpha-2 > alpha-1 above E(sub k). The maximum likelihood procedure is developed for estimating these three spectral parameters of the broken power law energy spectrum from simulated detector responses. These estimates and their surrounding statistical uncertainty are being used to derive the requirements in energy resolution, calorimeter size, and energy response of a proposed sampling calorimeter for the Advanced Cosmic-ray Composition Experiment for the Space Station (ACCESS). This study thereby permits instrument developers to make important trade studies in design parameters as a function of the science objectives, which is particularly important for space-based detectors where physical parameters, such as dimension and weight, impose rigorous practical limits to the design envelope.

Absolute parameters and chemical composition of the binary star OU Gem

NASA Astrophysics Data System (ADS)

Glazunova, L. V.; Mishenina, T. V.; Soubiran, C.; Kovtyukh, V. V.

2014-10-01

The absolute parameters and chemical composition of the BY Dra-type spectroscopic binary OU Gem (HD 45088) were determined on the basis of 10 high-resolution spectra. A new orbital solution of the binary system was determined, the binary ephemerides were specified, and the main physical and atmospheric parameters of the binary components were obtained. The chemical composition of both components was estimated for the first time for the stars of such type.

Effect of microwave radiation on Jayadhar cotton fibers: WAXS studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Niranjana, A. R., E-mail: arnphysics@gmail.com; Mahesh, S. S., E-mail: arnphysics@gmail.com; Divakara, S., E-mail: arnphysics@gmail.com

Thermal effect in the form of micro wave energy on Jayadhar cotton fiber has been investigated. Microstructural parameters have been estimated using wide angle x-ray scattering (WAXS) data and line profile analysis program developed by us. Physical properties like tensile strength are correlated with X-ray results. We observe that the microwave radiation do affect significantly many parameters and we have suggested a multivariate analysis of these parameters to arrive at a significant result.

Measurement-based perturbation theory and differential equation parameter estimation with applications to satellite gravimetry

NASA Astrophysics Data System (ADS)

Xu, Peiliang

2018-06-01

The numerical integration method has been routinely used by major institutions worldwide, for example, NASA Goddard Space Flight Center and German Research Center for Geosciences (GFZ), to produce global gravitational models from satellite tracking measurements of CHAMP and/or GRACE types. Such Earth's gravitational products have found widest possible multidisciplinary applications in Earth Sciences. The method is essentially implemented by solving the differential equations of the partial derivatives of the orbit of a satellite with respect to the unknown harmonic coefficients under the conditions of zero initial values. From the mathematical and statistical point of view, satellite gravimetry from satellite tracking is essentially the problem of estimating unknown parameters in the Newton's nonlinear differential equations from satellite tracking measurements. We prove that zero initial values for the partial derivatives are incorrect mathematically and not permitted physically. The numerical integration method, as currently implemented and used in mathematics and statistics, chemistry and physics, and satellite gravimetry, is groundless, mathematically and physically. Given the Newton's nonlinear governing differential equations of satellite motion with unknown equation parameters and unknown initial conditions, we develop three methods to derive new local solutions around a nominal reference orbit, which are linked to measurements to estimate the unknown corrections to approximate values of the unknown parameters and the unknown initial conditions. Bearing in mind that satellite orbits can now be tracked almost continuously at unprecedented accuracy, we propose the measurement-based perturbation theory and derive global uniformly convergent solutions to the Newton's nonlinear governing differential equations of satellite motion for the next generation of global gravitational models. Since the solutions are global uniformly convergent, theoretically speaking, they are able to extract smallest possible gravitational signals from modern and future satellite tracking measurements, leading to the production of global high-precision, high-resolution gravitational models. By directly turning the nonlinear differential equations of satellite motion into the nonlinear integral equations, and recognizing the fact that satellite orbits are measured with random errors, we further reformulate the links between satellite tracking measurements and the global uniformly convergent solutions to the Newton's governing differential equations as a condition adjustment model with unknown parameters, or equivalently, the weighted least squares estimation of unknown differential equation parameters with equality constraints, for the reconstruction of global high-precision, high-resolution gravitational models from modern (and future) satellite tracking measurements.

Delineating parameter unidentifiabilities in complex models

NASA Astrophysics Data System (ADS)

Raman, Dhruva V.; Anderson, James; Papachristodoulou, Antonis

2017-03-01

Scientists use mathematical modeling as a tool for understanding and predicting the properties of complex physical systems. In highly parametrized models there often exist relationships between parameters over which model predictions are identical, or nearly identical. These are known as structural or practical unidentifiabilities, respectively. They are hard to diagnose and make reliable parameter estimation from data impossible. They furthermore imply the existence of an underlying model simplification. We describe a scalable method for detecting unidentifiabilities, as well as the functional relations defining them, for generic models. This allows for model simplification, and appreciation of which parameters (or functions thereof) cannot be estimated from data. Our algorithm can identify features such as redundant mechanisms and fast time-scale subsystems, as well as the regimes in parameter space over which such approximations are valid. We base our algorithm on a quantification of regional parametric sensitivity that we call `multiscale sloppiness'. Traditionally, the link between parametric sensitivity and the conditioning of the parameter estimation problem is made locally, through the Fisher information matrix. This is valid in the regime of infinitesimal measurement uncertainty. We demonstrate the duality between multiscale sloppiness and the geometry of confidence regions surrounding parameter estimates made where measurement uncertainty is non-negligible. Further theoretical relationships are provided linking multiscale sloppiness to the likelihood-ratio test. From this, we show that a local sensitivity analysis (as typically done) is insufficient for determining the reliability of parameter estimation, even with simple (non)linear systems. Our algorithm can provide a tractable alternative. We finally apply our methods to a large-scale, benchmark systems biology model of necrosis factor (NF)-κ B , uncovering unidentifiabilities.

Multivariate Statistical Analysis of Cigarette Design Feature Influence on ISO TNCO Yields.

PubMed

Agnew-Heard, Kimberly A; Lancaster, Vicki A; Bravo, Roberto; Watson, Clifford; Walters, Matthew J; Holman, Matthew R

2016-06-20

The aim of this study is to explore how differences in cigarette physical design parameters influence tar, nicotine, and carbon monoxide (TNCO) yields in mainstream smoke (MSS) using the International Organization of Standardization (ISO) smoking regimen. Standardized smoking methods were used to evaluate 50 U.S. domestic brand cigarettes and a reference cigarette representing a range of TNCO yields in MSS collected from linear smoking machines using a nonintense smoking regimen. Multivariate statistical methods were used to form clusters of cigarettes based on their ISO TNCO yields and then to explore the relationship between the ISO generated TNCO yields and the nine cigarette physical design parameters between and within each cluster simultaneously. The ISO generated TNCO yields in MSS are 1.1-17.0 mg tar/cigarette, 0.1-2.2 mg nicotine/cigarette, and 1.6-17.3 mg CO/cigarette. Cluster analysis divided the 51 cigarettes into five discrete clusters based on their ISO TNCO yields. No one physical parameter dominated across all clusters. Predicting ISO machine generated TNCO yields based on these nine physical design parameters is complex due to the correlation among and between the nine physical design parameters and TNCO yields. From these analyses, it is estimated that approximately 20% of the variability in the ISO generated TNCO yields comes from other parameters (e.g., filter material, filter type, inclusion of expanded or reconstituted tobacco, and tobacco blend composition, along with differences in tobacco leaf origin and stalk positions and added ingredients). A future article will examine the influence of these physical design parameters on TNCO yields under a Canadian Intense (CI) smoking regimen. Together, these papers will provide a more robust picture of the design features that contribute to TNCO exposure across the range of real world smoking patterns.

Physical fundamentals of criterial estimation of nitriding technology for parts of friction units

NASA Astrophysics Data System (ADS)

Kuksenova, L. I.; Gerasimov, S. A.; Lapteva, V. G.; Alekseeva, M. S.

2013-03-01

Characteristics of the structure and properties of surface layers of nitrided structural steels and alloys, which affect the level of surface fracture under friction, are studied. A generalized structural parameter for optimizing the nitriding process and a rapid method for estimating the quality of the surface layer of nitrided parts of friction units are developed.

Simultaneous Position, Velocity, Attitude, Angular Rates, and Surface Parameter Estimation Using Astrometric and Photometric Observations

DTIC Science & Technology

2013-07-01

Additionally, a physically consistent BRDF and radiation pressure model is utilized thus enabling an accurate physical link between the observed... BRDF and radiation pressure model is utilized thus enabling an accurate physical link between the observed photometric brightness and the attitudinal...source and the observer is ( ) VLVLH ˆˆˆˆˆ ++= (2) with angles α and β from N̂ and is used in many analytic BRDF models . There are many

The structure of the ISM in the Zone of Avoidance by high-resolution multi-wavelength observations

NASA Astrophysics Data System (ADS)

Tóth, L. V.; Doi, Y.; Pinter, S.; Kovács, T.; Zahorecz, S.; Bagoly, Z.; Balázs, L. G.; Horvath, I.; Racz, I. I.; Onishi, T.

2018-05-01

We estimate the column density of the Galactic foreground interstellar medium (GFISM) in the direction of extragalactic sources. All-sky AKARI FIS infrared sky survey data might be used to trace the GFISM with a resolution of 2 arcminutes. The AKARI based GFISM hydrogen column density estimates are compared with similar quantities based on HI 21cm measurements of various resolution and of Planck results. High spatial resolution observations of the GFISM may be important recalculating the physical parameters of gamma-ray burst (GRB) host galaxies using the updated foreground parameters.

Development of an analytical solution for the Budyko watershed parameter in terms of catchment physical features

NASA Astrophysics Data System (ADS)

Reaver, N.; Kaplan, D. A.; Jawitz, J. W.

2017-12-01

The Budyko hypothesis states that a catchment's long-term water and energy balances are dependent on two relatively easy to measure quantities: rainfall depth and potential evaporation. This hypothesis is expressed as a simple function, the Budyko equation, which allows for the prediction of a catchment's actual evapotranspiration and discharge from measured rainfall depth and potential evaporation, data which are widely available. However, the two main analytically derived forms of the Budyko equation contain a single unknown watershed parameter, whose value varies across catchments; variation in this parameter has been used to explain the hydrological behavior of different catchments. The watershed parameter is generally thought of as a lumped quantity that represents the influence of all catchment biophysical features (e.g. soil type and depth, vegetation type, timing of rainfall, etc). Previous work has shown that the parameter is statistically correlated with catchment properties, but an explicit expression has been elusive. While the watershed parameter can be determined empirically by fitting the Budyko equation to measured data in gauged catchments where actual evapotranspiration can be estimated, this limits the utility of the framework for predicting impacts to catchment hydrology due to changing climate and land use. In this study, we developed an analytical solution for the lumped catchment parameter for both forms of the Budyko equation. We combined these solutions with a statistical soil moisture model to obtain analytical solutions for the Budyko equation parameter as a function of measurable catchment physical features, including rooting depth, soil porosity, and soil wilting point. We tested the predictive power of these solutions using the U.S. catchments in the MOPEX database. We also compared the Budyko equation parameter estimates generated from our analytical solutions (i.e. predicted parameters) with those obtained through the calibration of the Budyko equation to discharge data (i.e. empirical parameters), and found good agreement. These results suggest that it is possible to predict the Budyko equation watershed parameter directly from physical features, even for ungauged catchments.

Automated modal parameter estimation using correlation analysis and bootstrap sampling

NASA Astrophysics Data System (ADS)

Yaghoubi, Vahid; Vakilzadeh, Majid K.; Abrahamsson, Thomas J. S.

2018-02-01

The estimation of modal parameters from a set of noisy measured data is a highly judgmental task, with user expertise playing a significant role in distinguishing between estimated physical and noise modes of a test-piece. Various methods have been developed to automate this procedure. The common approach is to identify models with different orders and cluster similar modes together. However, most proposed methods based on this approach suffer from high-dimensional optimization problems in either the estimation or clustering step. To overcome this problem, this study presents an algorithm for autonomous modal parameter estimation in which the only required optimization is performed in a three-dimensional space. To this end, a subspace-based identification method is employed for the estimation and a non-iterative correlation-based method is used for the clustering. This clustering is at the heart of the paper. The keys to success are correlation metrics that are able to treat the problems of spatial eigenvector aliasing and nonunique eigenvectors of coalescent modes simultaneously. The algorithm commences by the identification of an excessively high-order model from frequency response function test data. The high number of modes of this model provides bases for two subspaces: one for likely physical modes of the tested system and one for its complement dubbed the subspace of noise modes. By employing the bootstrap resampling technique, several subsets are generated from the same basic dataset and for each of them a model is identified to form a set of models. Then, by correlation analysis with the two aforementioned subspaces, highly correlated modes of these models which appear repeatedly are clustered together and the noise modes are collected in a so-called Trashbox cluster. Stray noise modes attracted to the mode clusters are trimmed away in a second step by correlation analysis. The final step of the algorithm is a fuzzy c-means clustering procedure applied to a three-dimensional feature space to assign a degree of physicalness to each cluster. The proposed algorithm is applied to two case studies: one with synthetic data and one with real test data obtained from a hammer impact test. The results indicate that the algorithm successfully clusters similar modes and gives a reasonable quantification of the extent to which each cluster is physical.

VERIFICATION AND VALIDATION OF THE SPARC MODEL

EPA Science Inventory

Mathematical models for predicting the transport and fate of pollutants in the environment require reactivity parameter values--that is, the physical and chemical constants that govern reactivity. Although empirical structure-activity relationships that allow estimation of some ...

Predicting root zone soil moisture with soil properties and satellite near-surface moisture data across the conterminous United States

NASA Astrophysics Data System (ADS)

Baldwin, D.; Manfreda, S.; Keller, K.; Smithwick, E. A. H.

2017-03-01

Satellite-based near-surface (0-2 cm) soil moisture estimates have global coverage, but do not capture variations of soil moisture in the root zone (up to 100 cm depth) and may be biased with respect to ground-based soil moisture measurements. Here, we present an ensemble Kalman filter (EnKF) hydrologic data assimilation system that predicts bias in satellite soil moisture data to support the physically based Soil Moisture Analytical Relationship (SMAR) infiltration model, which estimates root zone soil moisture with satellite soil moisture data. The SMAR-EnKF model estimates a regional-scale bias parameter using available in situ data. The regional bias parameter is added to satellite soil moisture retrievals before their use in the SMAR model, and the bias parameter is updated continuously over time with the EnKF algorithm. In this study, the SMAR-EnKF assimilates in situ soil moisture at 43 Soil Climate Analysis Network (SCAN) monitoring locations across the conterminous U.S. Multivariate regression models are developed to estimate SMAR parameters using soil physical properties and the moderate resolution imaging spectroradiometer (MODIS) evapotranspiration data product as covariates. SMAR-EnKF root zone soil moisture predictions are in relatively close agreement with in situ observations when using optimal model parameters, with root mean square errors averaging 0.051 [cm3 cm-3] (standard error, s.e. = 0.005). The average root mean square error associated with a 20-fold cross-validation analysis with permuted SMAR parameter regression models increases moderately (0.082 [cm3 cm-3], s.e. = 0.004). The expected regional-scale satellite correction bias is negative in four out of six ecoregions studied (mean = -0.12 [-], s.e. = 0.002), excluding the Great Plains and Eastern Temperate Forests (0.053 [-], s.e. = 0.001). With its capability of estimating regional-scale satellite bias, the SMAR-EnKF system can predict root zone soil moisture over broad extents and has applications in drought predictions and other operational hydrologic modeling purposes.

Method And Apparatus For Two Dimensional Surface Property Analysis Based On Boundary Measurement

DOEpatents

Richardson, John G.

2005-11-15

An apparatus and method for determining properties of a conductive film is disclosed. A plurality of probe locations selected around a periphery of the conductive film define a plurality of measurement lines between each probe location and all other probe locations. Electrical resistance may be measured along each of the measurement lines. A lumped parameter model may be developed based on the measured values of electrical resistance. The lumped parameter model may be used to estimate resistivity at one or more selected locations encompassed by the plurality of probe locations. The resistivity may be extrapolated to other physical properties if the conductive film includes a correlation between resistivity and the other physical properties. A profile of the conductive film may be developed by determining resistivity at a plurality of locations. The conductive film may be applied to a structure such that resistivity may be estimated and profiled for the structure's surface.

Bayesian parameter estimation for chiral effective field theory

NASA Astrophysics Data System (ADS)

Wesolowski, Sarah; Furnstahl, Richard; Phillips, Daniel; Klco, Natalie

2016-09-01

The low-energy constants (LECs) of a chiral effective field theory (EFT) interaction in the two-body sector are fit to observable data using a Bayesian parameter estimation framework. By using Bayesian prior probability distributions (pdfs), we quantify relevant physical expectations such as LEC naturalness and include them in the parameter estimation procedure. The final result is a posterior pdf for the LECs, which can be used to propagate uncertainty resulting from the fit to data to the final observable predictions. The posterior pdf also allows an empirical test of operator redundancy and other features of the potential. We compare results of our framework with other fitting procedures, interpreting the underlying assumptions in Bayesian probabilistic language. We also compare results from fitting all partial waves of the interaction simultaneously to cross section data compared to fitting to extracted phase shifts, appropriately accounting for correlations in the data. Supported in part by the NSF and DOE.

Estimating model parameters for an impact-produced shock-wave simulation: Optimal use of partial data with the extended Kalman filter

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kao, Jim; Flicker, Dawn; Ide, Kayo

2006-05-20

This paper builds upon our recent data assimilation work with the extended Kalman filter (EKF) method [J. Kao, D. Flicker, R. Henninger, S. Frey, M. Ghil, K. Ide, Data assimilation with an extended Kalman filter for an impact-produced shock-wave study, J. Comp. Phys. 196 (2004) 705-723.]. The purpose is to test the capability of EKF in optimizing a model's physical parameters. The problem is to simulate the evolution of a shock produced through a high-speed flyer plate. In the earlier work, we have showed that the EKF allows one to estimate the evolving state of the shock wave from amore » single pressure measurement, assuming that all model parameters are known. In the present paper, we show that imperfectly known model parameters can also be estimated accordingly, along with the evolving model state, from the same single measurement. The model parameter optimization using the EKF can be achieved through a simple modification of the original EKF formalism by including the model parameters into an augmented state variable vector. While the regular state variables are governed by both deterministic and stochastic forcing mechanisms, the parameters are only subject to the latter. The optimally estimated model parameters are thus obtained through a unified assimilation operation. We show that improving the accuracy of the model parameters also improves the state estimate. The time variation of the optimized model parameters results from blending the data and the corresponding values generated from the model and lies within a small range, of less than 2%, from the parameter values of the original model. The solution computed with the optimized parameters performs considerably better and has a smaller total variance than its counterpart using the original time-constant parameters. These results indicate that the model parameters play a dominant role in the performance of the shock-wave hydrodynamic code at hand.« less

Interactions of solutes and streambed sediment: 2. A dynamic analysis of coupled hydrologic and chemical processes that determine solute transport

USGS Publications Warehouse

Bencala, Kenneth E.

1984-01-01

Solute transport in streams is determined by the interaction of physical and chemical processes. Data from an injection experiment for chloride and several cations indicate significant influence of solutestreambed processes on transport in a mountain stream. These data are interpreted in terms of transient storage processes for all tracers and sorption processes for the cations. Process parameter values are estimated with simulations based on coupled quasi-two-dimensional transport and first-order mass transfer sorption. Comparative simulations demonstrate the relative roles of the physical and chemical processes in determining solute transport. During the first 24 hours of the experiment, chloride concentrations were attenuated relative to expected plateau levels. Additional attenuation occurred for the sorbing cation strontium. The simulations account for these storage processes. Parameter values determined by calibration compare favorably with estimates from other studies in mountain streams. Without further calibration, the transport of potassium and lithium is adequately simulated using parameters determined in the chloride-strontium simulation and with measured cation distribution coefficients.

Hyper-Spectral Image Analysis With Partially Latent Regression and Spatial Markov Dependencies

NASA Astrophysics Data System (ADS)

Deleforge, Antoine; Forbes, Florence; Ba, Sileye; Horaud, Radu

2015-09-01

Hyper-spectral data can be analyzed to recover physical properties at large planetary scales. This involves resolving inverse problems which can be addressed within machine learning, with the advantage that, once a relationship between physical parameters and spectra has been established in a data-driven fashion, the learned relationship can be used to estimate physical parameters for new hyper-spectral observations. Within this framework, we propose a spatially-constrained and partially-latent regression method which maps high-dimensional inputs (hyper-spectral images) onto low-dimensional responses (physical parameters such as the local chemical composition of the soil). The proposed regression model comprises two key features. Firstly, it combines a Gaussian mixture of locally-linear mappings (GLLiM) with a partially-latent response model. While the former makes high-dimensional regression tractable, the latter enables to deal with physical parameters that cannot be observed or, more generally, with data contaminated by experimental artifacts that cannot be explained with noise models. Secondly, spatial constraints are introduced in the model through a Markov random field (MRF) prior which provides a spatial structure to the Gaussian-mixture hidden variables. Experiments conducted on a database composed of remotely sensed observations collected from the Mars planet by the Mars Express orbiter demonstrate the effectiveness of the proposed model.

Remote sensing-aided systems for snow qualification, evapotranspiration estimation, and their application in hydrologic models

NASA Technical Reports Server (NTRS)

Korram, S.

1977-01-01

The design of general remote sensing-aided methodologies was studied to provide the estimates of several important inputs to water yield forecast models. These input parameters are snow area extent, snow water content, and evapotranspiration. The study area is Feather River Watershed (780,000 hectares), Northern California. The general approach involved a stepwise sequence of identification of the required information, sample design, measurement/estimation, and evaluation of results. All the relevent and available information types needed in the estimation process are being defined. These include Landsat, meteorological satellite, and aircraft imagery, topographic and geologic data, ground truth data, and climatic data from ground stations. A cost-effective multistage sampling approach was employed in quantification of all the required parameters. The physical and statistical models for both snow quantification and evapotranspiration estimation was developed. These models use the information obtained by aerial and ground data through appropriate statistical sampling design.

Estimation of soil hydraulic properties with microwave techniques

NASA Technical Reports Server (NTRS)

Oneill, P. E.; Gurney, R. J.; Camillo, P. J.

1985-01-01

Useful quantitative information about soil properties may be obtained by calibrating energy and moisture balance models with remotely sensed data. A soil physics model solves heat and moisture flux equations in the soil profile and is driven by the surface energy balance. Model generated surface temperature and soil moisture and temperature profiles are then used in a microwave emission model to predict the soil brightness temperature. The model hydraulic parameters are varied until the predicted temperatures agree with the remotely sensed values. This method is used to estimate values for saturated hydraulic conductivity, saturated matrix potential, and a soil texture parameter. The conductivity agreed well with a value measured with an infiltration ring and the other parameters agreed with values in the literature.

Prediction of HDR quality by combining perceptually transformed display measurements with machine learning

NASA Astrophysics Data System (ADS)

Choudhury, Anustup; Farrell, Suzanne; Atkins, Robin; Daly, Scott

2017-09-01

We present an approach to predict overall HDR display quality as a function of key HDR display parameters. We first performed subjective experiments on a high quality HDR display that explored five key HDR display parameters: maximum luminance, minimum luminance, color gamut, bit-depth and local contrast. Subjects rated overall quality for different combinations of these display parameters. We explored two models | a physical model solely based on physically measured display characteristics and a perceptual model that transforms physical parameters using human vision system models. For the perceptual model, we use a family of metrics based on a recently published color volume model (ICT-CP), which consists of the PQ luminance non-linearity (ST2084) and LMS-based opponent color, as well as an estimate of the display point spread function. To predict overall visual quality, we apply linear regression and machine learning techniques such as Multilayer Perceptron, RBF and SVM networks. We use RMSE and Pearson/Spearman correlation coefficients to quantify performance. We found that the perceptual model is better at predicting subjective quality than the physical model and that SVM is better at prediction than linear regression. The significance and contribution of each display parameter was investigated. In addition, we found that combined parameters such as contrast do not improve prediction. Traditional perceptual models were also evaluated and we found that models based on the PQ non-linearity performed better.

Patient-specific parameter estimation in single-ventricle lumped circulation models under uncertainty

PubMed Central

Schiavazzi, Daniele E.; Baretta, Alessia; Pennati, Giancarlo; Hsia, Tain-Yen; Marsden, Alison L.

2017-01-01

Summary Computational models of cardiovascular physiology can inform clinical decision-making, providing a physically consistent framework to assess vascular pressures and flow distributions, and aiding in treatment planning. In particular, lumped parameter network (LPN) models that make an analogy to electrical circuits offer a fast and surprisingly realistic method to reproduce the circulatory physiology. The complexity of LPN models can vary significantly to account, for example, for cardiac and valve function, respiration, autoregulation, and time-dependent hemodynamics. More complex models provide insight into detailed physiological mechanisms, but their utility is maximized if one can quickly identify patient specific parameters. The clinical utility of LPN models with many parameters will be greatly enhanced by automated parameter identification, particularly if parameter tuning can match non-invasively obtained clinical data. We present a framework for automated tuning of 0D lumped model parameters to match clinical data. We demonstrate the utility of this framework through application to single ventricle pediatric patients with Norwood physiology. Through a combination of local identifiability, Bayesian estimation and maximum a posteriori simplex optimization, we show the ability to automatically determine physiologically consistent point estimates of the parameters and to quantify uncertainty induced by errors and assumptions in the collected clinical data. We show that multi-level estimation, that is, updating the parameter prior information through sub-model analysis, can lead to a significant reduction in the parameter marginal posterior variance. We first consider virtual patient conditions, with clinical targets generated through model solutions, and second application to a cohort of four single-ventricle patients with Norwood physiology. PMID:27155892

Modal Damping Ratio and Optimal Elastic Moduli of Human Body Segments for Anthropometric Vibratory Model of Standing Subjects.

PubMed

Gupta, Manoj; Gupta, T C

2017-10-01

The present study aims to accurately estimate inertial, physical, and dynamic parameters of human body vibratory model consistent with physical structure of the human body that also replicates its dynamic response. A 13 degree-of-freedom (DOF) lumped parameter model for standing person subjected to support excitation is established. Model parameters are determined from anthropometric measurements, uniform mass density, elastic modulus of individual body segments, and modal damping ratios. Elastic moduli of ellipsoidal body segments are initially estimated by comparing stiffness of spring elements, calculated from a detailed scheme, and values available in literature for same. These values are further optimized by minimizing difference between theoretically calculated platform-to-head transmissibility ratio (TR) and experimental measurements. Modal damping ratios are estimated from experimental transmissibility response using two dominant peaks in the frequency range of 0-25 Hz. From comparison between dynamic response determined form modal analysis and experimental results, a set of elastic moduli for different segments of human body and a novel scheme to determine modal damping ratios from TR plots, are established. Acceptable match between transmissibility values calculated from the vibratory model and experimental measurements for 50th percentile U.S. male, except at very low frequencies, establishes the human body model developed. Also, reasonable agreement obtained between theoretical response curve and experimental response envelop for average Indian male, affirms the technique used for constructing vibratory model of a standing person. Present work attempts to develop effective technique for constructing subject specific damped vibratory model based on its physical measurements.

Multiple Damage Progression Paths in Model-Based Prognostics

NASA Technical Reports Server (NTRS)

Daigle, Matthew; Goebel, Kai Frank

2011-01-01

Model-based prognostics approaches employ domain knowledge about a system, its components, and how they fail through the use of physics-based models. Component wear is driven by several different degradation phenomena, each resulting in their own damage progression path, overlapping to contribute to the overall degradation of the component. We develop a model-based prognostics methodology using particle filters, in which the problem of characterizing multiple damage progression paths is cast as a joint state-parameter estimation problem. The estimate is represented as a probability distribution, allowing the prediction of end of life and remaining useful life within a probabilistic framework that supports uncertainty management. We also develop a novel variance control mechanism that maintains an uncertainty bound around the hidden parameters to limit the amount of estimation uncertainty and, consequently, reduce prediction uncertainty. We construct a detailed physics-based model of a centrifugal pump, to which we apply our model-based prognostics algorithms. We illustrate the operation of the prognostic solution with a number of simulation-based experiments and demonstrate the performance of the chosen approach when multiple damage mechanisms are active

Theory based scaling of edge turbulence and implications for the scrape-off layer width

NASA Astrophysics Data System (ADS)

Myra, J. R.; Russell, D. A.; Zweben, S. J.

2016-11-01

Turbulence and plasma parameter data from the National Spherical Torus Experiment (NSTX) [Ono et al., Nucl. Fusion 40, 557 (2000)] is examined and interpreted based on various theoretical estimates. In particular, quantities of interest for assessing the role of turbulent transport on the midplane scrape-off layer heat flux width are assessed. Because most turbulence quantities exhibit large scatter and little scaling within a given operation mode, this paper focuses on length and time scales and dimensionless parameters between operational modes including Ohmic, low (L), and high (H) modes using a large NSTX edge turbulence database [Zweben et al., Nucl. Fusion 55, 093035 (2015)]. These are compared with theoretical estimates for drift and interchange rates, profile modification saturation levels, a resistive ballooning condition, and dimensionless parameters characterizing L and H mode conditions. It is argued that the underlying instability physics governing edge turbulence in different operational modes is, in fact, similar, and is consistent with curvature-driven drift ballooning. Saturation physics, however, is dependent on the operational mode. Five dimensionless parameters for drift-interchange turbulence are obtained and employed to assess the importance of turbulence in setting the scrape-off layer heat flux width λq and its scaling. An explicit proportionality of the width λq to the safety factor and major radius (qR) is obtained under these conditions. Quantitative estimates and reduced model numerical simulations suggest that the turbulence mechanism is not negligible in determining λq in NSTX, at least for high plasma current discharges.

Theory based scaling of edge turbulence and implications for the scrape-off layer width

DOE PAGES

Myra, J. R.; Russell, D. A.; Zweben, S. J.

2016-11-01

Turbulence and plasma parameter data from the National Spherical Torus Experiment (NSTX) is examined and interpreted based on various theoretical estimates. In particular, quantities of interest for assessing the role of turbulent transport on the midplane scrape-off layer heat flux width are assessed. Because most turbulence quantities exhibit large scatter and little scaling within a given operation mode, this paper focuses on length and time scales and dimensionless parameters between operational modes including Ohmic, low (L), and high (H) modes using a large NSTX edge turbulence database. These are compared with theoretical estimates for drift and interchange rates, profile modificationmore » saturation levels, a resistive ballooning condition, and dimensionless parameters characterizing L and H mode conditions. It is argued that the underlying instability physics governing edge turbulence in different operational modes is, in fact, similar, and is consistent with curvature-driven drift ballooning. Saturation physics, however, is dependent on the operational mode. Five dimensionless parameters for drift-interchange turbulence are obtained and employed to assess the importance of turbulence in setting the scrape-off layer heat flux width λ q and its scaling. An explicit proportionality of the width λ q to the safety factor and major radius (qR) is obtained under these conditions. Lastly, quantitative estimates and reduced model numerical simulations suggest that the turbulence mechanism is not negligible in determining λ q in NSTX, at least for high plasma current discharges.« less

Parameter Estimation and Sensitivity Analysis of an Urban Surface Energy Balance Parameterization at a Tropical Suburban Site

NASA Astrophysics Data System (ADS)

Harshan, S.; Roth, M.; Velasco, E.

2014-12-01

Forecasting of the urban weather and climate is of great importance as our cities become more populated and considering the combined effects of global warming and local land use changes which make urban inhabitants more vulnerable to e.g. heat waves and flash floods. In meso/global scale models, urban parameterization schemes are used to represent the urban effects. However, these schemes require a large set of input parameters related to urban morphological and thermal properties. Obtaining all these parameters through direct measurements are usually not feasible. A number of studies have reported on parameter estimation and sensitivity analysis to adjust and determine the most influential parameters for land surface schemes in non-urban areas. Similar work for urban areas is scarce, in particular studies on urban parameterization schemes in tropical cities have so far not been reported. In order to address above issues, the town energy balance (TEB) urban parameterization scheme (part of the SURFEX land surface modeling system) was subjected to a sensitivity and optimization/parameter estimation experiment at a suburban site in, tropical Singapore. The sensitivity analysis was carried out as a screening test to identify the most sensitive or influential parameters. Thereafter, an optimization/parameter estimation experiment was performed to calibrate the input parameter. The sensitivity experiment was based on the "improved Sobol's global variance decomposition method" . The analysis showed that parameters related to road, roof and soil moisture have significant influence on the performance of the model. The optimization/parameter estimation experiment was performed using the AMALGM (a multi-algorithm genetically adaptive multi-objective method) evolutionary algorithm. The experiment showed a remarkable improvement compared to the simulations using the default parameter set. The calibrated parameters from this optimization experiment can be used for further model validation studies to identify inherent deficiencies in model physics.

The Limitations of Model-Based Experimental Design and Parameter Estimation in Sloppy Systems.

PubMed

White, Andrew; Tolman, Malachi; Thames, Howard D; Withers, Hubert Rodney; Mason, Kathy A; Transtrum, Mark K

2016-12-01

We explore the relationship among experimental design, parameter estimation, and systematic error in sloppy models. We show that the approximate nature of mathematical models poses challenges for experimental design in sloppy models. In many models of complex biological processes it is unknown what are the relevant physical mechanisms that must be included to explain system behaviors. As a consequence, models are often overly complex, with many practically unidentifiable parameters. Furthermore, which mechanisms are relevant/irrelevant vary among experiments. By selecting complementary experiments, experimental design may inadvertently make details that were ommitted from the model become relevant. When this occurs, the model will have a large systematic error and fail to give a good fit to the data. We use a simple hyper-model of model error to quantify a model's discrepancy and apply it to two models of complex biological processes (EGFR signaling and DNA repair) with optimally selected experiments. We find that although parameters may be accurately estimated, the discrepancy in the model renders it less predictive than it was in the sloppy regime where systematic error is small. We introduce the concept of a sloppy system-a sequence of models of increasing complexity that become sloppy in the limit of microscopic accuracy. We explore the limits of accurate parameter estimation in sloppy systems and argue that identifying underlying mechanisms controlling system behavior is better approached by considering a hierarchy of models of varying detail rather than focusing on parameter estimation in a single model.

The Limitations of Model-Based Experimental Design and Parameter Estimation in Sloppy Systems

PubMed Central

Tolman, Malachi; Thames, Howard D.; Mason, Kathy A.

2016-01-01

We explore the relationship among experimental design, parameter estimation, and systematic error in sloppy models. We show that the approximate nature of mathematical models poses challenges for experimental design in sloppy models. In many models of complex biological processes it is unknown what are the relevant physical mechanisms that must be included to explain system behaviors. As a consequence, models are often overly complex, with many practically unidentifiable parameters. Furthermore, which mechanisms are relevant/irrelevant vary among experiments. By selecting complementary experiments, experimental design may inadvertently make details that were ommitted from the model become relevant. When this occurs, the model will have a large systematic error and fail to give a good fit to the data. We use a simple hyper-model of model error to quantify a model’s discrepancy and apply it to two models of complex biological processes (EGFR signaling and DNA repair) with optimally selected experiments. We find that although parameters may be accurately estimated, the discrepancy in the model renders it less predictive than it was in the sloppy regime where systematic error is small. We introduce the concept of a sloppy system–a sequence of models of increasing complexity that become sloppy in the limit of microscopic accuracy. We explore the limits of accurate parameter estimation in sloppy systems and argue that identifying underlying mechanisms controlling system behavior is better approached by considering a hierarchy of models of varying detail rather than focusing on parameter estimation in a single model. PMID:27923060

Verification Techniques for Parameter Selection and Bayesian Model Calibration Presented for an HIV Model

NASA Astrophysics Data System (ADS)

Wentworth, Mami Tonoe

Uncertainty quantification plays an important role when making predictive estimates of model responses. In this context, uncertainty quantification is defined as quantifying and reducing uncertainties, and the objective is to quantify uncertainties in parameter, model and measurements, and propagate the uncertainties through the model, so that one can make a predictive estimate with quantified uncertainties. Two of the aspects of uncertainty quantification that must be performed prior to propagating uncertainties are model calibration and parameter selection. There are several efficient techniques for these processes; however, the accuracy of these methods are often not verified. This is the motivation for our work, and in this dissertation, we present and illustrate verification frameworks for model calibration and parameter selection in the context of biological and physical models. First, HIV models, developed and improved by [2, 3, 8], describe the viral infection dynamics of an HIV disease. These are also used to make predictive estimates of viral loads and T-cell counts and to construct an optimal control for drug therapy. Estimating input parameters is an essential step prior to uncertainty quantification. However, not all the parameters are identifiable, implying that they cannot be uniquely determined by the observations. These unidentifiable parameters can be partially removed by performing parameter selection, a process in which parameters that have minimal impacts on the model response are determined. We provide verification techniques for Bayesian model calibration and parameter selection for an HIV model. As an example of a physical model, we employ a heat model with experimental measurements presented in [10]. A steady-state heat model represents a prototypical behavior for heat conduction and diffusion process involved in a thermal-hydraulic model, which is a part of nuclear reactor models. We employ this simple heat model to illustrate verification techniques for model calibration. For Bayesian model calibration, we employ adaptive Metropolis algorithms to construct densities for input parameters in the heat model and the HIV model. To quantify the uncertainty in the parameters, we employ two MCMC algorithms: Delayed Rejection Adaptive Metropolis (DRAM) [33] and Differential Evolution Adaptive Metropolis (DREAM) [66, 68]. The densities obtained using these methods are compared to those obtained through the direct numerical evaluation of the Bayes' formula. We also combine uncertainties in input parameters and measurement errors to construct predictive estimates for a model response. A significant emphasis is on the development and illustration of techniques to verify the accuracy of sampling-based Metropolis algorithms. We verify the accuracy of DRAM and DREAM by comparing chains, densities and correlations obtained using DRAM, DREAM and the direct evaluation of Bayes formula. We also perform similar analysis for credible and prediction intervals for responses. Once the parameters are estimated, we employ energy statistics test [63, 64] to compare the densities obtained by different methods for the HIV model. The energy statistics are used to test the equality of distributions. We also consider parameter selection and verification techniques for models having one or more parameters that are noninfluential in the sense that they minimally impact model outputs. We illustrate these techniques for a dynamic HIV model but note that the parameter selection and verification framework is applicable to a wide range of biological and physical models. To accommodate the nonlinear input to output relations, which are typical for such models, we focus on global sensitivity analysis techniques, including those based on partial correlations, Sobol indices based on second-order model representations, and Morris indices, as well as a parameter selection technique based on standard errors. A significant objective is to provide verification strategies to assess the accuracy of those techniques, which we illustrate in the context of the HIV model. Finally, we examine active subspace methods as an alternative to parameter subset selection techniques. The objective of active subspace methods is to determine the subspace of inputs that most strongly affect the model response, and to reduce the dimension of the input space. The major difference between active subspace methods and parameter selection techniques is that parameter selection identifies influential parameters whereas subspace selection identifies a linear combination of parameters that impacts the model responses significantly. We employ active subspace methods discussed in [22] for the HIV model and present a verification that the active subspace successfully reduces the input dimensions.

Mathematical models and photogrammetric exploitation of image sensing

NASA Astrophysics Data System (ADS)

Puatanachokchai, Chokchai

Mathematical models of image sensing are generally categorized into physical/geometrical sensor models and replacement sensor models. While the former is determined from image sensing geometry, the latter is based on knowledge of the physical/geometric sensor models and on using such models for its implementation. The main thrust of this research is in replacement sensor models which have three important characteristics: (1) Highly accurate ground-to-image functions; (2) Rigorous error propagation that is essentially of the same accuracy as the physical model; and, (3) Adjustability, or the ability to upgrade the replacement sensor model parameters when additional control information becomes available after the replacement sensor model has replaced the physical model. In this research, such replacement sensor models are considered as True Replacement Models or TRMs. TRMs provide a significant advantage of universality, particularly for image exploitation functions. There have been several writings about replacement sensor models, and except for the so called RSM (Replacement Sensor Model as a product described in the Manual of Photogrammetry), almost all of them pay very little or no attention to errors and their propagation. This is because, it is suspected, the few physical sensor parameters are usually replaced by many more parameters, thus presenting a potential error estimation difficulty. The third characteristic, adjustability, is perhaps the most demanding. It provides an equivalent flexibility to that of triangulation using the physical model. Primary contributions of this thesis include not only "the eigen-approach", a novel means of replacing the original sensor parameter covariance matrices at the time of estimating the TRM, but also the implementation of the hybrid approach that combines the eigen-approach with the added parameters approach used in the RSM. Using either the eigen-approach or the hybrid approach, rigorous error propagation can be performed during image exploitation. Further, adjustability can be performed when additional control information becomes available after the TRM has been implemented. The TRM is shown to apply to imagery from sensors having different geometries, including an aerial frame camera, a spaceborne linear array sensor, an airborne pushbroom sensor, and an airborne whiskbroom sensor. TRM results show essentially negligible differences as compared to those from rigorous physical sensor models, both for geopositioning from single and overlapping images. Simulated as well as real image data are used to address all three characteristics of the TRM.

Efficient Ensemble State-Parameters Estimation Techniques in Ocean Ecosystem Models: Application to the North Atlantic

NASA Astrophysics Data System (ADS)

El Gharamti, M.; Bethke, I.; Tjiputra, J.; Bertino, L.

2016-02-01

Given the recent strong international focus on developing new data assimilation systems for biological models, we present in this comparative study the application of newly developed state-parameters estimation tools to an ocean ecosystem model. It is quite known that the available physical models are still too simple compared to the complexity of the ocean biology. Furthermore, various biological parameters remain poorly unknown and hence wrong specifications of such parameters can lead to large model errors. Standard joint state-parameters augmentation technique using the ensemble Kalman filter (Stochastic EnKF) has been extensively tested in many geophysical applications. Some of these assimilation studies reported that jointly updating the state and the parameters might introduce significant inconsistency especially for strongly nonlinear models. This is usually the case for ecosystem models particularly during the period of the spring bloom. A better handling of the estimation problem is often carried out by separating the update of the state and the parameters using the so-called Dual EnKF. The dual filter is computationally more expensive than the Joint EnKF but is expected to perform more accurately. Using a similar separation strategy, we propose a new EnKF estimation algorithm in which we apply a one-step-ahead smoothing to the state. The new state-parameters estimation scheme is derived in a consistent Bayesian filtering framework and results in separate update steps for the state and the parameters. Unlike the classical filtering path, the new scheme starts with an update step and later a model propagation step is performed. We test the performance of the new smoothing-based schemes against the standard EnKF in a one-dimensional configuration of the Norwegian Earth System Model (NorESM) in the North Atlantic. We use nutrients profile (up to 2000 m deep) data and surface partial CO2 measurements from Mike weather station (66o N, 2o E) to estimate different biological parameters of phytoplanktons and zooplanktons. We analyze the performance of the filters in terms of complexity and accuracy of the state and parameters estimates.

Device-independent point estimation from finite data and its application to device-independent property estimation

NASA Astrophysics Data System (ADS)

Lin, Pei-Sheng; Rosset, Denis; Zhang, Yanbao; Bancal, Jean-Daniel; Liang, Yeong-Cherng

2018-03-01

The device-independent approach to physics is one where conclusions are drawn directly from the observed correlations between measurement outcomes. In quantum information, this approach allows one to make strong statements about the properties of the underlying systems or devices solely via the observation of Bell-inequality-violating correlations. However, since one can only perform a finite number of experimental trials, statistical fluctuations necessarily accompany any estimation of these correlations. Consequently, an important gap remains between the many theoretical tools developed for the asymptotic scenario and the experimentally obtained raw data. In particular, a physical and concurrently practical way to estimate the underlying quantum distribution has so far remained elusive. Here, we show that the natural analogs of the maximum-likelihood estimation technique and the least-square-error estimation technique in the device-independent context result in point estimates of the true distribution that are physical, unique, computationally tractable, and consistent. They thus serve as sound algorithmic tools allowing one to bridge the aforementioned gap. As an application, we demonstrate how such estimates of the underlying quantum distribution can be used to provide, in certain cases, trustworthy estimates of the amount of entanglement present in the measured system. In stark contrast to existing approaches to device-independent parameter estimations, our estimation does not require the prior knowledge of any Bell inequality tailored for the specific property and the specific distribution of interest.

Bayesian Methods for Effective Field Theories

NASA Astrophysics Data System (ADS)

Wesolowski, Sarah

Microscopic predictions of the properties of atomic nuclei have reached a high level of precision in the past decade. This progress mandates improved uncertainty quantification (UQ) for a robust comparison of experiment with theory. With the uncertainty from many-body methods under control, calculations are now sensitive to the input inter-nucleon interactions. These interactions include parameters that must be fit to experiment, inducing both uncertainty from the fit and from missing physics in the operator structure of the Hamiltonian. Furthermore, the implementation of the inter-nucleon interactions is not unique, which presents the additional problem of assessing results using different interactions. Effective field theories (EFTs) take advantage of a separation of high- and low-energy scales in the problem to form a power-counting scheme that allows the organization of terms in the Hamiltonian based on their expected contribution to observable predictions. This scheme gives a natural framework for quantification of uncertainty due to missing physics. The free parameters of the EFT, called the low-energy constants (LECs), must be fit to data, but in a properly constructed EFT these constants will be natural-sized, i.e., of order unity. The constraints provided by the EFT, namely the size of the systematic uncertainty from truncation of the theory and the natural size of the LECs, are assumed information even before a calculation is performed or a fit is done. Bayesian statistical methods provide a framework for treating uncertainties that naturally incorporates prior information as well as putting stochastic and systematic uncertainties on an equal footing. For EFT UQ Bayesian methods allow the relevant EFT properties to be incorporated quantitatively as prior probability distribution functions (pdfs). Following the logic of probability theory, observable quantities and underlying physical parameters such as the EFT breakdown scale may be expressed as pdfs that incorporate the prior pdfs. Problems of model selection, such as distinguishing between competing EFT implementations, are also natural in a Bayesian framework. In this thesis we focus on two complementary topics for EFT UQ using Bayesian methods--quantifying EFT truncation uncertainty and parameter estimation for LECs. Using the order-by-order calculations and underlying EFT constraints as prior information, we show how to estimate EFT truncation uncertainties. We then apply the result to calculating truncation uncertainties on predictions of nucleon-nucleon scattering in chiral effective field theory. We apply model-checking diagnostics to our calculations to ensure that the statistical model of truncation uncertainty produces consistent results. A framework for EFT parameter estimation based on EFT convergence properties and naturalness is developed which includes a series of diagnostics to ensure the extraction of the maximum amount of available information from data to estimate LECs with minimal bias. We develop this framework using model EFTs and apply it to the problem of extrapolating lattice quantum chromodynamics results for the nucleon mass. We then apply aspects of the parameter estimation framework to perform case studies in chiral EFT parameter estimation, investigating a possible operator redundancy at fourth order in the chiral expansion and the appropriate inclusion of truncation uncertainty in estimating LECs.

Controls on the physical properties of gas-hydrate-bearing sediments because of the interaction between gas hydrate and porous media

USGS Publications Warehouse

Lee, Myung W.; Collett, Timothy S.

2005-01-01

Physical properties of gas-hydrate-bearing sediments depend on the pore-scale interaction between gas hydrate and porous media as well as the amount of gas hydrate present. Well log measurements such as proton nuclear magnetic resonance (NMR) relaxation and electromagnetic propagation tool (EPT) techniques depend primarily on the bulk volume of gas hydrate in the pore space irrespective of the pore-scale interaction. However, elastic velocities or permeability depend on how gas hydrate is distributed in the pore space as well as the amount of gas hydrate. Gas-hydrate saturations estimated from NMR and EPT measurements are free of adjustable parameters; thus, the estimations are unbiased estimates of gas hydrate if the measurement is accurate. However, the amount of gas hydrate estimated from elastic velocities or electrical resistivities depends on many adjustable parameters and models related to the interaction of gas hydrate and porous media, so these estimates are model dependent and biased. NMR, EPT, elastic-wave velocity, electrical resistivity, and permeability measurements acquired in the Mallik 5L-38 well in the Mackenzie Delta, Canada, show that all of the well log evaluation techniques considered provide comparable gas-hydrate saturations in clean (low shale content) sandstone intervals with high gas-hydrate saturations. However, in shaly intervals, estimates from log measurement depending on the pore-scale interaction between gas hydrate and host sediments are higher than those estimates from measurements depending on the bulk volume of gas hydrate.

Measurement of the PPN parameter γ by testing the geometry of near-Earth space

NASA Astrophysics Data System (ADS)

Luo, Jie; Tian, Yuan; Wang, Dian-Hong; Qin, Cheng-Gang; Shao, Cheng-Gang

2016-06-01

The Beyond Einstein Advanced Coherent Optical Network (BEACON) mission was designed to achieve an accuracy of 10^{-9} in measuring the Eddington parameter γ , which is perhaps the most fundamental Parameterized Post-Newtonian parameter. However, this ideal accuracy was just estimated as a ratio of the measurement accuracy of the inter-spacecraft distances to the magnitude of the departure from Euclidean geometry. Based on the BEACON concept, we construct a measurement model to estimate the parameter γ with the least squares method. Influences of the measurement noise and the out-of-plane error on the estimation accuracy are evaluated based on the white noise model. Though the BEACON mission does not require expensive drag-free systems and avoids physical dynamical models of spacecraft, the relatively low accuracy of initial inter-spacecraft distances poses a great challenge, which reduces the estimation accuracy in about two orders of magnitude. Thus the noise requirements may need to be more stringent in the design in order to achieve the target accuracy, which is demonstrated in the work. Considering that, we have given the limits on the power spectral density of both noise sources for the accuracy of 10^{-9}.

Interference correction by extracting the information of interference dominant regions: Application to near-infrared spectra

NASA Astrophysics Data System (ADS)

Bi, Yiming; Tang, Liang; Shan, Peng; Xie, Qiong; Hu, Yong; Peng, Silong; Tan, Jie; Li, Changwen

2014-08-01

Interference such as baseline drift and light scattering can degrade the model predictability in multivariate analysis of near-infrared (NIR) spectra. Usually interference can be represented by an additive and a multiplicative factor. In order to eliminate these interferences, correction parameters are needed to be estimated from spectra. However, the spectra are often mixed of physical light scattering effects and chemical light absorbance effects, making it difficult for parameter estimation. Herein, a novel algorithm was proposed to find a spectral region automatically that the interesting chemical absorbance and noise are low, that is, finding an interference dominant region (IDR). Based on the definition of IDR, a two-step method was proposed to find the optimal IDR and the corresponding correction parameters estimated from IDR. Finally, the correction was performed to the full spectral range using previously obtained parameters for the calibration set and test set, respectively. The method can be applied to multi target systems with one IDR suitable for all targeted analytes. Tested on two benchmark data sets of near-infrared spectra, the performance of the proposed method provided considerable improvement compared with full spectral estimation methods and comparable with other state-of-art methods.

Astrometric and Photometric Data Fusion for Mass and Surface Material Estimation using Refined Bidirectional Reflectance Distribution Functions-Solar Radiation Pressure Model

DTIC Science & Technology

2013-09-01

model and the BRDF in the SRP model are not consistent with each other, then the resulting estimated albedo-areas and mass are inaccurate and biased...This work studies the use of physically consistent BRDF -SRP models for mass estimation. Simulation studies are used to provide an indication of the...benefits of using these new models . An unscented Kalman filter approach that includes BRDF and mass parameters in the state vector is used. The

Time scale variation of NV resonance line profiles of HD203064

NASA Astrophysics Data System (ADS)

Strantzalis, A.

2012-01-01

Hot emission star, such as Be and Oe, present many spectral lines with very complex and peculiar profiles. Therefore, we cannot find a classical distribution to fit theoretically those physical line profiles. So, many physical parameters of the regions, where spectral lines are created, are difficult to estimate. Here, in this poster paper we study the UV NV (λλ 1238.821, 1242.804 A) resonance lines of the Be star HD203064 at three different dates. We using the Gauss-Rotation model, that proposed the idea that these complex profiles consist of a number of independent Discrete or Satellite Absorption Components (DACs, SACs). Our purpose is to calculate the values of a group of physical parameters as the apparent rotational, radial, and random velocities of the thermal motions of the ions. Also the Full Width at Half Maximum (FWHM) and the column density, as well as the absorbed energy of the independent regions of matter, which produce the main and the satellite components of the studied spectral lines. In addition, we determine the time scale variations of the above physical parameters.

Time scale variation of MgII resonance lines of HD 41335 in UV region

NASA Astrophysics Data System (ADS)

Nikolaou, I.

2012-01-01

It is known that hot emission stars (Be and Oe) present peculiar and very complex spectral line profiles. Due to these perplexed lines that appear, it is difficult to actually fit a classical distribution to those physical profiles. Therefore many physical parameters of the regions, where these lines are created, can not be determined. In this paper, we study the Ultraviolet (UV) MgII (?? 2795.523, 2802.698 A) resonance lines of the HD 41335 star, at three different periods. Considering that these profiles consist of a number of independent Discrete or Satellite Absorption Components (DACs, SACs), we use the Gauss-Rotation model (GR-model). From this analysis we can estimate the values of a group of physical parameters, such as the apparent rotational and radial velocities, the random velocities of the thermal motions of the ions, as well as the Full Width at Half Maximum (FWHM), the column density and the absorbed energy of the independent regions of matter, which produce the main and the satellite components of the studied spectral lines. Eventually, we calculate the time scale variations of the above physical parameters.

Energetic investigation of the adsorption process of CH4, C2H6 and N2 on activated carbon: Numerical and statistical physics treatment

NASA Astrophysics Data System (ADS)

Ben Torkia, Yosra; Ben Yahia, Manel; Khalfaoui, Mohamed; Al-Muhtaseb, Shaheen A.; Ben Lamine, Abdelmottaleb

2014-01-01

The adsorption energy distribution (AED) function of a commercial activated carbon (BDH-activated carbon) was investigated. For this purpose, the integral equation is derived by using a purely analytical statistical physics treatment. The description of the heterogeneity of the adsorbent is significantly clarified by defining the parameter N(E). This parameter represents the energetic density of the spatial density of the effectively occupied sites. To solve the integral equation, a numerical method was used based on an adequate algorithm. The Langmuir model was adopted as a local adsorption isotherm. This model is developed by using the grand canonical ensemble, which allows defining the physico-chemical parameters involved in the adsorption process. The AED function is estimated by a normal Gaussian function. This method is applied to the adsorption isotherms of nitrogen, methane and ethane at different temperatures. The development of the AED using a statistical physics treatment provides an explanation of the gas molecules behaviour during the adsorption process and gives new physical interpretations at microscopic levels.

Nanostructure Sensing and Transmission of Gas Data

NASA Technical Reports Server (NTRS)

Li, Jing (Inventor)

2011-01-01

A system for receiving, analyzing and communicating results of sensing chemical and/or physical parameter values, using wireless transmission of the data. Presence or absence of one or more of a group of selected chemicals in a gas or vapor is determined, using suitably functionalized carbon nanostructures that are exposed to the gas. One or more physical parameter values, such as temperature, vapor pressure, relative humidity and distance from a reference location, are also sensed for the gas, using nanostructures and/or microstructures. All parameter values are transmitted wirelessly to a data processing site or to a control site, using an interleaving pattern for data received from different sensor groups, using I.E.E.E. 802.11 or 802.15 protocol, for example. Methods for estimating chemical concentration are discussed.

A Bayesian approach to the modelling of α Cen A

NASA Astrophysics Data System (ADS)

Bazot, M.; Bourguignon, S.; Christensen-Dalsgaard, J.

2012-12-01

Determining the physical characteristics of a star is an inverse problem consisting of estimating the parameters of models for the stellar structure and evolution, and knowing certain observable quantities. We use a Bayesian approach to solve this problem for α Cen A, which allows us to incorporate prior information on the parameters to be estimated, in order to better constrain the problem. Our strategy is based on the use of a Markov chain Monte Carlo (MCMC) algorithm to estimate the posterior probability densities of the stellar parameters: mass, age, initial chemical composition, etc. We use the stellar evolutionary code ASTEC to model the star. To constrain this model both seismic and non-seismic observations were considered. Several different strategies were tested to fit these values, using either two free parameters or five free parameters in ASTEC. We are thus able to show evidence that MCMC methods become efficient with respect to more classical grid-based strategies when the number of parameters increases. The results of our MCMC algorithm allow us to derive estimates for the stellar parameters and robust uncertainties thanks to the statistical analysis of the posterior probability densities. We are also able to compute odds for the presence of a convective core in α Cen A. When using core-sensitive seismic observational constraints, these can rise above ˜40 per cent. The comparison of results to previous studies also indicates that these seismic constraints are of critical importance for our knowledge of the structure of this star.

Spillover effects in epidemiology: parameters, study designs and methodological considerations

PubMed Central

Benjamin-Chung, Jade; Arnold, Benjamin F; Berger, David; Luby, Stephen P; Miguel, Edward; Colford Jr, John M; Hubbard, Alan E

2018-01-01

Abstract Many public health interventions provide benefits that extend beyond their direct recipients and impact people in close physical or social proximity who did not directly receive the intervention themselves. A classic example of this phenomenon is the herd protection provided by many vaccines. If these ‘spillover effects’ (i.e. ‘herd effects’) are present in the same direction as the effects on the intended recipients, studies that only estimate direct effects on recipients will likely underestimate the full public health benefits of the intervention. Causal inference assumptions for spillover parameters have been articulated in the vaccine literature, but many studies measuring spillovers of other types of public health interventions have not drawn upon that literature. In conjunction with a systematic review we conducted of spillovers of public health interventions delivered in low- and middle-income countries, we classified the most widely used spillover parameters reported in the empirical literature into a standard notation. General classes of spillover parameters include: cluster-level spillovers; spillovers conditional on treatment or outcome density, distance or the number of treated social network links; and vaccine efficacy parameters related to spillovers. We draw on high quality empirical examples to illustrate each of these parameters. We describe study designs to estimate spillovers and assumptions required to make causal inferences about spillovers. We aim to advance and encourage methods for spillover estimation and reporting by standardizing spillover parameter nomenclature and articulating the causal inference assumptions required to estimate spillovers. PMID:29106568

A "total parameter estimation" method in the varification of distributed hydrological models

NASA Astrophysics Data System (ADS)

Wang, M.; Qin, D.; Wang, H.

2011-12-01

Conventionally hydrological models are used for runoff or flood forecasting, hence the determination of model parameters are common estimated based on discharge measurements at the catchment outlets. With the advancement in hydrological sciences and computer technology, distributed hydrological models based on the physical mechanism such as SWAT, MIKESHE, and WEP, have gradually become the mainstream models in hydrology sciences. However, the assessments of distributed hydrological models and model parameter determination still rely on runoff and occasionally, groundwater level measurements. It is essential in many countries, including China, to understand the local and regional water cycle: not only do we need to simulate the runoff generation process and for flood forecasting in wet areas, we also need to grasp the water cycle pathways and consumption process of transformation in arid and semi-arid regions for the conservation and integrated water resources management. As distributed hydrological model can simulate physical processes within a catchment, we can get a more realistic representation of the actual water cycle within the simulation model. Runoff is the combined result of various hydrological processes, using runoff for parameter estimation alone is inherits problematic and difficult to assess the accuracy. In particular, in the arid areas, such as the Haihe River Basin in China, runoff accounted for only 17% of the rainfall, and very concentrated during the rainy season from June to August each year. During other months, many of the perennial rivers within the river basin dry up. Thus using single runoff simulation does not fully utilize the distributed hydrological model in arid and semi-arid regions. This paper proposed a "total parameter estimation" method to verify the distributed hydrological models within various water cycle processes, including runoff, evapotranspiration, groundwater, and soil water; and apply it to the Haihe river basin in China. The application results demonstrate that this comprehensive testing method is very useful in the development of a distributed hydrological model and it provides a new way of thinking in hydrological sciences.

Automatic Calibration of a Semi-Distributed Hydrologic Model Using Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Bekele, E. G.; Nicklow, J. W.

2005-12-01

Hydrologic simulation models need to be calibrated and validated before using them for operational predictions. Spatially-distributed hydrologic models generally have a large number of parameters to capture the various physical characteristics of a hydrologic system. Manual calibration of such models is a very tedious and daunting task, and its success depends on the subjective assessment of a particular modeler, which includes knowledge of the basic approaches and interactions in the model. In order to alleviate these shortcomings, an automatic calibration model, which employs an evolutionary optimization technique known as Particle Swarm Optimizer (PSO) for parameter estimation, is developed. PSO is a heuristic search algorithm that is inspired by social behavior of bird flocking or fish schooling. The newly-developed calibration model is integrated to the U.S. Department of Agriculture's Soil and Water Assessment Tool (SWAT). SWAT is a physically-based, semi-distributed hydrologic model that was developed to predict the long term impacts of land management practices on water, sediment and agricultural chemical yields in large complex watersheds with varying soils, land use, and management conditions. SWAT was calibrated for streamflow and sediment concentration. The calibration process involves parameter specification, whereby sensitive model parameters are identified, and parameter estimation. In order to reduce the number of parameters to be calibrated, parameterization was performed. The methodology is applied to a demonstration watershed known as Big Creek, which is located in southern Illinois. Application results show the effectiveness of the approach and model predictions are significantly improved.

Estimates of the atmospheric parameters of M-type stars: a machine-learning perspective

NASA Astrophysics Data System (ADS)

Sarro, L. M.; Ordieres-Meré, J.; Bello-García, A.; González-Marcos, A.; Solano, E.

2018-05-01

Estimating the atmospheric parameters of M-type stars has been a difficult task due to the lack of simple diagnostics in the stellar spectra. We aim at uncovering good sets of predictive features of stellar atmospheric parameters (Teff, log (g), [M/H]) in spectra of M-type stars. We define two types of potential features (equivalent widths and integrated flux ratios) able to explain the atmospheric physical parameters. We search the space of feature sets using a genetic algorithm that evaluates solutions by their prediction performance in the framework of the BT-Settl library of stellar spectra. Thereafter, we construct eight regression models using different machine-learning techniques and compare their performances with those obtained using the classical χ2 approach and independent component analysis (ICA) coefficients. Finally, we validate the various alternatives using two sets of real spectra from the NASA Infrared Telescope Facility (IRTF) and Dwarf Archives collections. We find that the cross-validation errors are poor measures of the performance of regression models in the context of physical parameter prediction in M-type stars. For R ˜ 2000 spectra with signal-to-noise ratios typical of the IRTF and Dwarf Archives, feature selection with genetic algorithms or alternative techniques produces only marginal advantages with respect to representation spaces that are unconstrained in wavelength (full spectrum or ICA). We make available the atmospheric parameters for the two collections of observed spectra as online material.

Limnology of Sawtooth Valley Lakes in 1995

DOE Office of Scientific and Technical Information (OSTI.GOV)

Luecke, C.; Slater, M.; Budy, P.

1996-05-01

Included in this section of the report on limnology of Lakes in the Snake River Plain are descriptions of the limnological characteristics of the four lakes in reference to their potential effect of growth and survival of juvenile sockeye salmon. Physical parameters included light penetration, Secchi transparency, and water temperature; chemical parameters included oxygen, and both dissolved and particulate forms of nitrogen and phosphorus. Phytoplankton parameters included chlorophyll concentration, biovolume of dominant taxa, and rates of primary production; zooplankton parameters included density and biomass estimate, length frequencies, and the number of eggs carried by female cladocerans. 11 figs., 5 tabs.

Earth-moon system: Dynamics and parameter estimation

NASA Technical Reports Server (NTRS)

Breedlove, W. J., Jr.

1975-01-01

A theoretical development of the equations of motion governing the earth-moon system is presented. The earth and moon were treated as finite rigid bodies and a mutual potential was utilized. The sun and remaining planets were treated as particles. Relativistic, non-rigid, and dissipative effects were not included. The translational and rotational motion of the earth and moon were derived in a fully coupled set of equations. Euler parameters were used to model the rotational motions. The mathematical model is intended for use with data analysis software to estimate physical parameters of the earth-moon system using primarily LURE type data. Two program listings are included. Program ANEAMO computes the translational/rotational motion of the earth and moon from analytical solutions. Program RIGEM numerically integrates the fully coupled motions as described above.

SENSITIVITY OF STRUCTURAL RESPONSE TO GROUND MOTION SOURCE AND SITE PARAMETERS.

USGS Publications Warehouse

Safak, Erdal; Brebbia, C.A.; Cakmak, A.S.; Abdel Ghaffar, A.M.

1985-01-01

Designing structures to withstand earthquakes requires an accurate estimation of the expected ground motion. While engineers use the peak ground acceleration (PGA) to model the strong ground motion, seismologists use physical characteristics of the source and the rupture mechanism, such as fault length, stress drop, shear wave velocity, seismic moment, distance, and attenuation. This study presents a method for calculating response spectra from seismological models using random vibration theory. It then investigates the effect of various source and site parameters on peak response. Calculations are based on a nonstationary stochastic ground motion model, which can incorporate all the parameters both in frequency and time domains. The estimation of the peak response accounts for the effects of the non-stationarity, bandwidth and peak correlations of the response.

State and Parameter Estimation for a Coupled Ocean--Atmosphere Model

NASA Astrophysics Data System (ADS)

Ghil, M.; Kondrashov, D.; Sun, C.

2006-12-01

The El-Nino/Southern-Oscillation (ENSO) dominates interannual climate variability and plays, therefore, a key role in seasonal-to-interannual prediction. Much is known by now about the main physical mechanisms that give rise to and modulate ENSO, but the values of several parameters that enter these mechanisms are an important unknown. We apply Extended Kalman Filtering (EKF) for both model state and parameter estimation in an intermediate, nonlinear, coupled ocean--atmosphere model of ENSO. The coupled model consists of an upper-ocean, reduced-gravity model of the Tropical Pacific and a steady-state atmospheric response to the sea surface temperature (SST). The model errors are assumed to be mainly in the atmospheric wind stress, and assimilated data are equatorial Pacific SSTs. Model behavior is very sensitive to two key parameters: (i) μ, the ocean-atmosphere coupling coefficient between SST and wind stress anomalies; and (ii) δs, the surface-layer coefficient. Previous work has shown that δs determines the period of the model's self-sustained oscillation, while μ measures the degree of nonlinearity. Depending on the values of these parameters, the spatio-temporal pattern of model solutions is either that of a delayed oscillator or of a westward propagating mode. Estimation of these parameters is tested first on synthetic data and allows us to recover the delayed-oscillator mode starting from model parameter values that correspond to the westward-propagating case. Assimilation of SST data from the NCEP-NCAR Reanalysis-2 shows that the parameters can vary on fairly short time scales and switch between values that approximate the two distinct modes of ENSO behavior. Rapid adjustments of these parameters occur, in particular, during strong ENSO events. Ways to apply EKF parameter estimation efficiently to state-of-the-art coupled ocean--atmosphere GCMs will be discussed.

Combining experimental techniques with non-linear numerical models to assess the sorption of pesticides on soils

NASA Astrophysics Data System (ADS)

Magga, Zoi; Tzovolou, Dimitra N.; Theodoropoulou, Maria A.; Tsakiroglou, Christos D.

2012-03-01

The risk assessment of groundwater pollution by pesticides may be based on pesticide sorption and biodegradation kinetic parameters estimated with inverse modeling of datasets from either batch or continuous flow soil column experiments. In the present work, a chemical non-equilibrium and non-linear 2-site sorption model is incorporated into solute transport models to invert the datasets of batch and soil column experiments, and estimate the kinetic sorption parameters for two pesticides: N-phosphonomethyl glycine (glyphosate) and 2,4-dichlorophenoxy-acetic acid (2,4-D). When coupling the 2-site sorption model with the 2-region transport model, except of the kinetic sorption parameters, the soil column datasets enable us to estimate the mass-transfer coefficients associated with solute diffusion between mobile and immobile regions. In order to improve the reliability of models and kinetic parameter values, a stepwise strategy that combines batch and continuous flow tests with adequate true-to-the mechanism analytical of numerical models, and decouples the kinetics of purely reactive steps of sorption from physical mass-transfer processes is required.

Assessment of catchments' flooding potential: a physically-based analytical tool

NASA Astrophysics Data System (ADS)

Botter, G.; Basso, S.; Schirmer, M.

2016-12-01

The assessment of the flooding potential of river catchments is critical in many research and applied fields, ranging from river science and geomorphology to urban planning and the insurance industry. Predicting magnitude and frequency of floods is key to prevent and mitigate the negative effects of high flows, and has therefore long been the focus of hydrologic research. Here, the recurrence intervals of seasonal flow maxima are estimated through a novel physically-based analytic approach, which links the extremal distribution of streamflows to the stochastic dynamics of daily discharge. An analytical expression of the seasonal flood-frequency curve is provided, whose parameters embody climate and landscape attributes of the contributing catchment and can be estimated from daily rainfall and streamflow data. Only one parameter, which expresses catchment saturation prior to rainfall events, needs to be calibrated on the observed maxima. The method has been tested in a set of catchments featuring heterogeneous daily flow regimes. The model is able to reproduce characteristic shapes of flood-frequency curves emerging in erratic and persistent flow regimes and provides good estimates of seasonal flow maxima in different climatic regions. Performances are steady when the magnitude of events with return times longer than the available sample size is estimated. This makes the approach especially valuable for regions affected by data scarcity.

Compost mixture influence of interactive physical parameters on microbial kinetics and substrate fractionation.

PubMed

Mohajer, Ardavan; Tremier, Anne; Barrington, Suzelle; Teglia, Cecile

2010-01-01

Composting is a feasible biological treatment for the recycling of wastewater sludge as a soil amendment. The process can be optimized by selecting an initial compost recipe with physical properties that enhance microbial activity. The present study measured the microbial O(2) uptake rate (OUR) in 16 sludge and wood residue mixtures to estimate the kinetics parameters of maximum growth rate mu(m) and rate of organic matter hydrolysis K(h), as well as the initial biodegradable organic matter fractions present. The starting mixtures consisted of a wide range of moisture content (MC), waste to bulking agent (BA) ratio (W/BA ratio) and BA particle size, which were placed in a laboratory respirometry apparatus to measure their OUR over 4 weeks. A microbial model based on the activated sludge process was used to calculate the kinetic parameters and was found to adequately reproduced OUR curves over time, except for the lag phase and peak OUR, which was not represented and generally over-estimated, respectively. The maximum growth rate mu(m), was found to have a quadratic relationship with MC and a negative association with BA particle size. As a result, increasing MC up to 50% and using a smaller BA particle size of 8-12 mm was seen to maximize mu(m). The rate of hydrolysis K(h) was found to have a linear association with both MC and BA particle size. The model also estimated the initial readily biodegradable organic matter fraction, MB(0), and the slower biodegradable matter requiring hydrolysis, MH(0). The sum of MB(0) and MH(0) was associated with MC, W/BA ratio and the interaction between these two parameters, suggesting that O(2) availability was a key factor in determining the value of these two fractions. The study reinforced the idea that optimization of the physical characteristics of a compost mixture requires a holistic approach. 2010 Elsevier Ltd. All rights reserved.

Statistical properties of a filtered Poisson process with additive random noise: distributions, correlations and moment estimation

NASA Astrophysics Data System (ADS)

Theodorsen, A.; E Garcia, O.; Rypdal, M.

2017-05-01

Filtered Poisson processes are often used as reference models for intermittent fluctuations in physical systems. Such a process is here extended by adding a noise term, either as a purely additive term to the process or as a dynamical term in a stochastic differential equation. The lowest order moments, probability density function, auto-correlation function and power spectral density are derived and used to identify and compare the effects of the two different noise terms. Monte-Carlo studies of synthetic time series are used to investigate the accuracy of model parameter estimation and to identify methods for distinguishing the noise types. It is shown that the probability density function and the three lowest order moments provide accurate estimations of the model parameters, but are unable to separate the noise types. The auto-correlation function and the power spectral density also provide methods for estimating the model parameters, as well as being capable of identifying the noise type. The number of times the signal crosses a prescribed threshold level in the positive direction also promises to be able to differentiate the noise type.

An improved swarm optimization for parameter estimation and biological model selection.

PubMed

Abdullah, Afnizanfaizal; Deris, Safaai; Mohamad, Mohd Saberi; Anwar, Sohail

2013-01-01

One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This study is hoped to provide a new insight in developing more accurate and reliable biological models based on limited and low quality experimental data.

Adaptive control based on an on-line parameter estimation of an upper limb exoskeleton.

PubMed

Riani, Akram; Madani, Tarek; Hadri, Abdelhafid El; Benallegue, Abdelaziz

2017-07-01

This paper presents an adaptive control strategy for an upper-limb exoskeleton based on an on-line dynamic parameter estimator. The objective is to improve the control performance of this system that plays a critical role in assisting patients for shoulder, elbow and wrist joint movements. In general, the dynamic parameters of the human limb are unknown and differ from a person to another, which degrade the performances of the exoskeleton-human control system. For this reason, the proposed control scheme contains a supplementary loop based on a new efficient on-line estimator of the dynamic parameters. Indeed, the latter is acting upon the parameter adaptation of the controller to ensure the performances of the system in the presence of parameter uncertainties and perturbations. The exoskeleton used in this work is presented and a physical model of the exoskeleton interacting with a 7 Degree of Freedom (DoF) upper limb model is generated using the SimMechanics library of MatLab/Simulink. To illustrate the effectiveness of the proposed approach, an example of passive rehabilitation movements is performed using multi-body dynamic simulation. The aims is to maneuver the exoskeleton that drive the upper limb to track desired trajectories in the case of the passive arm movements.

Modeling Far-UV Fluorescent Emission Features of Warm Molecular Hydrogen in the Inner Regions of Protoplanetary Disks

NASA Astrophysics Data System (ADS)

Hoadley, Keri; France, Kevin

2015-01-01

Probing the surviving molecular gas within the inner regions of protoplanetary disks (PPDs) around T Tauri stars (1 - 10 Myr) provides insight into the conditions in which planet formation and migration occurs while the gas disk is still present. We model observed far ultraviolet (FUV) molecular hydrogen (H₂) fluorescent emission lines that originate within the inner regions (< 10 AU) of 9 well-studied Classic T Tauri stars, using the Hubble Space Telescope Cosmic Origins Spectrograph (COS), to explore the physical structure of the molecular disk at different PPD dust evolutionary stages. We created a 2D radiative transfer model that estimates the density and temperature distributions of warm, inner radial H₂ (T > 1500 K) with a set of 6 free parameters and produces a data cube of expected emission line profiles that describe the physical structure of the inner molecular disk atmosphere. By comparing the modeled emission lines with COS H₂ fluorescence emission features, we estimate the physical structure of the molecular disk atmosphere for each target with the set of free parameters that best replicate the observed lines. First results suggest that, for all dust evolutionary stages of disks considered, ground-state H₂ populations are described by a roughly constant temperature T(H₂) = 2500 +/- 1000 K. Possible evolution of the density structure of the H₂ atmosphere between intact and depleting dust disks may be distinguishable, but large errors in the inferred best-fit parameter sets prevent us from making this conclusion. Further improvements to the modeling framework and statistical comparison in determining the best-fit model-to-data parameter sets are ongoing, beginning with improvements to the radiative transfer model and use of up-to-date HI Lyman α absorption optical depths (see McJunkin in posters) to better estimate disk structural parameters. Once improvements are implemented, we will investigate the possible presence of a molecular wind component in the observed H₂ fluorescence features by determining blue-shifted flux residuals in the data after best-fit model-to-data comparisons are complete.

Estimating the Maximum Magnitude of Induced Earthquakes With Dynamic Rupture Simulations

NASA Astrophysics Data System (ADS)

Gilmour, E.; Daub, E. G.

2017-12-01

Seismicity in Oklahoma has been sharply increasing as the result of wastewater injection. The earthquakes, thought to be induced from changes in pore pressure due to fluid injection, nucleate along existing faults. Induced earthquakes currently dominate central and eastern United States seismicity (Keranen et al. 2016). Induced earthquakes have only been occurring in the central US for a short time; therefore, too few induced earthquakes have been observed in this region to know their maximum magnitude. The lack of knowledge regarding the maximum magnitude of induced earthquakes means that large uncertainties exist in the seismic hazard for the central United States. While induced earthquakes follow the Gutenberg-Richter relation (van der Elst et al. 2016), it is unclear if there are limits to their magnitudes. An estimate of the maximum magnitude of the induced earthquakes is crucial for understanding their impact on seismic hazard. While other estimates of the maximum magnitude exist, those estimates are observational or statistical, and cannot take into account the possibility of larger events that have not yet been observed. Here, we take a physical approach to studying the maximum magnitude based on dynamic ruptures simulations. We run a suite of two-dimensional ruptures simulations to physically determine how ruptures propagate. The simulations use the known parameters of principle stress orientation and rupture locations. We vary the other unknown parameters of the ruptures simulations to obtain a large number of rupture simulation results reflecting different possible sets of parameters, and use these results to train a neural network to complete the ruptures simulations. Then using a Markov Chain Monte Carlo method to check different combinations of parameters, the trained neural network is used to create synthetic magnitude-frequency distributions to compare to the real earthquake catalog. This method allows us to find sets of parameters that are consistent with earthquakes observed in Oklahoma and find which parameters effect the rupture propagation. Our results show that the stress orientation and magnitude, pore pressure, and friction properties combine to determine the final magnitude of the simulated event.

Slip-based terrain estimation with a skid-steer vehicle

NASA Astrophysics Data System (ADS)

Reina, Giulio; Galati, Rocco

2016-10-01

In this paper, a novel approach for online terrain characterisation is presented using a skid-steer vehicle. In the context of this research, terrain characterisation refers to the estimation of physical parameters that affects the terrain ability to support vehicular motion. These parameters are inferred from the modelling of the kinematic and dynamic behaviour of a skid-steer vehicle that reveals the underlying relationships governing the vehicle-terrain interaction. The concept of slip track is introduced as a measure of the slippage experienced by the vehicle during turning motion. The proposed terrain estimation system includes common onboard sensors, that is, wheel encoders, electrical current sensors and yaw rate gyroscope. Using these components, the system can characterise terrain online during normal vehicle operations. Experimental results obtained from different surfaces are presented to validate the system in the field showing its effectiveness and potential benefits to implement adaptive driving assistance systems or to automatically update the parameters of onboard control and planning algorithms.

Physical models, cross sections, and numerical approximations used in MCNP and GEANT4 Monte Carlo codes for photon and electron absorbed fraction calculation.

PubMed

Yoriyaz, Hélio; Moralles, Maurício; Siqueira, Paulo de Tarso Dalledone; Guimarães, Carla da Costa; Cintra, Felipe Belonsi; dos Santos, Adimir

2009-11-01

Radiopharmaceutical applications in nuclear medicine require a detailed dosimetry estimate of the radiation energy delivered to the human tissues. Over the past years, several publications addressed the problem of internal dose estimate in volumes of several sizes considering photon and electron sources. Most of them used Monte Carlo radiation transport codes. Despite the widespread use of these codes due to the variety of resources and potentials they offered to carry out dose calculations, several aspects like physical models, cross sections, and numerical approximations used in the simulations still remain an object of study. Accurate dose estimate depends on the correct selection of a set of simulation options that should be carefully chosen. This article presents an analysis of several simulation options provided by two of the most used codes worldwide: MCNP and GEANT4. For this purpose, comparisons of absorbed fraction estimates obtained with different physical models, cross sections, and numerical approximations are presented for spheres of several sizes and composed as five different biological tissues. Considerable discrepancies have been found in some cases not only between the different codes but also between different cross sections and algorithms in the same code. Maximum differences found between the two codes are 5.0% and 10%, respectively, for photons and electrons. Even for simple problems as spheres and uniform radiation sources, the set of parameters chosen by any Monte Carlo code significantly affects the final results of a simulation, demonstrating the importance of the correct choice of parameters in the simulation.

Nonlinear Kalman filters for calibration in radio interferometry

NASA Astrophysics Data System (ADS)

Tasse, C.

2014-06-01

The data produced by the new generation of interferometers are affected by a wide variety of partially unknown complex effects such as pointing errors, phased array beams, ionosphere, troposphere, Faraday rotation, or clock drifts. Most algorithms addressing direction-dependent calibration solve for the effective Jones matrices, and cannot constrain the underlying physical quantities of the radio interferometry measurement equation (RIME). A related difficulty is that they lack robustness in the presence of low signal-to-noise ratios, and when solving for moderate to large numbers of parameters they can be subject to ill-conditioning. These effects can have dramatic consequences in the image plane such as source or even thermal noise suppression. The advantage of solvers directly estimating the physical terms appearing in the RIME is that they can potentially reduce the number of free parameters by orders of magnitudes while dramatically increasing the size of usable data, thereby improving conditioning. We present here a new calibration scheme based on a nonlinear version of the Kalman filter that aims at estimating the physical terms appearing in the RIME. We enrich the filter's structure with a tunable data representation model, together with an augmented measurement model for regularization. Using simulations we show that it can properly estimate the physical effects appearing in the RIME. We found that this approach is particularly useful in the most extreme cases such as when ionospheric and clock effects are simultaneously present. Combined with the ability to provide prior knowledge on the expected structure of the physical instrumental effects (expected physical state and dynamics), we obtain a fairly computationally cheap algorithm that we believe to be robust, especially in low signal-to-noise regimes. Potentially, the use of filters and other similar methods can represent an improvement for calibration in radio interferometry, under the condition that the effects corrupting visibilities are understood and analytically stable. Recursive algorithms are particularly well adapted for pre-calibration and sky model estimate in a streaming way. This may be useful for the SKA-type instruments that produce huge amounts of data that have to be calibrated before being averaged.

Using Remotely-Sensed Estimates of Soil Moisture to Infer Soil Texture and Hydraulic Properties across a Semi-arid Watershed

NASA Technical Reports Server (NTRS)

Santanello, Joseph A.; Peters-Lidard, Christa D.; Garcia, Matthew E.; Mocko, David M.; Tischler, Michael A.; Moran, M. Susan; Thoma, D. P.

2007-01-01

Near-surface soil moisture is a critical component of land surface energy and water balance studies encompassing a wide range of disciplines. However, the processes of infiltration, runoff, and evapotranspiration in the vadose zone of the soil are not easy to quantify or predict because of the difficulty in accurately representing soil texture and hydraulic properties in land surface models. This study approaches the problem of parameterizing soils from a unique perspective based on components originally developed for operational estimation of soil moisture for mobility assessments. Estimates of near-surface soil moisture derived from passive (L-band) microwave remote sensing were acquired on six dates during the Monsoon '90 experiment in southeastern Arizona, and used to calibrate hydraulic properties in an offline land surface model and infer information on the soil conditions of the region. Specifically, a robust parameter estimation tool (PEST) was used to calibrate the Noah land surface model and run at very high spatial resolution across the Walnut Gulch Experimental Watershed. Errors in simulated versus observed soil moisture were minimized by adjusting the soil texture, which in turn controls the hydraulic properties through the use of pedotransfer functions. By estimating a continuous range of widely applicable soil properties such as sand, silt, and clay percentages rather than applying rigid soil texture classes, lookup tables, or large parameter sets as in previous studies, the physical accuracy and consistency of the resulting soils could then be assessed. In addition, the sensitivity of this calibration method to the number and timing of microwave retrievals is determined in relation to the temporal patterns in precipitation and soil drying. The resultant soil properties were applied to an extended time period demonstrating the improvement in simulated soil moisture over that using default or county-level soil parameters. The methodology is also applied to an independent case at Walnut Gulch using a new soil moisture product from active (C-band) radar imagery with much lower spatial and temporal resolution. Overall, results demonstrate the potential to gain physically meaningful soils information using simple parameter estimation with few but appropriately timed remote sensing retrievals.

Using a Betabinomial distribution to estimate the prevalence of adherence to physical activity guidelines among children and youth.

PubMed

Garriguet, Didier

2016-04-01

Estimates of the prevalence of adherence to physical activity guidelines in the population are generally the result of averaging individual probability of adherence based on the number of days people meet the guidelines and the number of days they are assessed. Given this number of active and inactive days (days assessed minus days active), the conditional probability of meeting the guidelines that has been used in the past is a Beta (1 + active days, 1 + inactive days) distribution assuming the probability p of a day being active is bounded by 0 and 1 and averages 50%. A change in the assumption about the distribution of p is required to better match the discrete nature of the data and to better assess the probability of adherence when the percentage of active days in the population differs from 50%. Using accelerometry data from the Canadian Health Measures Survey, the probability of adherence to physical activity guidelines is estimated using a conditional probability given the number of active and inactive days distributed as a Betabinomial(n, a + active days , β + inactive days) assuming that p is randomly distributed as Beta(a, β) where the parameters a and β are estimated by maximum likelihood. The resulting Betabinomial distribution is discrete. For children aged 6 or older, the probability of meeting physical activity guidelines 7 out of 7 days is similar to published estimates. For pre-schoolers, the Betabinomial distribution yields higher estimates of adherence to the guidelines than the Beta distribution, in line with the probability of being active on any given day. In estimating the probability of adherence to physical activity guidelines, the Betabinomial distribution has several advantages over the previously used Beta distribution. It is a discrete distribution and maximizes the richness of accelerometer data.

Simulation-Based Joint Estimation of Body Deformation and Elasticity Parameters for Medical Image Analysis

PubMed Central

Foskey, Mark; Niethammer, Marc; Krajcevski, Pavel; Lin, Ming C.

2014-01-01

Estimation of tissue stiffness is an important means of noninvasive cancer detection. Existing elasticity reconstruction methods usually depend on a dense displacement field (inferred from ultrasound or MR images) and known external forces. Many imaging modalities, however, cannot provide details within an organ and therefore cannot provide such a displacement field. Furthermore, force exertion and measurement can be difficult for some internal organs, making boundary forces another missing parameter. We propose a general method for estimating elasticity and boundary forces automatically using an iterative optimization framework, given the desired (target) output surface. During the optimization, the input model is deformed by the simulator, and an objective function based on the distance between the deformed surface and the target surface is minimized numerically. The optimization framework does not depend on a particular simulation method and is therefore suitable for different physical models. We show a positive correlation between clinical prostate cancer stage (a clinical measure of severity) and the recovered elasticity of the organ. Since the surface correspondence is established, our method also provides a non-rigid image registration, where the quality of the deformation fields is guaranteed, as they are computed using a physics-based simulation. PMID:22893381

An Inequality Constrained Least-Squares Approach as an Alternative Estimation Procedure for Atmospheric Parameters from VLBI Observations

NASA Astrophysics Data System (ADS)

Halsig, Sebastian; Artz, Thomas; Iddink, Andreas; Nothnagel, Axel

2016-12-01

On its way through the atmosphere, radio signals are delayed and affected by bending and attenuation effects relative to a theoretical path in vacuum. In particular, the neutral part of the atmosphere contributes considerably to the error budget of space-geodetic observations. At the same time, space-geodetic techniques become more and more important in the understanding of the Earth's atmosphere, because atmospheric parameters can be linked to the water vapor content in the atmosphere. The tropospheric delay is usually taken into account by applying an adequate model for the hydrostatic component and by additionally estimating zenith wet delays for the highly variable wet component. Sometimes, the Ordinary Least Squares (OLS) approach leads to negative estimates, which would be equivalent to negative water vapor in the atmosphere and does, of course, not reflect meteorological and physical conditions in a plausible way. To cope with this phenomenon, we introduce an Inequality Constrained Least Squares (ICLS) method from the field of convex optimization and use inequality constraints to force the tropospheric parameters to be non-negative allowing for a more realistic tropospheric parameter estimation in a meteorological sense. Because deficiencies in the a priori hydrostatic modeling are almost fully compensated by the tropospheric estimates, the ICLS approach urgently requires suitable a priori hydrostatic delays. In this paper, we briefly describe the ICLS method and validate its impact with regard to station positions.

[Afterschool physical activity programs: Literature review].

PubMed

Reloba-Martínez, Sergio; Martín-Tamayo, Ignacio; Martínez-López, Emilio José; Guerrero-Almeida, Laura

2015-01-01

The purpose of this review was to analyze the scientific production about extra-curricular physical activity (PA) in western children of 6-12 years. Medline / Pub-Med, Scopus and Google Scholar were used. This search collects articles published between January 1990 and May 2013. A total of 104 publications were analyzed. The body composition parameters are best used to assess the results of the studies, followed by those which estimate the maximum aerobic capacity. Articles of intervention are presented with very heterogeneous methodological features but there are clear trends in the use of certain aspects. As for the reviews, most are systematic and include meta-analysis. In this studies, body mass index (BMI) is the most used parameter.

A Software Toolkit to Study Systematic Uncertainties of the Physics Models of the Geant4 Simulation Package

DOE Office of Scientific and Technical Information (OSTI.GOV)

Genser, Krzysztof; Hatcher, Robert; Kelsey, Michael

The Geant4 simulation toolkit is used to model interactions between particles and matter. Geant4 employs a set of validated physics models that span a wide range of interaction energies. These models rely on measured cross-sections and phenomenological models with the physically motivated parameters that are tuned to cover many application domains. To study what uncertainties are associated with the Geant4 physics models we have designed and implemented a comprehensive, modular, user-friendly software toolkit that allows the variation of one or more parameters of one or more Geant4 physics models involved in simulation studies. It also enables analysis of multiple variantsmore » of the resulting physics observables of interest in order to estimate the uncertainties associated with the simulation model choices. Based on modern event-processing infrastructure software, the toolkit offers a variety of attractive features, e.g. exible run-time con gurable work ow, comprehensive bookkeeping, easy to expand collection of analytical components. Design, implementation technology, and key functionalities of the toolkit are presented in this paper and illustrated with selected results.« less

Physics issues in diffraction limited storage ring design

NASA Astrophysics Data System (ADS)

Fan, Wei; Bai, ZhengHe; Gao, WeiWei; Feng, GuangYao; Li, WeiMin; Wang, Lin; He, DuoHui

2012-05-01

Diffraction limited electron storage ring is considered a promising candidate for future light sources, whose main characteristics are higher brilliance, better transverse coherence and better stability. The challenge of diffraction limited storage ring design is how to achieve the ultra low beam emittance with acceptable nonlinear performance. Effective linear and nonlinear parameter optimization methods based on Artificial Intelligence were developed for the storage ring physical design. As an example of application, partial physical design of HALS (Hefei Advanced Light Source), which is a diffraction limited VUV and soft X-ray light source, was introduced. Severe emittance growth due to the Intra Beam Scattering effect, which is the main obstacle to achieve ultra low emittance, was estimated quantitatively and possible cures were discussed. It is inspiring that better performance of diffraction limited storage ring can be achieved in principle with careful parameter optimization.

Parameter and observation importance in modelling virus transport in saturated porous media - Investigations in a homogenous system

USGS Publications Warehouse

Barth, Gilbert R.; Hill, M.C.

2005-01-01

This paper evaluates the importance of seven types of parameters to virus transport: hydraulic conductivity, porosity, dispersivity, sorption rate and distribution coefficient (representing physical-chemical filtration), and in-solution and adsorbed inactivation (representing virus inactivation). The first three parameters relate to subsurface transport in general while the last four, the sorption rate, distribution coefficient, and in-solution and adsorbed inactivation rates, represent the interaction of viruses with the porous medium and their ability to persist. The importance of four types of observations to estimate the virus-transport parameters are evaluated: hydraulic heads, flow, temporal moments of conservative-transport concentrations, and virus concentrations. The evaluations are conducted using one- and two-dimensional homogeneous simulations, designed from published field experiments, and recently developed sensitivity-analysis methods. Sensitivity to the transport-simulation time-step size is used to evaluate the importance of numerical solution difficulties. Results suggest that hydraulic conductivity, porosity, and sorption are most important to virus-transport predictions. Most observation types provide substantial information about hydraulic conductivity and porosity; only virus-concentration observations provide information about sorption and inactivation. The observations are not sufficient to estimate these important parameters uniquely. Even with all observation types, there is extreme parameter correlation between porosity and hydraulic conductivity and between the sorption rate and in-solution inactivation. Parameter estimation was accomplished by fixing values of porosity and in-solution inactivation.

On-line adaptive battery impedance parameter and state estimation considering physical principles in reduced order equivalent circuit battery models part 2. Parameter and state estimation

NASA Astrophysics Data System (ADS)

Fleischer, Christian; Waag, Wladislaw; Heyn, Hans-Martin; Sauer, Dirk Uwe

2014-09-01

Lithium-ion battery systems employed in high power demanding systems such as electric vehicles require a sophisticated monitoring system to ensure safe and reliable operation. Three major states of the battery are of special interest and need to be constantly monitored. These include: battery state of charge (SoC), battery state of health (capacity fade determination, SoH), and state of function (power fade determination, SoF). The second paper concludes the series by presenting a multi-stage online parameter identification technique based on a weighted recursive least quadratic squares parameter estimator to determine the parameters of the proposed battery model from the first paper during operation. A novel mutation based algorithm is developed to determine the nonlinear current dependency of the charge-transfer resistance. The influence of diffusion is determined by an on-line identification technique and verified on several batteries at different operation conditions. This method guarantees a short response time and, together with its fully recursive structure, assures a long-term stable monitoring of the battery parameters. The relative dynamic voltage prediction error of the algorithm is reduced to 2%. The changes of parameters are used to determine the states of the battery. The algorithm is real-time capable and can be implemented on embedded systems.

Electron-hole pairs generation rate estimation irradiated by isotope Nickel-63 in silicone using GEANT4

NASA Astrophysics Data System (ADS)

Kovalev, I. V.; Sidorov, V. G.; Zelenkov, P. V.; Khoroshko, A. Y.; Lelekov, A. T.

2015-10-01

To optimize parameters of beta-electrical converter of isotope Nickel-63 radiation, model of the distribution of EHP generation rate in semiconductor must be derived. By using Monte-Carlo methods in GEANT4 system with ultra-low energy electron physics models this distribution in silicon calculated and approximated with Gauss function. Maximal efficient isotope layer thickness and maximal energy efficiency of EHP generation were estimated.

Uncertainty Analysis and Parameter Estimation For Nearshore Hydrodynamic Models

NASA Astrophysics Data System (ADS)

Ardani, S.; Kaihatu, J. M.

2012-12-01

Numerical models represent deterministic approaches used for the relevant physical processes in the nearshore. Complexity of the physics of the model and uncertainty involved in the model inputs compel us to apply a stochastic approach to analyze the robustness of the model. The Bayesian inverse problem is one powerful way to estimate the important input model parameters (determined by apriori sensitivity analysis) and can be used for uncertainty analysis of the outputs. Bayesian techniques can be used to find the range of most probable parameters based on the probability of the observed data and the residual errors. In this study, the effect of input data involving lateral (Neumann) boundary conditions, bathymetry and off-shore wave conditions on nearshore numerical models are considered. Monte Carlo simulation is applied to a deterministic numerical model (the Delft3D modeling suite for coupled waves and flow) for the resulting uncertainty analysis of the outputs (wave height, flow velocity, mean sea level and etc.). Uncertainty analysis of outputs is performed by random sampling from the input probability distribution functions and running the model as required until convergence to the consistent results is achieved. The case study used in this analysis is the Duck94 experiment, which was conducted at the U.S. Army Field Research Facility at Duck, North Carolina, USA in the fall of 1994. The joint probability of model parameters relevant for the Duck94 experiments will be found using the Bayesian approach. We will further show that, by using Bayesian techniques to estimate the optimized model parameters as inputs and applying them for uncertainty analysis, we can obtain more consistent results than using the prior information for input data which means that the variation of the uncertain parameter will be decreased and the probability of the observed data will improve as well. Keywords: Monte Carlo Simulation, Delft3D, uncertainty analysis, Bayesian techniques, MCMC

An MCMC determination of the primordial helium abundance

NASA Astrophysics Data System (ADS)

Aver, Erik; Olive, Keith A.; Skillman, Evan D.

2012-04-01

Spectroscopic observations of the chemical abundances in metal-poor H II regions provide an independent method for estimating the primordial helium abundance. H II regions are described by several physical parameters such as electron density, electron temperature, and reddening, in addition to y, the ratio of helium to hydrogen. It had been customary to estimate or determine self-consistently these parameters to calculate y. Frequentist analyses of the parameter space have been shown to be successful in these parameter determinations, and Markov Chain Monte Carlo (MCMC) techniques have proven to be very efficient in sampling this parameter space. Nevertheless, accurate determination of the primordial helium abundance from observations of H II regions is constrained by both systematic and statistical uncertainties. In an attempt to better reduce the latter, and continue to better characterize the former, we apply MCMC methods to the large dataset recently compiled by Izotov, Thuan, & Stasińska (2007). To improve the reliability of the determination, a high quality dataset is needed. In pursuit of this, a variety of cuts are explored. The efficacy of the He I λ4026 emission line as a constraint on the solutions is first examined, revealing the introduction of systematic bias through its absence. As a clear measure of the quality of the physical solution, a χ2 analysis proves instrumental in the selection of data compatible with the theoretical model. Nearly two-thirds of the observations fall outside a standard 95% confidence level cut, which highlights the care necessary in selecting systems and warrants further investigation into potential deficiencies of the model or data. In addition, the method also allows us to exclude systems for which parameter estimations are statistical outliers. As a result, the final selected dataset gains in reliability and exhibits improved consistency. Regression to zero metallicity yields Yp = 0.2534 ± 0.0083, in broad agreement with the WMAP result. The inclusion of more observations shows promise for further reducing the uncertainty, but more high quality spectra are required.

A new zonation algorithm with parameter estimation using hydraulic head and subsidence observations.

PubMed

Zhang, Meijing; Burbey, Thomas J; Nunes, Vitor Dos Santos; Borggaard, Jeff

2014-01-01

Parameter estimation codes such as UCODE_2005 are becoming well-known tools in groundwater modeling investigations. These programs estimate important parameter values such as transmissivity (T) and aquifer storage values (Sa ) from known observations of hydraulic head, flow, or other physical quantities. One drawback inherent in these codes is that the parameter zones must be specified by the user. However, such knowledge is often unknown even if a detailed hydrogeological description is available. To overcome this deficiency, we present a discrete adjoint algorithm for identifying suitable zonations from hydraulic head and subsidence measurements, which are highly sensitive to both elastic (Sske) and inelastic (Sskv) skeletal specific storage coefficients. With the advent of interferometric synthetic aperture radar (InSAR), distributed spatial and temporal subsidence measurements can be obtained. A synthetic conceptual model containing seven transmissivity zones, one aquifer storage zone and three interbed zones for elastic and inelastic storage coefficients were developed to simulate drawdown and subsidence in an aquifer interbedded with clay that exhibits delayed drainage. Simulated delayed land subsidence and groundwater head data are assumed to be the observed measurements, to which the discrete adjoint algorithm is called to create approximate spatial zonations of T, Sske , and Sskv . UCODE-2005 is then used to obtain the final optimal parameter values. Calibration results indicate that the estimated zonations calculated from the discrete adjoint algorithm closely approximate the true parameter zonations. This automation algorithm reduces the bias established by the initial distribution of zones and provides a robust parameter zonation distribution. © 2013, National Ground Water Association.

Relating stick-slip friction experiments to earthquake source parameters

USGS Publications Warehouse

McGarr, Arthur F.

2012-01-01

Analytical results for parameters, such as static stress drop, for stick-slip friction experiments, with arbitrary input parameters, can be determined by solving an energy-balance equation. These results can then be related to a given earthquake based on its seismic moment and the maximum slip within its rupture zone, assuming that the rupture process entails the same physics as stick-slip friction. This analysis yields overshoots and ratios of apparent stress to static stress drop of about 0.25. The inferred earthquake source parameters static stress drop, apparent stress, slip rate, and radiated energy are robust inasmuch as they are largely independent of the experimental parameters used in their estimation. Instead, these earthquake parameters depend on C, the ratio of maximum slip to the cube root of the seismic moment. C is controlled by the normal stress applied to the rupture plane and the difference between the static and dynamic coefficients of friction. Estimating yield stress and seismic efficiency using the same procedure is only possible when the actual static and dynamic coefficients of friction are known within the earthquake rupture zone.

Quantification of whispering gallery mode spectrum variability in application to sensing nanobiophotonics

NASA Astrophysics Data System (ADS)

Saetchnikov, Anton; Skakun, Victor; Saetchnikov, Vladimir; Tcherniavskaia, Elina; Ostendorf, Andreas

2017-10-01

An approach for the automated whispering gallery mode (WGM) signal decomposition and its parameter estimation is discussed. The algorithm is based on the peak picking and can be applied for the preprocessing of the raw signal acquired from the multiplied WGM-based biosensing chips. Quantitative estimations representing physically meaningful parameters of the external disturbing factors on the WGM spectral shape are the output values. Derived parameters can be directly applied to the further deep qualitative and quantitative interpretations of the sensed disturbing factors. The algorithm is tested on both simulated and experimental data taken from the bovine serum albumin biosensing task. The proposed solution is expected to be a useful contribution to the preprocessing phase of the complete data analysis engine and is expected to push the WGM technology toward the real-live sensing nanobiophotonics.

Using microwave observations to estimate land surface temperature during cloudy conditions

USDA-ARS?s Scientific Manuscript database

Land surface temperature (LST), a key ingredient for physically-based retrieval algorithms of hydrological states and fluxes, remains a poorly constrained parameter for global scale studies. The main two observational methods to remotely measure T are based on thermal infrared (TIR) observations and...

The Effect of Nondeterministic Parameters on Shock-Associated Noise Prediction Modeling

NASA Technical Reports Server (NTRS)

Dahl, Milo D.; Khavaran, Abbas

2010-01-01

Engineering applications for aircraft noise prediction contain models for physical phenomenon that enable solutions to be computed quickly. These models contain parameters that have an uncertainty not accounted for in the solution. To include uncertainty in the solution, nondeterministic computational methods are applied. Using prediction models for supersonic jet broadband shock-associated noise, fixed model parameters are replaced by probability distributions to illustrate one of these methods. The results show the impact of using nondeterministic parameters both on estimating the model output uncertainty and on the model spectral level prediction. In addition, a global sensitivity analysis is used to determine the influence of the model parameters on the output, and to identify the parameters with the least influence on model output.

Linearly Supporting Feature Extraction for Automated Estimation of Stellar Atmospheric Parameters

NASA Astrophysics Data System (ADS)

Li, Xiangru; Lu, Yu; Comte, Georges; Luo, Ali; Zhao, Yongheng; Wang, Yongjun

2015-05-01

We describe a scheme to extract linearly supporting (LSU) features from stellar spectra to automatically estimate the atmospheric parameters {{T}{\\tt{eff} }}, log g, and [Fe/H]. “Linearly supporting” means that the atmospheric parameters can be accurately estimated from the extracted features through a linear model. The successive steps of the process are as follow: first, decompose the spectrum using a wavelet packet (WP) and represent it by the derived decomposition coefficients; second, detect representative spectral features from the decomposition coefficients using the proposed method Least Absolute Shrinkage and Selection Operator (LARS)bs; third, estimate the atmospheric parameters {{T}{\\tt{eff} }}, log g, and [Fe/H] from the detected features using a linear regression method. One prominent characteristic of this scheme is its ability to evaluate quantitatively the contribution of each detected feature to the atmospheric parameter estimate and also to trace back the physical significance of that feature. This work also shows that the usefulness of a component depends on both the wavelength and frequency. The proposed scheme has been evaluated on both real spectra from the Sloan Digital Sky Survey (SDSS)/SEGUE and synthetic spectra calculated from Kurucz's NEWODF models. On real spectra, we extracted 23 features to estimate {{T}{\\tt{eff} }}, 62 features for log g, and 68 features for [Fe/H]. Test consistencies between our estimates and those provided by the Spectroscopic Parameter Pipeline of SDSS show that the mean absolute errors (MAEs) are 0.0062 dex for log {{T}{\\tt{eff} }} (83 K for {{T}{\\tt{eff} }}), 0.2345 dex for log g, and 0.1564 dex for [Fe/H]. For the synthetic spectra, the MAE test accuracies are 0.0022 dex for log {{T}{\\tt{eff} }} (32 K for {{T}{\\tt{eff} }}), 0.0337 dex for log g, and 0.0268 dex for [Fe/H].

Analytical Estimation of the Scale of Earth-Like Planetary Magnetic Fields

NASA Astrophysics Data System (ADS)

Bologna, Mauro; Tellini, Bernardo

2014-10-01

In this paper we analytically estimate the magnetic field scale of planets with physical core conditions similar to that of Earth from a statistical physics point of view. We evaluate the magnetic field on the basis of the physical parameters of the center of the planet, such as density, temperature, and core size. We look at the contribution of the Seebeck effect on the magnetic field, showing that a thermally induced electrical current can exist in a rotating fluid sphere. We apply our calculations to Earth, where the currents would be driven by the temperature difference at the outer-inner core boundary, Jupiter and the Jupiter's satellite Ganymede. In each case we show that the thermal generation of currents leads to a magnetic field scale comparable to the observed fields of the considered celestial bodies.

Late type close binary system CM Dra

NASA Astrophysics Data System (ADS)

Kalomeni, Belinda

2015-08-01

In this study, we present new observations of the close binary system CM Dra. We analyzed all the available data of the system and estimated the physical parameters of the system stars highly accurately. Using the newly obtained parameters the distance of the system is determined to be 11.6 pc. A possible giant planet orbiting the close binary system has been detected. This orbital period would likely make it one of the longest known orbital period planet.

A Data-Driven Approach to Develop Physically Sound Predictors: Application to Depth-Averaged Velocities and Drag Coefficients on Vegetated Flows

NASA Astrophysics Data System (ADS)

Tinoco, R. O.; Goldstein, E. B.; Coco, G.

2016-12-01

We use a machine learning approach to seek accurate, physically sound predictors, to estimate two relevant flow parameters for open-channel vegetated flows: mean velocities and drag coefficients. A genetic programming algorithm is used to find a robust relationship between properties of the vegetation and flow parameters. We use data published from several laboratory experiments covering a broad range of conditions to obtain: a) in the case of mean flow, an equation that matches the accuracy of other predictors from recent literature while showing a less complex structure, and b) for drag coefficients, a predictor that relies on both single element and array parameters. We investigate different criteria for dataset size and data selection to evaluate their impact on the resulting predictor, as well as simple strategies to obtain only dimensionally consistent equations, and avoid the need for dimensional coefficients. The results show that a proper methodology can deliver physically sound models representative of the processes involved, such that genetic programming and machine learning techniques can be used as powerful tools to study complicated phenomena and develop not only purely empirical, but "hybrid" models, coupling results from machine learning methodologies into physics-based models.

Structured filtering

NASA Astrophysics Data System (ADS)

Granade, Christopher; Wiebe, Nathan

2017-08-01

A major challenge facing existing sequential Monte Carlo methods for parameter estimation in physics stems from the inability of existing approaches to robustly deal with experiments that have different mechanisms that yield the results with equivalent probability. We address this problem here by proposing a form of particle filtering that clusters the particles that comprise the sequential Monte Carlo approximation to the posterior before applying a resampler. Through a new graphical approach to thinking about such models, we are able to devise an artificial-intelligence based strategy that automatically learns the shape and number of the clusters in the support of the posterior. We demonstrate the power of our approach by applying it to randomized gap estimation and a form of low circuit-depth phase estimation where existing methods from the physics literature either exhibit much worse performance or even fail completely.

A transfer function type of simplified electrochemical model with modified boundary conditions and Padé approximation for Li-ion battery: Part 2. Modeling and parameter estimation

NASA Astrophysics Data System (ADS)

Yuan, Shifei; Jiang, Lei; Yin, Chengliang; Wu, Hongjie; Zhang, Xi

2017-06-01

The electrochemistry-based battery model can provide physics-meaningful knowledge about the lithium-ion battery system with extensive computation burdens. To motivate the development of reduced order battery model, three major contributions have been made throughout this paper: (1) the transfer function type of simplified electrochemical model is proposed to address the current-voltage relationship with Padé approximation method and modified boundary conditions for electrolyte diffusion equations. The model performance has been verified under pulse charge/discharge and dynamic stress test (DST) profiles with the standard derivation less than 0.021 V and the runtime 50 times faster. (2) the parametric relationship between the equivalent circuit model and simplified electrochemical model has been established, which will enhance the comprehension level of two models with more in-depth physical significance and provide new methods for electrochemical model parameter estimation. (3) four simplified electrochemical model parameters: equivalent resistance Req, effective diffusion coefficient in electrolyte phase Deeff, electrolyte phase volume fraction ε and open circuit voltage (OCV), have been identified by the recursive least square (RLS) algorithm with the modified DST profiles under 45, 25 and 0 °C. The simulation results indicate that the proposed model coupled with RLS algorithm can achieve high accuracy for electrochemical parameter identification in dynamic scenarios.

Thermodynamically consistent model calibration in chemical kinetics

PubMed Central

2011-01-01

Background The dynamics of biochemical reaction systems are constrained by the fundamental laws of thermodynamics, which impose well-defined relationships among the reaction rate constants characterizing these systems. Constructing biochemical reaction systems from experimental observations often leads to parameter values that do not satisfy the necessary thermodynamic constraints. This can result in models that are not physically realizable and may lead to inaccurate, or even erroneous, descriptions of cellular function. Results We introduce a thermodynamically consistent model calibration (TCMC) method that can be effectively used to provide thermodynamically feasible values for the parameters of an open biochemical reaction system. The proposed method formulates the model calibration problem as a constrained optimization problem that takes thermodynamic constraints (and, if desired, additional non-thermodynamic constraints) into account. By calculating thermodynamically feasible values for the kinetic parameters of a well-known model of the EGF/ERK signaling cascade, we demonstrate the qualitative and quantitative significance of imposing thermodynamic constraints on these parameters and the effectiveness of our method for accomplishing this important task. MATLAB software, using the Systems Biology Toolbox 2.1, can be accessed from http://www.cis.jhu.edu/~goutsias/CSS lab/software.html. An SBML file containing the thermodynamically feasible EGF/ERK signaling cascade model can be found in the BioModels database. Conclusions TCMC is a simple and flexible method for obtaining physically plausible values for the kinetic parameters of open biochemical reaction systems. It can be effectively used to recalculate a thermodynamically consistent set of parameter values for existing thermodynamically infeasible biochemical reaction models of cellular function as well as to estimate thermodynamically feasible values for the parameters of new models. Furthermore, TCMC can provide dimensionality reduction, better estimation performance, and lower computational complexity, and can help to alleviate the problem of data overfitting. PMID:21548948

Nonlinear Inference in Partially Observed Physical Systems and Deep Neural Networks

NASA Astrophysics Data System (ADS)

Rozdeba, Paul J.

The problem of model state and parameter estimation is a significant challenge in nonlinear systems. Due to practical considerations of experimental design, it is often the case that physical systems are partially observed, meaning that data is only available for a subset of the degrees of freedom required to fully model the observed system's behaviors and, ultimately, predict future observations. Estimation in this context is highly complicated by the presence of chaos, stochasticity, and measurement noise in dynamical systems. One of the aims of this dissertation is to simultaneously analyze state and parameter estimation in as a regularized inverse problem, where the introduction of a model makes it possible to reverse the forward problem of partial, noisy observation; and as a statistical inference problem using data assimilation to transfer information from measurements to the model states and parameters. Ultimately these two formulations achieve the same goal. Similar aspects that appear in both are highlighted as a means for better understanding the structure of the nonlinear inference problem. An alternative approach to data assimilation that uses model reduction is then examined as a way to eliminate unresolved nonlinear gating variables from neuron models. In this formulation, only measured variables enter into the model, and the resulting errors are themselves modeled by nonlinear stochastic processes with memory. Finally, variational annealing, a data assimilation method previously applied to dynamical systems, is introduced as a potentially useful tool for understanding deep neural network training in machine learning by exploiting similarities between the two problems.

Mixing effects on apparent reaction rates and isotope fractionation during denitrification in a heterogeneous aquifer

USGS Publications Warehouse

Green, Christopher T.; Böhlke, John Karl; Bekins, Barbara A.; Phillips, Steven P.

2010-01-01

Gradients in contaminant concentrations and isotopic compositions commonly are used to derive reaction parameters for natural attenuation in aquifers. Differences between field‐scale (apparent) estimated reaction rates and isotopic fractionations and local‐scale (intrinsic) effects are poorly understood for complex natural systems. For a heterogeneous alluvial fan aquifer, numerical models and field observations were used to study the effects of physical heterogeneity on reaction parameter estimates. Field measurements included major ions, age tracers, stable isotopes, and dissolved gases. Parameters were estimated for the O2 reduction rate, denitrification rate, O2 threshold for denitrification, and stable N isotope fractionation during denitrification. For multiple geostatistical realizations of the aquifer, inverse modeling was used to establish reactive transport simulations that were consistent with field observations and served as a basis for numerical experiments to compare sample‐based estimates of “apparent” parameters with “true“ (intrinsic) values. For this aquifer, non‐Gaussian dispersion reduced the magnitudes of apparent reaction rates and isotope fractionations to a greater extent than Gaussian mixing alone. Apparent and true rate constants and fractionation parameters can differ by an order of magnitude or more, especially for samples subject to slow transport, long travel times, or rapid reactions. The effect of mixing on apparent N isotope fractionation potentially explains differences between previous laboratory and field estimates. Similarly, predicted effects on apparent O2threshold values for denitrification are consistent with previous reports of higher values in aquifers than in the laboratory. These results show that hydrogeological complexity substantially influences the interpretation and prediction of reactive transport.

Do Concept Inventories Actually Measure Anything?

ERIC Educational Resources Information Center

Wallace, Colin S.; Bailey, Janelle M.

2010-01-01

Although concept inventories are among the most frequently used tools in the physics and astronomy education communities, they are rarely evaluated using item response theory (IRT). When IRT models fit the data, they offer sample-independent estimates of item and person parameters. IRT may also provide a way to measure students' learning gains…

Multi-Parameter Linear Least-Squares Fitting to Poisson Data One Count at a Time

NASA Technical Reports Server (NTRS)

Wheaton, W.; Dunklee, A.; Jacobson, A.; Ling, J.; Mahoney, W.; Radocinski, R.

1993-01-01

A standard problem in gamma-ray astronomy data analysis is the decomposition of a set of observed counts, described by Poisson statistics, according to a given multi-component linear model, with underlying physical count rates or fluxes which are to be estimated from the data.

Techniques for determining partial size distribution of particulate matter: Laser diffraction versus electrical sensing zone

USDA-ARS?s Scientific Manuscript database

The study of health impacts, emission estimation of particulate matter (PM), and development of new control technologies require knowledge of PM characteristics. Among these PM characteristics, the particle size distribution (PSD) is perhaps the most important physical parameter governing particle b...

A Toolkit to Study Sensitivity of the Geant4 Predictions to the Variations of the Physics Model Parameters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fields, Laura; Genser, Krzysztof; Hatcher, Robert

Geant4 is the leading detector simulation toolkit used in high energy physics to design detectors and to optimize calibration and reconstruction software. It employs a set of carefully validated physics models to simulate interactions of particles with matter across a wide range of interaction energies. These models, especially the hadronic ones, rely largely on directly measured cross-sections and phenomenological predictions with physically motivated parameters estimated by theoretical calculation or measurement. Because these models are tuned to cover a very wide range of possible simulation tasks, they may not always be optimized for a given process or a given material. Thismore » raises several critical questions, e.g. how sensitive Geant4 predictions are to the variations of the model parameters, or what uncertainties are associated with a particular tune of a Geant4 physics model, or a group of models, or how to consistently derive guidance for Geant4 model development and improvement from a wide range of available experimental data. We have designed and implemented a comprehensive, modular, user-friendly software toolkit to study and address such questions. It allows one to easily modify parameters of one or several Geant4 physics models involved in the simulation, and to perform collective analysis of multiple variants of the resulting physics observables of interest and comparison against a variety of corresponding experimental data. Based on modern event-processing infrastructure software, the toolkit offers a variety of attractive features, e.g. flexible run-time configurable workflow, comprehensive bookkeeping, easy to expand collection of analytical components. Design, implementation technology, and key functionalities of the toolkit are presented and illustrated with results obtained with Geant4 key hadronic models.« less

Trilogy, a Planetary Geodesy Mission Concept for Measuring the Expansion of the Solar System.

PubMed

Smith, David E; Zuber, Maria T; Mazarico, Erwan; Genova, Antonio; Neumann, Gregory A; Sun, Xiaoli; Torrence, Mark H; Mao, Dan-Dan

2018-04-01

The scale of the solar system is slowly changing, likely increasing as a result of solar mass loss, with additional change possible if there is a secular variation of the gravitational constant, G . The measurement of the change of scale could provide insight into the past and the future of the solar system, and in addition a better understanding of planetary motion and fundamental physics. Estimates for the expansion of the scale of the solar system are of order 1.5 cm year -1 AU -1 , which over several years is an observable quantity with present-day laser ranging systems. This estimate suggests that laser measurements between planets could provide an accurate estimate of the solar system expansion rate. We examine distance measurements between three bodies in the inner solar system -- Earth's Moon, Mars and Venus -- and outline a mission concept for making the measurements. The concept involves placing spacecraft that carry laser ranging transponders in orbit around each body and measuring the distances between the three spacecraft over a period of several years. The analysis of these range measurements would allow the co-estimation of the spacecraft orbit, planetary ephemerides, other geophysical parameters related to the constitution and dynamics of the central bodies, and key geodetic parameters related to the solar system expansion, the Sun, and theoretical physics.

Trilogy, a planetary geodesy mission concept for measuring the expansion of the solar system

NASA Astrophysics Data System (ADS)

Smith, David E.; Zuber, Maria T.; Mazarico, Erwan; Genova, Antonio; Neumann, Gregory A.; Sun, Xiaoli; Torrence, Mark H.; Mao, Dan-dan

2018-04-01

The scale of the solar system is slowly changing, likely increasing as a result of solar mass loss, with additional change possible if there is a secular variation of the gravitational constant, G. The measurement of the change of scale could provide insight into the past and the future of the solar system, and in addition a better understanding of planetary motion and fundamental physics. Estimates for the expansion of the scale of the solar system are of order 1.5 cm year-1 AU-1, which over several years is an observable quantity with present-day laser ranging systems. This estimate suggests that laser measurements between planets could provide an accurate estimate of the solar system expansion rate. We examine distance measurements between three bodies in the inner solar system - Earth's Moon, Mars and Venus - and outline a mission concept for making the measurements. The concept involves placing spacecraft that carry laser ranging transponders in orbit around each body and measuring the distances between the three spacecraft over a period of several years. The analysis of these range measurements would allow the co-estimation of the spacecraft orbit, planetary ephemerides, other geophysical parameters related to the constitution and dynamics of the central bodies, and key geodetic parameters related to the solar system expansion, the Sun, and theoretical physics.

NWP model forecast skill optimization via closure parameter variations

NASA Astrophysics Data System (ADS)

Järvinen, H.; Ollinaho, P.; Laine, M.; Solonen, A.; Haario, H.

2012-04-01

We present results of a novel approach to tune predictive skill of numerical weather prediction (NWP) models. These models contain tunable parameters which appear in parameterizations schemes of sub-grid scale physical processes. The current practice is to specify manually the numerical parameter values, based on expert knowledge. We developed recently a concept and method (QJRMS 2011) for on-line estimation of the NWP model parameters via closure parameter variations. The method called EPPES ("Ensemble prediction and parameter estimation system") utilizes ensemble prediction infra-structure for parameter estimation in a very cost-effective way: practically no new computations are introduced. The approach provides an algorithmic decision making tool for model parameter optimization in operational NWP. In EPPES, statistical inference about the NWP model tunable parameters is made by (i) generating an ensemble of predictions so that each member uses different model parameter values, drawn from a proposal distribution, and (ii) feeding-back the relative merits of the parameter values to the proposal distribution, based on evaluation of a suitable likelihood function against verifying observations. In this presentation, the method is first illustrated in low-order numerical tests using a stochastic version of the Lorenz-95 model which effectively emulates the principal features of ensemble prediction systems. The EPPES method correctly detects the unknown and wrongly specified parameters values, and leads to an improved forecast skill. Second, results with an ensemble prediction system emulator, based on the ECHAM5 atmospheric GCM show that the model tuning capability of EPPES scales up to realistic models and ensemble prediction systems. Finally, preliminary results of EPPES in the context of ECMWF forecasting system are presented.

Quantum-enhanced multiparameter estimation in multiarm interferometers

PubMed Central

Ciampini, Mario A.; Spagnolo, Nicolò; Vitelli, Chiara; Pezzè, Luca; Smerzi, Augusto; Sciarrino, Fabio

2016-01-01

Quantum metrology is the state-of-the-art measurement technology. It uses quantum resources to enhance the sensitivity of phase estimation over that achievable by classical physics. While single parameter estimation theory has been widely investigated, much less is known about the simultaneous estimation of multiple phases, which finds key applications in imaging and sensing. In this manuscript we provide conditions of useful particle (qudit) entanglement for multiphase estimation and adapt them to multiarm Mach-Zehnder interferometry. We theoretically discuss benchmark multimode Fock states containing useful qudit entanglement and overcoming the sensitivity of separable qudit states in three and four arm Mach-Zehnder-like interferometers - currently within the reach of integrated photonics technology. PMID:27381743

Estimation of Enthalpy of Formation of Liquid Transition Metal Alloys: A Modified Prescription Based on Macroscopic Atom Model of Cohesion

NASA Astrophysics Data System (ADS)

Raju, Subramanian; Saibaba, Saroja

2016-09-01

The enthalpy of formation Δo H f is an important thermodynamic quantity, which sheds significant light on fundamental cohesive and structural characteristics of an alloy. However, being a difficult one to determine accurately through experiments, simple estimation procedures are often desirable. In the present study, a modified prescription for estimating Δo H f L of liquid transition metal alloys is outlined, based on the Macroscopic Atom Model of cohesion. This prescription relies on self-consistent estimation of liquid-specific model parameters, namely electronegativity ( ϕ L) and bonding electron density ( n b L ). Such unique identification is made through the use of well-established relationships connecting surface tension, compressibility, and molar volume of a metallic liquid with bonding charge density. The electronegativity is obtained through a consistent linear scaling procedure. The preliminary set of values for ϕ L and n b L , together with other auxiliary model parameters, is subsequently optimized to obtain a good numerical agreement between calculated and experimental values of Δo H f L for sixty liquid transition metal alloys. It is found that, with few exceptions, the use of liquid-specific model parameters in Macroscopic Atom Model yields a physically consistent methodology for reliable estimation of mixing enthalpies of liquid alloys.

New physicochemical interpretations for the adsorption of food dyes on chitosan films using statistical physics treatment.

PubMed

Dotto, G L; Pinto, L A A; Hachicha, M A; Knani, S

2015-03-15

In this work, statistical physics treatment was employed to study the adsorption of food dyes onto chitosan films, in order to obtain new physicochemical interpretations at molecular level. Experimental equilibrium curves were obtained for the adsorption of four dyes (FD&C red 2, FD&C yellow 5, FD&C blue 2, Acid Red 51) at different temperatures (298, 313 and 328 K). A statistical physics formula was used to interpret these curves, and the parameters such as, number of adsorbed dye molecules per site (n), anchorage number (n'), receptor sites density (NM), adsorbed quantity at saturation (N asat), steric hindrance (τ), concentration at half saturation (c1/2) and molar adsorption energy (ΔE(a)) were estimated. The relation of the above mentioned parameters with the chemical structure of the dyes and temperature was evaluated and interpreted. Copyright © 2014 Elsevier Ltd. All rights reserved.

Upper limb strength estimation of physically impaired persons using a musculoskeletal model: A sensitivity analysis.

PubMed

Carmichael, Marc G; Liu, Dikai

2015-01-01

Sensitivity of upper limb strength calculated from a musculoskeletal model was analyzed, with focus on how the sensitivity is affected when the model is adapted to represent a person with physical impairment. Sensitivity was calculated with respect to four muscle-tendon parameters: muscle peak isometric force, muscle optimal length, muscle pennation, and tendon slack length. Results obtained from a musculoskeletal model of average strength showed highest sensitivity to tendon slack length, followed by muscle optimal length and peak isometric force, which is consistent with existing studies. Muscle pennation angle was relatively insensitive. The analysis was repeated after adapting the musculoskeletal model to represent persons with varying severities of physical impairment. Results showed that utilizing the weakened model significantly increased the sensitivity of the calculated strength at the hand, with parameters previously insensitive becoming highly sensitive. This increased sensitivity presents a significant challenge in applications utilizing musculoskeletal models to represent impaired individuals.

The use of subjective rating of exertion in Ergonomics.

PubMed

Capodaglio, P

2002-01-01

In Ergonomics, the use of psychophysical methods for subjectively evaluating work tasks and determining acceptable loads has become more common. Daily activities at the work site are studied not only with physiological methods but also with perceptual estimation and production methods. The psychophysical methods are of special interest in field studies of short-term work tasks for which valid physiological measurements are difficult to obtain. The perceived exertion, difficulty and fatigue that a person experiences in a certain work situation is an important sign of a real or objective load. Measurement of the physical load with physiological parameters is not sufficient since it does not take into consideration the particular difficulty of the performance or the capacity of the individual. It is often difficult from technical and biomechanical analyses to understand the seriousness of a difficulty that a person experiences. Physiological determinations give important information, but they may be insufficient due to the technical problems in obtaining relevant but simple measurements for short-term activities or activities involving special movement patterns. Perceptual estimations using Borg's scales give important information because the severity of a task's difficulty depends on the individual doing the work. Observation is the most simple and used means to assess job demands. Other evaluations integrating observation are the followings: indirect estimation of energy expenditure based on prediction equations or direct measurement of oxygen consumption; measurements of forces, angles and biomechanical parameters; measurements of physiological and neurophysiological parameters during tasks. It is recommended that determinations of performances of occupational activities assess rating of perceived exertion and integrate these measurements of intensity levels with those of activity's type, duration and frequency. A better estimate of the degree of physical activity of individuals thus can be obtained.

Bayesian approach to the analysis of neutron Brillouin scattering data on liquid metals

NASA Astrophysics Data System (ADS)

De Francesco, A.; Guarini, E.; Bafile, U.; Formisano, F.; Scaccia, L.

2016-08-01

When the dynamics of liquids and disordered systems at mesoscopic level is investigated by means of inelastic scattering (e.g., neutron or x ray), spectra are often characterized by a poor definition of the excitation lines and spectroscopic features in general and one important issue is to establish how many of these lines need to be included in the modeling function and to estimate their parameters. Furthermore, when strongly damped excitations are present, commonly used and widespread fitting algorithms are particularly affected by the choice of initial values of the parameters. An inadequate choice may lead to an inefficient exploration of the parameter space, resulting in the algorithm getting stuck in a local minimum. In this paper, we present a Bayesian approach to the analysis of neutron Brillouin scattering data in which the number of excitation lines is treated as unknown and estimated along with the other model parameters. We propose a joint estimation procedure based on a reversible-jump Markov chain Monte Carlo algorithm, which efficiently explores the parameter space, producing a probabilistic measure to quantify the uncertainty on the number of excitation lines as well as reliable parameter estimates. The method proposed could turn out of great importance in extracting physical information from experimental data, especially when the detection of spectral features is complicated not only because of the properties of the sample, but also because of the limited instrumental resolution and count statistics. The approach is tested on generated data set and then applied to real experimental spectra of neutron Brillouin scattering from a liquid metal, previously analyzed in a more traditional way.

Automatic Estimation of Osteoporotic Fracture Cases by Using Ensemble Learning Approaches.

PubMed

Kilic, Niyazi; Hosgormez, Erkan

2016-03-01

Ensemble learning methods are one of the most powerful tools for the pattern classification problems. In this paper, the effects of ensemble learning methods and some physical bone densitometry parameters on osteoporotic fracture detection were investigated. Six feature set models were constructed including different physical parameters and they fed into the ensemble classifiers as input features. As ensemble learning techniques, bagging, gradient boosting and random subspace (RSM) were used. Instance based learning (IBk) and random forest (RF) classifiers applied to six feature set models. The patients were classified into three groups such as osteoporosis, osteopenia and control (healthy), using ensemble classifiers. Total classification accuracy and f-measure were also used to evaluate diagnostic performance of the proposed ensemble classification system. The classification accuracy has reached to 98.85 % by the combination of model 6 (five BMD + five T-score values) using RSM-RF classifier. The findings of this paper suggest that the patients will be able to be warned before a bone fracture occurred, by just examining some physical parameters that can easily be measured without invasive operations.

Towards Improved High-Resolution Land Surface Hydrologic Reanalysis Using a Physically-Based Hydrologic Model and Data Assimilation

NASA Astrophysics Data System (ADS)

Shi, Y.; Davis, K. J.; Zhang, F.; Duffy, C.; Yu, X.

2014-12-01

A coupled physically based land surface hydrologic model, Flux-PIHM, has been developed by incorporating a land surface scheme into the Penn State Integrated Hydrologic Model (PIHM). The land surface scheme is adapted from the Noah land surface model. Flux-PIHM has been implemented and manually calibrated at the Shale Hills watershed (0.08 km2) in central Pennsylvania. Model predictions of discharge, point soil moisture, point water table depth, sensible and latent heat fluxes, and soil temperature show good agreement with observations. When calibrated only using discharge, and soil moisture and water table depth at one point, Flux-PIHM is able to resolve the observed 101 m scale soil moisture pattern at the Shale Hills watershed when an appropriate map of soil hydraulic properties is provided. A Flux-PIHM data assimilation system has been developed by incorporating EnKF for model parameter and state estimation. Both synthetic and real data assimilation experiments have been performed at the Shale Hills watershed. Synthetic experiment results show that the data assimilation system is able to simultaneously provide accurate estimates of multiple parameters. In the real data experiment, the EnKF estimated parameters and manually calibrated parameters yield similar model performances, but the EnKF method significantly decreases the time and labor required for calibration. The data requirements for accurate Flux-PIHM parameter estimation via data assimilation using synthetic observations have been tested. Results show that by assimilating only in situ outlet discharge, soil water content at one point, and the land surface temperature averaged over the whole watershed, the data assimilation system can provide an accurate representation of watershed hydrology. Observations of these key variables are available with national and even global spatial coverage (e.g., MODIS surface temperature, SMAP soil moisture, and the USGS gauging stations). National atmospheric reanalysis products, soil databases and land cover databases (e.g., NLDAS-2, SSURGO, NLCD) can provide high resolution forcing and input data. Therefore the Flux-PIHM data assimilation system could be readily expanded to other watersheds to provide regional scale land surface and hydrologic reanalysis with high spatial temporal resolution.

Retrieval of land parameters by multi-sensor information using the Earth Observation Land Data Assimilation System

NASA Astrophysics Data System (ADS)

Chernetskiy, Maxim; Gobron, Nadine; Gomez-Dans, Jose; Disney, Mathias

2016-07-01

Upcoming satellite constellations will substantially increase the amount of Earth Observation (EO) data, and presents us with the challenge of consistently using all these available information to infer the state of the land surface, parameterised through Essential Climate Variables (ECVs). A promising approach to this problem is the use of physically based models that describe the processes that generate the images, using e.g. radiative transfer (RT) theory. However, these models need to be inverted to infer the land surface parameters from the observations, and there is often not enough information in the EO data to satisfactorily achieve this. Data assimilation (DA) approaches supplement the EO data with prior information in the form of models or prior parameter distributions, and have the potential for solving the inversion problem. These methods however are computationally expensive. In this study, we show the use of fast surrogate models of the RT codes (emulators) based on Gaussian Processes (Gomez-Dans et al, 2016) embedded with the Earth Observation Land Data Assimilation System (EO-LDAS) framework (Lewis et al 2012) in order to estimate the surface of the land surface from a heterogeneous set of optical observations. The study uses time series of moderate spatial resolution observations from MODIS (250 m), MERIS (300 m) and MISR (275 m) over one site to infer the temporal evolution of a number of land surface parameters (and associated uncertainties) related to vegetation: leaf area index (LAI), leaf chlorophyll content, etc. These parameter estimates are then used as input to an RT model (semidiscrete or PROSAIL, for example) to calculate fluxes such as broad band albedo or fAPAR. The study demonstrates that blending different sensors in a consistent way using physical models results in a rich and coherent set of land surface parameters retrieved, with quantified uncertainties. The use of RT models also allows for the consistent prediction of fluxes, with a simple mechanism for propagating the uncertainty in the land surface parameters to the flux estimates.

Equilibrium sensitivities of the Greenland ice sheet inferred from the adjoint of the three- dimensional thermo-mechanical model SICOPOLIS

NASA Astrophysics Data System (ADS)

Heimbach, P.; Bugnion, V.

2008-12-01

We present a new and original approach to understanding the sensitivity of the Greenland ice sheet to key model parameters and environmental conditions. At the heart of this approach is the use of an adjoint ice sheet model. MacAyeal (1992) introduced adjoints in the context of applying control theory to estimate basal sliding parameters (basal shear stress, basal friction) of an ice stream model which minimize a least-squares model vs. observation misfit. Since then, this method has become widespread to fit ice stream models to the increasing number and diversity of satellite observations, and to estimate uncertain model parameters. However, no attempt has been made to extend this method to comprehensive ice sheet models. Here, we present a first step toward moving beyond limiting the use of control theory to ice stream models. We have generated an adjoint of the three-dimensional thermo-mechanical ice sheet model SICOPOLIS of Greve (1997). The adjoint was generated using the automatic differentiation (AD) tool TAF. TAF generates exact source code representing the tangent linear and adjoint model of the parent model provided. Model sensitivities are given by the partial derivatives of a scalar-valued model diagnostic or "cost function" with respect to the controls, and can be efficiently calculated via the adjoint. An effort to generate an efficient adjoint with the newly developed open-source AD tool OpenAD is also under way. To gain insight into the adjoint solutions, we explore various cost functions, such as local and domain-integrated ice temperature, total ice volume or the velocity of ice at the margins of the ice sheet. Elements of our control space include initial cold ice temperatures, surface mass balance, as well as parameters such as appear in Glen's flow law, or in the surface degree-day or basal sliding parameterizations. Sensitivity maps provide a comprehensive view, and allow a quantification of where and to which variables the ice sheet model is most sensitive to. The model used in the present study includes simplifications in the model physics, parameterizations which rely on uncertain empirical constants, and is unable to capture fast ice streams. Nevertheless, as a proof-of-concept, this method can readily be extended to incorporate higher-order physics or parameterizations (or be applied to other models). It also opens the door to ice sheet state estimation: using the model's physics jointly with field and satellite observations to estimate a best estimate of the state of the ice sheets.

Bayesian Modeling of Exposure and Airflow Using Two-Zone Models

PubMed Central

Zhang, Yufen; Banerjee, Sudipto; Yang, Rui; Lungu, Claudiu; Ramachandran, Gurumurthy

2009-01-01

Mathematical modeling is being increasingly used as a means for assessing occupational exposures. However, predicting exposure in real settings is constrained by lack of quantitative knowledge of exposure determinants. Validation of models in occupational settings is, therefore, a challenge. Not only do the model parameters need to be known, the models also need to predict the output with some degree of accuracy. In this paper, a Bayesian statistical framework is used for estimating model parameters and exposure concentrations for a two-zone model. The model predicts concentrations in a zone near the source and far away from the source as functions of the toluene generation rate, air ventilation rate through the chamber, and the airflow between near and far fields. The framework combines prior or expert information on the physical model along with the observed data. The framework is applied to simulated data as well as data obtained from the experiments conducted in a chamber. Toluene vapors are generated from a source under different conditions of airflow direction, the presence of a mannequin, and simulated body heat of the mannequin. The Bayesian framework accounts for uncertainty in measurement as well as in the unknown rate of airflow between the near and far fields. The results show that estimates of the interzonal airflow are always close to the estimated equilibrium solutions, which implies that the method works efficiently. The predictions of near-field concentration for both the simulated and real data show nice concordance with the true values, indicating that the two-zone model assumptions agree with the reality to a large extent and the model is suitable for predicting the contaminant concentration. Comparison of the estimated model and its margin of error with the experimental data thus enables validation of the physical model assumptions. The approach illustrates how exposure models and information on model parameters together with the knowledge of uncertainty and variability in these quantities can be used to not only provide better estimates of model outputs but also model parameters. PMID:19403840

Degree-of-Freedom Strengthened Cascade Array for DOD-DOA Estimation in MIMO Array Systems.

PubMed

Yao, Bobin; Dong, Zhi; Zhang, Weile; Wang, Wei; Wu, Qisheng

2018-05-14

In spatial spectrum estimation, difference co-array can provide extra degrees-of-freedom (DOFs) for promoting parameter identifiability and parameter estimation accuracy. For the sake of acquiring as more DOFs as possible with a given number of physical sensors, we herein design a novel sensor array geometry named cascade array. This structure is generated by systematically connecting a uniform linear array (ULA) and a non-uniform linear array, and can provide more DOFs than some exist array structures but less than the upper-bound indicated by minimum redundant array (MRA). We further apply this cascade array into multiple input multiple output (MIMO) array systems, and propose a novel joint direction of departure (DOD) and direction of arrival (DOA) estimation algorithm, which is based on a reduced-dimensional weighted subspace fitting technique. The algorithm is angle auto-paired and computationally efficient. Theoretical analysis and numerical simulations prove the advantages and effectiveness of the proposed array structure and the related algorithm.

Building vulnerability to hydro-geomorphic hazards: Estimating damage probability from qualitative vulnerability assessment using logistic regression

NASA Astrophysics Data System (ADS)

Ettinger, Susanne; Mounaud, Loïc; Magill, Christina; Yao-Lafourcade, Anne-Françoise; Thouret, Jean-Claude; Manville, Vern; Negulescu, Caterina; Zuccaro, Giulio; De Gregorio, Daniela; Nardone, Stefano; Uchuchoque, Juan Alexis Luque; Arguedas, Anita; Macedo, Luisa; Manrique Llerena, Nélida

2016-10-01

The focus of this study is an analysis of building vulnerability through investigating impacts from the 8 February 2013 flash flood event along the Avenida Venezuela channel in the city of Arequipa, Peru. On this day, 124.5 mm of rain fell within 3 h (monthly mean: 29.3 mm) triggering a flash flood that inundated at least 0.4 km2 of urban settlements along the channel, affecting more than 280 buildings, 23 of a total of 53 bridges (pedestrian, vehicle and railway), and leading to the partial collapse of sections of the main road, paralyzing central parts of the city for more than one week. This study assesses the aspects of building design and site specific environmental characteristics that render a building vulnerable by considering the example of a flash flood event in February 2013. A statistical methodology is developed that enables estimation of damage probability for buildings. The applied method uses observed inundation height as a hazard proxy in areas where more detailed hydrodynamic modeling data is not available. Building design and site-specific environmental conditions determine the physical vulnerability. The mathematical approach considers both physical vulnerability and hazard related parameters and helps to reduce uncertainty in the determination of descriptive parameters, parameter interdependency and respective contributions to damage. This study aims to (1) enable the estimation of damage probability for a certain hazard intensity, and (2) obtain data to visualize variations in damage susceptibility for buildings in flood prone areas. Data collection is based on a post-flood event field survey and the analysis of high (sub-metric) spatial resolution images (Pléiades 2012, 2013). An inventory of 30 city blocks was collated in a GIS database in order to estimate the physical vulnerability of buildings. As many as 1103 buildings were surveyed along the affected drainage and 898 buildings were included in the statistical analysis. Univariate and bivariate analyses were applied to better characterize each vulnerability parameter. Multiple corresponding analyses revealed strong relationships between the "Distance to channel or bridges", "Structural building type", "Building footprint" and the observed damage. Logistic regression enabled quantification of the contribution of each explanatory parameter to potential damage, and determination of the significant parameters that express the damage susceptibility of a building. The model was applied 200 times on different calibration and validation data sets in order to examine performance. Results show that 90% of these tests have a success rate of more than 67%. Probabilities (at building scale) of experiencing different damage levels during a future event similar to the 8 February 2013 flash flood are the major outcomes of this study.

Evaluating performances of simplified physically based landslide susceptibility models.

NASA Astrophysics Data System (ADS)

Capparelli, Giovanna; Formetta, Giuseppe; Versace, Pasquale

2015-04-01

Rainfall induced shallow landslides cause significant damages involving loss of life and properties. Prediction of shallow landslides susceptible locations is a complex task that involves many disciplines: hydrology, geotechnical science, geomorphology, and statistics. Usually to accomplish this task two main approaches are used: statistical or physically based model. This paper presents a package of GIS based models for landslide susceptibility analysis. It was integrated in the NewAge-JGrass hydrological model using the Object Modeling System (OMS) modeling framework. The package includes three simplified physically based models for landslides susceptibility analysis (M1, M2, and M3) and a component for models verifications. It computes eight goodness of fit indices (GOF) by comparing pixel-by-pixel model results and measurements data. Moreover, the package integration in NewAge-JGrass allows the use of other components such as geographic information system tools to manage inputs-output processes, and automatic calibration algorithms to estimate model parameters. The system offers the possibility to investigate and fairly compare the quality and the robustness of models and models parameters, according a procedure that includes: i) model parameters estimation by optimizing each of the GOF index separately, ii) models evaluation in the ROC plane by using each of the optimal parameter set, and iii) GOF robustness evaluation by assessing their sensitivity to the input parameter variation. This procedure was repeated for all three models. The system was applied for a case study in Calabria (Italy) along the Salerno-Reggio Calabria highway, between Cosenza and Altilia municipality. The analysis provided that among all the optimized indices and all the three models, Average Index (AI) optimization coupled with model M3 is the best modeling solution for our test case. This research was funded by PON Project No. 01_01503 "Integrated Systems for Hydrogeological Risk Monitoring, Early Warning and Mitigation Along the Main Lifelines", CUP B31H11000370005, in the framework of the National Operational Program for "Research and Competitiveness" 2007-2013.

Estimating Escherichia coli loads in streams based on various physical, chemical, and biological factors

PubMed Central

Dwivedi, Dipankar; Mohanty, Binayak P.; Lesikar, Bruce J.

2013-01-01

Microbes have been identified as a major contaminant of water resources. Escherichia coli (E. coli) is a commonly used indicator organism. It is well recognized that the fate of E. coli in surface water systems is governed by multiple physical, chemical, and biological factors. The aim of this work is to provide insight into the physical, chemical, and biological factors along with their interactions that are critical in the estimation of E. coli loads in surface streams. There are various models to predict E. coli loads in streams, but they tend to be system or site specific or overly complex without enhancing our understanding of these factors. Hence, based on available data, a Bayesian Neural Network (BNN) is presented for estimating E. coli loads based on physical, chemical, and biological factors in streams. The BNN has the dual advantage of overcoming the absence of quality data (with regards to consistency in data) and determination of mechanistic model parameters by employing a probabilistic framework. This study evaluates whether the BNN model can be an effective alternative tool to mechanistic models for E. coli loads estimation in streams. For this purpose, a comparison with a traditional model (LOADEST, USGS) is conducted. The models are compared for estimated E. coli loads based on available water quality data in Plum Creek, Texas. All the model efficiency measures suggest that overall E. coli loads estimations by the BNN model are better than the E. coli loads estimations by the LOADEST model on all the three occasions (three-fold cross validation). Thirteen factors were used for estimating E. coli loads with the exhaustive feature selection technique, which indicated that six of thirteen factors are important for estimating E. coli loads. Physical factors included temperature and dissolved oxygen; chemical factors include phosphate and ammonia; biological factors include suspended solids and chlorophyll. The results highlight that the LOADEST model estimates E. coli loads better in the smaller ranges, whereas the BNN model estimates E. coli loads better in the higher ranges. Hence, the BNN model can be used to design targeted monitoring programs and implement regulatory standards through TMDL programs. PMID:24511166

Surveying implicit solvent models for estimating small molecule absolute hydration free energies

PubMed Central

Knight, Jennifer L.

2011-01-01

Implicit solvent models are powerful tools in accounting for the aqueous environment at a fraction of the computational expense of explicit solvent representations. Here, we compare the ability of common implicit solvent models (TC, OBC, OBC2, GBMV, GBMV2, GBSW, GBSW/MS, GBSW/MS2 and FACTS) to reproduce experimental absolute hydration free energies for a series of 499 small neutral molecules that are modeled using AMBER/GAFF parameters and AM1-BCC charges. Given optimized surface tension coefficients for scaling the surface area term in the nonpolar contribution, most implicit solvent models demonstrate reasonable agreement with extensive explicit solvent simulations (average difference 1.0-1.7 kcal/mol and R2=0.81-0.91) and with experimental hydration free energies (average unsigned errors=1.1-1.4 kcal/mol and R2=0.66-0.81). Chemical classes of compounds are identified that need further optimization of their ligand force field parameters and others that require improvement in the physical parameters of the implicit solvent models themselves. More sophisticated nonpolar models are also likely necessary to more effectively represent the underlying physics of solvation and take the quality of hydration free energies estimated from implicit solvent models to the next level. PMID:21735452

A physics-based fractional order model and state of energy estimation for lithium ion batteries. Part I: Model development and observability analysis

NASA Astrophysics Data System (ADS)

Li, Xiaoyu; Fan, Guodong; Pan, Ke; Wei, Guo; Zhu, Chunbo; Rizzoni, Giorgio; Canova, Marcello

2017-11-01

The design of a lumped parameter battery model preserving physical meaning is especially desired by the automotive researchers and engineers due to the strong demand for battery system control, estimation, diagnosis and prognostics. In light of this, a novel simplified fractional order electrochemical model is developed for electric vehicle (EV) applications in this paper. In the model, a general fractional order transfer function is designed for the solid phase lithium ion diffusion approximation. The dynamic characteristics of the electrolyte concentration overpotential are approximated by a first-order resistance-capacitor transfer function in the electrolyte phase. The Ohmic resistances and electrochemical reaction kinetics resistance are simplified to a lumped Ohmic resistance parameter. Overall, the number of model parameters is reduced from 30 to 9, yet the accuracy of the model is still guaranteed. In order to address the dynamics of phase-change phenomenon in the active particle during charging and discharging, variable solid-state diffusivity is taken into consideration in the model. Also, the observability of the model is analyzed on two types of lithium ion batteries subsequently. Results show the fractional order model with variable solid-state diffusivity agrees very well with experimental data at various current input conditions and is suitable for electric vehicle applications.

Fitting ordinary differential equations to short time course data.

PubMed

Brewer, Daniel; Barenco, Martino; Callard, Robin; Hubank, Michael; Stark, Jaroslav

2008-02-28

Ordinary differential equations (ODEs) are widely used to model many systems in physics, chemistry, engineering and biology. Often one wants to compare such equations with observed time course data, and use this to estimate parameters. Surprisingly, practical algorithms for doing this are relatively poorly developed, particularly in comparison with the sophistication of numerical methods for solving both initial and boundary value problems for differential equations, and for locating and analysing bifurcations. A lack of good numerical fitting methods is particularly problematic in the context of systems biology where only a handful of time points may be available. In this paper, we present a survey of existing algorithms and describe the main approaches. We also introduce and evaluate a new efficient technique for estimating ODEs linear in parameters particularly suited to situations where noise levels are high and the number of data points is low. It employs a spline-based collocation scheme and alternates linear least squares minimization steps with repeated estimates of the noise-free values of the variables. This is reminiscent of expectation-maximization methods widely used for problems with nuisance parameters or missing data.

Migration of antioxidants from polylactic acid films, a parameter estimation approach: Part I - A model including convective mass transfer coefficient.

PubMed

Samsudin, Hayati; Auras, Rafael; Burgess, Gary; Dolan, Kirk; Soto-Valdez, Herlinda

2018-03-01

A two-step solution based on the boundary conditions of Crank's equations for mass transfer in a film was developed. Three driving factors, the diffusion (D), partition (K p,f ) and convective mass transfer coefficients (h), govern the sorption and/or desorption kinetics of migrants from polymer films. These three parameters were simultaneously estimated. They provide in-depth insight into the physics of a migration process. The first step was used to find the combination of D, K p,f and h that minimized the sums of squared errors (SSE) between the predicted and actual results. In step 2, an ordinary least square (OLS) estimation was performed by using the proposed analytical solution containing D, K p,f and h. Three selected migration studies of PLA/antioxidant-based films were used to demonstrate the use of this two-step solution. Additional parameter estimation approaches such as sequential and bootstrap were also performed to acquire a better knowledge about the kinetics of migration. The proposed model successfully provided the initial guesses for D, K p,f and h. The h value was determined without performing a specific experiment for it. By determining h together with D, under or overestimation issues pertaining to a migration process can be avoided since these two parameters are correlated. Copyright © 2017 Elsevier Ltd. All rights reserved.

A biodynamic feedthrough model based on neuromuscular principles.

PubMed

Venrooij, Joost; Abbink, David A; Mulder, Mark; van Paassen, Marinus M; Mulder, Max; van der Helm, Frans C T; Bulthoff, Heinrich H

2014-07-01

A biodynamic feedthrough (BDFT) model is proposed that describes how vehicle accelerations feed through the human body, causing involuntary limb motions and so involuntary control inputs. BDFT dynamics strongly depend on limb dynamics, which can vary between persons (between-subject variability), but also within one person over time, e.g., due to the control task performed (within-subject variability). The proposed BDFT model is based on physical neuromuscular principles and is derived from an established admittance model-describing limb dynamics-which was extended to include control device dynamics and account for acceleration effects. The resulting BDFT model serves primarily the purpose of increasing the understanding of the relationship between neuromuscular admittance and biodynamic feedthrough. An added advantage of the proposed model is that its parameters can be estimated using a two-stage approach, making the parameter estimation more robust, as the procedure is largely based on the well documented procedure required for the admittance model. To estimate the parameter values of the BDFT model, data are used from an experiment in which both neuromuscular admittance and biodynamic feedthrough are measured. The quality of the BDFT model is evaluated in the frequency and time domain. Results provide strong evidence that the BDFT model and the proposed method of parameter estimation put forward in this paper allows for accurate BDFT modeling across different subjects (accounting for between-subject variability) and across control tasks (accounting for within-subject variability).

Final STS-11 (41-B) best estimate trajectory products: Development and results from the first Cape landing

NASA Technical Reports Server (NTRS)

Kelly, G. M.; Mcconnell, J. G.; Findlay, J. T.; Heck, M. L.; Henry, M. W.

1984-01-01

The STS-11 (41-B) postflight data processing is completed and the results published. The final reconstructed entry trajectory is presented. The various atmospheric sources available for this flight are discussed. Aerodynamic Best Estimate of Trajectory BET generation and plots from this file are presented. A definition of the major maneuvers effected is given. Physical constants, including spacecraft mass properties; final residuals from the reconstruction process; trajectory parameter listings; and an archival section are included.

Phytoplankton production in the Sargasso Sea as determined using optical mooring data

NASA Technical Reports Server (NTRS)

Waters, K. J.; Smith, R. C.; Marra, J.

1994-01-01

Optical measurements from an untended mooring provide high-frequency observations of in-water optical properties and permit the estimation of important biological parameters continuously as a function of time. A 9-month time series, composed of three separate deployments, of optical data from the BIOWATT 1987 deep-sea mooring located in the oligotrophic waters of the Sargasso Sea at 34 deg N, 70 deg W are presented. These data have been tested using several bio-optical models for the purpose of providing a continuous estimate of phytoplankton productivity. The data are discussed in the context of contemporaneous shipboard observations and for future ocean color satellite observations. We present a continuous estimation of phytoplankton productivity for the 9-month time series. Results from the first 70-day deployment are emphasized to demonstrate the utility of optical observations as proxy measures of biological parameters, to present preliminary analysis, and to compare our bio-optical observations with concurrent physical observations. The bio-optical features show variation in response to physical forcings including diel variations of incident solar irradiance, episodic changes corresponding to wind forcing, variability caused by advective mesoscale eddy events in the vicinity of the mooring, and seasonal variability corresponding to changes in solar radiation, shoaling of the mixed layer depth, and succession of phytoplankton populations.

Predicting nonstationary flood frequencies: Evidence supports an updated stationarity thesis in the United States

NASA Astrophysics Data System (ADS)

Luke, Adam; Vrugt, Jasper A.; AghaKouchak, Amir; Matthew, Richard; Sanders, Brett F.

2017-07-01

Nonstationary extreme value analysis (NEVA) can improve the statistical representation of observed flood peak distributions compared to stationary (ST) analysis, but management of flood risk relies on predictions of out-of-sample distributions for which NEVA has not been comprehensively evaluated. In this study, we apply split-sample testing to 1250 annual maximum discharge records in the United States and compare the predictive capabilities of NEVA relative to ST extreme value analysis using a log-Pearson Type III (LPIII) distribution. The parameters of the LPIII distribution in the ST and nonstationary (NS) models are estimated from the first half of each record using Bayesian inference. The second half of each record is reserved to evaluate the predictions under the ST and NS models. The NS model is applied for prediction by (1) extrapolating the trend of the NS model parameters throughout the evaluation period and (2) using the NS model parameter values at the end of the fitting period to predict with an updated ST model (uST). Our analysis shows that the ST predictions are preferred, overall. NS model parameter extrapolation is rarely preferred. However, if fitting period discharges are influenced by physical changes in the watershed, for example from anthropogenic activity, the uST model is strongly preferred relative to ST and NS predictions. The uST model is therefore recommended for evaluation of current flood risk in watersheds that have undergone physical changes. Supporting information includes a MATLAB® program that estimates the (ST/NS/uST) LPIII parameters from annual peak discharge data through Bayesian inference.

An Improved Swarm Optimization for Parameter Estimation and Biological Model Selection

PubMed Central

Abdullah, Afnizanfaizal; Deris, Safaai; Mohamad, Mohd Saberi; Anwar, Sohail

2013-01-01

One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This study is hoped to provide a new insight in developing more accurate and reliable biological models based on limited and low quality experimental data. PMID:23593445

BAYESIAN ESTIMATION OF THERMONUCLEAR REACTION RATES

DOE Office of Scientific and Technical Information (OSTI.GOV)

Iliadis, C.; Anderson, K. S.; Coc, A.

The problem of estimating non-resonant astrophysical S -factors and thermonuclear reaction rates, based on measured nuclear cross sections, is of major interest for nuclear energy generation, neutrino physics, and element synthesis. Many different methods have been applied to this problem in the past, almost all of them based on traditional statistics. Bayesian methods, on the other hand, are now in widespread use in the physical sciences. In astronomy, for example, Bayesian statistics is applied to the observation of extrasolar planets, gravitational waves, and Type Ia supernovae. However, nuclear physics, in particular, has been slow to adopt Bayesian methods. We presentmore » astrophysical S -factors and reaction rates based on Bayesian statistics. We develop a framework that incorporates robust parameter estimation, systematic effects, and non-Gaussian uncertainties in a consistent manner. The method is applied to the reactions d(p, γ ){sup 3}He, {sup 3}He({sup 3}He,2p){sup 4}He, and {sup 3}He( α , γ ){sup 7}Be, important for deuterium burning, solar neutrinos, and Big Bang nucleosynthesis.« less

Large electroweak penguin contribution in B{yields}K{pi} and {pi}{pi} decay modes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mishima, Satoshi; Yoshikawa, Tadashi

2004-11-01

We discuss a possibility of large electroweak penguin contribution in B{yields}K{pi} and {pi}{pi} from recent experimental data. The experimental data may be suggesting that there are some discrepancies between the data and theoretical estimation in the branching ratios of them. In B{yields}K{pi} decays, to explain it, a large electroweak penguin contribution and large strong phase differences seem to be needed. The contributions should appear also in B{yields}{pi}{pi}. We show, as an example, a solution to solve the discrepancies in both B{yields}K{pi} and B{yields}{pi}{pi}. However the magnitude of the parameters and the strong phase estimated from experimental data are quite largemore » compared with the theoretical estimations. It may be suggesting some new physics effects are included in these processes. We will have to discuss about the dependence of the new physics. To explain both modes at once, we may need large electroweak penguin contribution with new weak phases and some SU(3) breaking effects by new physics in both QCD and electroweak penguin-type processes.« less

Kinematical Focus on NGC 7086

NASA Astrophysics Data System (ADS)

Tadross, A. L.

2005-12-01

The main physical parameters; the cluster center, distance, radius, age, reddening, and visual absorbtion; have been re-estimated and improved for the open cluster NGC 7086. The metal abundance, galactic distances, membership richness, luminosity function, mass function, and the total mass of NGC 7086 have been examined for the first time here using Monet et al. (2003) catalog.

40 CFR 63.1323 - Batch process vents-methods and procedures for group determination.

Code of Federal Regulations, 2014 CFR

2014-07-01

... accepted chemical engineering principles, measurable process parameters, or physical or chemical laws or... paragraph (b)(5) of this section. Engineering assessment may be used to estimate emissions from a batch... defined in paragraph (b)(5) of this section, through engineering assessment, as defined in paragraph (b)(6...

40 CFR 63.1323 - Batch process vents-methods and procedures for group determination.

Code of Federal Regulations, 2011 CFR

2011-07-01

... accepted chemical engineering principles, measurable process parameters, or physical or chemical laws or... paragraph (b)(5) of this section. Engineering assessment may be used to estimate emissions from a batch... defined in paragraph (b)(5) of this section, through engineering assessment, as defined in paragraph (b)(6...

40 CFR 63.1323 - Batch process vents-methods and procedures for group determination.

Code of Federal Regulations, 2013 CFR

2013-07-01

... accepted chemical engineering principles, measurable process parameters, or physical or chemical laws or... paragraph (b)(5) of this section. Engineering assessment may be used to estimate emissions from a batch... defined in paragraph (b)(5) of this section, through engineering assessment, as defined in paragraph (b)(6...

40 CFR 63.1323 - Batch process vents-methods and procedures for group determination.

Code of Federal Regulations, 2012 CFR

2012-07-01

... accepted chemical engineering principles, measurable process parameters, or physical or chemical laws or... paragraph (b)(5) of this section. Engineering assessment may be used to estimate emissions from a batch... defined in paragraph (b)(5) of this section, through engineering assessment, as defined in paragraph (b)(6...

CAN WE DETERMINE PENETRATION COEFFICIENTS AND DEPOSITION RATES FROM FIELD STUDIES? RESULTS OF A 37-PERSON PANEL STUDY IN NORTH CAROLINA

EPA Science Inventory

The contribution of outdoor particles to indoor concentrations is governed by three physical processes: air exchange, penetration, and deposition. Air exchange rates can be measured during field studies, but the other two parameters must be estimated. Over the past few years,...

Anisotropic physical properties of myocardium characterized by ultrasonic measurements of backscatter, attenuation, and velocity

NASA Astrophysics Data System (ADS)

Baldwin, Steven L.

The goal of elucidating the physical mechanisms underlying the propagation of ultrasonic waves in anisotropic soft tissue such as myocardium has posed an interesting and largely unsolved problem in the field of physics for the past 30 years. In part because of the vast complexity of the system being studied, progress towards understanding and modeling the mechanisms that underlie observed acoustic parameters may first require the guidance of careful experiment. Knowledge of the causes of observed ultrasonic properties in soft tissue including attenuation, speed of sound, and backscatter, and how those properties are altered with specific pathophysiologies, may lead to new noninvasive approaches to the diagnosis of disease. The primary aim of this Dissertation is to contribute to an understanding of the physics that underlies the mechanisms responsible for the observed interaction of ultrasound with myocardium. To this end, through-transmission and backscatter measurements were performed by varying acoustic properties as a function of angle of insonification relative to the predominant myofiber direction and by altering the material properties of myocardium by increased protein cross-linking induced by chemical fixation as an extreme form of changes that may occur in certain pathologies such as diabetes. Techniques to estimate acoustic parameters from backscatter were broadened and challenges to implementing these techniques in vivo were addressed. Provided that specific challenges identified in this Dissertation can be overcome, techniques to estimate attenuation from ultrasonic backscatter show promise as a means to investigate the physical interaction of ultrasound with anisotropic biological media in vivo. This Dissertation represents a step towards understanding the physics of the interaction of ultrasonic waves with anisotropic biological media.

Application of maximum-likelihood estimation in optical coherence tomography for nanometer-class thickness estimation

NASA Astrophysics Data System (ADS)

Huang, Jinxin; Yuan, Qun; Tankam, Patrice; Clarkson, Eric; Kupinski, Matthew; Hindman, Holly B.; Aquavella, James V.; Rolland, Jannick P.

2015-03-01

In biophotonics imaging, one important and quantitative task is layer-thickness estimation. In this study, we investigate the approach of combining optical coherence tomography and a maximum-likelihood (ML) estimator for layer thickness estimation in the context of tear film imaging. The motivation of this study is to extend our understanding of tear film dynamics, which is the prerequisite to advance the management of Dry Eye Disease, through the simultaneous estimation of the thickness of the tear film lipid and aqueous layers. The estimator takes into account the different statistical processes associated with the imaging chain. We theoretically investigated the impact of key system parameters, such as the axial point spread functions (PSF) and various sources of noise on measurement uncertainty. Simulations show that an OCT system with a 1 μm axial PSF (FWHM) allows unbiased estimates down to nanometers with nanometer precision. In implementation, we built a customized Fourier domain OCT system that operates in the 600 to 1000 nm spectral window and achieves 0.93 micron axial PSF in corneal epithelium. We then validated the theoretical framework with physical phantoms made of custom optical coatings, with layer thicknesses from tens of nanometers to microns. Results demonstrate unbiased nanometer-class thickness estimates in three different physical phantoms.

First-order exchange coefficient coupling for simulating surface water-groundwater interactions: Parameter sensitivity and consistency with a physics-based approach

USGS Publications Warehouse

Ebel, B.A.; Mirus, B.B.; Heppner, C.S.; VanderKwaak, J.E.; Loague, K.

2009-01-01

Distributed hydrologic models capable of simulating fully-coupled surface water and groundwater flow are increasingly used to examine problems in the hydrologic sciences. Several techniques are currently available to couple the surface and subsurface; the two most frequently employed approaches are first-order exchange coefficients (a.k.a., the surface conductance method) and enforced continuity of pressure and flux at the surface-subsurface boundary condition. The effort reported here examines the parameter sensitivity of simulated hydrologic response for the first-order exchange coefficients at a well-characterized field site using the fully coupled Integrated Hydrology Model (InHM). This investigation demonstrates that the first-order exchange coefficients can be selected such that the simulated hydrologic response is insensitive to the parameter choice, while simulation time is considerably reduced. Alternatively, the ability to choose a first-order exchange coefficient that intentionally decouples the surface and subsurface facilitates concept-development simulations to examine real-world situations where the surface-subsurface exchange is impaired. While the parameters comprising the first-order exchange coefficient cannot be directly estimated or measured, the insensitivity of the simulated flow system to these parameters (when chosen appropriately) combined with the ability to mimic actual physical processes suggests that the first-order exchange coefficient approach can be consistent with a physics-based framework. Copyright ?? 2009 John Wiley & Sons, Ltd.

Physically based model for extracting dual permeability parameters using non-Newtonian fluids

NASA Astrophysics Data System (ADS)

Abou Najm, M. R.; Basset, C.; Stewart, R. D.; Hauswirth, S.

2017-12-01

Dual permeability models are effective for the assessment of flow and transport in structured soils with two dominant structures. The major challenge to those models remains in the ability to determine appropriate and unique parameters through affordable, simple, and non-destructive methods. This study investigates the use of water and a non-Newtonian fluid in saturated flow experiments to derive physically-based parameters required for improved flow predictions using dual permeability models. We assess the ability of these two fluids to accurately estimate the representative pore sizes in dual-domain soils, by determining the effective pore sizes of macropores and micropores. We developed two sub-models that solve for the effective macropore size assuming either cylindrical (e.g., biological pores) or planar (e.g., shrinkage cracks and fissures) pore geometries, with the micropores assumed to be represented by a single effective radius. Furthermore, the model solves for the percent contribution to flow (wi) corresponding to the representative macro and micro pores. A user-friendly solver was developed to numerically solve the system of equations, given that relevant non-Newtonian viscosity models lack forms conducive to analytical integration. The proposed dual-permeability model is a unique attempt to derive physically based parameters capable of measuring dual hydraulic conductivities, and therefore may be useful in reducing parameter uncertainty and improving hydrologic model predictions.

Consequences of using different soil texture determination methodologies for soil physical quality and unsaturated zone time lag estimates

NASA Astrophysics Data System (ADS)

Fenton, O.; Vero, S.; Ibrahim, T. G.; Murphy, P. N. C.; Sherriff, S. C.; Ó hUallacháin, D.

2015-11-01

Elucidation of when the loss of pollutants, below the rooting zone in agricultural landscapes, affects water quality is important when assessing the efficacy of mitigation measures. Investigation of this inherent time lag (tT) is divided into unsaturated (tu) and saturated (ts) components. The duration of these components relative to each other differs depending on soil characteristics and the landscape position. The present field study focuses on tu estimation in a scenario where the saturated zone is likely to constitute a higher proportion of tT. In such instances, or where only initial breakthrough (IBT) or centre of mass (COM) is of interest, utilisation of site and depth specific "simple" textural class or actual sand-silt-clay percentages to generate soil water characteristic curves with associated soil hydraulic parameters is acceptable. With the same data it is also possible to estimate a soil physical quality (S) parameter for each soil layer which can be used to infer many other physical, chemical and biological quality indicators. In this study, hand texturing in the field was used to determine textural classes of a soil profile. Laboratory methods, including hydrometer, pipette and laser diffraction methods were used to determine actual sand-silt-clay percentages of sections of the same soil profile. Results showed that in terms of S, hand texturing resulted in a lower index value (inferring a degraded soil) than that of pipette, hydrometer and laser equivalents. There was no difference between S index values determined using the pipette, hydrometer and laser diffraction methods. The difference between the three laboratory methods on both the IBT and COM stages of tu were negligible, and in this instance were unlikely to affect either groundwater monitoring decisions, or to be of consequence from a policy perspective. When tu estimates are made over the full depth of the vadose zone, which may extend to several metres, errors resulting from the use of hydraulic parameters generated from hand texture data will be resultantly greater, and may lead to flawed predictions regarding the achievability of water policy targets. For this reason laboratory analysis, regardless of method, should be preferred to simple field assessments.

Estimation of k-ε parameters using surrogate models and jet-in-crossflow data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lefantzi, Sophia; Ray, Jaideep; Arunajatesan, Srinivasan

2014-11-01

We demonstrate a Bayesian method that can be used to calibrate computationally expensive 3D RANS (Reynolds Av- eraged Navier Stokes) models with complex response surfaces. Such calibrations, conditioned on experimental data, can yield turbulence model parameters as probability density functions (PDF), concisely capturing the uncertainty in the parameter estimates. Methods such as Markov chain Monte Carlo (MCMC) estimate the PDF by sampling, with each sample requiring a run of the RANS model. Consequently a quick-running surrogate is used instead to the RANS simulator. The surrogate can be very difficult to design if the model's response i.e., the dependence of themore » calibration variable (the observable) on the parameter being estimated is complex. We show how the training data used to construct the surrogate can be employed to isolate a promising and physically realistic part of the parameter space, within which the response is well-behaved and easily modeled. We design a classifier, based on treed linear models, to model the "well-behaved region". This classifier serves as a prior in a Bayesian calibration study aimed at estimating 3 k - ε parameters ( C μ, C ε2 , C ε1 ) from experimental data of a transonic jet-in-crossflow interaction. The robustness of the calibration is investigated by checking its predictions of variables not included in the cal- ibration data. We also check the limit of applicability of the calibration by testing at off-calibration flow regimes. We find that calibration yield turbulence model parameters which predict the flowfield far better than when the nomi- nal values of the parameters are used. Substantial improvements are still obtained when we use the calibrated RANS model to predict jet-in-crossflow at Mach numbers and jet strengths quite different from those used to generate the ex- perimental (calibration) data. Thus the primary reason for poor predictive skill of RANS, when using nominal values of the turbulence model parameters, was parametric uncertainty, which was rectified by calibration. Post-calibration, the dominant contribution to model inaccuraries are due to the structural errors in RANS.« less

Combined inverse-forward artificial neural networks for fast and accurate estimation of the diffusion coefficients of cartilage based on multi-physics models.

PubMed

Arbabi, Vahid; Pouran, Behdad; Weinans, Harrie; Zadpoor, Amir A

2016-09-06

Analytical and numerical methods have been used to extract essential engineering parameters such as elastic modulus, Poisson׳s ratio, permeability and diffusion coefficient from experimental data in various types of biological tissues. The major limitation associated with analytical techniques is that they are often only applicable to problems with simplified assumptions. Numerical multi-physics methods, on the other hand, enable minimizing the simplified assumptions but require substantial computational expertise, which is not always available. In this paper, we propose a novel approach that combines inverse and forward artificial neural networks (ANNs) which enables fast and accurate estimation of the diffusion coefficient of cartilage without any need for computational modeling. In this approach, an inverse ANN is trained using our multi-zone biphasic-solute finite-bath computational model of diffusion in cartilage to estimate the diffusion coefficient of the various zones of cartilage given the concentration-time curves. Robust estimation of the diffusion coefficients, however, requires introducing certain levels of stochastic variations during the training process. Determining the required level of stochastic variation is performed by coupling the inverse ANN with a forward ANN that receives the diffusion coefficient as input and returns the concentration-time curve as output. Combined together, forward-inverse ANNs enable computationally inexperienced users to obtain accurate and fast estimation of the diffusion coefficients of cartilage zones. The diffusion coefficients estimated using the proposed approach are compared with those determined using direct scanning of the parameter space as the optimization approach. It has been shown that both approaches yield comparable results. Copyright © 2016 Elsevier Ltd. All rights reserved.

Estimating fractional vegetation cover and the vegetation index of bare soil and highly dense vegetation with a physically based method

NASA Astrophysics Data System (ADS)

Song, Wanjuan; Mu, Xihan; Ruan, Gaiyan; Gao, Zhan; Li, Linyuan; Yan, Guangjian

2017-06-01

Normalized difference vegetation index (NDVI) of highly dense vegetation (NDVIv) and bare soil (NDVIs), identified as the key parameters for Fractional Vegetation Cover (FVC) estimation, are usually obtained with empirical statistical methods However, it is often difficult to obtain reasonable values of NDVIv and NDVIs at a coarse resolution (e.g., 1 km), or in arid, semiarid, and evergreen areas. The uncertainty of estimated NDVIs and NDVIv can cause substantial errors in FVC estimations when a simple linear mixture model is used. To address this problem, this paper proposes a physically based method. The leaf area index (LAI) and directional NDVI are introduced in a gap fraction model and a linear mixture model for FVC estimation to calculate NDVIv and NDVIs. The model incorporates the Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) model parameters product (MCD43B1) and LAI product, which are convenient to acquire. Two types of evaluation experiments are designed 1) with data simulated by a canopy radiative transfer model and 2) with satellite observations. The root-mean-square deviation (RMSD) for simulated data is less than 0.117, depending on the type of noise added on the data. In the real data experiment, the RMSD for cropland is 0.127, for grassland is 0.075, and for forest is 0.107. The experimental areas respectively lack fully vegetated and non-vegetated pixels at 1 km resolution. Consequently, a relatively large uncertainty is found while using the statistical methods and the RMSD ranges from 0.110 to 0.363 based on the real data. The proposed method is convenient to produce NDVIv and NDVIs maps for FVC estimation on regional and global scales.

Enabling Predictive Simulation and UQ of Complex Multiphysics PDE Systems by the Development of Goal-Oriented Variational Sensitivity Analysis and a-Posteriori Error Estimation Methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Estep, Donald

2015-11-30

This project addressed the challenge of predictive computational analysis of strongly coupled, highly nonlinear multiphysics systems characterized by multiple physical phenomena that span a large range of length- and time-scales. Specifically, the project was focused on computational estimation of numerical error and sensitivity analysis of computational solutions with respect to variations in parameters and data. In addition, the project investigated the use of accurate computational estimates to guide efficient adaptive discretization. The project developed, analyzed and evaluated new variational adjoint-based techniques for integration, model, and data error estimation/control and sensitivity analysis, in evolutionary multiphysics multiscale simulations.

CMB bispectrum, trispectrum, non-Gaussianity, and the Cramer-Rao bound

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kamionkowski, Marc; Smith, Tristan L.; Heavens, Alan

Minimum-variance estimators for the parameter f{sub nl} that quantifies local-model non-Gaussianity can be constructed from the cosmic microwave background (CMB) bispectrum (three-point function) and also from the trispectrum (four-point function). Some have suggested that a comparison between the estimates for the values of f{sub nl} from the bispectrum and trispectrum allow a consistency test for the model. But others argue that the saturation of the Cramer-Rao bound--which gives a lower limit to the variance of an estimator--by the bispectrum estimator implies that no further information on f{sub nl} can be obtained from the trispectrum. Here, we elaborate the nature ofmore » the correlation between the bispectrum and trispectrum estimators for f{sub nl}. We show that the two estimators become statistically independent in the limit of large number of CMB pixels, and thus that the trispectrum estimator does indeed provide additional information on f{sub nl} beyond that obtained from the bispectrum. We explain how this conclusion is consistent with the Cramer-Rao bound. Our discussion of the Cramer-Rao bound may be of interest to those doing Fisher-matrix parameter-estimation forecasts or data analysis in other areas of physics as well.« less

Sensor Fusion Based on an Integrated Neural Network and Probability Density Function (PDF) Dual Kalman Filter for On-Line Estimation of Vehicle Parameters and States.

PubMed

Vargas-Melendez, Leandro; Boada, Beatriz L; Boada, Maria Jesus L; Gauchia, Antonio; Diaz, Vicente

2017-04-29

Vehicles with a high center of gravity (COG), such as light trucks and heavy vehicles, are prone to rollover. This kind of accident causes nearly 33 % of all deaths from passenger vehicle crashes. Nowadays, these vehicles are incorporating roll stability control (RSC) systems to improve their safety. Most of the RSC systems require the vehicle roll angle as a known input variable to predict the lateral load transfer. The vehicle roll angle can be directly measured by a dual antenna global positioning system (GPS), but it is expensive. For this reason, it is important to estimate the vehicle roll angle from sensors installed onboard in current vehicles. On the other hand, the knowledge of the vehicle's parameters values is essential to obtain an accurate vehicle response. Some of vehicle parameters cannot be easily obtained and they can vary over time. In this paper, an algorithm for the simultaneous on-line estimation of vehicle's roll angle and parameters is proposed. This algorithm uses a probability density function (PDF)-based truncation method in combination with a dual Kalman filter (DKF), to guarantee that both vehicle's states and parameters are within bounds that have a physical meaning, using the information obtained from sensors mounted on vehicles. Experimental results show the effectiveness of the proposed algorithm.

Sensor Fusion Based on an Integrated Neural Network and Probability Density Function (PDF) Dual Kalman Filter for On-Line Estimation of Vehicle Parameters and States

PubMed Central

Vargas-Melendez, Leandro; Boada, Beatriz L.; Boada, Maria Jesus L.; Gauchia, Antonio; Diaz, Vicente

2017-01-01

Vehicles with a high center of gravity (COG), such as light trucks and heavy vehicles, are prone to rollover. This kind of accident causes nearly 33% of all deaths from passenger vehicle crashes. Nowadays, these vehicles are incorporating roll stability control (RSC) systems to improve their safety. Most of the RSC systems require the vehicle roll angle as a known input variable to predict the lateral load transfer. The vehicle roll angle can be directly measured by a dual antenna global positioning system (GPS), but it is expensive. For this reason, it is important to estimate the vehicle roll angle from sensors installed onboard in current vehicles. On the other hand, the knowledge of the vehicle’s parameters values is essential to obtain an accurate vehicle response. Some of vehicle parameters cannot be easily obtained and they can vary over time. In this paper, an algorithm for the simultaneous on-line estimation of vehicle’s roll angle and parameters is proposed. This algorithm uses a probability density function (PDF)-based truncation method in combination with a dual Kalman filter (DKF), to guarantee that both vehicle’s states and parameters are within bounds that have a physical meaning, using the information obtained from sensors mounted on vehicles. Experimental results show the effectiveness of the proposed algorithm. PMID:28468252

Variability in soil-water retention properties and implications for physics-based simulation of landslide early warning criteria

USGS Publications Warehouse

Thomas, Matthew A.; Mirus, Benjamin B.; Collins, Brian D.; Lu, Ning; Godt, Jonathan W.

2018-01-01

Rainfall-induced shallow landsliding is a persistent hazard to human life and property. Despite the observed connection between infiltration through the unsaturated zone and shallow landslide initiation, there is considerable uncertainty in how estimates of unsaturated soil-water retention properties affect slope stability assessment. This source of uncertainty is critical to evaluating the utility of physics-based hydrologic modeling as a tool for landslide early warning. We employ a numerical model of variably saturated groundwater flow parameterized with an ensemble of texture-, laboratory-, and field-based estimates of soil-water retention properties for an extensively monitored landslide-prone site in the San Francisco Bay Area, CA, USA. Simulations of soil-water content, pore-water pressure, and the resultant factor of safety show considerable variability across and within these different parameter estimation techniques. In particular, we demonstrate that with the same permeability structure imposed across all simulations, the variability in soil-water retention properties strongly influences predictions of positive pore-water pressure coincident with widespread shallow landsliding. We also find that the ensemble of soil-water retention properties imposes an order-of-magnitude and nearly two-fold variability in seasonal and event-scale landslide susceptibility, respectively. Despite the reduced factor of safety uncertainty during wet conditions, parameters that control the dry end of the soil-water retention function markedly impact the ability of a hydrologic model to capture soil-water content dynamics observed in the field. These results suggest that variability in soil-water retention properties should be considered for objective physics-based simulation of landslide early warning criteria.

The applications of statistical quantification techniques in nanomechanics and nanoelectronics.

PubMed

Mai, Wenjie; Deng, Xinwei

2010-10-08

Although nanoscience and nanotechnology have been developing for approximately two decades and have achieved numerous breakthroughs, the experimental results from nanomaterials with a higher noise level and poorer repeatability than those from bulk materials still remain as a practical issue, and challenge many techniques of quantification of nanomaterials. This work proposes a physical-statistical modeling approach and a global fitting statistical method to use all the available discrete data or quasi-continuous curves to quantify a few targeted physical parameters, which can provide more accurate, efficient and reliable parameter estimates, and give reasonable physical explanations. In the resonance method for measuring the elastic modulus of ZnO nanowires (Zhou et al 2006 Solid State Commun. 139 222-6), our statistical technique gives E = 128.33 GPa instead of the original E = 108 GPa, and unveils a negative bias adjustment f(0). The causes are suggested by the systematic bias in measuring the length of the nanowires. In the electronic measurement of the resistivity of a Mo nanowire (Zach et al 2000 Science 290 2120-3), the proposed new method automatically identified the importance of accounting for the Ohmic contact resistance in the model of the Ohmic behavior in nanoelectronics experiments. The 95% confidence interval of resistivity in the proposed one-step procedure is determined to be 3.57 +/- 0.0274 x 10( - 5) ohm cm, which should be a more reliable and precise estimate. The statistical quantification technique should find wide applications in obtaining better estimations from various systematic errors and biased effects that become more significant at the nanoscale.

Initialization of a fractional order identification algorithm applied for Lithium-ion battery modeling in time domain

NASA Astrophysics Data System (ADS)

Nasser Eddine, Achraf; Huard, Benoît; Gabano, Jean-Denis; Poinot, Thierry

2018-06-01

This paper deals with the initialization of a non linear identification algorithm used to accurately estimate the physical parameters of Lithium-ion battery. A Randles electric equivalent circuit is used to describe the internal impedance of the battery. The diffusion phenomenon related to this modeling is presented using a fractional order method. The battery model is thus reformulated into a transfer function which can be identified through Levenberg-Marquardt algorithm to ensure the algorithm's convergence to the physical parameters. An initialization method is proposed in this paper by taking into account previously acquired information about the static and dynamic system behavior. The method is validated using noisy voltage response, while precision of the final identification results is evaluated using Monte-Carlo method.

A physically based analytical model of flood frequency curves

NASA Astrophysics Data System (ADS)

Basso, S.; Schirmer, M.; Botter, G.

2016-09-01

Predicting magnitude and frequency of floods is a key issue in hydrology, with implications in many fields ranging from river science and geomorphology to the insurance industry. In this paper, a novel physically based approach is proposed to estimate the recurrence intervals of seasonal flow maxima. The method links the extremal distribution of streamflows to the stochastic dynamics of daily discharge, providing an analytical expression of the seasonal flood frequency curve. The parameters involved in the formulation embody climate and landscape attributes of the contributing catchment and can be estimated from daily rainfall and streamflow data. Only one parameter, which is linked to the antecedent wetness condition in the watershed, needs to be calibrated on the observed maxima. The performance of the method is discussed through a set of applications in four rivers featuring heterogeneous daily flow regimes. The model provides reliable estimates of seasonal maximum flows in different climatic settings and is able to capture diverse shapes of flood frequency curves emerging in erratic and persistent flow regimes. The proposed method exploits experimental information on the full range of discharges experienced by rivers. As a consequence, model performances do not deteriorate when the magnitude of events with return times longer than the available sample size is estimated. The approach provides a framework for the prediction of floods based on short data series of rainfall and daily streamflows that may be especially valuable in data scarce regions of the world.

A robust nonlinear position observer for synchronous motors with relaxed excitation conditions

NASA Astrophysics Data System (ADS)

Bobtsov, Alexey; Bazylev, Dmitry; Pyrkin, Anton; Aranovskiy, Stanislav; Ortega, Romeo

2017-04-01

A robust, nonlinear and globally convergent rotor position observer for surface-mounted permanent magnet synchronous motors was recently proposed by the authors. The key feature of this observer is that it requires only the knowledge of the motor's resistance and inductance. Using some particular properties of the mathematical model it is shown that the problem of state observation can be translated into one of estimation of two constant parameters, which is carried out with a standard gradient algorithm. In this work, we propose to replace this estimator with a new one called dynamic regressor extension and mixing, which has the following advantages with respect to gradient estimators: (1) the stringent persistence of excitation (PE) condition of the regressor is not necessary to ensure parameter convergence; (2) the latter is guaranteed requiring instead a non-square-integrability condition that has a clear physical meaning in terms of signal energy; (3) if the regressor is PE, the new observer (like the old one) ensures convergence is exponential, entailing some robustness properties to the observer; (4) the new estimator includes an additional filter that constitutes an additional degree of freedom to satisfy the non-square integrability condition. Realistic simulation results show significant performance improvement of the position observer using the new parameter estimator, with a less oscillatory behaviour and a faster convergence speed.

Advanced Machine Learning Emulators of Radiative Transfer Models

NASA Astrophysics Data System (ADS)

Camps-Valls, G.; Verrelst, J.; Martino, L.; Vicent, J.

2017-12-01

Physically-based model inversion methodologies are based on physical laws and established cause-effect relationships. A plethora of remote sensing applications rely on the physical inversion of a Radiative Transfer Model (RTM), which lead to physically meaningful bio-geo-physical parameter estimates. The process is however computationally expensive, needs expert knowledge for both the selection of the RTM, its parametrization and the the look-up table generation, as well as its inversion. Mimicking complex codes with statistical nonlinear machine learning algorithms has become the natural alternative very recently. Emulators are statistical constructs able to approximate the RTM, although at a fraction of the computational cost, providing an estimation of uncertainty, and estimations of the gradient or finite integral forms. We review the field and recent advances of emulation of RTMs with machine learning models. We posit Gaussian processes (GPs) as the proper framework to tackle the problem. Furthermore, we introduce an automatic methodology to construct emulators for costly RTMs. The Automatic Gaussian Process Emulator (AGAPE) methodology combines the interpolation capabilities of GPs with the accurate design of an acquisition function that favours sampling in low density regions and flatness of the interpolation function. We illustrate the good capabilities of our emulators in toy examples, leaf and canopy levels PROSPECT and PROSAIL RTMs, and for the construction of an optimal look-up-table for atmospheric correction based on MODTRAN5.

Improving Wind Predictions in the Marine Atmospheric Boundary Layer Through Parameter Estimation in a Single Column Model

DOE PAGES

Lee, Jared A.; Hacker, Joshua P.; Monache, Luca Delle; ...

2016-08-03

A current barrier to greater deployment of offshore wind turbines is the poor quality of numerical weather prediction model wind and turbulence forecasts over open ocean. The bulk of development for atmospheric boundary layer (ABL) parameterization schemes has focused on land, partly due to a scarcity of observations over ocean. The 100-m FINO1 tower in the North Sea is one of the few sources worldwide of atmospheric profile observations from the sea surface to turbine hub height. These observations are crucial to developing a better understanding and modeling of physical processes in the marine ABL. In this paper we usemore » the WRF single column model (SCM), coupled with an ensemble Kalman filter from the Data Assimilation Research Testbed (DART), to create 100-member ensembles at the FINO1 location. The goal of this study is to determine the extent to which model parameter estimation can improve offshore wind forecasts. Combining two datasets that provide lateral forcing for the SCM and two methods for determining z 0, the time-varying sea-surface roughness length, we conduct four WRF-SCM/DART experiments over the October-December 2006 period. The two methods for determining z 0 are the default Fairall-adjusted Charnock formulation in WRF, and using parameter estimation techniques to estimate z 0 in DART. Using DART to estimate z 0 is found to reduce 1-h forecast errors of wind speed over the Charnock-Fairall z 0 ensembles by 4%–22%. Finally, however, parameter estimation of z 0 does not simultaneously reduce turbulent flux forecast errors, indicating limitations of this approach and the need for new marine ABL parameterizations.« less

Colloid Transport in Saturated Porous Media: Elimination of Attachment Efficiency in a New Colloid Transport Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Landkamer, Lee L.; Harvey, Ronald W.; Scheibe, Timothy D.

A new colloid transport model is introduced that is conceptually simple but captures the essential features of complicated attachment and detachment behavior of colloids when conditions of secondary minimum attachment exist. This model eliminates the empirical concept of collision efficiency; the attachment rate is computed directly from colloid filtration theory. Also, a new paradigm for colloid detachment based on colloid population heterogeneity is introduced. Assuming the dispersion coefficient can be estimated from tracer behavior, this model has only two fitting parameters: (1) the fraction of colloids that attach irreversibly and (2) the rate at which reversibly attached colloids leave themore » surface. These two parameters were correlated to physical parameters that control colloid transport such as the depth of the secondary minimum and pore water velocity. Given this correlation, the model serves as a heuristic tool for exploring the influence of physical parameters such as surface potential and fluid velocity on colloid transport. This model can be extended to heterogeneous systems characterized by both primary and secondary minimum deposition by simply increasing the fraction of colloids that attach irreversibly.« less

Detecting cis-regulatory binding sites for cooperatively binding proteins

PubMed Central

van Oeffelen, Liesbeth; Cornelis, Pierre; Van Delm, Wouter; De Ridder, Fedor; De Moor, Bart; Moreau, Yves

2008-01-01

Several methods are available to predict cis-regulatory modules in DNA based on position weight matrices. However, the performance of these methods generally depends on a number of additional parameters that cannot be derived from sequences and are difficult to estimate because they have no physical meaning. As the best way to detect cis-regulatory modules is the way in which the proteins recognize them, we developed a new scoring method that utilizes the underlying physical binding model. This method requires no additional parameter to account for multiple binding sites; and the only necessary parameters to model homotypic cooperative interactions are the distances between adjacent protein binding sites in basepairs, and the corresponding cooperative binding constants. The heterotypic cooperative binding model requires one more parameter per cooperatively binding protein, which is the concentration multiplied by the partition function of this protein. In a case study on the bacterial ferric uptake regulator, we show that our scoring method for homotypic cooperatively binding proteins significantly outperforms other PWM-based methods where biophysical cooperativity is not taken into account. PMID:18400778

A Recommended Procedure for Estimating the Cosmic-Ray Spectral Parameter of a Simple Power Law With Applications to Detector Design

NASA Technical Reports Server (NTRS)

Howell, L. W.

2001-01-01

A simple power law model consisting of a single spectral index alpha-1 is believed to be an adequate description of the galactic cosmic-ray (GCR) proton flux at energies below 10(exp 13) eV. Two procedures for estimating alpha-1 the method of moments and maximum likelihood (ML), are developed and their statistical performance compared. It is concluded that the ML procedure attains the most desirable statistical properties and is hence the recommended statistical estimation procedure for estimating alpha-1. The ML procedure is then generalized for application to a set of real cosmic-ray data and thereby makes this approach applicable to existing cosmic-ray data sets. Several other important results, such as the relationship between collecting power and detector energy resolution, as well as inclusion of a non-Gaussian detector response function, are presented. These results have many practical benefits in the design phase of a cosmic-ray detector as they permit instrument developers to make important trade studies in design parameters as a function of one of the science objectives. This is particularly important for space-based detectors where physical parameters, such as dimension and weight, impose rigorous practical limits to the design envelope.

Development of a transient, lumped hydrologic model for geomorphologic units in a geomorphology based rainfall-runoff modelling framework

NASA Astrophysics Data System (ADS)

Vannametee, E.; Karssenberg, D.; Hendriks, M. R.; de Jong, S. M.; Bierkens, M. F. P.

2010-05-01

We propose a modelling framework for distributed hydrological modelling of 103-105 km2 catchments by discretizing the catchment in geomorphologic units. Each of these units is modelled using a lumped model representative for the processes in the unit. Here, we focus on the development and parameterization of this lumped model as a component of our framework. The development of the lumped model requires rainfall-runoff data for an extensive set of geomorphological units. Because such large observational data sets do not exist, we create artificial data. With a high-resolution, physically-based, rainfall-runoff model, we create artificial rainfall events and resulting hydrographs for an extensive set of different geomorphological units. This data set is used to identify the lumped model of geomorphologic units. The advantage of this approach is that it results in a lumped model with a physical basis, with representative parameters that can be derived from point-scale measurable physical parameters. The approach starts with the development of the high-resolution rainfall-runoff model that generates an artificial discharge dataset from rainfall inputs as a surrogate of a real-world dataset. The model is run for approximately 105 scenarios that describe different characteristics of rainfall, properties of the geomorphologic units (i.e. slope gradient, unit length and regolith properties), antecedent moisture conditions and flow patterns. For each scenario-run, the results of the high-resolution model (i.e. runoff and state variables) at selected simulation time steps are stored in a database. The second step is to develop the lumped model of a geomorphological unit. This forward model consists of a set of simple equations that calculate Hortonian runoff and state variables of the geomorphologic unit over time. The lumped model contains only three parameters: a ponding factor, a linear reservoir parameter, and a lag time. The model is capable of giving an appropriate representation of the transient rainfall-runoff relations that exist in the artificial data set generated with the high-resolution model. The third step is to find the values of empirical parameters in the lumped forward model using the artificial dataset. For each scenario of the high-resolution model run, a set of lumped model parameters is determined with a fitting method using the corresponding time series of state variables and outputs retrieved from the database. Thus, the parameters in the lumped model can be estimated by using the artificial data set. The fourth step is to develop an approach to assign lumped model parameters based upon the properties of the geomorphological unit. This is done by finding relationships between the measurable physical properties of geomorphologic units (i.e. slope gradient, unit length, and regolith properties) and the lumped forward model parameters using multiple regression techniques. In this way, a set of lumped forward model parameters can be estimated as a function of morphology and physical properties of the geomorphologic units. The lumped forward model can then be applied to different geomorphologic units. Finally, the performance of the lumped forward model is evaluated; the outputs of the lumped forward model are compared with the results of the high-resolution model. Our results show that the lumped forward model gives the best estimates of total discharge volumes and peak discharges when rain intensities are not significantly larger than the infiltration capacities of the units and when the units are small with a flat gradient. Hydrograph shapes are fairly well reproduced for most cases except for flat and elongated units with large runoff volumes. The results of this study provide a first step towards developing low-dimensional models for large ungauged basins.

A PDE-based methodology for modeling, parameter estimation and feedback control in structural and structural acoustic systems

NASA Technical Reports Server (NTRS)

Banks, H. T.; Brown, D. E.; Metcalf, Vern L.; Silcox, R. J.; Smith, Ralph C.; Wang, Yun

1994-01-01

A problem of continued interest concerns the control of vibrations in a flexible structure and the related problem of reducing structure-borne noise in structural acoustic systems. In both cases, piezoceramic patches bonded to the structures have been successfully used as control actuators. Through the application of a controlling voltage, the patches can be used to reduce structural vibrations which in turn lead to methods for reducing structure-borne noise. A PDE-based methodology for modeling, estimating physical parameters, and implementing a feedback control scheme for problems of this type is discussed. While the illustrating example is a circular plate, the methodology is sufficiently general so as to be applicable in a variety of structural and structural acoustic systems.

Propagation dynamics of successive emissions in laboratory and astrophysical jets and problem of their collimation

NASA Astrophysics Data System (ADS)

Kalashnikov, I.; Chardonnet, P.; Chechetkin, V.; Dodin, A.; Krauz, V.

2018-06-01

This paper presents the results of numerical simulation of the propagation of a sequence of plasma knots in laboratory conditions and in the astrophysical environment. The physical and geometric parameters of the simulation have been chosen close to the parameters of the PF-3 facility (Kurchatov Institute) and the jet of the star RW Aur. We found that the low-density region formed after the first knot propagation plays an important role in the collimation of the subsequent ones. Assuming only the thermal expansion of the subsequent emissions, qualitative estimates of the time taken to fill this area with the surrounding matter and the angle of jet scattering have been made. These estimates are consistent with observations and results of our modeling.

A Simple Model of Global Aerosol Indirect Effects

NASA Technical Reports Server (NTRS)

Ghan, Steven J.; Smith, Steven J.; Wang, Minghuai; Zhang, Kai; Pringle, Kirsty; Carslaw, Kenneth; Pierce, Jeffrey; Bauer, Susanne; Adams, Peter

2013-01-01

Most estimates of the global mean indirect effect of anthropogenic aerosol on the Earth's energy balance are from simulations by global models of the aerosol lifecycle coupled with global models of clouds and the hydrologic cycle. Extremely simple models have been developed for integrated assessment models, but lack the flexibility to distinguish between primary and secondary sources of aerosol. Here a simple but more physically based model expresses the aerosol indirect effect (AIE) using analytic representations of cloud and aerosol distributions and processes. Although the simple model is able to produce estimates of AIEs that are comparable to those from some global aerosol models using the same global mean aerosol properties, the estimates by the simple model are sensitive to preindustrial cloud condensation nuclei concentration, preindustrial accumulation mode radius, width of the accumulation mode, size of primary particles, cloud thickness, primary and secondary anthropogenic emissions, the fraction of the secondary anthropogenic emissions that accumulates on the coarse mode, the fraction of the secondary mass that forms new particles, and the sensitivity of liquid water path to droplet number concentration. Estimates of present-day AIEs as low as 5 W/sq m and as high as 0.3 W/sq m are obtained for plausible sets of parameter values. Estimates are surprisingly linear in emissions. The estimates depend on parameter values in ways that are consistent with results from detailed global aerosol-climate simulation models, which adds to understanding of the dependence on AIE uncertainty on uncertainty in parameter values.

Genetic variability in krill.

PubMed

Valentine, J W; Ayala, F J

1976-02-01

We have estimated genetic variability by gel electrophoresis in three species of krill, genus Euphausia (Arthropoda: Crustacea). Genetic variability is low where trophic resources are most seasonal, and high where trophic resources are most stable. Simlar trends have been found in benthic marine invertebrates. The observed trends of genetic variability do not correlate with trends in the stability of physical environment parameters.

UCODE, a computer code for universal inverse modeling

USGS Publications Warehouse

Poeter, E.P.; Hill, M.C.

1999-01-01

This article presents the US Geological Survey computer program UCODE, which was developed in collaboration with the US Army Corps of Engineers Waterways Experiment Station and the International Ground Water Modeling Center of the Colorado School of Mines. UCODE performs inverse modeling, posed as a parameter-estimation problem, using nonlinear regression. Any application model or set of models can be used; the only requirement is that they have numerical (ASCII or text only) input and output files and that the numbers in these files have sufficient significant digits. Application models can include preprocessors and postprocessors as well as models related to the processes of interest (physical, chemical and so on), making UCODE extremely powerful for model calibration. Estimated parameters can be defined flexibly with user-specified functions. Observations to be matched in the regression can be any quantity for which a simulated equivalent value can be produced, thus simulated equivalent values are calculated using values that appear in the application model output files and can be manipulated with additive and multiplicative functions, if necessary. Prior, or direct, information on estimated parameters also can be included in the regression. The nonlinear regression problem is solved by minimizing a weighted least-squares objective function with respect to the parameter values using a modified Gauss-Newton method. Sensitivities needed for the method are calculated approximately by forward or central differences and problems and solutions related to this approximation are discussed. Statistics are calculated and printed for use in (1) diagnosing inadequate data or identifying parameters that probably cannot be estimated with the available data, (2) evaluating estimated parameter values, (3) evaluating the model representation of the actual processes and (4) quantifying the uncertainty of model simulated values. UCODE is intended for use on any computer operating system: it consists of algorithms programmed in perl, a freeware language designed for text manipulation and Fortran90, which efficiently performs numerical calculations.

Dynamic imaging model and parameter optimization for a star tracker.

PubMed

Yan, Jinyun; Jiang, Jie; Zhang, Guangjun

2016-03-21

Under dynamic conditions, star spots move across the image plane of a star tracker and form a smeared star image. This smearing effect increases errors in star position estimation and degrades attitude accuracy. First, an analytical energy distribution model of a smeared star spot is established based on a line segment spread function because the dynamic imaging process of a star tracker is equivalent to the static imaging process of linear light sources. The proposed model, which has a clear physical meaning, explicitly reflects the key parameters of the imaging process, including incident flux, exposure time, velocity of a star spot in an image plane, and Gaussian radius. Furthermore, an analytical expression of the centroiding error of the smeared star spot is derived using the proposed model. An accurate and comprehensive evaluation of centroiding accuracy is obtained based on the expression. Moreover, analytical solutions of the optimal parameters are derived to achieve the best performance in centroid estimation. Finally, we perform numerical simulations and a night sky experiment to validate the correctness of the dynamic imaging model, the centroiding error expression, and the optimal parameters.

Optimization of tissue physical parameters for accurate temperature estimation from finite-element simulation of radiofrequency ablation.

PubMed

Subramanian, Swetha; Mast, T Douglas

2015-10-07

Computational finite element models are commonly used for the simulation of radiofrequency ablation (RFA) treatments. However, the accuracy of these simulations is limited by the lack of precise knowledge of tissue parameters. In this technical note, an inverse solver based on the unscented Kalman filter (UKF) is proposed to optimize values for specific heat, thermal conductivity, and electrical conductivity resulting in accurately simulated temperature elevations. A total of 15 RFA treatments were performed on ex vivo bovine liver tissue. For each RFA treatment, 15 finite-element simulations were performed using a set of deterministically chosen tissue parameters to estimate the mean and variance of the resulting tissue ablation. The UKF was implemented as an inverse solver to recover the specific heat, thermal conductivity, and electrical conductivity corresponding to the measured area of the ablated tissue region, as determined from gross tissue histology. These tissue parameters were then employed in the finite element model to simulate the position- and time-dependent tissue temperature. Results show good agreement between simulated and measured temperature.

Robust optimal design of diffusion-weighted magnetic resonance experiments for skin microcirculation

NASA Astrophysics Data System (ADS)

Choi, J.; Raguin, L. G.

2010-10-01

Skin microcirculation plays an important role in several diseases including chronic venous insufficiency and diabetes. Magnetic resonance (MR) has the potential to provide quantitative information and a better penetration depth compared with other non-invasive methods such as laser Doppler flowmetry or optical coherence tomography. The continuous progress in hardware resulting in higher sensitivity must be coupled with advances in data acquisition schemes. In this article, we first introduce a physical model for quantifying skin microcirculation using diffusion-weighted MR (DWMR) based on an effective dispersion model for skin leading to a q-space model of the DWMR complex signal, and then design the corresponding robust optimal experiments. The resulting robust optimal DWMR protocols improve the worst-case quality of parameter estimates using nonlinear least squares optimization by exploiting available a priori knowledge of model parameters. Hence, our approach optimizes the gradient strengths and directions used in DWMR experiments to robustly minimize the size of the parameter estimation error with respect to model parameter uncertainty. Numerical evaluations are presented to demonstrate the effectiveness of our approach as compared to conventional DWMR protocols.

U.S. Workshop on the Physics and Chemistry of II-VI Materials (a.k.a. II-VI Workshop) - ARO Research Area 9: Materials Science - Physical Properties of Materials

DTIC Science & Technology

2015-12-21

5.5: Evaluation of MBE-Grown MCT on GaAs for HOT Applications .................................................... 99 J. Wenisch, W. Schirmacher, R...on-p architecture and is well adapted for low flux detection or high operating temperature. This architecture has been evaluated for space...estimate the ER of the Hg1-xCdxTe in real time is described. In this work, the output parameters from the ICP etcher are evaluated for their correlation

Consolidation of Bimetallic Nanosized Particles and Formation of Nanocomposites Depending on Conditions of Shock Wave Compaction

NASA Astrophysics Data System (ADS)

Vorozhtsov, S. A.; Kudryashova, O. B.; Lerner, M. I.; Vorozhtsov, A. B.; Khrustalyov, A. P.; Pervikov, A. V.

2017-11-01

The authors consider and evaluate the physical parameters and regularities of the process of consolidation of Fe-Cu, Cu-Nb, Ag-Ni, Fe-Pb nanoparticles when creating composite materials by means of shock wave compaction. As a result of theoretical consideration of explosive compaction process, researchers established and discussed the physical process conditions, established a number of threshold pressure values corresponding to different target indicators of the state of the compact. The time of shock wave impact on powders for powder consolidation was estimated.

Linking flowability and granulometry of lactose powders.

PubMed

Boschini, F; Delaval, V; Traina, K; Vandewalle, N; Lumay, G

2015-10-15

The flowing properties of 10 lactose powders commonly used in pharmaceutical industries have been analyzed with three recently improved measurement methods. The first method is based on the heap shape measurement. This straightforward measurement method provides two physical parameters (angle of repose αr and static cohesive index σr) allowing to make a first screening of the powder properties. The second method allows to estimate the rheological properties of a powder by analyzing the powder flow in a rotating drum. This more advanced method gives a large set of physical parameters (flowing angle αf, dynamic cohesive index σf, angle of first avalanche αa and powder aeration %ae) leading to deeper interpretations. The third method is an improvement of the classical bulk and tapped density measurements. In addition to the improvement of the measurement precision, the densification dynamics of the powder bulk submitted to taps is analyzed. The link between the macroscopic physical parameters obtained with these methods and the powder granulometry is analyzed. Moreover, the correlations between the different flowability indexes are discussed. Finally, the link between grain shape and flowability is discussed qualitatively. Copyright © 2015 Elsevier B.V. All rights reserved.

Equations for estimating synthetic unit-hydrograph parameter values for small watersheds in Lake County, Illinois

USGS Publications Warehouse

Melching, C.S.; Marquardt, J.S.

1997-01-01

Design hydrographs computed from design storms, simple models of abstractions (interception, depression storage, and infiltration), and synthetic unit hydrographs provide vital information for stormwater, flood-plain, and water-resources management throughout the United States. Rainfall and runoff data for small watersheds in Lake County collected between 1990 and 1995 were studied to develop equations for estimation of synthetic unit-hydrograph parameters on the basis of watershed and storm characteristics. The synthetic unit-hydrograph parameters of interest were the time of concentration (TC) and watershed-storage coefficient (R) for the Clark unit-hydrograph method, the unit-graph lag (UL) for the Soil Conservation Service (now known as the Natural Resources Conservation Service) dimensionless unit hydrograph, and the hydrograph-time lag (TL) for the linear-reservoir method for unit-hydrograph estimation. Data from 66 storms with effective-precipitation depths greater than 0.4 inches on 9 small watersheds (areas between 0.06 and 37 square miles (mi2)) were utilized to develop the estimation equations, and data from 11 storms on 8 of these watersheds were utilized to verify (test) the estimation equations. The synthetic unit-hydrograph parameters were determined by calibration using the U.S. Army Corps of Engineers Flood Hydrograph Package HEC-1 (TC, R, and UL) or by manual analysis of the rainfall and run-off data (TL). The relation between synthetic unit-hydrograph parameters, and watershed and storm characteristics was determined by multiple linear regression of the logarithms of the parameters and characteristics. Separate sets of equations were developed with watershed area and main channel length as the starting parameters. Percentage of impervious cover, main channel slope, and depth of effective precipitation also were identified as important characteristics for estimation of synthetic unit-hydrograph parameters. The estimation equations utilizing area had multiple correlation coefficients of 0.873, 0.961, 0.968, and 0.963 for TC, R, UL, and TL, respectively, and the estimation equations utilizing main channel length had multiple correlation coefficients of 0.845, 0.957, 0.961, and 0.963 for TC, R, UL, and TL, respectively. Simulation of the measured hydrographs for the verification storms utilizing TC and R obtained from the estimation equations yielded good results without calibration. The peak discharge for 8 of the 11 storms was estimated within 25 percent and the time-to-peak discharge for 10 of the 11 storms was estimated within 20 percent. Thus, application of the estimation equations to determine synthetic unit-hydrograph parameters for design-storm simulation may result in reliable design hydrographs; as long as the physical characteristics of the watersheds under consideration are within the range of those for the watersheds in this study (area: 0.06-37 mi2, main channel length: 0.33-16.6 miles, main channel slope: 3.13-55.3 feet per mile, and percentage of impervious cover: 7.32-40.6 percent). The estimation equations are most reliable when applied to watersheds with areas less than 25 mi2.

Prediction of Building Limestone Physical and Mechanical Properties by Means of Ultrasonic P-Wave Velocity

PubMed Central

Concu, Giovanna; De Nicolo, Barbara; Valdes, Monica

2014-01-01

The aim of this study was to evaluate ultrasonic P-wave velocity as a feature for predicting some physical and mechanical properties that describe the behavior of local building limestone. To this end, both ultrasonic testing and compressive tests were carried out on several limestone specimens and statistical correlation between ultrasonic velocity and density, compressive strength, and modulus of elasticity was studied. The effectiveness of ultrasonic velocity was evaluated by regression, with the aim of observing the coefficient of determination r 2 between ultrasonic velocity and the aforementioned parameters, and the mathematical expressions of the correlations were found and discussed. The strong relations that were established between ultrasonic velocity and limestone properties indicate that these parameters can be reasonably estimated by means of this nondestructive parameter. This may be of great value in a preliminary phase of the diagnosis and inspection of stone masonry conditions, especially when the possibility of sampling material cores is reduced. PMID:24511286

Prediction of building limestone physical and mechanical properties by means of ultrasonic P-wave velocity.

PubMed

Concu, Giovanna; De Nicolo, Barbara; Valdes, Monica

2014-01-01

The aim of this study was to evaluate ultrasonic P-wave velocity as a feature for predicting some physical and mechanical properties that describe the behavior of local building limestone. To this end, both ultrasonic testing and compressive tests were carried out on several limestone specimens and statistical correlation between ultrasonic velocity and density, compressive strength, and modulus of elasticity was studied. The effectiveness of ultrasonic velocity was evaluated by regression, with the aim of observing the coefficient of determination r(2) between ultrasonic velocity and the aforementioned parameters, and the mathematical expressions of the correlations were found and discussed. The strong relations that were established between ultrasonic velocity and limestone properties indicate that these parameters can be reasonably estimated by means of this nondestructive parameter. This may be of great value in a preliminary phase of the diagnosis and inspection of stone masonry conditions, especially when the possibility of sampling material cores is reduced.

THE NuSTAR X-RAY SPECTRUM OF HERCULES X-1: A RADIATION-DOMINATED RADIATIVE SHOCK

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wolff, Michael T.; Wood, Kent S.; Becker, Peter A.

2016-11-10

We report on new spectral modeling of the accreting X-ray pulsar Hercules X-1. Our radiation-dominated radiative shock model is an implementation of the analytic work of Becker and Wolff on Comptonized accretion flows onto magnetic neutron stars. We obtain a good fit to the spin-phase-averaged 4–78 keV X-ray spectrum observed by the Nuclear Spectroscopic Telescope Array during a main-on phase of the Her X-1 35 day accretion disk precession period. This model allows us to estimate the accretion rate, the Comptonizing temperature of the radiating plasma, the radius of the magnetic polar cap, and the average scattering opacity parameters inmore » the accretion column. This is in contrast to previous phenomenological models that characterized the shape of the X-ray spectrum, but could not determine the physical parameters of the accretion flow. We describe the spectral fitting details and discuss the interpretation of the accretion flow physical parameters.« less

Experiences in multiyear combined state-parameter estimation with an ecosystem model of the North Atlantic and Arctic Oceans using the Ensemble Kalman Filter

NASA Astrophysics Data System (ADS)

Simon, Ehouarn; Samuelsen, Annette; Bertino, Laurent; Mouysset, Sandrine

2015-12-01

A sequence of one-year combined state-parameter estimation experiments has been conducted in a North Atlantic and Arctic Ocean configuration of the coupled physical-biogeochemical model HYCOM-NORWECOM over the period 2007-2010. The aim is to evaluate the ability of an ensemble-based data assimilation method to calibrate ecosystem model parameters in a pre-operational setting, namely the production of the MyOcean pilot reanalysis of the Arctic biology. For that purpose, four biological parameters (two phyto- and two zooplankton mortality rates) are estimated by assimilating weekly data such as, satellite-derived Sea Surface Temperature, along-track Sea Level Anomalies, ice concentrations and chlorophyll-a concentrations with an Ensemble Kalman Filter. The set of optimized parameters locally exhibits seasonal variations suggesting that time-dependent parameters should be used in ocean ecosystem models. A clustering analysis of the optimized parameters is performed in order to identify consistent ecosystem regions. In the north part of the domain, where the ecosystem model is the most reliable, most of them can be associated with Longhurst provinces and new provinces emerge in the Arctic Ocean. However, the clusters do not coincide anymore with the Longhurst provinces in the Tropics due to large model errors. Regarding the ecosystem state variables, the assimilation of satellite-derived chlorophyll concentration leads to significant reduction of the RMS errors in the observed variables during the first year, i.e. 2008, compared to a free run simulation. However, local filter divergences of the parameter component occur in 2009 and result in an increase in the RMS error at the time of the spring bloom.

Recent results and perspectives on cosmology and fundamental physics from microwave surveys

NASA Astrophysics Data System (ADS)

Burigana, Carlo; Battistelli, Elia Stefano; Benetti, Micol; Cabass, Giovanni; de Bernardis, Paolo; di Serego Alighieri, Sperello; di Valentino, Eleonora; Gerbino, Martina; Giusarma, Elena; Gruppuso, Alessandro; Liguori, Michele; Masi, Silvia; Norgaard-Nielsen, Hans Ulrik; Rosati, Piero; Salvati, Laura; Trombetti, Tiziana; Vielva, Patricio

2016-04-01

Recent cosmic microwave background (CMB) data in temperature and polarization have reached high precision in estimating all the parameters that describe the current so-called standard cosmological model. Recent results about the integrated Sachs-Wolfe (ISW) effect from CMB anisotropies, galaxy surveys, and their cross-correlations are presented. Looking at fine signatures in the CMB, such as the lack of power at low multipoles, the primordial power spectrum (PPS) and the bounds on non-Gaussianities, complemented by galaxy surveys, we discuss inflationary physics and the generation of primordial perturbations in the early universe. Three important topics in particle physics, the bounds on neutrinos masses and parameters, on thermal axion mass and on the neutron lifetime derived from cosmological data are reviewed, with attention to the comparison with laboratory experiment results. Recent results from cosmic polarization rotation (CPR) analyses aimed at testing the Einstein equivalence principle (EEP) are presented. Finally, we discuss the perspectives of next radio facilities for the improvement of the analysis of future CMB spectral distortion experiments.

Resolution and Orbit Reconstruction of Spectroscopic Binary Stars with the Palomar Testbed Interferometer

NASA Astrophysics Data System (ADS)

Boden, A. F.; Lane, B. F.; Creech-Eakman, M. J.; Queloz, D.; Koresko, C. D.

2000-05-01

The Palomar Testbed Interferometer (PTI) is a long-baseline near-infrared interferometer located at Palomar Observatory. For the past several years we have had an ongoing program of resolving and reconstructing the visual and physical orbits of spectroscopic binary stars with PTI, with the goal of obtaining precise dynamical mass estimates and other physical parameters. We will present a number of new visual and physical orbit determinations derived from integrated reductions of PTI visibility and archival and new spectroscopic radial velocity data. The systems for which we will discuss our orbit models are: iota Pegasi (HD 210027), 64 Psc (HD 4676), 12 Boo (HD 123999), 75 Cnc (HD 78418), 47 And (HD 8374), HD 205539, BY Draconis (HDE 234677), and 3 Boo (HD 120064), and 3 Boo (HD 120064). All of these systems are double-lined binary systems (SB2), and integrated astrometric/radial velocity orbit modeling provides precise fundamental parameters (mass, luminosity) and system distance determinations comparable with Hipparcos precisions.

A simple approach for estimating the refractive index structure parameter (Cn²) profile in the atmosphere.

PubMed

Basu, Sukanta

2015-09-01

Utilizing the so-called Thorpe scale as a measure of the turbulence outer scale, we propose a physically-based approach for the estimation of Cn2 profiles in the lower atmosphere. This approach only requires coarse-resolution temperature profiles (a.k.a., soundings) as input, yet it has the intrinsic ability to capture layers of high optical turbulence. The prowess of this computationally inexpensive approach is demonstrated by validations against observational data from a field campaign over Mauna Kea, Hawaii.

Probabilistic inversion of AVO seismic data for reservoir properties and related uncertainty estimation

NASA Astrophysics Data System (ADS)

Zunino, Andrea; Mosegaard, Klaus

2017-04-01

Sought-after reservoir properties of interest are linked only indirectly to the observable geophysical data which are recorded at the earth's surface. In this framework, seismic data represent one of the most reliable tool to study the structure and properties of the subsurface for natural resources. Nonetheless, seismic analysis is not an end in itself, as physical properties such as porosity are often of more interest for reservoir characterization. As such, inference of those properties implies taking into account also rock physics models linking porosity and other physical properties to elastic parameters. In the framework of seismic reflection data, we address this challenge for a reservoir target zone employing a probabilistic method characterized by a multi-step complex nonlinear forward modeling that combines: 1) a rock physics model with 2) the solution of full Zoeppritz equations and 3) a convolutional seismic forward modeling. The target property of this work is porosity, which is inferred using a Monte Carlo approach where porosity models, i.e., solutions to the inverse problem, are directly sampled from the posterior distribution. From a theoretical point of view, the Monte Carlo strategy can be particularly useful in the presence of nonlinear forward models, which is often the case when employing sophisticated rock physics models and full Zoeppritz equations and to estimate related uncertainty. However, the resulting computational challenge is huge. We propose to alleviate this computational burden by assuming some smoothness of the subsurface parameters and consequently parameterizing the model in terms of spline bases. This allows us a certain flexibility in that the number of spline bases and hence the resolution in each spatial direction can be controlled. The method is tested on a 3-D synthetic case and on a 2-D real data set.

Neural Network-Based Retrieval of Surface and Root Zone Soil Moisture using Multi-Frequency Remotely-Sensed Observations

NASA Astrophysics Data System (ADS)

Hamed Alemohammad, Seyed; Kolassa, Jana; Prigent, Catherine; Aires, Filipe; Gentine, Pierre

2017-04-01

Knowledge of root zone soil moisture is essential in studying plant's response to different stress conditions since plant photosynthetic activity and transpiration rate are constrained by the water available through their roots. Current global root zone soil moisture estimates are based on either outputs from physical models constrained by observations, or assimilation of remotely-sensed microwave-based surface soil moisture estimates with physical model outputs. However, quality of these estimates are limited by the accuracy of the model representations of physical processes (such as radiative transfer, infiltration, percolation, and evapotranspiration) as well as errors in the estimates of the surface parameters. Additionally, statistical approaches provide an alternative efficient platform to develop root zone soil moisture retrieval algorithms from remotely-sensed observations. In this study, we present a new neural network based retrieval algorithm to estimate surface and root zone soil moisture from passive microwave observations of SMAP satellite (L-band) and AMSR2 instrument (X-band). SMAP early morning observations are ideal for surface soil moisture retrieval. AMSR2 mid-night observations are used here as an indicator of plant hydraulic properties that are related to root zone soil moisture. The combined observations from SMAP and AMSR2 together with other ancillary observations including the Solar-Induced Fluorescence (SIF) estimates from GOME-2 instrument provide necessary information to estimate surface and root zone soil moisture. The algorithm is applied to observations from the first 18 months of SMAP mission and retrievals are validated against in-situ observations and other global datasets.

A classification of U.S. estuaries based on physical and hydrologic attributes

USGS Publications Warehouse

Engle, V.D.; Kurtz, J.C.; Smith, L.M.; Chancy, C.; Bourgeois, P.

2007-01-01

A classification of U.S. estuaries is presented based on estuarine characteristics that have been identified as important for quantifying stressor-response relationships in coastal systems. Estuaries within a class have similar physical and hydrologic characteristics and would be expected to demonstrate similar biological responses to stressor loads from the adjacent watersheds. Nine classes of estuaries were identified by applying cluster analysis to a database for 138 U.S. estuarine drainage areas. The database included physical measures of estuarine areas, depth and volume, as well as hydrologic parameters (i.e., tide height, tidal prism volume, freshwater inflow rates, salinity, and temperature). The ability of an estuary to dilute or flush pollutants can be estimated using physical and hydrologic properties such as volume, bathymetry, freshwater inflow and tidal exchange rates which influence residence time and affect pollutant loading rates. Thus, physical and hydrologic characteristics can be used to estimate the susceptibility of estuaries to pollutant effects. This classification of estuaries can be used by natural resource managers to describe and inventory coastal systems, understand stressor impacts, predict which systems are most sensitive to stressors, and manage and protect coastal resources. ?? Springer Science+Business Media B.V. 2007.

Application of empirical and dynamical closure methods to simple climate models

NASA Astrophysics Data System (ADS)

Padilla, Lauren Elizabeth

This dissertation applies empirically- and physically-based methods for closure of uncertain parameters and processes to three model systems that lie on the simple end of climate model complexity. Each model isolates one of three sources of closure uncertainty: uncertain observational data, large dimension, and wide ranging length scales. They serve as efficient test systems toward extension of the methods to more realistic climate models. The empirical approach uses the Unscented Kalman Filter (UKF) to estimate the transient climate sensitivity (TCS) parameter in a globally-averaged energy balance model. Uncertainty in climate forcing and historical temperature make TCS difficult to determine. A range of probabilistic estimates of TCS computed for various assumptions about past forcing and natural variability corroborate ranges reported in the IPCC AR4 found by different means. Also computed are estimates of how quickly uncertainty in TCS may be expected to diminish in the future as additional observations become available. For higher system dimensions the UKF approach may become prohibitively expensive. A modified UKF algorithm is developed in which the error covariance is represented by a reduced-rank approximation, substantially reducing the number of model evaluations required to provide probability densities for unknown parameters. The method estimates the state and parameters of an abstract atmospheric model, known as Lorenz 96, with accuracy close to that of a full-order UKF for 30-60% rank reduction. The physical approach to closure uses the Multiscale Modeling Framework (MMF) to demonstrate closure of small-scale, nonlinear processes that would not be resolved directly in climate models. A one-dimensional, abstract test model with a broad spatial spectrum is developed. The test model couples the Kuramoto-Sivashinsky equation to a transport equation that includes cloud formation and precipitation-like processes. In the test model, three main sources of MMF error are evaluated independently. Loss of nonlinear multi-scale interactions and periodic boundary conditions in closure models were dominant sources of error. Using a reduced order modeling approach to maximize energy content allowed reduction of the closure model dimension up to 75% without loss in accuracy. MMF and a comparable alternative model peformed equally well compared to direct numerical simulation.

Estimating age from recapture data: integrating incremental growth measures with ancillary data to infer age-at-length

USGS Publications Warehouse

Eaton, Mitchell J.; Link, William A.

2011-01-01

Estimating the age of individuals in wild populations can be of fundamental importance for answering ecological questions, modeling population demographics, and managing exploited or threatened species. Significant effort has been devoted to determining age through the use of growth annuli, secondary physical characteristics related to age, and growth models. Many species, however, either do not exhibit physical characteristics useful for independent age validation or are too rare to justify sacrificing a large number of individuals to establish the relationship between size and age. Length-at-age models are well represented in the fisheries and other wildlife management literature. Many of these models overlook variation in growth rates of individuals and consider growth parameters as population parameters. More recent models have taken advantage of hierarchical structuring of parameters and Bayesian inference methods to allow for variation among individuals as functions of environmental covariates or individual-specific random effects. Here, we describe hierarchical models in which growth curves vary as individual-specific stochastic processes, and we show how these models can be fit using capture–recapture data for animals of unknown age along with data for animals of known age. We combine these independent data sources in a Bayesian analysis, distinguishing natural variation (among and within individuals) from measurement error. We illustrate using data for African dwarf crocodiles, comparing von Bertalanffy and logistic growth models. The analysis provides the means of predicting crocodile age, given a single measurement of head length. The von Bertalanffy was much better supported than the logistic growth model and predicted that dwarf crocodiles grow from 19.4 cm total length at birth to 32.9 cm in the first year and 45.3 cm by the end of their second year. Based on the minimum size of females observed with hatchlings, reproductive maturity was estimated to be at nine years. These size benchmarks are believed to represent thresholds for important demographic parameters; improved estimates of age, therefore, will increase the precision of population projection models. The modeling approach that we present can be applied to other species and offers significant advantages when multiple sources of data are available and traditional aging techniques are not practical.

Cost-estimating relationships for space programs

NASA Technical Reports Server (NTRS)

Mandell, Humboldt C., Jr.

1992-01-01

Cost-estimating relationships (CERs) are defined and discussed as they relate to the estimation of theoretical costs for space programs. The paper primarily addresses CERs based on analogous relationships between physical and performance parameters to estimate future costs. Analytical estimation principles are reviewed examining the sources of errors in cost models, and the use of CERs is shown to be affected by organizational culture. Two paradigms for cost estimation are set forth: (1) the Rand paradigm for single-culture single-system methods; and (2) the Price paradigms that incorporate a set of cultural variables. For space programs that are potentially subject to even small cultural changes, the Price paradigms are argued to be more effective. The derivation and use of accurate CERs is important for developing effective cost models to analyze the potential of a given space program.

Stream Discharge and Evapotranspiration Responses to Climate Change and Their Associated Uncertainties in a Large Semi-Arid Basin

NASA Astrophysics Data System (ADS)

Bassam, S.; Ren, J.

2017-12-01

Predicting future water availability in watersheds is very important for proper water resources management, especially in semi-arid regions with scarce water resources. Hydrological models have been considered as powerful tools in predicting future hydrological conditions in watershed systems in the past two decades. Streamflow and evapotranspiration are the two important components in watershed water balance estimation as the former is the most commonly-used indicator of the overall water budget estimation, and the latter is the second biggest component of water budget (biggest outflow from the system). One of the main concerns in watershed scale hydrological modeling is the uncertainties associated with model prediction, which could arise from errors in model parameters and input meteorological data, or errors in model representation of the physics of hydrological processes. Understanding and quantifying these uncertainties are vital to water resources managers for proper decision making based on model predictions. In this study, we evaluated the impacts of different climate change scenarios on the future stream discharge and evapotranspiration, and their associated uncertainties, throughout a large semi-arid basin using a stochastically-calibrated, physically-based, semi-distributed hydrological model. The results of this study could provide valuable insights in applying hydrological models in large scale watersheds, understanding the associated sensitivity and uncertainties in model parameters, and estimating the corresponding impacts on interested hydrological process variables under different climate change scenarios.

Complex mode indication function and its applications to spatial domain parameter estimation

NASA Astrophysics Data System (ADS)

Shih, C. Y.; Tsuei, Y. G.; Allemang, R. J.; Brown, D. L.

1988-10-01

This paper introduces the concept of the Complex Mode Indication Function (CMIF) and its application in spatial domain parameter estimation. The concept of CMIF is developed by performing singular value decomposition (SVD) of the Frequency Response Function (FRF) matrix at each spectral line. The CMIF is defined as the eigenvalues, which are the square of the singular values, solved from the normal matrix formed from the FRF matrix, [ H( jω)] H[ H( jω)], at each spectral line. The CMIF appears to be a simple and efficient method for identifying the modes of the complex system. The CMIF identifies modes by showing the physical magnitude of each mode and the damped natural frequency for each root. Since multiple reference data is applied in CMIF, repeated roots can be detected. The CMIF also gives global modal parameters, such as damped natural frequencies, mode shapes and modal participation vectors. Since CMIF works in the spatial domain, uneven frequency spacing data such as data from spatial sine testing can be used. A second-stage procedure for accurate damped natural frequency and damping estimation as well as mode shape scaling is also discussed in this paper.

Extending amulti-scale parameter regionalization (MPR) method by introducing parameter constrained optimization and flexible transfer functions

NASA Astrophysics Data System (ADS)

Klotz, Daniel; Herrnegger, Mathew; Schulz, Karsten

2015-04-01

A multi-scale parameter-estimation method, as presented by Samaniego et al. (2010), is implemented and extended for the conceptual hydrological model COSERO. COSERO is a HBV-type model that is specialized for alpine-environments, but has been applied over a wide range of basins all over the world (see: Kling et al., 2014 for an overview). Within the methodology available small-scale information (DEM, soil texture, land cover, etc.) is used to estimate the coarse-scale model parameters by applying a set of transfer-functions (TFs) and subsequent averaging methods, whereby only TF hyper-parameters are optimized against available observations (e.g. runoff data). The parameter regionalisation approach was extended in order to allow for a more meta-heuristical handling of the transfer-functions. The two main novelties are: 1. An explicit introduction of constrains into parameter estimation scheme: The constraint scheme replaces invalid parts of the transfer-function-solution space with valid solutions. It is inspired by applications in evolutionary algorithms and related to the combination of learning and evolution. This allows the consideration of physical and numerical constraints as well as the incorporation of a priori modeller-experience into the parameter estimation. 2. Spline-based transfer-functions: Spline-based functions enable arbitrary forms of transfer-functions: This is of importance since in many cases the general relationship between sub-grid information and parameters are known, but not the form of the transfer-function itself. The contribution presents the results and experiences with the adopted method and the introduced extensions. Simulation are performed for the pre-alpine/alpine Traisen catchment in Lower Austria. References: Samaniego, L., Kumar, R., Attinger, S. (2010): Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale, Water Resour. Res., doi: 10.1029/2008WR007327 Kling, H., Stanzel, P., Fuchs, M., and Nachtnebel, H. P. (2014): Performance of the COSERO precipitation-runoff model under non-stationary conditions in basins with different climates, Hydrolog. Sci. J., doi: 10.1080/02626667.2014.959956.

Sensitivity analysis of pulse pileup model parameter in photon counting detectors

NASA Astrophysics Data System (ADS)

Shunhavanich, Picha; Pelc, Norbert J.

2017-03-01

Photon counting detectors (PCDs) may provide several benefits over energy-integrating detectors (EIDs), including spectral information for tissue characterization and the elimination of electronic noise. PCDs, however, suffer from pulse pileup, which distorts the detected spectrum and degrades the accuracy of material decomposition. Several analytical models have been proposed to address this problem. The performance of these models are dependent on the assumptions used, including the estimated pulse shape whose parameter values could differ from the actual physical ones. As the incident flux increases and the corrections become more significant the needed parameter value accuracy may be more crucial. In this work, the sensitivity of model parameter accuracies is analyzed for the pileup model of Taguchi et al. The spectra distorted by pileup at different count rates are simulated using either the model or Monte Carlo simulations, and the basis material thicknesses are estimated by minimizing the negative log-likelihood with Poisson or multivariate Gaussian distributions. From simulation results, we find that the accuracy of the deadtime, the height of pulse negative tail, and the timing to the end of the pulse are more important than most other parameters, and they matter more with increasing count rate. This result can help facilitate further work on parameter calibrations.

A meta-model based approach for rapid formability estimation of continuous fibre reinforced components

NASA Astrophysics Data System (ADS)

Zimmerling, Clemens; Dörr, Dominik; Henning, Frank; Kärger, Luise

2018-05-01

Due to their high mechanical performance, continuous fibre reinforced plastics (CoFRP) become increasingly important for load bearing structures. In many cases, manufacturing CoFRPs comprises a forming process of textiles. To predict and optimise the forming behaviour of a component, numerical simulations are applied. However, for maximum part quality, both the geometry and the process parameters must match in mutual regard, which in turn requires numerous numerically expensive optimisation iterations. In both textile and metal forming, a lot of research has focused on determining optimum process parameters, whilst regarding the geometry as invariable. In this work, a meta-model based approach on component level is proposed, that provides a rapid estimation of the formability for variable geometries based on pre-sampled, physics-based draping data. Initially, a geometry recognition algorithm scans the geometry and extracts a set of doubly-curved regions with relevant geometry parameters. If the relevant parameter space is not part of an underlying data base, additional samples via Finite-Element draping simulations are drawn according to a suitable design-table for computer experiments. Time saving parallel runs of the physical simulations accelerate the data acquisition. Ultimately, a Gaussian Regression meta-model is built from the data base. The method is demonstrated on a box-shaped generic structure. The predicted results are in good agreement with physics-based draping simulations. Since evaluations of the established meta-model are numerically inexpensive, any further design exploration (e.g. robustness analysis or design optimisation) can be performed in short time. It is expected that the proposed method also offers great potential for future applications along virtual process chains: For each process step along the chain, a meta-model can be set-up to predict the impact of design variations on manufacturability and part performance. Thus, the method is considered to facilitate a lean and economic part and process design under consideration of manufacturing effects.

Recovering Galaxy Properties Using Gaussian Process SED Fitting

NASA Astrophysics Data System (ADS)

Iyer, Kartheik; Awan, Humna

2018-01-01

Information about physical quantities like the stellar mass, star formation rates, and ages for distant galaxies is contained in their spectral energy distributions (SEDs), obtained through photometric surveys like SDSS, CANDELS, LSST etc. However, noise in the photometric observations often is a problem, and using naive machine learning methods to estimate physical quantities can result in overfitting the noise, or converging on solutions that lie outside the physical regime of parameter space.We use Gaussian Process regression trained on a sample of SEDs corresponding to galaxies from a Semi-Analytic model (Somerville+15a) to estimate their stellar masses, and compare its performance to a variety of different methods, including simple linear regression, Random Forests, and k-Nearest Neighbours. We find that the Gaussian Process method is robust to noise and predicts not only stellar masses but also their uncertainties. The method is also robust in the cases where the distribution of the training data is not identical to the target data, which can be extremely useful when generalized to more subtle galaxy properties.

Critical Parameters of the Initiation Zone for Spontaneous Dynamic Rupture Propagation

NASA Astrophysics Data System (ADS)

Galis, M.; Pelties, C.; Kristek, J.; Moczo, P.; Ampuero, J. P.; Mai, P. M.

2014-12-01

Numerical simulations of rupture propagation are used to study both earthquake source physics and earthquake ground motion. Under linear slip-weakening friction, artificial procedures are needed to initiate a self-sustained rupture. The concept of an overstressed asperity is often applied, in which the asperity is characterized by its size, shape and overstress. The physical properties of the initiation zone may have significant impact on the resulting dynamic rupture propagation. A trial-and-error approach is often necessary for successful initiation because 2D and 3D theoretical criteria for estimating the critical size of the initiation zone do not provide general rules for designing 3D numerical simulations. Therefore, it is desirable to define guidelines for efficient initiation with minimal artificial effects on rupture propagation. We perform an extensive parameter study using numerical simulations of 3D dynamic rupture propagation assuming a planar fault to examine the critical size of square, circular and elliptical initiation zones as a function of asperity overstress and background stress. For a fixed overstress, we discover that the area of the initiation zone is more important for the nucleation process than its shape. Comparing our numerical results with published theoretical estimates, we find that the estimates by Uenishi & Rice (2004) are applicable to configurations with low background stress and small overstress. None of the published estimates are consistent with numerical results for configurations with high background stress. We therefore derive new equations to estimate the initiation zone size in environments with high background stress. Our results provide guidelines for defining the size of the initiation zone and overstress with minimal effects on the subsequent spontaneous rupture propagation.

Improving inferences from fisheries capture-recapture studies through remote detection of PIT tags

USGS Publications Warehouse

Hewitt, David A.; Janney, Eric C.; Hayes, Brian S.; Shively, Rip S.

2010-01-01

Models for capture-recapture data are commonly used in analyses of the dynamics of fish and wildlife populations, especially for estimating vital parameters such as survival. Capture-recapture methods provide more reliable inferences than other methods commonly used in fisheries studies. However, for rare or elusive fish species, parameter estimation is often hampered by small probabilities of re-encountering tagged fish when encounters are obtained through traditional sampling methods. We present a case study that demonstrates how remote antennas for passive integrated transponder (PIT) tags can increase encounter probabilities and the precision of survival estimates from capture-recapture models. Between 1999 and 2007, trammel nets were used to capture and tag over 8,400 endangered adult Lost River suckers (Deltistes luxatus) during the spawning season in Upper Klamath Lake, Oregon. Despite intensive sampling at relatively discrete spawning areas, encounter probabilities from Cormack-Jolly-Seber models were consistently low (< 0.2) and the precision of apparent annual survival estimates was poor. Beginning in 2005, remote PIT tag antennas were deployed at known spawning locations to increase the probability of re-encountering tagged fish. We compare results based only on physical recaptures with results based on both physical recaptures and remote detections to demonstrate the substantial improvement in estimates of encounter probabilities (approaching 100%) and apparent annual survival provided by the remote detections. The richer encounter histories provided robust inferences about the dynamics of annual survival and have made it possible to explore more realistic models and hypotheses about factors affecting the conservation and recovery of this endangered species. Recent advances in technology related to PIT tags have paved the way for creative implementation of large-scale tagging studies in systems where they were previously considered impracticable.

Phobos laser ranging: Numerical Geodesy experiments for Martian system science

NASA Astrophysics Data System (ADS)

Dirkx, D.; Vermeersen, L. L. A.; Noomen, R.; Visser, P. N. A. M.

2014-09-01

Laser ranging is emerging as a technology for use over (inter)planetary distances, having the advantage of high (mm-cm) precision and accuracy and low mass and power consumption. We have performed numerical simulations to assess the science return in terms of geodetic observables of a hypothetical Phobos lander performing active two-way laser ranging with Earth-based stations. We focus our analysis on the estimation of Phobos and Mars gravitational, tidal and rotational parameters. We explicitly include systematic error sources in addition to uncorrelated random observation errors. This is achieved through the use of consider covariance parameters, specifically the ground station position and observation biases. Uncertainties for the consider parameters are set at 5 mm and at 1 mm for the Gaussian uncorrelated observation noise (for an observation integration time of 60 s). We perform the analysis for a mission duration up to 5 years. It is shown that a Phobos Laser Ranging (PLR) can contribute to a better understanding of the Martian system, opening the possibility for improved determination of a variety of physical parameters of Mars and Phobos. The simulations show that the mission concept is especially suited for estimating Mars tidal deformation parameters, estimating degree 2 Love numbers with absolute uncertainties at the 10-2 to 10-4 level after 1 and 4 years, respectively and providing separate estimates for the Martian quality factors at Sun and Phobos-forced frequencies. The estimation of Phobos libration amplitudes and gravity field coefficients provides an estimate of Phobos' relative equatorial and polar moments of inertia with an absolute uncertainty of 10-4 and 10-7, respectively, after 1 year. The observation of Phobos tidal deformation will be able to differentiate between a rubble pile and monolithic interior within 2 years. For all parameters, systematic errors have a much stronger influence (per unit uncertainty) than the uncorrelated Gaussian observation noise. This indicates the need for the inclusion of systematic errors in simulation studies and special attention to the mitigation of these errors in mission and system design.

Perm-Fit: a new program to estimate permeability at high P-T conditions

NASA Astrophysics Data System (ADS)

Moulas, Evangelos; Madonna, Claudio

2016-04-01

Several geological processes are controlled by porous fluid flow. The circulation of porous fluids influences many physical phenomena and in turn it depends on the rock permeability. The permeability of rocks is a physical property that needs to be measured since it depends on many factors such as secondary porosity (fractures etc). We present a numerical approach to estimate permeability using the transient step method (Brace et al., 1968). When a non-reacting, compressible fluid is considered in a relative incompressible solid matrix, the only unknown parameter in the equations of porous flow is permeability. Porosity is assumed to be known and the physical properties of the fluid (compressibility, density, viscosity) are taken from the NIST database. Forward numerical calculations for different values of permeability are used and the results are compared to experimental measurements. The extracted permeability value is the one that minimizes the misfit between experimental and numerical results. The uncertainty on the value of permeability is estimated using a Monte Carlo method. REFERENCES Brace, W.F., Walsh J.B., & Frangos, W.T. 1968: Permeability of Granite under High Pressure, Journal of Geophysical Research, 73, 6, 2225-2236

On the relation of earthquake stress drop and ground motion variability

NASA Astrophysics Data System (ADS)

Oth, Adrien; Miyake, Hiroe; Bindi, Dino

2017-07-01

One of the key parameters for earthquake source physics is stress drop since it can be directly linked to the spectral level of ground motion. Stress drop estimates from moment corner frequency analysis have been shown to be extremely variable, and this to a much larger degree than expected from the between-event ground motion variability. This discrepancy raises the question whether classically determined stress drop variability is too large, which would have significant consequences for seismic hazard analysis. We use a large high-quality data set from Japan with well-studied stress drop data to address this issue. Nonparametric and parametric reference ground motion models are derived, and the relation of between-event residuals for Japan Meteorological Agency equivalent seismic intensity and peak ground acceleration with stress drop is analyzed for crustal earthquakes. We find a clear correlation of the between-event residuals with stress drops estimates; however, while the island of Kyushu is characterized by substantially larger stress drops than Honshu, the between-event residuals do not reflect this observation, leading to the appearance of two event families with different stress drop levels yet similar range of between-event residuals. Both the within-family and between-family stress drop variations are larger than expected from the ground motion between-event variability. A systematic common analysis of these parameters holds the potential to provide important constraints on the relative robustness of different groups of data in the different parameter spaces and to improve our understanding on how much of the observed source parameter variability is likely to be true source physics variability.

Estimation of the solubility parameters of model plant surfaces and agrochemicals: a valuable tool for understanding plant surface interactions

PubMed Central

2012-01-01

Background Most aerial plant parts are covered with a hydrophobic lipid-rich cuticle, which is the interface between the plant organs and the surrounding environment. Plant surfaces may have a high degree of hydrophobicity because of the combined effects of surface chemistry and roughness. The physical and chemical complexity of the plant cuticle limits the development of models that explain its internal structure and interactions with surface-applied agrochemicals. In this article we introduce a thermodynamic method for estimating the solubilities of model plant surface constituents and relating them to the effects of agrochemicals. Results Following the van Krevelen and Hoftyzer method, we calculated the solubility parameters of three model plant species and eight compounds that differ in hydrophobicity and polarity. In addition, intact tissues were examined by scanning electron microscopy and the surface free energy, polarity, solubility parameter and work of adhesion of each were calculated from contact angle measurements of three liquids with different polarities. By comparing the affinities between plant surface constituents and agrochemicals derived from (a) theoretical calculations and (b) contact angle measurements we were able to distinguish the physical effect of surface roughness from the effect of the chemical nature of the epicuticular waxes. A solubility parameter model for plant surfaces is proposed on the basis of an increasing gradient from the cuticular surface towards the underlying cell wall. Conclusions The procedure enabled us to predict the interactions among agrochemicals, plant surfaces, and cuticular and cell wall components, and promises to be a useful tool for improving our understanding of biological surface interactions. PMID:23151272

Estimation of the solubility parameters of model plant surfaces and agrochemicals: a valuable tool for understanding plant surface interactions.

PubMed

Khayet, Mohamed; Fernández, Victoria

2012-11-14

Most aerial plant parts are covered with a hydrophobic lipid-rich cuticle, which is the interface between the plant organs and the surrounding environment. Plant surfaces may have a high degree of hydrophobicity because of the combined effects of surface chemistry and roughness. The physical and chemical complexity of the plant cuticle limits the development of models that explain its internal structure and interactions with surface-applied agrochemicals. In this article we introduce a thermodynamic method for estimating the solubilities of model plant surface constituents and relating them to the effects of agrochemicals. Following the van Krevelen and Hoftyzer method, we calculated the solubility parameters of three model plant species and eight compounds that differ in hydrophobicity and polarity. In addition, intact tissues were examined by scanning electron microscopy and the surface free energy, polarity, solubility parameter and work of adhesion of each were calculated from contact angle measurements of three liquids with different polarities. By comparing the affinities between plant surface constituents and agrochemicals derived from (a) theoretical calculations and (b) contact angle measurements we were able to distinguish the physical effect of surface roughness from the effect of the chemical nature of the epicuticular waxes. A solubility parameter model for plant surfaces is proposed on the basis of an increasing gradient from the cuticular surface towards the underlying cell wall. The procedure enabled us to predict the interactions among agrochemicals, plant surfaces, and cuticular and cell wall components, and promises to be a useful tool for improving our understanding of biological surface interactions.

Spectral Analysis and Experimental Modeling of Ice Accretion Roughness

NASA Technical Reports Server (NTRS)

Orr, D. J.; Breuer, K. S.; Torres, B. E.; Hansman, R. J., Jr.

1996-01-01

A self-consistent scheme for relating wind tunnel ice accretion roughness to the resulting enhancement of heat transfer is described. First, a spectral technique of quantitative analysis of early ice roughness images is reviewed. The image processing scheme uses a spectral estimation technique (SET) which extracts physically descriptive parameters by comparing scan lines from the experimentally-obtained accretion images to a prescribed test function. Analysis using this technique for both streamwise and spanwise directions of data from the NASA Lewis Icing Research Tunnel (IRT) are presented. An experimental technique is then presented for constructing physical roughness models suitable for wind tunnel testing that match the SET parameters extracted from the IRT images. The icing castings and modeled roughness are tested for enhancement of boundary layer heat transfer using infrared techniques in a "dry" wind tunnel.

Randic and Schultz molecular topological indices and their correlation with some X-ray absorption parameters

NASA Astrophysics Data System (ADS)

Khatri, Sunil; Kekre, Pravin A.; Mishra, Ashutosh

2016-10-01

The properties of a molecular system are affected by the topology of molecule. Therefore many studies have been made where the various physic-chemical properties are correlated with the topological indices. These studies have shown a very good correlation demonstrating the utility of the graph theoretical approach. It is, therefore, very natural to expect that the various physical properties obtained by the X-ray absorption spectra may also show correlation with the topological indices. Some complexes were used to establish correlation between topological indices and some X-ray absorption parameters like chemical shift. The chemical shift is on the higher energy side of the metal edge in these complexes. The result obtained in these studies shows that the topological indices of organic molecule acting as a legands can be used for estimating edge shift theoretically.

Physical retrieval of precipitation water contents from Special Sensor Microwave/Imager (SSM/I) data. Part 2: Retrieval method and applications (report version)

NASA Technical Reports Server (NTRS)

Olson, William S.

1990-01-01

A physical retrieval method for estimating precipitating water distributions and other geophysical parameters based upon measurements from the DMSP-F8 SSM/I is developed. Three unique features of the retrieval method are (1) sensor antenna patterns are explicitly included to accommodate varying channel resolution; (2) precipitation-brightness temperature relationships are quantified using the cloud ensemble/radiative parameterization; and (3) spatial constraints are imposed for certain background parameters, such as humidity, which vary more slowly in the horizontal than the cloud and precipitation water contents. The general framework of the method will facilitate the incorporation of measurements from the SSMJT, SSM/T-2 and geostationary infrared measurements, as well as information from conventional sources (e.g., radiosondes) or numerical forecast model fields.

Modeling Verwey transition temperature of Fe3O4 nanocrystals

NASA Astrophysics Data System (ADS)

Jiang, Xiao bao; Xiao, Bei bei; Yang, Hong yu; Gu, Xiao yan; Sheng, Hong chao; Zhang, Xing hua

2016-11-01

The Verwey transition in nanoscale is an important physical property for Fe3O4 nanocrystals and has attracted extensive attention in recent years. In this work, an analytic thermodynamic model without any adjusting parameters is developed to estimate the size and shape effects on modulating the Verwey transition temperature of Fe3O4 nanocrystals. The results show that the Verwey transition temperature reduces with increasing shape parameter λ or decreasing size D. A good agreement between the prediction and the experimental data verified our physical insight that the Verwey transition of Fe3O4 can be directly related to the atomic thermal vibration. The results presented in this work will be of benefit to the understanding of the microscopic mechanism of the Verwey transition and the design of future generation switching and memory devices.

iSEDfit: Bayesian spectral energy distribution modeling of galaxies

NASA Astrophysics Data System (ADS)

Moustakas, John

2017-08-01

iSEDfit uses Bayesian inference to extract the physical properties of galaxies from their observed broadband photometric spectral energy distribution (SED). In its default mode, the inputs to iSEDfit are the measured photometry (fluxes and corresponding inverse variances) and a measurement of the galaxy redshift. Alternatively, iSEDfit can be used to estimate photometric redshifts from the input photometry alone. After the priors have been specified, iSEDfit calculates the marginalized posterior probability distributions for the physical parameters of interest, including the stellar mass, star-formation rate, dust content, star formation history, and stellar metallicity. iSEDfit also optionally computes K-corrections and produces multiple "quality assurance" (QA) plots at each stage of the modeling procedure to aid in the interpretation of the prior parameter choices and subsequent fitting results. The software is distributed as part of the impro IDL suite.

Goddard Laboratory for Atmospheric Sciences physical retrieval system for remote determination of weather and climate parameter from HIRS2 and MSU observations

NASA Technical Reports Server (NTRS)

Susskind, J.

1984-01-01

At the Goddard Laboratory for Atmospheric Sciences (GLAS) a physically based satellite temperature sounding retrieval system, involving the simultaneous analysis of HIRS2 and MSU sounding data, was developed for determining atmospheric and surface conditions which are consistent with the observed radiances. In addition to determining accurate atmospheric temperature profiles even in the presence of cloud contamination, the system provides global estimates of day and night sea or land surface temperatures, snow and ice cover, and parameters related to cloud cover. Details of the system are described elsewhere. A brief overview of the system is presented, as well as recent improvements and previously unpublished results, relating to the sea-surface intercomparison workshop, the diurnal variation of ground temperatures, and forecast impact tests.

Evaluation of the Patient-Reported Outcomes Information System (PROMIS(®)) Spanish-language physical functioning items.

PubMed

Paz, Sylvia H; Spritzer, Karen L; Morales, Leo S; Hays, Ron D

2013-09-01

To evaluate the equivalence of the PROMIS(®) physical functioning item bank by language of administration (English versus Spanish). The PROMIS(®) wave 1 English-language physical functioning bank consists of 124 items, and 114 of these were translated into Spanish. Item frequencies, means and standard deviations, item-scale correlations, and internal consistency reliability were calculated. The IRT assumption of unidimensionality was evaluated by fitting a single-factor confirmatory factor analytic model. IRT threshold and discrimination parameters were estimated using Samejima's Graded Response Model. DIF by language of administration was evaluated. Item means ranged from 2.53 (SD = 1.36) to 4.62 (SD = 0.82). Coefficient alpha was 0.99, and item-rest correlations ranged from 0.41 to 0.89. A one-factor model fits the data well (CFI = 0.971, TLI = 0.970, and RMSEA = 0.052). The slope parameters ranged from 0.45 ("Are you able to run 10 miles?") to 4.50 ("Are you able to put on a shirt or blouse?"). The threshold parameters ranged from -1.92 ("How much do physical health problems now limit your usual physical activities (such as walking or climbing stairs)?") to 6.06 ("Are you able to run 10 miles?"). Fifty of the 114 items were flagged for DIF based on an R(2) of 0.02 or above criterion. The expected total score was higher for Spanish- than English-language respondents. English- and Spanish-speaking subjects with the same level of underlying physical function responded differently to 50 of 114 items. This study has important implications in the study of physical functioning among diverse populations.

Simultaneous inversion of multiple land surface parameters from MODIS optical-thermal observations

NASA Astrophysics Data System (ADS)

Ma, Han; Liang, Shunlin; Xiao, Zhiqiang; Shi, Hanyu

2017-06-01

Land surface parameters from remote sensing observations are critical in monitoring and modeling of global climate change and biogeochemical cycles. Current methods for estimating land surface variables usually focus on individual parameters separately even from the same satellite observations, resulting in inconsistent products. Moreover, no efforts have been made to generate global products from integrated observations from the optical to Thermal InfraRed (TIR) spectrum. Particularly, Middle InfraRed (MIR) observations have received little attention due to the complexity of the radiometric signal, which contains both reflected and emitted radiation. In this paper, we propose a unified algorithm for simultaneously retrieving six land surface parameters - Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), land surface albedo, Land Surface Emissivity (LSE), Land Surface Temperature (LST), and Upwelling Longwave radiation (LWUP) by exploiting MODIS visible-to-TIR observations. We incorporate a unified physical radiative transfer model into a data assimilation framework. The MODIS visible-to-TIR time series datasets include the daily surface reflectance product and MIR-to-TIR surface radiance, which are atmospherically corrected from the MODIS data using the Moderate Resolution Transmittance program (MODTRAN, ver. 5.0). LAI was first estimated using a data assimilation method that combines MODIS daily reflectance data and a LAI phenology model, and then the LAI was input to the unified radiative transfer model to simulate spectral surface reflectance and surface emissivity for calculating surface broadband albedo and emissivity, and FAPAR. LST was estimated from the MIR-TIR surface radiance data and the simulated emissivity, using an iterative optimization procedure. Lastly, LWUP was estimated using the LST and surface emissivity. The retrieved six parameters were extensively validated across six representative sites with different biome types, and compared with MODIS, GLASS, and GlobAlbedo land surface products. The results demonstrate that the unified inversion algorithm can retrieve temporally complete and physically consistent land surface parameters, and provides more accurate estimates of surface albedo, LST, and LWUP than existing products, with R2 values of 0.93 and 0.62, RMSE of 0.029 and 0.037, and BIAS values of 0.016 and 0.012 for the retrieved and MODIS albedo products, respectively, compared with field albedo measurements; R2 values of 0.95 and 0.93, RMSE of 2.7 and 4.2 K, and BIAS values of -0.6 and -2.7 K for the retrieved and MODIS LST products, respectively, compared with field LST measurements; and R2 values of 0.93 and 0.94, RMSE of 18.2 and 22.8 W/m2, and BIAS values of -2.7 and -14.6 W/m2 for the retrieved and MODIS LWUP products, respectively, compared with field LWUP measurements.

Prediction of changes in important physical parameters during composting of separated animal slurry solid fractions.

PubMed

Chowdhury, Md Albarune; de Neergaard, Andreas; Jensen, Lars Stoumann

2014-01-01

Solid-liquid separation of animal slurry, with solid fractions used for composting, has gained interest recently. However, efficient composting of separated animal slurry solid fractions (SSFs) requires a better understanding of the process dynamics in terms of important physical parameters and their interacting physical relationships in the composting matrix. Here we monitored moisture content, bulk density, particle density and air-filled porosity (AFP) during composting of SSF collected from four commercially available solid-liquid separators. Composting was performed in laboratory-scale reactors for 30 days (d) under forced aeration and measurements were conducted on the solid samples at the beginning of composting and at 10-d intervals during composting. The results suggest that differences in initial physical properties of SSF influence the development of compost maximum temperatures (40-70 degreeC). Depending on SSF, total wet mass and volume losses (expressed as % of initial value) were up to 37% and 34%, respectively. After 30 d of composting, relative losses of total solids varied from 17.9% to 21.7% and of volatile solids (VS) from 21.3% to 27.5%, depending on SSF. VS losses in all composts showed different dynamics as described by the first-order kinetic equation. The estimated component particle density of 1441 kg m-3 for VS and 2625 kg m-3 for fixed solids can be used to improve estimates of AFP for SSF within the range tested. The linear relationship between wet bulk density and AFP reported by previous researchers held true for SSF.

Application of the Junge- and Pankow-equation for estimating indoor gas/particle distribution and exposure to SVOCs

NASA Astrophysics Data System (ADS)

Salthammer, Tunga; Schripp, Tobias

2015-04-01

In the indoor environment, distribution and dynamics of an organic compound between gas phase, particle phase and settled dust must be known for estimating human exposure. This, however, requires a detailed understanding of the environmentally important compound parameters, their interrelation and of the algorithms for calculating partitioning coefficients. The parameters of major concern are: (I) saturation vapor pressure (PS) (of the subcooled liquid); (II) Henry's law constant (H); (III) octanol/water partition coefficient (KOW); (IV) octanol/air partition coefficient (KOA); (V) air/water partition coefficient (KAW) and (VI) settled dust properties like density and organic content. For most of the relevant compounds reliable experimental data are not available and calculated gas/particle distributions can widely differ due to the uncertainty in predicted Ps and KOA values. This is not a big problem if the target compound is of low (<10-6 Pa) or high (>10-2 Pa) volatility, but in the intermediate region even small changes in Ps or KOA will have a strong impact on the result. Moreover, the related physical processes might bear large uncertainties. The KOA value can only be used for particle absorption from the gas phase if the organic portion of the particle or dust is high. The Junge- and Pankow-equation for calculating the gas/particle distribution coefficient KP do not consider the physical and chemical properties of the particle surface area. It is demonstrated by error propagation theory and Monte-Carlo simulations that parameter uncertainties from estimation methods for molecular properties and variations of indoor conditions might strongly influence the calculated distribution behavior of compounds in the indoor environment.

Genetic variability in krill.

PubMed Central

Valentine, J W; Ayala, F J

1976-01-01

We have estimated genetic variability by gel electrophoresis in three species of krill, genus Euphausia (Arthropoda: Crustacea). Genetic variability is low where trophic resources are most seasonal, and high where trophic resources are most stable. Simlar trends have been found in benthic marine invertebrates. The observed trends of genetic variability do not correlate with trends in the stability of physical environment parameters. Images PMID:1061166

Wave-Current Conditions and Navigation Safety at an Inlet Entrance

DTIC Science & Technology

2015-06-26

effects of physical processes. Wave simulations with refraction, shoaling, and breaking provide estimates of wave-related parameters of interest to...summer and winter months and to better understand the cause- effect relationship between navigability conditions at Tillamook Inlet and characteristics of...the Coriolis force, wind stress, wave stress, bottom stress, vegetation flow drag, bottom friction, wave roller, and turbulent diffusion. Governing

The Effect of Petrographic Characteristics on Engineering Properties of Conglomerates from Famenin Region, Northeast of Hamedan, Iran

NASA Astrophysics Data System (ADS)

Khanlari, G. R.; Heidari, M.; Noori, M.; Momeni, A.

2016-07-01

To assess relationship between engineering characteristics and petrographic features, conglomerates samples related to Qom formation from Famenin region in northeast of Hamedan province were studied. Samples were tested in laboratory to determine the uniaxial compressive strength, point load strength index, modulus of elasticity, porosity, dry and saturation densities. For determining petrographic features, textural and mineralogical parameters, thin sections of the samples were prepared and studied. The results show that the effect of textural characteristics on the engineering properties of conglomerates supposed to be more important than mineralogical composition. It also was concluded that the packing proximity, packing density, grain shape and mean grain size, cement and matrix frequency are as textural features that have a significant effect on the physical and mechanical properties of the studied conglomerates. In this study, predictive statistical relationships were developed to estimate the physical and mechanical properties of the rocks based on the results of petrographic features. Furthermore, multivariate linear regression was used in four different steps comprising various combinations of petrographical characteristics for each engineering parameters. Finally, the best equations with specific arrangement were suggested to estimate engineering properties of the Qom formation conglomerates.

Anomalous solute transport in saturated porous media: Relating transport model parameters to electrical and nuclear magnetic resonance properties

USGS Publications Warehouse

Swanson, Ryan D; Binley, Andrew; Keating, Kristina; France, Samantha; Osterman, Gordon; Day-Lewis, Frederick D.; Singha, Kamini

2015-01-01

The advection-dispersion equation (ADE) fails to describe commonly observed non-Fickian solute transport in saturated porous media, necessitating the use of other models such as the dual-domain mass-transfer (DDMT) model. DDMT model parameters are commonly calibrated via curve fitting, providing little insight into the relation between effective parameters and physical properties of the medium. There is a clear need for material characterization techniques that can provide insight into the geometry and connectedness of pore spaces related to transport model parameters. Here, we consider proton nuclear magnetic resonance (NMR), direct-current (DC) resistivity, and complex conductivity (CC) measurements for this purpose, and assess these methods using glass beads as a control and two different samples of the zeolite clinoptilolite, a material that demonstrates non-Fickian transport due to intragranular porosity. We estimate DDMT parameters via calibration of a transport model to column-scale solute tracer tests, and compare NMR, DC resistivity, CC results, which reveal that grain size alone does not control transport properties and measured geophysical parameters; rather, volume and arrangement of the pore space play important roles. NMR cannot provide estimates of more-mobile and less-mobile pore volumes in the absence of tracer tests because these estimates depend critically on the selection of a material-dependent and flow-dependent cutoff time. Increased electrical connectedness from DC resistivity measurements are associated with greater mobile pore space determined from transport model calibration. CC was hypothesized to be related to length scales of mass transfer, but the CC response is unrelated to DDMT.

Elastic full-waveform inversion and parameterization analysis applied to walk-away vertical seismic profile data for unconventional (heavy oil) reservoir characterization

NASA Astrophysics Data System (ADS)

Pan, Wenyong; Innanen, Kristopher A.; Geng, Yu

2018-03-01

Seismic full-waveform inversion (FWI) methods hold strong potential to recover multiple subsurface elastic properties for hydrocarbon reservoir characterization. Simultaneously updating multiple physical parameters introduces the problem of interparameter tradeoff, arising from the covariance between different physical parameters, which increases nonlinearity and uncertainty of multiparameter FWI. The coupling effects of different physical parameters are significantly influenced by model parameterization and acquisition arrangement. An appropriate choice of model parameterization is critical to successful field data applications of multiparameter FWI. The objective of this paper is to examine the performance of various model parameterizations in isotropic-elastic FWI with walk-away vertical seismic profile (W-VSP) dataset for unconventional heavy oil reservoir characterization. Six model parameterizations are considered: velocity-density (α, β and ρ΄), modulus-density (κ, μ and ρ), Lamé-density (λ, μ΄ and ρ‴), impedance-density (IP, IS and ρ″), velocity-impedance-I (α΄, β΄ and I_P^'), and velocity-impedance-II (α″, β″ and I_S^'). We begin analyzing the interparameter tradeoff by making use of scattering radiation patterns, which is a common strategy for qualitative parameter resolution analysis. In this paper, we discuss the advantages and limitations of the scattering radiation patterns and recommend that interparameter tradeoffs be evaluated using interparameter contamination kernels, which provide quantitative, second-order measurements of the interparameter contaminations and can be constructed efficiently with an adjoint-state approach. Synthetic W-VSP isotropic-elastic FWI experiments in the time domain verify our conclusions about interparameter tradeoffs for various model parameterizations. Density profiles are most strongly influenced by the interparameter contaminations; depending on model parameterization, the inverted density profile can be over-estimated, under-estimated or spatially distorted. Among the six cases, only the velocity-density parameterization provides stable and informative density features not included in the starting model. Field data applications of multicomponent W-VSP isotropic-elastic FWI in the time domain were also carried out. The heavy oil reservoir target zone, characterized by low α-to-β ratios and low Poisson's ratios, can be identified clearly with the inverted isotropic-elastic parameters.

Elastic full-waveform inversion and parameterization analysis applied to walk-away vertical seismic profile data for unconventional (heavy oil) reservoir characterization

DOE PAGES

Pan, Wenyong; Innanen, Kristopher A.; Geng, Yu

2018-03-06

We report seismic full-waveform inversion (FWI) methods hold strong potential to recover multiple subsurface elastic properties for hydrocarbon reservoir characterization. Simultaneously updating multiple physical parameters introduces the problem of interparameter tradeoff, arising from the covariance between different physical parameters, which increases nonlinearity and uncertainty of multiparameter FWI. The coupling effects of different physical parameters are significantly influenced by model parameterization and acquisition arrangement. An appropriate choice of model parameterization is critical to successful field data applications of multiparameter FWI. The objective of this paper is to examine the performance of various model parameterizations in isotropic-elastic FWI with walk-away vertical seismicmore » profile (W-VSP) dataset for unconventional heavy oil reservoir characterization. Six model parameterizations are considered: velocity-density (α, β and ρ'), modulus-density (κ, μ and ρ), Lamé-density (λ, μ' and ρ'''), impedance-density (IP, IS and ρ''), velocity-impedance-I (α', β' and I' P), and velocity-impedance-II (α'', β'' and I'S). We begin analyzing the interparameter tradeoff by making use of scattering radiation patterns, which is a common strategy for qualitative parameter resolution analysis. In this paper, we discuss the advantages and limitations of the scattering radiation patterns and recommend that interparameter tradeoffs be evaluated using interparameter contamination kernels, which provide quantitative, second-order measurements of the interparameter contaminations and can be constructed efficiently with an adjoint-state approach. Synthetic W-VSP isotropic-elastic FWI experiments in the time domain verify our conclusions about interparameter tradeoffs for various model parameterizations. Density profiles are most strongly influenced by the interparameter contaminations; depending on model parameterization, the inverted density profile can be over-estimated, under-estimated or spatially distorted. Among the six cases, only the velocity-density parameterization provides stable and informative density features not included in the starting model. Field data applications of multicomponent W-VSP isotropic-elastic FWI in the time domain were also carried out. Finally, the heavy oil reservoir target zone, characterized by low α-to-β ratios and low Poisson’s ratios, can be identified clearly with the inverted isotropic-elastic parameters.« less

Elastic full-waveform inversion and parameterization analysis applied to walk-away vertical seismic profile data for unconventional (heavy oil) reservoir characterization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pan, Wenyong; Innanen, Kristopher A.; Geng, Yu

We report seismic full-waveform inversion (FWI) methods hold strong potential to recover multiple subsurface elastic properties for hydrocarbon reservoir characterization. Simultaneously updating multiple physical parameters introduces the problem of interparameter tradeoff, arising from the covariance between different physical parameters, which increases nonlinearity and uncertainty of multiparameter FWI. The coupling effects of different physical parameters are significantly influenced by model parameterization and acquisition arrangement. An appropriate choice of model parameterization is critical to successful field data applications of multiparameter FWI. The objective of this paper is to examine the performance of various model parameterizations in isotropic-elastic FWI with walk-away vertical seismicmore » profile (W-VSP) dataset for unconventional heavy oil reservoir characterization. Six model parameterizations are considered: velocity-density (α, β and ρ'), modulus-density (κ, μ and ρ), Lamé-density (λ, μ' and ρ'''), impedance-density (IP, IS and ρ''), velocity-impedance-I (α', β' and I' P), and velocity-impedance-II (α'', β'' and I'S). We begin analyzing the interparameter tradeoff by making use of scattering radiation patterns, which is a common strategy for qualitative parameter resolution analysis. In this paper, we discuss the advantages and limitations of the scattering radiation patterns and recommend that interparameter tradeoffs be evaluated using interparameter contamination kernels, which provide quantitative, second-order measurements of the interparameter contaminations and can be constructed efficiently with an adjoint-state approach. Synthetic W-VSP isotropic-elastic FWI experiments in the time domain verify our conclusions about interparameter tradeoffs for various model parameterizations. Density profiles are most strongly influenced by the interparameter contaminations; depending on model parameterization, the inverted density profile can be over-estimated, under-estimated or spatially distorted. Among the six cases, only the velocity-density parameterization provides stable and informative density features not included in the starting model. Field data applications of multicomponent W-VSP isotropic-elastic FWI in the time domain were also carried out. Finally, the heavy oil reservoir target zone, characterized by low α-to-β ratios and low Poisson’s ratios, can be identified clearly with the inverted isotropic-elastic parameters.« less

Quasar microlensing models with constraints on the Quasar light curves

NASA Astrophysics Data System (ADS)

Tie, S. S.; Kochanek, C. S.

2018-01-01

Quasar microlensing analyses implicitly generate a model of the variability of the source quasar. The implied source variability may be unrealistic yet its likelihood is generally not evaluated. We used the damped random walk (DRW) model for quasar variability to evaluate the likelihood of the source variability and applied the revized algorithm to a microlensing analysis of the lensed quasar RX J1131-1231. We compared estimates of the size of the quasar disc and the average stellar mass of the lens galaxy with and without applying the DRW likelihoods for the source variability model and found no significant effect on the estimated physical parameters. The most likely explanation is that unreliastic source light-curve models are generally associated with poor microlensing fits that already make a negligible contribution to the probability distributions of the derived parameters.

Sensorless Estimation and Nonlinear Control of a Rotational Energy Harvester

NASA Astrophysics Data System (ADS)

Nunna, Kameswarie; Toh, Tzern T.; Mitcheson, Paul D.; Astolfi, Alessandro

2013-12-01

It is important to perform sensorless monitoring of parameters in energy harvesting devices in order to determine the operating states of the system. However, physical measurements of these parameters is often a challenging task due to the unavailability of access points. This paper presents, as an example application, the design of a nonlinear observer and a nonlinear feedback controller for a rotational energy harvester. A dynamic model of a rotational energy harvester with its power electronic interface is derived and validated. This model is then used to design a nonlinear observer and a nonlinear feedback controller which yield a sensorless closed-loop system. The observer estimates the mechancial quantities from the measured electrical quantities while the control law sustains power generation across a range of source rotation speeds. The proposed scheme is assessed through simulations and experiments.

Evaluating CONUS-Scale Runoff Simulation across the National Water Model WRF-Hydro Implementation to Disentangle Regional Controls on Streamflow Generation and Model Error Contribution

NASA Astrophysics Data System (ADS)

Dugger, A. L.; Rafieeinasab, A.; Gochis, D.; Yu, W.; McCreight, J. L.; Karsten, L. R.; Pan, L.; Zhang, Y.; Sampson, K. M.; Cosgrove, B.

2016-12-01

Evaluation of physically-based hydrologic models applied across large regions can provide insight into dominant controls on runoff generation and how these controls vary based on climatic, biological, and geophysical setting. To make this leap, however, we need to combine knowledge of regional forcing skill, model parameter and physics assumptions, and hydrologic theory. If we can successfully do this, we also gain information on how well our current approximations of these dominant physical processes are represented in continental-scale models. In this study, we apply this diagnostic approach to a 5-year retrospective implementation of the WRF-Hydro community model configured for the U.S. National Weather Service's National Water Model (NWM). The NWM is a water prediction model in operations over the contiguous U.S. as of summer 2016, providing real-time estimates and forecasts out to 30 days of streamflow across 2.7 million stream reaches as well as distributed snowpack, soil moisture, and evapotranspiration at 1-km resolution. The WRF-Hydro system permits not only the standard simulation of vertical energy and water fluxes common in continental-scale models, but augments these processes with lateral redistribution of surface and subsurface water, simple groundwater dynamics, and channel routing. We evaluate 5 years of NLDAS-2 precipitation forcing and WRF-Hydro streamflow and evapotranspiration simulation across the contiguous U.S. at a range of spatial (gage, basin, ecoregion) and temporal (hourly, daily, monthly) scales and look for consistencies and inconsistencies in performance in terms of bias, timing, and extremes. Leveraging results from other CONUS-scale hydrologic evaluation studies, we translate our performance metrics into a matrix of likely dominant process controls and error sources (forcings, parameter estimates, and model physics). We test our hypotheses in a series of controlled model experiments on a subset of representative basins from distinct "problem" environments (Southeast U.S. Coastal Plain, Central and Coastal Texas, Northern Plains, and Arid Southwest). The results from these longer-term model diagnostics will inform future improvements in forcing bias correction, parameter calibration, and physics developments in the National Water Model.

A New Insight into the Earthquake Recurrence Studies from the Three-parameter Generalized Exponential Distributions

NASA Astrophysics Data System (ADS)

Pasari, S.; Kundu, D.; Dikshit, O.

2012-12-01

Earthquake recurrence interval is one of the important ingredients towards probabilistic seismic hazard assessment (PSHA) for any location. Exponential, gamma, Weibull and lognormal distributions are quite established probability models in this recurrence interval estimation. However, they have certain shortcomings too. Thus, it is imperative to search for some alternative sophisticated distributions. In this paper, we introduce a three-parameter (location, scale and shape) exponentiated exponential distribution and investigate the scope of this distribution as an alternative of the afore-mentioned distributions in earthquake recurrence studies. This distribution is a particular member of the exponentiated Weibull distribution. Despite of its complicated form, it is widely accepted in medical and biological applications. Furthermore, it shares many physical properties with gamma and Weibull family. Unlike gamma distribution, the hazard function of generalized exponential distribution can be easily computed even if the shape parameter is not an integer. To contemplate the plausibility of this model, a complete and homogeneous earthquake catalogue of 20 events (M ≥ 7.0) spanning for the period 1846 to 1995 from North-East Himalayan region (20-32 deg N and 87-100 deg E) has been used. The model parameters are estimated using maximum likelihood estimator (MLE) and method of moment estimator (MOME). No geological or geophysical evidences have been considered in this calculation. The estimated conditional probability reaches quite high after about a decade for an elapsed time of 17 years (i.e. 2012). Moreover, this study shows that the generalized exponential distribution fits the above data events more closely compared to the conventional models and hence it is tentatively concluded that generalized exponential distribution can be effectively considered in earthquake recurrence studies.

Dynamic State Estimation of Terrestrial and Solar Plasmas

NASA Astrophysics Data System (ADS)

Kamalabadi, Farzad

A pervasive problem in virtually all branches of space science is the estimation of multi-dimensional state parameters of a dynamical system from a collection of indirect, often incomplete, and imprecise measurements. Subsequent scientific inference is predicated on rigorous analysis, interpretation, and understanding of physical observations and on the reliability of the associated quantitative statistical bounds and performance characteristics of the algorithms used. In this work, we focus on these dynamic state estimation problems and illustrate their importance in the context of two timely activities in space remote sensing. First, we discuss the estimation of multi-dimensional ionospheric state parameters from UV spectral imaging measurements anticipated to be acquired the recently selected NASA Heliophysics mission, Ionospheric Connection Explorer (ICON). Next, we illustrate that similar state-space formulations provide the means for the estimation of 3D, time-dependent densities and temperatures in the solar corona from a series of white-light and EUV measurements. We demonstrate that, while a general framework for the stochastic formulation of the state estimation problem is suited for systematic inference of the parameters of a hidden Markov process, several challenges must be addressed in the assimilation of an increasing volume and diversity of space observations. These challenges are: (1) the computational tractability when faced with voluminous and multimodal data, (2) the inherent limitations of the underlying models which assume, often incorrectly, linear dynamics and Gaussian noise, and (3) the unavailability or inaccuracy of transition probabilities and noise statistics. We argue that pursuing answers to these questions necessitates cross-disciplinary research that enables progress toward systematically reconciling observational and theoretical understanding of the space environment.

Applying petrophysical models to radar travel time and electrical resistivity tomograms: Resolution-dependent limitations

USGS Publications Warehouse

Day-Lewis, F. D.; Singha, K.; Binley, A.M.

2005-01-01

Geophysical imaging has traditionally provided qualitative information about geologic structure; however, there is increasing interest in using petrophysical models to convert tomograms to quantitative estimates of hydrogeologic, mechanical, or geochemical parameters of interest (e.g., permeability, porosity, water content, and salinity). Unfortunately, petrophysical estimation based on tomograms is complicated by limited and variable image resolution, which depends on (1) measurement physics (e.g., electrical conduction or electromagnetic wave propagation), (2) parameterization and regularization, (3) measurement error, and (4) spatial variability. We present a framework to predict how core-scale relations between geophysical properties and hydrologic parameters are altered by the inversion, which produces smoothly varying pixel-scale estimates. We refer to this loss of information as "correlation loss." Our approach upscales the core-scale relation to the pixel scale using the model resolution matrix from the inversion, random field averaging, and spatial statistics of the geophysical property. Synthetic examples evaluate the utility of radar travel time tomography (RTT) and electrical-resistivity tomography (ERT) for estimating water content. This work provides (1) a framework to assess tomograms for geologic parameter estimation and (2) insights into the different patterns of correlation loss for ERT and RTT. Whereas ERT generally performs better near boreholes, RTT performs better in the interwell region. Application of petrophysical models to the tomograms in our examples would yield misleading estimates of water content. Although the examples presented illustrate the problem of correlation loss in the context of near-surface geophysical imaging, our results have clear implications for quantitative analysis of tomograms for diverse geoscience applications. Copyright 2005 by the American Geophysical Union.

Application of Multi-task Sparse Lasso Feature Extraction and Support Vector Machine Regression in the Stellar Atmospheric Parameterization

NASA Astrophysics Data System (ADS)

Gao, Wei; Li, Xiang-ru

2017-07-01

The multi-task learning takes the multiple tasks together to make analysis and calculation, so as to dig out the correlations among them, and therefore to improve the accuracy of the analyzed results. This kind of methods have been widely applied to the machine learning, pattern recognition, computer vision, and other related fields. This paper investigates the application of multi-task learning in estimating the stellar atmospheric parameters, including the surface temperature (Teff), surface gravitational acceleration (lg g), and chemical abundance ([Fe/H]). Firstly, the spectral features of the three stellar atmospheric parameters are extracted by using the multi-task sparse group Lasso algorithm, then the support vector machine is used to estimate the atmospheric physical parameters. The proposed scheme is evaluated on both the Sloan stellar spectra and the theoretical spectra computed from the Kurucz's New Opacity Distribution Function (NEWODF) model. The mean absolute errors (MAEs) on the Sloan spectra are: 0.0064 for lg (Teff /K), 0.1622 for lg (g/(cm · s-2)), and 0.1221 dex for [Fe/H]; the MAEs on the synthetic spectra are 0.0006 for lg (Teff /K), 0.0098 for lg (g/(cm · s-2)), and 0.0082 dex for [Fe/H]. Experimental results show that the proposed scheme has a rather high accuracy for the estimation of stellar atmospheric parameters.

Empirical Scaling Laws of Rocket Exhaust Cratering

NASA Technical Reports Server (NTRS)

Donahue, Carly M.; Metzger, Philip T.; Immer, Christopher D.

2005-01-01

When launching or landing a space craft on the regolith of a terrestrial surface, special attention needs to be paid to the rocket exhaust cratering effects. If the effects are not controlled, the rocket cratering could damage the spacecraft or other surrounding hardware. The cratering effects of a rocket landing on a planet's surface are not understood well, especially for the lunar case with the plume expanding in vacuum. As a result, the blast effects cannot be estimated sufficiently using analytical theories. It is necessary to develop physics-based simulation tools in order to calculate mission-essential parameters. In this work we test out the scaling laws of the physics in regard to growth rate of the crater depth. This will provide the physical insight necessary to begin the physics-based modeling.

Detrended fluctuation analysis as a regression framework: Estimating dependence at different scales

NASA Astrophysics Data System (ADS)

Kristoufek, Ladislav

2015-02-01

We propose a framework combining detrended fluctuation analysis with standard regression methodology. The method is built on detrended variances and covariances and it is designed to estimate regression parameters at different scales and under potential nonstationarity and power-law correlations. The former feature allows for distinguishing between effects for a pair of variables from different temporal perspectives. The latter ones make the method a significant improvement over the standard least squares estimation. Theoretical claims are supported by Monte Carlo simulations. The method is then applied on selected examples from physics, finance, environmental science, and epidemiology. For most of the studied cases, the relationship between variables of interest varies strongly across scales.

Dynamic control of remelting processes

DOEpatents

Bertram, Lee A.; Williamson, Rodney L.; Melgaard, David K.; Beaman, Joseph J.; Evans, David G.

2000-01-01

An apparatus and method of controlling a remelting process by providing measured process variable values to a process controller; estimating process variable values using a process model of a remelting process; and outputting estimated process variable values from the process controller. Feedback and feedforward control devices receive the estimated process variable values and adjust inputs to the remelting process. Electrode weight, electrode mass, electrode gap, process current, process voltage, electrode position, electrode temperature, electrode thermal boundary layer thickness, electrode velocity, electrode acceleration, slag temperature, melting efficiency, cooling water temperature, cooling water flow rate, crucible temperature profile, slag skin temperature, and/or drip short events are employed, as are parameters representing physical constraints of electroslag remelting or vacuum arc remelting, as applicable.

Consequences of using different soil texture determination methodologies for soil physical quality and unsaturated zone time lag estimates.

PubMed

Fenton, O; Vero, S; Ibrahim, T G; Murphy, P N C; Sherriff, S C; Ó hUallacháin, D

2015-11-01

Elucidation of when the loss of pollutants, below the rooting zone in agricultural landscapes, affects water quality is important when assessing the efficacy of mitigation measures. Investigation of this inherent time lag (t(T)) is divided into unsaturated (t(u)) and saturated (t(s)) components. The duration of these components relative to each other differs depending on soil characteristics and the landscape position. The present field study focuses on tu estimation in a scenario where the saturated zone is likely to constitute a higher proportion of t(T). In such instances, or where only initial breakthrough (IBT) or centre of mass (COM) is of interest, utilisation of site and depth specific "simple" textural class or actual sand-silt-clay percentages to generate soil water characteristic curves with associated soil hydraulic parameters is acceptable. With the same data it is also possible to estimate a soil physical quality (S) parameter for each soil layer which can be used to infer many other physical, chemical and biological quality indicators. In this study, hand texturing in the field was used to determine textural classes of a soil profile. Laboratory methods, including hydrometer, pipette and laser diffraction methods were used to determine actual sand-silt-clay percentages of sections of the same soil profile. Results showed that in terms of S, hand texturing resulted in a lower index value (inferring a degraded soil) than that of pipette, hydrometer and laser equivalents. There was no difference between S index values determined using the pipette, hydrometer and laser diffraction methods. The difference between the three laboratory methods on both the IBT and COM stages of t(u) were negligible, and in this instance were unlikely to affect either groundwater monitoring decisions, or to be of consequence from a policy perspective. When t(u) estimates are made over the full depth of the vadose zone, which may extend to several metres, errors resulting from the use of hydraulic parameters generated from hand texture data will be resultantly greater, and may lead to flawed predictions regarding the achievability of water policy targets. For this reason laboratory analysis, regardless of method, should be preferred to simple field assessments. Copyright © 2015 Elsevier B.V. All rights reserved.

Aggressive-antisocial boys develop into physically strong young men.

PubMed

Isen, Joshua D; McGue, Matthew K; Iacono, William G

2015-04-01

Young men with superior upper-body strength typically show a greater proclivity for physical aggression than their weaker male counterparts. The traditional interpretation of this phenomenon is that young men calibrate their attitudes and behaviors to their physical formidability. Physical strength is thus viewed as a causal antecedent of aggressive behavior. The present study is the first to examine this phenomenon within a developmental framework. We capitalized on the fact that physical strength is a male secondary sex characteristic. In two longitudinal cohorts of children, we estimated adolescent change in upper-body strength using the slope parameter from a latent growth model. We found that males' antisocial tendencies temporally precede their physical formidability. Boys, but not girls, with greater antisocial tendencies in childhood attained larger increases in physical strength between the ages of 11 and 17. These results support sexual selection theory, indicating an adaptive congruence between male-typical behavioral dispositions and subsequent physical masculinization during puberty. © The Author(s) 2015.

Physical assessment of coastal vulnerability under enhanced land subsidence in Semarang, Indonesia, using multi-sensor satellite data

NASA Astrophysics Data System (ADS)

Husnayaen; Rimba, A. Besse; Osawa, Takahiro; Parwata, I. Nyoman Sudi; As-syakur, Abd. Rahman; Kasim, Faizal; Astarini, Ida Ayu

2018-04-01

Research has been conducted in Semarang, Indonesia, to assess coastal vulnerability under enhanced land subsidence using multi-sensor satellite data, including the Advanced Land Observing Satellite (ALOS) Phased Array type L-band SAR (PALSAR), Landsat TM, IKONOS, and TOPEX/Poseidon. A coastal vulnerability index (CVI) was constructed to estimate the level of vulnerability of a coastline approximately 48.68 km in length using seven physical variables, namely, land subsidence, relative sea level change, coastal geomorphology, coastal slope, shoreline change, mean tidal range, and significant wave height. A comparison was also performed between a CVI calculated using seven parameters and a CVI using six parameters, the latter of which excludes the land subsidence parameter, to determine the effects of land subsidence during the coastal vulnerability assessment. This study showed that the accuracy of coastal vulnerability was increased 40% by adding the land subsidence factor (i.e., CVI 6 parameters = 53%, CVI 7 parameters = 93%). Moreover, Kappa coefficient indicated very good agreement (0.90) for CVI 7 parameters and fair agreement (0.3) for CVI 6 parameters. The results indicate that the area of very high vulnerability increased by 7% when land subsidence was added. Hence, using the CVI calculation including land subsidence parameters, the very high vulnerability area is determined to be 20% of the total coastline or 9.7 km of the total 48.7 km of coastline. This study proved that land subsidence has significant influence on coastal vulnerability in Semarang.

Significance of modeling internal damping in the control of structures

NASA Technical Reports Server (NTRS)

Banks, H. T.; Inman, D. J.

1992-01-01

Several simple systems are examined to illustrate the importance of the estimation of damping parameters in closed-loop system performance and stability. The negative effects of unmodeled damping are particularly pronounced in systems that do not use collocated sensors and actuators. An example is considered for which even the actuators (a tip jet nozzle and flexible hose) for a simple beam produce significant damping which, if ignored, results in a model that cannot yield a reasonable time response using physically meaningful parameter values. It is concluded that correct damping modeling is essential in structure control.

Investigation for improving Global Positioning System (GPS) orbits using a discrete sequential estimator and stochastic models of selected physical processes

NASA Technical Reports Server (NTRS)

Goad, Clyde C.; Chadwell, C. David

1993-01-01

GEODYNII is a conventional batch least-squares differential corrector computer program with deterministic models of the physical environment. Conventional algorithms were used to process differenced phase and pseudorange data to determine eight-day Global Positioning system (GPS) orbits with several meter accuracy. However, random physical processes drive the errors whose magnitudes prevent improving the GPS orbit accuracy. To improve the orbit accuracy, these random processes should be modeled stochastically. The conventional batch least-squares algorithm cannot accommodate stochastic models, only a stochastic estimation algorithm is suitable, such as a sequential filter/smoother. Also, GEODYNII cannot currently model the correlation among data values. Differenced pseudorange, and especially differenced phase, are precise data types that can be used to improve the GPS orbit precision. To overcome these limitations and improve the accuracy of GPS orbits computed using GEODYNII, we proposed to develop a sequential stochastic filter/smoother processor by using GEODYNII as a type of trajectory preprocessor. Our proposed processor is now completed. It contains a correlated double difference range processing capability, first order Gauss Markov models for the solar radiation pressure scale coefficient and y-bias acceleration, and a random walk model for the tropospheric refraction correction. The development approach was to interface the standard GEODYNII output files (measurement partials and variationals) with software modules containing the stochastic estimator, the stochastic models, and a double differenced phase range processing routine. Thus, no modifications to the original GEODYNII software were required. A schematic of the development is shown. The observational data are edited in the preprocessor and the data are passed to GEODYNII as one of its standard data types. A reference orbit is determined using GEODYNII as a batch least-squares processor and the GEODYNII measurement partial (FTN90) and variational (FTN80, V-matrix) files are generated. These two files along with a control statement file and a satellite identification and mass file are passed to the filter/smoother to estimate time-varying parameter states at each epoch, improved satellite initial elements, and improved estimates of constant parameters.

Adaptive optimal input design and parametric estimation of nonlinear dynamical systems: application to neuronal modeling.

PubMed

Madi, Mahmoud K; Karameh, Fadi N

2018-05-11

Many physical models of biological processes including neural systems are characterized by parametric nonlinear dynamical relations between driving inputs, internal states, and measured outputs of the process. Fitting such models using experimental data (data assimilation) is a challenging task since the physical process often operates in a noisy, possibly non-stationary environment; moreover, conducting multiple experiments under controlled and repeatable conditions can be impractical, time consuming or costly. The accuracy of model identification, therefore, is dictated principally by the quality and dynamic richness of collected data over single or few experimental sessions. Accordingly, it is highly desirable to design efficient experiments that, by exciting the physical process with smart inputs, yields fast convergence and increased accuracy of the model. We herein introduce an adaptive framework in which optimal input design is integrated with Square root Cubature Kalman Filters (OID-SCKF) to develop an online estimation procedure that first, converges significantly quicker, thereby permitting model fitting over shorter time windows, and second, enhances model accuracy when only few process outputs are accessible. The methodology is demonstrated on common nonlinear models and on a four-area neural mass model with noisy and limited measurements. Estimation quality (speed and accuracy) is benchmarked against high-performance SCKF-based methods that commonly employ dynamically rich informed inputs for accurate model identification. For all the tested models, simulated single-trial and ensemble averages showed that OID-SCKF exhibited (i) faster convergence of parameter estimates and (ii) lower dependence on inter-trial noise variability with gains up to around 1000 msec in speed and 81% increase in variability for the neural mass models. In terms of accuracy, OID-SCKF estimation was superior, and exhibited considerably less variability across experiments, in identifying model parameters of (a) systems with challenging model inversion dynamics and (b) systems with fewer measurable outputs that directly relate to the underlying processes. Fast and accurate identification therefore carries particular promise for modeling of transient (short-lived) neuronal network dynamics using a spatially under-sampled set of noisy measurements, as is commonly encountered in neural engineering applications. © 2018 IOP Publishing Ltd.

A consistent framework to predict mass fluxes and depletion times for DNAPL contaminations in heterogeneous aquifers under uncertainty

NASA Astrophysics Data System (ADS)

Koch, Jonas; Nowak, Wolfgang

2013-04-01

At many hazardous waste sites and accidental spills, dense non-aqueous phase liquids (DNAPLs) such as TCE, PCE, or TCA have been released into the subsurface. Once a DNAPL is released into the subsurface, it serves as persistent source of dissolved-phase contamination. In chronological order, the DNAPL migrates through the porous medium and penetrates the aquifer, it forms a complex pattern of immobile DNAPL saturation, it dissolves into the groundwater and forms a contaminant plume, and it slowly depletes and bio-degrades in the long-term. In industrial countries the number of such contaminated sites is tremendously high to the point that a ranking from most risky to least risky is advisable. Such a ranking helps to decide whether a site needs to be remediated or may be left to natural attenuation. Both the ranking and the designing of proper remediation or monitoring strategies require a good understanding of the relevant physical processes and their inherent uncertainty. To this end, we conceptualize a probabilistic simulation framework that estimates probability density functions of mass discharge, source depletion time, and critical concentration values at crucial target locations. Furthermore, it supports the inference of contaminant source architectures from arbitrary site data. As an essential novelty, the mutual dependencies of the key parameters and interacting physical processes are taken into account throughout the whole simulation. In an uncertain and heterogeneous subsurface setting, we identify three key parameter fields: the local velocities, the hydraulic permeabilities and the DNAPL phase saturations. Obviously, these parameters depend on each other during DNAPL infiltration, dissolution and depletion. In order to highlight the importance of these mutual dependencies and interactions, we present results of several model set ups where we vary the physical and stochastic dependencies of the input parameters and simulated processes. Under these changes, the probability density functions demonstrate strong statistical shifts in their expected values and in their uncertainty. Considering the uncertainties of all key parameters but neglecting their interactions overestimates the output uncertainty. However, consistently using all available physical knowledge when assigning input parameters and simulating all relevant interactions of the involved processes reduces the output uncertainty significantly back down to useful and plausible ranges. When using our framework in an inverse setting, omitting a parameter dependency within a crucial physical process would lead to physical meaningless identified parameters. Thus, we conclude that the additional complexity we propose is both necessary and adequate. Overall, our framework provides a tool for reliable and plausible prediction, risk assessment, and model based decision support for DNAPL contaminated sites.

Phenomenology of Ξb→Ξcτ ν decays

NASA Astrophysics Data System (ADS)

Dutta, Rupak

2018-04-01

Deviations from the standard model prediction have been reported in various semileptonic B decays mediated via b →c charged-current interactions. In this context, we analyze corresponding semileptonic baryon decays Ξb→Ξcτ ν using the helicity formalism. We report numerical results on various observables such as the decay rate, ratio of branching ratios, lepton-side forward-backward asymmetry, longitudinal polarization fraction of the charged lepton, and the convexity parameter for this decay mode using results from the relativistic quark model. We also provide an estimate of the new physics effect on these observables under various new physics scenarios.

Experimental Study on the Perception Characteristics of Haptic Texture by Multidimensional Scaling.

PubMed

Wu, Juan; Li, Na; Liu, Wei; Song, Guangming; Zhang, Jun

2015-01-01

Recent works regarding real texture perception demonstrate that physical factors such as stiffness and spatial period play a fundamental role in texture perception. This research used a multidimensional scaling (MDS) analysis to further characterize and quantify the effects of the simulation parameters on haptic texture rendering and perception. In a pilot experiment, 12 haptic texture samples were generated by using a 3-degrees-of-freedom (3-DOF) force-feedback device with varying spatial period, height, and stiffness coefficient parameter values. The subjects' perceptions of the virtual textures indicate that roughness, denseness, flatness and hardness are distinguishing characteristics of texture. In the main experiment, 19 participants rated the dissimilarities of the textures and estimated the magnitudes of their characteristics. The MDS method was used to recover the underlying perceptual space and reveal the significance of the space from the recorded data. The physical parameters and their combinations have significant effects on the perceptual characteristics. A regression model was used to quantitatively analyze the parameters and their effects on the perceptual characteristics. This paper is to illustrate that haptic texture perception based on force feedback can be modeled in two- or three-dimensional space and provide suggestions on improving perception-based haptic texture rendering.

Parameter estimation for lithium ion batteries

NASA Astrophysics Data System (ADS)

Santhanagopalan, Shriram

With an increase in the demand for lithium based batteries at the rate of about 7% per year, the amount of effort put into improving the performance of these batteries from both experimental and theoretical perspectives is increasing. There exist a number of mathematical models ranging from simple empirical models to complicated physics-based models to describe the processes leading to failure of these cells. The literature is also rife with experimental studies that characterize the various properties of the system in an attempt to improve the performance of lithium ion cells. However, very little has been done to quantify the experimental observations and relate these results to the existing mathematical models. In fact, the best of the physics based models in the literature show as much as 20% discrepancy when compared to experimental data. The reasons for such a big difference include, but are not limited to, numerical complexities involved in extracting parameters from experimental data and inconsistencies in interpreting directly measured values for the parameters. In this work, an attempt has been made to implement simplified models to extract parameter values that accurately characterize the performance of lithium ion cells. The validity of these models under a variety of experimental conditions is verified using a model discrimination procedure. Transport and kinetic properties are estimated using a non-linear estimation procedure. The initial state of charge inside each electrode is also maintained as an unknown parameter, since this value plays a significant role in accurately matching experimental charge/discharge curves with model predictions and is not readily known from experimental data. The second part of the dissertation focuses on parameters that change rapidly with time. For example, in the case of lithium ion batteries used in Hybrid Electric Vehicle (HEV) applications, the prediction of the State of Charge (SOC) of the cell under a variety of road conditions is important. An algorithm to predict the SOC in time intervals as small as 5 ms is of critical demand. In such cases, the conventional non-linear estimation procedure is not time-effective. There exist methodologies in the literature, such as those based on fuzzy logic; however, these techniques require a lot of computational storage space. Consequently, it is not possible to implement such techniques on a micro-chip for integration as a part of a real-time device. The Extended Kalman Filter (EKF) based approach presented in this work is a first step towards developing an efficient method to predict online, the State of Charge of a lithium ion cell based on an electrochemical model. The final part of the dissertation focuses on incorporating uncertainty in parameter values into electrochemical models using the polynomial chaos theory (PCT).

REVERBERATION AND PHOTOIONIZATION ESTIMATES OF THE BROAD-LINE REGION RADIUS IN LOW-z QUASARS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Negrete, C. Alenka; Dultzin, Deborah; Marziani, Paola

2013-07-01

Black hole mass estimation in quasars, especially at high redshift, involves the use of single-epoch spectra with signal-to-noise ratio and resolution that permit accurate measurement of the width of a broad line assumed to be a reliable virial estimator. Coupled with an estimate of the radius of the broad-line region (BLR) this yields the black hole mass M{sub BH}. The radius of the BLR may be inferred from an extrapolation of the correlation between source luminosity and reverberation-derived r{sub BLR} measures (the so-called Kaspi relation involving about 60 low-z sources). We are exploring a different method for estimating r{sub BLR}more » directly from inferred physical conditions in the BLR of each source. We report here on a comparison of r{sub BLR} estimates that come from our method and from reverberation mapping. Our ''photoionization'' method employs diagnostic line intensity ratios in the rest-frame range 1400-2000 A (Al III {lambda}1860/Si III] {lambda}1892, C IV {lambda}1549/Al III {lambda}1860) that enable derivation of the product of density and ionization parameter with the BLR distance derived from the definition of the ionization parameter. We find good agreement between our estimates of the density, ionization parameter, and r{sub BLR} and those from reverberation mapping. We suggest empirical corrections to improve the agreement between individual photoionization-derived r{sub BLR} values and those obtained from reverberation mapping. The results in this paper can be exploited to estimate M{sub BH} for large samples of high-z quasars using an appropriate virial broadening estimator. We show that the width of the UV intermediate emission lines are consistent with the width of H{beta}, thereby providing a reliable virial broadening estimator that can be measured in large samples of high-z quasars.« less

Performance Evaluation of EnKF-based Hydrogeological Site Characterization using Color Coherent Vectors

NASA Astrophysics Data System (ADS)

Moslehi, M.; de Barros, F.

2017-12-01

Complexity of hydrogeological systems arises from the multi-scale heterogeneity and insufficient measurements of their underlying parameters such as hydraulic conductivity and porosity. An inadequate characterization of hydrogeological properties can significantly decrease the trustworthiness of numerical models that predict groundwater flow and solute transport. Therefore, a variety of data assimilation methods have been proposed in order to estimate hydrogeological parameters from spatially scarce data by incorporating the governing physical models. In this work, we propose a novel framework for evaluating the performance of these estimation methods. We focus on the Ensemble Kalman Filter (EnKF) approach that is a widely used data assimilation technique. It reconciles multiple sources of measurements to sequentially estimate model parameters such as the hydraulic conductivity. Several methods have been used in the literature to quantify the accuracy of the estimations obtained by EnKF, including Rank Histograms, RMSE and Ensemble Spread. However, these commonly used methods do not regard the spatial information and variability of geological formations. This can cause hydraulic conductivity fields with very different spatial structures to have similar histograms or RMSE. We propose a vision-based approach that can quantify the accuracy of estimations by considering the spatial structure embedded in the estimated fields. Our new approach consists of adapting a new metric, Color Coherent Vectors (CCV), to evaluate the accuracy of estimated fields achieved by EnKF. CCV is a histogram-based technique for comparing images that incorporate spatial information. We represent estimated fields as digital three-channel images and use CCV to compare and quantify the accuracy of estimations. The sensitivity of CCV to spatial information makes it a suitable metric for assessing the performance of spatial data assimilation techniques. Under various factors of data assimilation methods such as number, layout, and type of measurements, we compare the performance of CCV with other metrics such as RMSE. By simulating hydrogeological processes using estimated and true fields, we observe that CCV outperforms other existing evaluation metrics.

Forward hadron calorimeter at MPD/NICA

NASA Astrophysics Data System (ADS)

Golubeva, M.; Guber, F.; Ivashkin, A.; Izvestnyy, A.; Kurepin, A.; Morozov, S.; Parfenov, P.; Petukhov, O.; Taranenko, A.; Selyuzhenkov, I.; Svintsov, I.

2017-01-01

Forward hadron calorimeter (FHCAL) at MPD/NICA experimental setup is described. The main purpose of the FHCAL is to provide an experimental measurement of a heavy-ion collision centrality (impact parameter) and orientation of its reaction plane. Precise event-by-event estimate of these basic observables is crucial for many physics phenomena studies to be performed by the MPD experiment. The simulation results of FHCAL performance are presented.

Modeling of microporous silicon betaelectric converter with 63Ni plating in GEANT4 toolkit*

NASA Astrophysics Data System (ADS)

Zelenkov, P. V.; Sidorov, V. G.; Lelekov, E. T.; Khoroshko, A. Y.; Bogdanov, S. V.; Lelekov, A. T.

2016-04-01

The model of electron-hole pairs generation rate distribution in semiconductor is needed to optimize the parameters of microporous silicon betaelectric converter, which uses 63Ni isotope radiation. By using Monte-Carlo methods of GEANT4 software with ultra-low energy electron physics models this distribution in silicon was calculated and approximated with exponential function. Optimal pore configuration was estimated.

Neutral hydrogen in the Fourcade-Figueroa galaxy (A 1332 - 45)

NASA Astrophysics Data System (ADS)

Colomb, F. R.; Loiseau, N.; Testori, J. C.

The H I 21-cm line has been observed around the Fourcade-Figueroa galaxy (A 1332 - 45), a late spiral-type galaxy seen edge-on, with a systematic velocity of 823 km/s. Its distance is estimated as 3.2 Mpc, and the mass and other parameters of the galaxy are derived from the observed 21-cm profiles. The distance given for A 1332 - 45 agrees with the one adopted by Dottori and Fourcade (1973), and is at variance with the distance estimated by other authors. The possibility of a physical association with the Centaurus Group is discussed.

Energy acceptance and on momentum aperture optimization for the Sirius project

NASA Astrophysics Data System (ADS)

Dester, P. S.; Sá, F. H.; Liu, L.

2017-07-01

A fast objective function to calculate Touschek lifetime and on momentum aperture is essential to explore the vast search space of strength of quadrupole and sextupole families in Sirius. Touschek lifetime is estimated by using the energy aperture (dynamic and physical), RF system parameters and driving terms. Non-linear induced betatron oscillations are considered to determine the energy aperture. On momentum aperture is estimated by using a chaos indicator and resonance crossing considerations. Touschek lifetime and on momentum aperture constitute the objective function, which was used in a multi-objective genetic algorithm to perform an optimization for Sirius.

An efficient energy response model for liquid scintillator detectors

NASA Astrophysics Data System (ADS)

Lebanowski, Logan; Wan, Linyan; Ji, Xiangpan; Wang, Zhe; Chen, Shaomin

2018-05-01

Liquid scintillator detectors are playing an increasingly important role in low-energy neutrino experiments. In this article, we describe a generic energy response model of liquid scintillator detectors that provides energy estimations of sub-percent accuracy. This model fits a minimal set of physically-motivated parameters that capture the essential characteristics of scintillator response and that can naturally account for changes in scintillator over time, helping to avoid associated biases or systematic uncertainties. The model employs a one-step calculation and look-up tables, yielding an immediate estimation of energy and an efficient framework for quantifying systematic uncertainties and correlations.

Estimating Sleep from Multisensory Armband Measurements: Validity and Reliability in Teens

PubMed Central

Roane, Brandy M.; Van Reen, Eliza; Hart, Chantelle N.; Wing, Rena; Carskadon, Mary A.

2015-01-01

SUMMARY Given the recognition that sleep may influence obesity risk, there is increasing interest in measuring sleep parameters within obesity studies. The goal of the current analyses was to determine whether the SenseWear® Pro3 Armband (armband), typically used to assess physical activity, is reliable at assessing sleep parameters. We compared the armband to the AMI Motionlogger® (actigraph), a validated activity monitor for sleep assessment and to polysomnography (PSG), the gold standard for assessing sleep. Participants were twenty adolescents (mean age=15.5 years) with a mean BMI %tile of 63.7. All participants wore the armband and actigraph on their non-dominant arm while in-lab during a nocturnal PSG recording (600 minutes). Epoch-by-epoch sleep/wake data and concordance of sleep parameters were examined. No significant sleep parameter differences were found between the armband and PSG; the actigraph tended to overestimate sleep and underestimate wake compared to PSG. Both devices showed high sleep sensitivity, but lower wake detection rates. Bland-Altman plots showed large individual differences in armband sleep parameter concordance rates. The armband did well estimating sleep overall with group results more similar to PSG than the actigraph; however, the armband was less accurate at an individual level than the actigraph. PMID:26126746

Estimating sleep from multisensory armband measurements: validity and reliability in teens.

PubMed

Roane, Brandy M; Van Reen, Eliza; Hart, Chantelle N; Wing, Rena; Carskadon, Mary A

2015-12-01

Given the recognition that sleep may influence obesity risk, there is increasing interest in measuring sleep parameters within obesity studies. The goal of the current analyses was to determine whether the SenseWear(®) Pro3 Armband (armband), typically used to assess physical activity, is reliable at assessing sleep parameters. The armband was compared with the AMI Motionlogger(®) (actigraph), a validated activity monitor for sleep assessment, and with polysomnography, the gold standard for assessing sleep. Participants were 20 adolescents (mean age = 15.5 years) with a mean body mass index percentile of 63.7. All participants wore the armband and actigraph on their non-dominant arm while in-lab during a nocturnal polysomnographic recording (600 min). Epoch-by-epoch sleep/wake data and concordance of sleep parameters were examined. No significant sleep parameter differences were found between the armband and polysomnography; the actigraph tended to overestimate sleep and underestimate wake compared with polysomnography. Both devices showed high sleep sensitivity, but lower wake detection rates. Bland-Altman plots showed large individual differences in armband sleep parameter concordance rates. The armband did well estimating sleep overall, with group results more similar to polysomnography than the actigraph; however, the armband was less accurate at an individual level than the actigraph. © 2015 European Sleep Research Society.

The contribution of NOAA/CMDL ground-based measurements to understanding long-term stratospheric changes

NASA Astrophysics Data System (ADS)

Montzka, S. A.; Butler, J. H.; Dutton, G.; Thompson, T. M.; Hall, B.; Mondeel, D. J.; Elkins, J. W.

2005-05-01

The El-Nino/Southern-Oscillation (ENSO) dominates interannual climate variability and plays, therefore, a key role in seasonal-to-interannual prediction. Much is known by now about the main physical mechanisms that give rise to and modulate ENSO, but the values of several parameters that enter these mechanisms are an important unknown. We apply Extended Kalman Filtering (EKF) for both model state and parameter estimation in an intermediate, nonlinear, coupled ocean--atmosphere model of ENSO. The coupled model consists of an upper-ocean, reduced-gravity model of the Tropical Pacific and a steady-state atmospheric response to the sea surface temperature (SST). The model errors are assumed to be mainly in the atmospheric wind stress, and assimilated data are equatorial Pacific SSTs. Model behavior is very sensitive to two key parameters: (i) μ, the ocean-atmosphere coupling coefficient between SST and wind stress anomalies; and (ii) δs, the surface-layer coefficient. Previous work has shown that δs determines the period of the model's self-sustained oscillation, while μ measures the degree of nonlinearity. Depending on the values of these parameters, the spatio-temporal pattern of model solutions is either that of a delayed oscillator or of a westward propagating mode. Estimation of these parameters is tested first on synthetic data and allows us to recover the delayed-oscillator mode starting from model parameter values that correspond to the westward-propagating case. Assimilation of SST data from the NCEP-NCAR Reanalysis-2 shows that the parameters can vary on fairly short time scales and switch between values that approximate the two distinct modes of ENSO behavior. Rapid adjustments of these parameters occur, in particular, during strong ENSO events. Ways to apply EKF parameter estimation efficiently to state-of-the-art coupled ocean--atmosphere GCMs will be discussed.

An empirical method for approximating stream baseflow time series using groundwater table fluctuations

NASA Astrophysics Data System (ADS)

Meshgi, Ali; Schmitter, Petra; Babovic, Vladan; Chui, Ting Fong May

2014-11-01

Developing reliable methods to estimate stream baseflow has been a subject of interest due to its importance in catchment response and sustainable watershed management. However, to date, in the absence of complex numerical models, baseflow is most commonly estimated using statistically derived empirical approaches that do not directly incorporate physically-meaningful information. On the other hand, Artificial Intelligence (AI) tools such as Genetic Programming (GP) offer unique capabilities to reduce the complexities of hydrological systems without losing relevant physical information. This study presents a simple-to-use empirical equation to estimate baseflow time series using GP so that minimal data is required and physical information is preserved. A groundwater numerical model was first adopted to simulate baseflow for a small semi-urban catchment (0.043 km2) located in Singapore. GP was then used to derive an empirical equation relating baseflow time series to time series of groundwater table fluctuations, which are relatively easily measured and are physically related to baseflow generation. The equation was then generalized for approximating baseflow in other catchments and validated for a larger vegetation-dominated basin located in the US (24 km2). Overall, this study used GP to propose a simple-to-use equation to predict baseflow time series based on only three parameters: minimum daily baseflow of the entire period, area of the catchment and groundwater table fluctuations. It serves as an alternative approach for baseflow estimation in un-gauged systems when only groundwater table and soil information is available, and is thus complementary to other methods that require discharge measurements.

Improving Simulated Soil Moisture Fields Through Assimilation of AMSR-E Soil Moisture Retrievals with an Ensemble Kalman Filter and a Mass Conservation Constraint

NASA Technical Reports Server (NTRS)

Li, Bailing; Toll, David; Zhan, Xiwu; Cosgrove, Brian

2011-01-01

Model simulated soil moisture fields are often biased due to errors in input parameters and deficiencies in model physics. Satellite derived soil moisture estimates, if retrieved appropriately, represent the spatial mean of soil moisture in a footprint area, and can be used to reduce model bias (at locations near the surface) through data assimilation techniques. While assimilating the retrievals can reduce model bias, it can also destroy the mass balance enforced by the model governing equation because water is removed from or added to the soil by the assimilation algorithm. In addition, studies have shown that assimilation of surface observations can adversely impact soil moisture estimates in the lower soil layers due to imperfect model physics, even though the bias near the surface is decreased. In this study, an ensemble Kalman filter (EnKF) with a mass conservation updating scheme was developed to assimilate the actual value of Advanced Microwave Scanning Radiometer (AMSR-E) soil moisture retrievals to improve the mean of simulated soil moisture fields by the Noah land surface model. Assimilation results using the conventional and the mass conservation updating scheme in the Little Washita watershed of Oklahoma showed that, while both updating schemes reduced the bias in the shallow root zone, the mass conservation scheme provided better estimates in the deeper profile. The mass conservation scheme also yielded physically consistent estimates of fluxes and maintained the water budget. Impacts of model physics on the assimilation results are discussed.

Magnetised Strings in Λ-Dominated Anisotropic Universe

NASA Astrophysics Data System (ADS)

Goswami, G. K.; Yadav, Anil Kumar; Dewangan, R. N.

2016-11-01

In this paper, we have searched the existence of Λ-dominated anisotropic universe filled with magnetized strings. The observed acceleration of universe has been explained by introducing a positive cosmological constant Λ in the Einstein's field equation which is mathematically equivalent to dark energy with equation of state (EOS) parameter set equal to -1. The present values of the matter and the dark energy parameters (Ω m )0 & (ΩΛ)0 are estimated for high red shift (.3 ≤ z ≤ 1.4) SN Ia supernova data's of observed apparent magnitude along with their possible error taken from Union 2.1 compilation. It is found that the best fit value for (Ω m )0 & (ΩΛ)0 are 0.2920 & 0.7076 respectively which are in good agreement with recent astrophysical observations in the latest surveys like WMAP and Plank. Various physical parameters such as the matter and dark energy densities, the present age of the universe and the present value of deceleration parameter have been obtained on the basis of the values of (Ω m )0 & (ΩΛ)0.Also, we have estimated that the acceleration would have begun in the past at z = 0.6845 i. e. 6.2341 Gyrs before from now.

First orbital solution and evolutionary state for the newly discovered eclipsing binaries USNO-B1.0 1091-0130715 and GSC-03449-0680

NASA Astrophysics Data System (ADS)

Elkhateeb, M. M.; Nouh, M. I.; Nelson, R. H.

2015-02-01

A first photometric study for the newly discovered systems USNO-B1.0 1091-0130715 and GSC-03449-0680 was carried out by means of recent a windows interface version of the Wilson and Devinney code based on model atmospheres by Kurucz (1993). The accepted models reveal some absolute parameters for both systems, which are used in deriving the spectral type of the system components and their evolutionary status. Distances to each systems and physical properties were estimated. Comparisons of the computed physical parameters with stellar models are discussed. The components of the system USNO-B1.0 1091-0130715 and the primary of the system GSC-03449-0680 are found to be on or near the ZAMS track, while the secondary of GSC-03449-0680 system found to be severely under luminous and too cool compared to its ZAMS mass.

Application of the device based on chirping of optical impulses for management of software-defined networks in dynamic mode

NASA Astrophysics Data System (ADS)

Vinogradova, Irina L.; Khasansin, Vadim R.; Andrianova, Anna V.; Yantilina, Liliya Z.; Vinogradov, Sergey L.

2016-03-01

The analysis of the influence of the physical layer concepts in optical networks on the performance of the whole network. It is concluded that the relevance of the search for new means of transmitting information on a physical level. It is proposed to use an optical chirp overhead transmission between controllers SDN. This article is devoted to research of a creation opportunity of optical neural switchboards controlled in addition by submitted optical radiation. It is supposed, that the managing radiation changes a parameter of refraction of optical environment of the device, and with it and length of a wave of information radiation. For the control by last is used multibeam interferometer. The brief estimation of technical aspects of construction of the device is carried out. The principle of using the device to an extensive network. Simulation of network performance parameters.

Different parameters support generalization and discrimination learning in Drosophila at the flight simulator.

PubMed

Brembs, Björn; Hempel de Ibarra, Natalie

2006-01-01

We have used a genetically tractable model system, the fruit fly Drosophila melanogaster to study the interdependence between sensory processing and associative processing on learning performance. We investigated the influence of variations in the physical and predictive properties of color stimuli in several different operant-conditioning procedures on the subsequent learning performance. These procedures included context and stimulus generalization as well as color, compound, and conditional discrimination (colors and patterns). A surprisingly complex dependence of the learning performance on the colors' physical and predictive properties emerged, which was clarified by taking into account the fly-subjective perception of the color stimuli. Based on estimates of the stimuli's color and brightness values, we propose that the different tasks are supported by different parameters of the color stimuli; generalization occurs only if the chromaticity is sufficiently similar, whereas discrimination learning relies on brightness differences.

Adaptive method for electron bunch profile prediction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Scheinker, Alexander; Gessner, Spencer

2015-10-01

We report on an experiment performed at the Facility for Advanced Accelerator Experimental Tests (FACET) at SLAC National Accelerator Laboratory, in which a new adaptive control algorithm, one with known, bounded update rates, despite operating on analytically unknown cost functions, was utilized in order to provide quasi-real-time bunch property estimates of the electron beam. Multiple parameters, such as arbitrary rf phase settings and other time-varying accelerator properties, were simultaneously tuned in order to match a simulated bunch energy spectrum with a measured energy spectrum. The simple adaptive scheme was digitally implemented using matlab and the experimental physics and industrial controlmore » system. The main result is a nonintrusive, nondestructive, real-time diagnostic scheme for prediction of bunch profiles, as well as other beam parameters, the precise control of which are important for the plasma wakefield acceleration experiments being explored at FACET. © 2015 authors. Published by the American Physical Society.« less

BIASES IN PHYSICAL PARAMETER ESTIMATES THROUGH DIFFERENTIAL LENSING MAGNIFICATION

DOE Office of Scientific and Technical Information (OSTI.GOV)

Er Xinzhong; Ge Junqiang; Mao Shude, E-mail: xer@nao.cas.cn

2013-06-20

We study the lensing magnification effect on background galaxies. Differential magnification due to different magnifications of different source regions of a galaxy will change the lensed composite spectra. The derived properties of the background galaxies are therefore biased. For simplicity, we model galaxies as a superposition of an axis-symmetric bulge and a face-on disk in order to study the differential magnification effect on the composite spectra. We find that some properties derived from the spectra (e.g., velocity dispersion, star formation rate, and metallicity) are modified. Depending on the relative positions of the source and the lens, the inferred results canmore » be either over- or underestimates of the true values. In general, for an extended source at strong lensing regions with high magnifications, the inferred physical parameters (e.g., metallicity) can be strongly biased. Therefore, detailed lens modeling is necessary to obtain the true properties of the lensed galaxies.« less

Estimation of static parameters based on dynamical and physical properties in limestone rocks

NASA Astrophysics Data System (ADS)

Ghafoori, Mohammad; Rastegarnia, Ahmad; Lashkaripour, Gholam Reza

2018-01-01

Due to the importance of uniaxial compressive strength (UCS), static Young's modulus (ES) and shear wave velocity, it is always worth to predict these parameters from empirical relations that suggested for other formations with same lithology. This paper studies the physical, mechanical and dynamical properties of limestone rocks using the results of laboratory tests which carried out on 60 the Jahrum and the Asmari formations core specimens. The core specimens were obtained from the Bazoft dam site, hydroelectric supply and double-curvature arch dam in Iran. The Dynamic Young's modulus (Ed) and dynamic Poisson ratio were calculated using the existing relations. Some empirical relations were presented to estimate uniaxial compressive strength, as well as static Young's modulus and shear wave velocity (Vs). Results showed the static parameters such as uniaxial compressive strength and static Young's modulus represented low correlation with water absorption. It is also found that the uniaxial compressive strength and static Young's modulus had high correlation with compressional wave velocity and dynamic Young's modulus, respectively. Dynamic Young's modulus was 5 times larger than static Young's modulus. Further, the dynamic Poisson ratio was 1.3 times larger than static Poisson ratio. The relationship between shear wave velocity (Vs) and compressional wave velocity (Vp) was power and positive with high correlation coefficient. Prediction of uniaxial compressive strength based on Vp was better than that based on Vs . Generally, both UCS and static Young's modulus (ES) had good correlation with Ed.

Evaluation of cardioprotective effect of silk cocoon (Abresham) on isoprenaline-induced myocardial infarction in rats.

PubMed

Srivastav, Ritesh Kumar; Siddiqui, Hefazat Hussain; Mahmood, Tarique; Ahsan, Farogh

2013-01-01

The study was conducted to evaluate cardioprotective effect of silk cocoon (Abresham) Bombyx mori (B. mori) on isoprenaline-induced myocardial infarction. This study deals with the cocoons, which is called Abresham in the Unani system of medicine. It is one of the 64 drugs which Avicenna has mentioned in Avicenna's tract on cardiac drugs and used in the treatment of cardiovascular diseases. Abresham is a chief ingredient of the two very famous Unani formulation viz. Khamira Abresham Sada, and Khamira Abresham Hakim Arshad Wala. The ethanolic extract of B. mori (Abresham) silk cocoons in the dose of 250 mg/kg and 500 mg/kg body weight was administered orally for 28 days before isoprenaline administration to test their cardioprotective effect. Isoprenaline (85 mg/kg) was administered subcutaneously on days 29(th) and 30(th), respectively in order to induce myocardial infarction. The parameters for evaluation of cardioprotective activity were the physical parameters and the biochemical estimations. The physical parameters were gross examination of heart, heart weight/body weight ratio and histopathology examination. In biochemical estimations, the activity of various cardiac enzymes such as aspartate transaminase, alanine transaminase, creatinine kinase, lactate dehydrogenase, and the gold marker troponin-I were determined. The levels altered by isoproterenol were restored significantly by the administration of the both doses of test extract especially at higher dose. The result of this study shows that alcoholic extract B. mori has significant cardioprotective activity against isoprenaline-induced myocardial infarction.

Theoretical Advances in Sequential Data Assimilation for the Atmosphere and Oceans

NASA Astrophysics Data System (ADS)

Ghil, M.

2007-05-01

We concentrate here on two aspects of advanced Kalman--filter-related methods: (i) the stability of the forecast- assimilation cycle, and (ii) parameter estimation for the coupled ocean-atmosphere system. The nonlinear stability of a prediction-assimilation system guarantees the uniqueness of the sequentially estimated solutions in the presence of partial and inaccurate observations, distributed in space and time; this stability is shown to be a necessary condition for the convergence of the state estimates to the true evolution of the turbulent flow. The stability properties of the governing nonlinear equations and of several data assimilation systems are studied by computing the spectrum of the associated Lyapunov exponents. These ideas are applied to a simple and an intermediate model of atmospheric variability and we show that the degree of stabilization depends on the type and distribution of the observations, as well as on the data assimilation method. These results represent joint work with A. Carrassi, A. Trevisan and F. Uboldi. Much is known by now about the main physical mechanisms that give rise to and modulate the El-Nino/Southern- Oscillation (ENSO), but the values of several parameters that enter these mechanisms are an important unknown. We apply Extended Kalman Filtering (EKF) for both model state and parameter estimation in an intermediate, nonlinear, coupled ocean-atmosphere model of ENSO. Model behavior is very sensitive to two key parameters: (a) "mu", the ocean-atmosphere coupling coefficient between the sea-surface temperature (SST) and wind stress anomalies; and (b) "delta-s", the surface-layer coefficient. Previous work has shown that "delta- s" determines the period of the model's self-sustained oscillation, while "mu' measures the degree of nonlinearity. Depending on the values of these parameters, the spatio-temporal pattern of model solutions is either that of a delayed oscillator or of a westward propagating mode. Assimilation of SST data from the NCEP- NCAR Reanalysis-2 shows that the parameters can vary on fairly short time scales and switch between values that approximate the two distinct modes of ENSO behavior. Rapid adjustments of these parameters occur, in particular, during strong ENSO events. Ways to apply EKF parameter estimation efficiently to state-of-the-art coupled ocean-atmosphere GCMs will be discussed. These results arise from joint work with D. Kondrashov and C.-j. Sun.

Rapid Computation of Thermodynamic Properties over Multidimensional Nonbonded Parameter Spaces Using Adaptive Multistate Reweighting.

PubMed

Naden, Levi N; Shirts, Michael R

2016-04-12

We show how thermodynamic properties of molecular models can be computed over a large, multidimensional parameter space by combining multistate reweighting analysis with a linear basis function approach. This approach reduces the computational cost to estimate thermodynamic properties from molecular simulations for over 130,000 tested parameter combinations from over 1000 CPU years to tens of CPU days. This speed increase is achieved primarily by computing the potential energy as a linear combination of basis functions, computed from either modified simulation code or as the difference of energy between two reference states, which can be done without any simulation code modification. The thermodynamic properties are then estimated with the Multistate Bennett Acceptance Ratio (MBAR) as a function of multiple model parameters without the need to define a priori how the states are connected by a pathway. Instead, we adaptively sample a set of points in parameter space to create mutual configuration space overlap. The existence of regions of poor configuration space overlap are detected by analyzing the eigenvalues of the sampled states' overlap matrix. The configuration space overlap to sampled states is monitored alongside the mean and maximum uncertainty to determine convergence, as neither the uncertainty or the configuration space overlap alone is a sufficient metric of convergence. This adaptive sampling scheme is demonstrated by estimating with high precision the solvation free energies of charged particles of Lennard-Jones plus Coulomb functional form with charges between -2 and +2 and generally physical values of σij and ϵij in TIP3P water. We also compute entropy, enthalpy, and radial distribution functions of arbitrary unsampled parameter combinations using only the data from these sampled states and use the estimates of free energies over the entire space to examine the deviation of atomistic simulations from the Born approximation to the solvation free energy.

[Relationship between physical activity and health in children and adolescents. Results of the German Health Interview and Examination Survey for Children and Adolescents (KiGGS) and the "Motorik-Modul" (MoMo)].

PubMed

Krug, S; Jekauc, D; Poethko-Müller, C; Woll, A; Schlaud, M

2012-01-01

The question of whether physical activity is associated with positive aspects of health becomes increasingly more important in the light of the health status in today's children and adolescents and due to the changing lifestyle with respect to everyday activity. The German Health Interview and Examination Survey for Children and Adolescents (KiGGS) collected the first set of nationwide representative cross-sectional data to examine the relationship between health and physical activity. Taking sociodemographic parameters into consideration, the results suggest a positive association between self-estimated general health and several types of physical activity. The results vary with respect to gender and type of physical activity. For methodological reasons, causal conclusions can only be drawn after longitudinal data of the second wave of KiGGS are available.

Quasi-dynamic earthquake fault systems with rheological heterogeneity

NASA Astrophysics Data System (ADS)

Brietzke, G. B.; Hainzl, S.; Zoeller, G.; Holschneider, M.

2009-12-01

Seismic risk and hazard estimates mostly use pure empirical, stochastic models of earthquake fault systems tuned specifically to the vulnerable areas of interest. Although such models allow for reasonable risk estimates, such models cannot allow for physical statements of the described seismicity. In contrary such empirical stochastic models, physics based earthquake fault systems models allow for a physical reasoning and interpretation of the produced seismicity and system dynamics. Recently different fault system earthquake simulators based on frictional stick-slip behavior have been used to study effects of stress heterogeneity, rheological heterogeneity, or geometrical complexity on earthquake occurrence, spatial and temporal clustering of earthquakes, and system dynamics. Here we present a comparison of characteristics of synthetic earthquake catalogs produced by two different formulations of quasi-dynamic fault system earthquake simulators. Both models are based on discretized frictional faults embedded in an elastic half-space. While one (1) is governed by rate- and state-dependent friction with allowing three evolutionary stages of independent fault patches, the other (2) is governed by instantaneous frictional weakening with scheduled (and therefore causal) stress transfer. We analyze spatial and temporal clustering of events and characteristics of system dynamics by means of physical parameters of the two approaches.

Stochastic and Perturbed Parameter Representations of Model Uncertainty in Convection Parameterization

NASA Astrophysics Data System (ADS)

Christensen, H. M.; Moroz, I.; Palmer, T.

2015-12-01

It is now acknowledged that representing model uncertainty in atmospheric simulators is essential for the production of reliable probabilistic ensemble forecasts, and a number of different techniques have been proposed for this purpose. Stochastic convection parameterization schemes use random numbers to represent the difference between a deterministic parameterization scheme and the true atmosphere, accounting for the unresolved sub grid-scale variability associated with convective clouds. An alternative approach varies the values of poorly constrained physical parameters in the model to represent the uncertainty in these parameters. This study presents new perturbed parameter schemes for use in the European Centre for Medium Range Weather Forecasts (ECMWF) convection scheme. Two types of scheme are developed and implemented. Both schemes represent the joint uncertainty in four of the parameters in the convection parametrisation scheme, which was estimated using the Ensemble Prediction and Parameter Estimation System (EPPES). The first scheme developed is a fixed perturbed parameter scheme, where the values of uncertain parameters are changed between ensemble members, but held constant over the duration of the forecast. The second is a stochastically varying perturbed parameter scheme. The performance of these schemes was compared to the ECMWF operational stochastic scheme, Stochastically Perturbed Parametrisation Tendencies (SPPT), and to a model which does not represent uncertainty in convection. The skill of probabilistic forecasts made using the different models was evaluated. While the perturbed parameter schemes improve on the stochastic parametrisation in some regards, the SPPT scheme outperforms the perturbed parameter approaches when considering forecast variables that are particularly sensitive to convection. Overall, SPPT schemes are the most skilful representations of model uncertainty due to convection parametrisation. Reference: H. M. Christensen, I. M. Moroz, and T. N. Palmer, 2015: Stochastic and Perturbed Parameter Representations of Model Uncertainty in Convection Parameterization. J. Atmos. Sci., 72, 2525-2544.

Application of Multi-task Lasso Regression in the Parametrization of Stellar Spectra

NASA Astrophysics Data System (ADS)

Chang, Li-Na; Zhang, Pei-Ai

2015-07-01

The multi-task learning approaches have attracted the increasing attention in the fields of machine learning, computer vision, and artificial intelligence. By utilizing the correlations in tasks, learning multiple related tasks simultaneously is better than learning each task independently. An efficient multi-task Lasso (Least Absolute Shrinkage Selection and Operator) regression algorithm is proposed in this paper to estimate the physical parameters of stellar spectra. It not only can obtain the information about the common features of the different physical parameters, but also can preserve effectively their own peculiar features. Experiments were done based on the ELODIE synthetic spectral data simulated with the stellar atmospheric model, and on the SDSS data released by the American large-scale survey Sloan. The estimation precision of our model is better than those of the methods in the related literature, especially for the estimates of the gravitational acceleration (lg g) and the chemical abundance ([Fe/H]). In the experiments we changed the spectral resolution, and applied the noises with different signal-to-noise ratios (SNRs) to the spectral data, so as to illustrate the stability of the model. The results show that the model is influenced by both the resolution and the noise. But the influence of the noise is larger than that of the resolution. In general, the multi-task Lasso regression algorithm is easy to operate, it has a strong stability, and can also improve the overall prediction accuracy of the model.

A study of physical properties of ODPA-p-PDA polyimide films

NASA Technical Reports Server (NTRS)

Singh, Jag J.; Eftekhari, Abe; St.clair, Terry L.

1990-01-01

Physical properties were investigated of ODPA-p-PDA polyimide films, including their lower molecular weight versions with phthalimide endcaps. Free volume, determined by low energy positron annihilation in the test films, was the major parameter of interest since all other physical properties are ostensibly related to it. It affects the dielectric constant as well as the saturation moisture pickup of the test films. An empirical relation was developed between the free volume and molecular weight of the test films, comparable to the Mark-Houwink relation between the polymer solution viscosity and the molecular weight. Development of such a relation constitutes a unique achievement since it enables researchers to estimate the molecular weight of an intractable polymer in solid state for the first time.

Density-based global sensitivity analysis of sheet-flow travel time: Kinematic wave-based formulations

NASA Astrophysics Data System (ADS)

Hosseini, Seiyed Mossa; Ataie-Ashtiani, Behzad; Simmons, Craig T.

2018-04-01

Despite advancements in developing physics-based formulations to estimate the sheet-flow travel time (tSHF), the quantification of the relative impacts of influential parameters on tSHF has not previously been considered. In this study, a brief review of the physics-based formulations to estimate tSHF including kinematic wave (K-W) theory in combination with Manning's roughness (K-M) and with Darcy-Weisbach friction formula (K-D) over single and multiple planes is provided. Then, the relative significance of input parameters to the developed approaches is quantified by a density-based global sensitivity analysis (GSA). The performance of K-M considering zero-upstream and uniform flow depth (so-called K-M1 and K-M2), and K-D formulae to estimate the tSHF over single plane surface were assessed using several sets of experimental data collected from the previous studies. The compatibility of the developed models to estimate tSHF over multiple planes considering temporal rainfall distributions of Natural Resources Conservation Service, NRCS (I, Ia, II, and III) are scrutinized by several real-world examples. The results obtained demonstrated that the main controlling parameters of tSHF through K-D and K-M formulae are the length of surface plane (mean sensitivity index T̂i = 0.72) and flow resistance (mean T̂i = 0.52), respectively. Conversely, the flow temperature and initial abstraction ratio of rainfall have the lowest influence on tSHF (mean T̂i is 0.11 and 0.12, respectively). The significant role of the flow regime on the estimation of tSHF over a single and a cascade of planes are also demonstrated. Results reveal that the K-D formulation provides more precise tSHF over the single plane surface with an average percentage of error, APE equal to 9.23% (the APE for K-M1 and K-M2 formulae were 13.8%, and 36.33%, respectively). The superiority of Manning-jointed formulae in estimation of tSHF is due to the incorporation of effects from different flow regimes as flow moves downgradient that is affected by one or more factors including high excess rainfall intensities, low flow resistance, high degrees of imperviousness, long surfaces, steep slope, and domination of rainfall distribution as NRCS Type I, II, or III.

A Method for Improving Hotspot Directional Signatures in BRDF Models Used for MODIS

NASA Technical Reports Server (NTRS)

Jiao, Ziti; Schaaf, Crystal B.; Dong, Yadong; Roman, Miguel; Hill, Michael J.; Chen, Jing M.; Wang, Zhuosen; Zhang, Hu; Saenz, Edward; Poudyal, Rajesh;

M-dwarf exoplanet surface density distribution. A log-normal fit from 0.07 to 400 AU

NASA Astrophysics Data System (ADS)

Meyer, Michael R.; Amara, Adam; Reggiani, Maddalena; Quanz, Sascha P.

2018-04-01

Aims: We fit a log-normal function to the M-dwarf orbital surface density distribution of gas giant planets, over the mass range 1-10 times that of Jupiter, from 0.07 to 400 AU. Methods: We used a Markov chain Monte Carlo approach to explore the likelihoods of various parameter values consistent with point estimates of the data given our assumed functional form. Results: This fit is consistent with radial velocity, microlensing, and direct-imaging observations, is well-motivated from theoretical and phenomenological points of view, and predicts results of future surveys. We present probability distributions for each parameter and a maximum likelihood estimate solution. Conclusions: We suggest that this function makes more physical sense than other widely used functions, and we explore the implications of our results on the design of future exoplanet surveys.

An interacting O + O supergiant close binary system: Cygnus OB2-5 (V729 Cyg)

NASA Astrophysics Data System (ADS)

Yaşarsoy, B.; Yakut, K.

2014-08-01

The massive interacting close binary system V729 Cyg (OIa + O/WN9), plausibly progenitor of a Wolf-Rayet system, is studied using new observations gathered over 65 nights and earlier published data. Radial velocity and five color light curves are analysed simultaneously. Estimated physical parameters of the components are M1=36±3 M, M2=10±1 M, R1=27±1 R, R2=15±0.6 R, log(L1/L⊙)=5.59±0.06, and log(L2/L⊙)=4.65±0.07. We give only the formal 1σ scatter, but we believe systematic errors in the luminosities, of uncertain origin as discussed in the text, are likely to be much bigger. The distance of the Cygnus OB2 association is estimated as 967±48 pc by using our newly obtained parameters.

An improved computer model for prediction of axial gas turbine performance losses

NASA Technical Reports Server (NTRS)

Jenkins, R. M.

1984-01-01

The calculation model performs a rapid preliminary pitchline optimization of axial gas turbine annular flowpath geometry, as well as an initial estimate of blade profile shapes, given only a minimum of thermodynamic cycle requirements. No geometric parameters need be specified. The following preliminary design data are determined: (1) the optimum flowpath geometry, within mechanical stress limits; (2) initial estimates of cascade blade shapes; and (3) predictions of expected turbine performance. The model uses an inverse calculation technique whereby blade profiles are generated by designing channels to yield a specified velocity distribution on the two walls. Velocity distributions are then used to calculate the cascade loss parameters. Calculated blade shapes are used primarily to determine whether the assumed velocity loadings are physically realistic. Model verification is accomplished by comparison of predicted turbine geometry and performance with an array of seven NASA single-stage axial gas turbine configurations.

A comprehensive method for preliminary design optimization of axial gas turbine stages

NASA Technical Reports Server (NTRS)

Jenkins, R. M.

1982-01-01

A method is presented that performs a rapid, reasonably accurate preliminary pitchline optimization of axial gas turbine annular flowpath geometry, as well as an initial estimate of blade profile shapes, given only a minimum of thermodynamic cycle requirements. No geometric parameters need be specified. The following preliminary design data are determined: (1) the optimum flowpath geometry, within mechanical stress limits; (2) initial estimates of cascade blade shapes; (3) predictions of expected turbine performance. The method uses an inverse calculation technique whereby blade profiles are generated by designing channels to yield a specified velocity distribution on the two walls. Velocity distributions are then used to calculate the cascade loss parameters. Calculated blade shapes are used primarily to determine whether the assumed velocity loadings are physically realistic. Model verification is accomplished by comparison of predicted turbine geometry and performance with four existing single stage turbines.

Multivariate space - time analysis of PRE-STORM precipitation

NASA Technical Reports Server (NTRS)

Polyak, Ilya; North, Gerald R.; Valdes, Juan B.

1994-01-01

This paper presents the methodologies and results of the multivariate modeling and two-dimensional spectral and correlation analysis of PRE-STORM rainfall gauge data. Estimated parameters of the models for the specific spatial averages clearly indicate the eastward and southeastward wave propagation of rainfall fluctuations. A relationship between the coefficients of the diffusion equation and the parameters of the stochastic model of rainfall fluctuations is derived that leads directly to the exclusive use of rainfall data to estimate advection speed (about 12 m/s) as well as other coefficients of the diffusion equation of the corresponding fields. The statistical methodology developed here can be used for confirmation of physical models by comparison of the corresponding second-moment statistics of the observed and simulated data, for generating multiple samples of any size, for solving the inverse problem of the hydrodynamic equations, and for application in some other areas of meteorological and climatological data analysis and modeling.

Mapping of bare soil surface parameters from TerraSAR-X radar images over a semi-arid region

NASA Astrophysics Data System (ADS)

Gorrab, A.; Zribi, M.; Baghdadi, N.; Lili Chabaane, Z.

2015-10-01

The goal of this paper is to analyze the sensitivity of X-band SAR (TerraSAR-X) signals as a function of different physical bare soil parameters (soil moisture, soil roughness), and to demonstrate that it is possible to estimate of both soil moisture and texture from the same experimental campaign, using a single radar signal configuration (one incidence angle, one polarization). Firstly, we analyzed statistically the relationships between X-band SAR (TerraSAR-X) backscattering signals function of soil moisture and different roughness parameters (the root mean square height Hrms, the Zs parameter and the Zg parameter) at HH polarization and for an incidence angle about 36°, over a semi-arid site in Tunisia (North Africa). Results have shown a high sensitivity of real radar data to the two soil parameters: roughness and moisture. A linear relationship is obtained between volumetric soil moisture and radar signal. A logarithmic correlation is observed between backscattering coefficient and all roughness parameters. The highest dynamic sensitivity is obtained with Zg parameter. Then, we proposed to retrieve of both soil moisture and texture using these multi-temporal X-band SAR images. Our approach is based on the change detection method and combines the seven radar images with different continuous thetaprobe measurements. To estimate soil moisture from X-band SAR data, we analyzed statistically the sensitivity between radar measurements and ground soil moisture derived from permanent thetaprobe stations. Our approaches are applied over bare soil class identified from an optical image SPOT / HRV acquired in the same period of measurements. Results have shown linear relationship for the radar signals as a function of volumetric soil moisture with high sensitivity about 0.21 dB/vol%. For estimation of change in soil moisture, we considered two options: (1) roughness variations during the three-month radar acquisition campaigns were not accounted for; (2) a simple correction for temporal variations in roughness was included. The results reveal a small improvement in the estimation of soil moisture when a correction for temporal variations in roughness is introduced. Finally, by considering the estimated temporal dynamics of soil moisture, a methodology is proposed for the retrieval of clay and sand content (expressed as percentages) in soil. Two empirical relationships were established between the mean moisture values retrieved from the seven acquired radar images and the two soil texture components over 36 test fields. Validation of the proposed approach was carried out over a second set of 34 fields, showing that highly accurate clay estimations can be achieved.

Adult survival and productivity of Northern Fulmars in Alaska

USGS Publications Warehouse

Hatch, Scott A.

1987-01-01

The population dynamics of Northern Fulmars (Fulmarus glacialis) were studied at the Semidi Islands in the western Gulf of Alaska. Fulmars occurred in a broad range of color phases, and annual survival was estimated from the return of birds in the rarer plumage classes. A raw estimate of mean annual survival over a 5-year period was 0.963, but a removal experiment indicated the raw value was probably biased downward. The estimate of annual survival adjusted accordingly was 0.969. Mortality during the breeding season was less than 10% of the annual total, and postbreeding mortality of failed breeders was three to four times higher than that of successful breeders. Breeding success averaged 41% over 9 years. About 5% of experienced birds failed to breed each year due to physical destruction of their breeding sites, mate-loss, or other causes. An estimated 30% of the birds near the colony in one year were of prebreeding age. A comparison of population parameters in Pacific and Atlantic fulmars indicates that higher survival in the prebreeding years is the likely basis for population growth in the northeastern Atlantic. The correlation of breeding success and survival suggests both parameters may decline with age.

Estimation of ice activation parameters within a particle tracking Lagrangian cloud model using the ensemble Kalman filter to match ISCDAC golden case observations

NASA Astrophysics Data System (ADS)

Reisner, J. M.; Dubey, M. K.

2010-12-01

To both quantify and reduce uncertainty in ice activation parameterizations for stratus clouds occurring in the temperature range between -5 to -10 C ensemble simulations of an ISDAC golden case have been conducted. To formulate the ensemble, three parameters found within an ice activation model have been sampled using a Latin hypercube technique over a parameter range that induces large variability in both number and mass of ice. The ice activation model is contained within a Lagrangian cloud model that simulates particle number as a function of radius for cloud ice, snow, graupel, cloud, and rain particles. A unique aspect of this model is that it produces very low levels of numerical diffusion that enable the model to accurately resolve the sharp cloud edges associated with the ISDAC stratus deck. Another important aspect of the model is that near the cloud edges the number of particles can be significantly increased to reduce sampling errors and accurately resolve physical processes such as collision-coalescence that occur in this region. Thus, given these relatively low numerical errors, as compared to traditional bin models, the sensitivity of a stratus deck to changes in parameters found within the activation model can be examined without fear of numerical contamination. Likewise, once the ensemble has been completed, ISDAC observations can be incorporated into a Kalman filter to optimally estimate the ice activation parameters and reduce overall model uncertainty. Hence, this work will highlight the ability of an ensemble Kalman filter system coupled to a highly accurate numerical model to estimate important parameters found within microphysical parameterizations containing high uncertainty.

Calibration of infiltration parameters on hydrological tank model using runoff coefficient of rational method

NASA Astrophysics Data System (ADS)

Suryoputro, Nugroho; Suhardjono, Soetopo, Widandi; Suhartanto, Ery

2017-09-01

In calibrating hydrological models, there are generally two stages of activity: 1) determining realistic model initial parameters in representing natural component physical processes, 2) entering initial parameter values which are then processed by trial error or automatically to obtain optimal values. To determine a realistic initial value, it takes experience and user knowledge of the model. This is a problem for beginner model users. This paper will present another approach to estimate the infiltration parameters in the tank model. The parameters will be approximated by the runoff coefficient of rational method. The value approach of infiltration parameter is simply described as the result of the difference in the percentage of total rainfall minus the percentage of runoff. It is expected that the results of this research will accelerate the calibration process of tank model parameters. The research was conducted on the sub-watershed Kali Bango in Malang Regency with an area of 239,71 km2. Infiltration measurements were carried out in January 2017 to March 2017. Analysis of soil samples at Soil Physics Laboratory, Department of Soil Science, Faculty of Agriculture, Universitas Brawijaya. Rainfall and discharge data were obtained from UPT PSAWS Bango Gedangan in Malang. Temperature, evaporation, relative humidity, wind speed data was obtained from BMKG station of Karang Ploso, Malang. The results showed that the infiltration coefficient at the top tank outlet can be determined its initial value by using the approach of the coefficient of runoff rational method with good result.

Numerical simulation and analysis for low-frequency rock physics measurements

NASA Astrophysics Data System (ADS)

Dong, Chunhui; Tang, Genyang; Wang, Shangxu; He, Yanxiao

2017-10-01

In recent years, several experimental methods have been introduced to measure the elastic parameters of rocks in the relatively low-frequency range, such as differential acoustic resonance spectroscopy (DARS) and stress-strain measurement. It is necessary to verify the validity and feasibility of the applied measurement method and to quantify the sources and levels of measurement error. Relying solely on the laboratory measurements, however, we cannot evaluate the complete wavefield variation in the apparatus. Numerical simulations of elastic wave propagation, on the other hand, are used to model the wavefield distribution and physical processes in the measurement systems, and to verify the measurement theory and analyze the measurement results. In this paper we provide a numerical simulation method to investigate the acoustic waveform response of the DARS system and the quasi-static responses of the stress-strain system, both of which use axisymmetric apparatus. We applied this method to parameterize the properties of the rock samples, the sample locations and the sensor (hydrophone and strain gauges) locations and simulate the measurement results, i.e. resonance frequencies and axial and radial strains on the sample surface, from the modeled wavefield following the physical experiments. Rock physical parameters were estimated by inversion or direct processing of these data, and showed a perfect match with the true values, thus verifying the validity of the experimental measurements. Error analysis was also conducted for the DARS system with 18 numerical samples, and the sources and levels of error are discussed. In particular, we propose an inversion method for estimating both density and compressibility of these samples. The modeled results also showed fairly good agreement with the real experiment results, justifying the effectiveness and feasibility of our modeling method.

Statistical analysis of target acquisition sensor modeling experiments

NASA Astrophysics Data System (ADS)

Deaver, Dawne M.; Moyer, Steve

2015-05-01

The U.S. Army RDECOM CERDEC NVESD Modeling and Simulation Division is charged with the development and advancement of military target acquisition models to estimate expected soldier performance when using all types of imaging sensors. Two elements of sensor modeling are (1) laboratory-based psychophysical experiments used to measure task performance and calibrate the various models and (2) field-based experiments used to verify the model estimates for specific sensors. In both types of experiments, it is common practice to control or measure environmental, sensor, and target physical parameters in order to minimize uncertainty of the physics based modeling. Predicting the minimum number of test subjects required to calibrate or validate the model should be, but is not always, done during test planning. The objective of this analysis is to develop guidelines for test planners which recommend the number and types of test samples required to yield a statistically significant result.

Physics-based, Bayesian sequential detection method and system for radioactive contraband

DOEpatents

Candy, James V; Axelrod, Michael C; Breitfeller, Eric F; Chambers, David H; Guidry, Brian L; Manatt, Douglas R; Meyer, Alan W; Sale, Kenneth E

2014-03-18

A distributed sequential method and system for detecting and identifying radioactive contraband from highly uncertain (noisy) low-count, radionuclide measurements, i.e. an event mode sequence (EMS), using a statistical approach based on Bayesian inference and physics-model-based signal processing based on the representation of a radionuclide as a monoenergetic decomposition of monoenergetic sources. For a given photon event of the EMS, the appropriate monoenergy processing channel is determined using a confidence interval condition-based discriminator for the energy amplitude and interarrival time and parameter estimates are used to update a measured probability density function estimate for a target radionuclide. A sequential likelihood ratio test is then used to determine one of two threshold conditions signifying that the EMS is either identified as the target radionuclide or not, and if not, then repeating the process for the next sequential photon event of the EMS until one of the two threshold conditions is satisfied.

Angular ellipticity correlations in a composite alignment model for elliptical and spiral galaxies and inference from weak lensing

NASA Astrophysics Data System (ADS)

Tugendhat, Tim M.; Schäfer, Björn Malte

2018-05-01

We investigate a physical, composite alignment model for both spiral and elliptical galaxies and its impact on cosmological parameter estimation from weak lensing for a tomographic survey. Ellipticity correlation functions and angular ellipticity spectra for spiral and elliptical galaxies are derived on the basis of tidal interactions with the cosmic large-scale structure and compared to the tomographic weak-lensing signal. We find that elliptical galaxies cause a contribution to the weak-lensing dominated ellipticity correlation on intermediate angular scales between ℓ ≃ 40 and ℓ ≃ 400 before that of spiral galaxies dominates on higher multipoles. The predominant term on intermediate scales is the negative cross-correlation between intrinsic alignments and weak gravitational lensing (GI-alignment). We simulate parameter inference from weak gravitational lensing with intrinsic alignments unaccounted; the bias induced by ignoring intrinsic alignments in a survey like Euclid is shown to be several times larger than the statistical error and can lead to faulty conclusions when comparing to other observations. The biases generally point into different directions in parameter space, such that in some cases one can observe a partial cancellation effect. Furthermore, it is shown that the biases increase with the number of tomographic bins used for the parameter estimation process. We quantify this parameter estimation bias in units of the statistical error and compute the loss of Bayesian evidence for a model due to the presence of systematic errors as well as the Kullback-Leibler divergence to quantify the distance between the true model and the wrongly inferred one.

[A new procedure for the estimation of physical fitness of patients during clinical rehabilitation using the 6-minute-walk-test].

PubMed

Marek, W; Marek, E; Vogel, P; Mückenhoff, K; Kotschy-Lang, N

2008-11-01

AIMS OF THE INVESTIGATION: The 6-minute-walk-test (6-MW) is an effective tool for measuring physical fitness in elderly patients. The increased walking distance is taken as a parameter for improved physical conditions. Frequently an unaltered walking distance is found after clinical treatment, but heart rate is significantly lower in the second challenge, indicating an improved physical fitness. This positive effect is not recognised when only the walking distance is analysed. An analysis of the 6-MW test was performed on 263 patients before and after 3 - 4 weeks clinical rehabilitation. In a control group of 26 patients 6-MW was repeated after recovery at the beginning and the end of the clinical treatment. Instrumented by a mobile pulse oximeter for recording oxygen saturation and heart rate, patients were instructed to walk as fast as they can do during 6 minutes. Measurements were performed every 30 seconds and printed out. Two new parameters, efficiency (E = S/f (C)), the ratio of distance and mean heart rate, and the theoretical increase in walking distance (S (z) = Delta f (C1)/Delta f (C2) x S (2) - S (1)) were introduced and tested. S (z) = theoretical increase in distance, Delta f (C1) = difference in heart rate at rest and mean heart rate at steady state during the first walk test with distance, S1. Delta f (C2), and S2 are measured during the second walk. Thus, the increase in distance is calculated under the assumption that the second walk test would have been performed by the patient with the same difference in heart rate that he/she achieved in the first walk. The patient groups walked 353 +/- 80 m at 106 +/- 14.3 beats/min in the 1st. 6-MW and 368 +/- 76.9 m at a heart rate of 105 +/- 14.0 beats/min in the final test. The increase of the walking distance was most significant in patients with shorter distances in the 1st 6-MW. A significant increase in the walking distance and in efficiency was found in patients with shorter walking distances or lower heart rates in the final test, using the numerical procedure described above. The patient's performance of the second walk test with an unchanged distance at a lower heart rate reveals an improved physical fitness. This is solely described by an increase by the parameter of efficiency, E. The calculation of the parameter, Sz, theoretical difference in walking distance (i. e., theoretical increase in almost all tests) provides a quantification of the effect of exercise training, even if the patient is not cooperative during the tests. Both parameters have proved to be suitable estimations for the assessment of physical fitness as a beneficial effect of clinical rehabilitation.

Identification of coal seam strata from geophysical logs of borehole using Adaptive Neuro-Fuzzy Inference System

NASA Astrophysics Data System (ADS)

Yegireddi, Satyanarayana; Uday Bhaskar, G.

2009-01-01

Different parameters obtained through well-logging geophysical sensors such as SP, resistivity, gamma-gamma, neutron, natural gamma and acoustic, help in identification of strata and estimation of the physical, electrical and acoustical properties of the subsurface lithology. Strong and conspicuous changes in some of the log parameters associated with any particular stratigraphy formation, are function of its composition, physical properties and help in classification. However some substrata show moderate values in respective log parameters and make difficult to identify or assess the type of strata, if we go by the standard variability ranges of any log parameters and visual inspection. The complexity increases further with more number of sensors involved. An attempt is made to identify the type of stratigraphy from borehole geophysical log data using a combined approach of neural networks and fuzzy logic, known as Adaptive Neuro-Fuzzy Inference System. A model is built based on a few data sets (geophysical logs) of known stratigraphy of in coal areas of Kothagudem, Godavari basin and further the network model is used as test model to infer the lithology of a borehole from their geophysical logs, not used in simulation. The results are very encouraging and the model is able to decipher even thin cola seams and other strata from borehole geophysical logs. The model can be further modified to assess the physical properties of the strata, if the corresponding ground truth is made available for simulation.

Numerical weather prediction model tuning via ensemble prediction system

NASA Astrophysics Data System (ADS)

Jarvinen, H.; Laine, M.; Ollinaho, P.; Solonen, A.; Haario, H.

2011-12-01

This paper discusses a novel approach to tune predictive skill of numerical weather prediction (NWP) models. NWP models contain tunable parameters which appear in parameterizations schemes of sub-grid scale physical processes. Currently, numerical values of these parameters are specified manually. In a recent dual manuscript (QJRMS, revised) we developed a new concept and method for on-line estimation of the NWP model parameters. The EPPES ("Ensemble prediction and parameter estimation system") method requires only minimal changes to the existing operational ensemble prediction infra-structure and it seems very cost-effective because practically no new computations are introduced. The approach provides an algorithmic decision making tool for model parameter optimization in operational NWP. In EPPES, statistical inference about the NWP model tunable parameters is made by (i) generating each member of the ensemble of predictions using different model parameter values, drawn from a proposal distribution, and (ii) feeding-back the relative merits of the parameter values to the proposal distribution, based on evaluation of a suitable likelihood function against verifying observations. In the presentation, the method is first illustrated in low-order numerical tests using a stochastic version of the Lorenz-95 model which effectively emulates the principal features of ensemble prediction systems. The EPPES method correctly detects the unknown and wrongly specified parameters values, and leads to an improved forecast skill. Second, results with an atmospheric general circulation model based ensemble prediction system show that the NWP model tuning capacity of EPPES scales up to realistic models and ensemble prediction systems. Finally, a global top-end NWP model tuning exercise with preliminary results is published.

A multi-scale ''soil water structure'' model based on the pedostructure concept

NASA Astrophysics Data System (ADS)

Braudeau, E.; Mohtar, R. H.; El Ghezal, N.; Crayol, M.; Salahat, M.; Martin, P.

2009-02-01

Current soil water models do not take into account the internal organization of the soil medium and, a fortiori, the physical interaction between the water film surrounding the solid particles of the soil structure, and the surface charges of this structure. In that sense they empirically deal with the physical soil properties that are all generated from this soil water-structure interaction. As a result, the thermodynamic state of the soil water medium, which constitutes the local physical conditions, namely the pedo-climate, for biological and geo-chemical processes in soil, is not defined in these models. The omission of soil structure from soil characterization and modeling does not allow for coupling disciplinary models for these processes with soil water models. This article presents a soil water structure model, Kamel®, which was developed based on a new paradigm in soil physics where the hierarchical soil structure is taken into account allowing for defining its thermodynamic properties. After a review of soil physics principles which forms the basis of the paradigm, we describe the basic relationships and functionality of the model. Kamel® runs with a set of 15 soil input parameters, the pedohydral parameters, which are parameters of the physically-based equations of four soil characteristic curves that can be measured in the laboratory. For cases where some of these parameters are not available, we show how to estimate these parameters from commonly available soil information using published pedotransfer functions. A published field experimental study on the dynamics of the soil moisture profile following a pounded infiltration rainfall event was used as an example to demonstrate soil characterization and Kamel® simulations. The simulated soil moisture profile for a period of 60 days showed very good agreement with experimental field data. Simulations using input data calculated from soil texture and pedotransfer functions were also generated and compared to simulations of the more ideal characterization. The later comparison illustrates how Kamel® can be used and adapt to any case of soil data availability. As physically based model on soil structure, it may be used as a standard reference to evaluate other soil-water models and also pedotransfer functions at a given location or agronomical situation.

Differential surface models for tactile perception of shape and on-line tracking of features

NASA Technical Reports Server (NTRS)

Hemami, H.

1987-01-01

Tactile perception of shape involves an on-line controller and a shape perceptor. The purpose of the on-line controller is to maintain gliding or rolling contact with the surface, and collect information, or track specific features of the surface such as edges of a certain sharpness. The shape perceptor uses the information to perceive, estimate the parameters of, or recognize the shape. The differential surface model depends on the information collected and on the a priori information known about the robot and its physical parameters. These differential models are certain functionals that are projections of the dynamics of the robot onto the surface gradient or onto the tangent plane. A number of differential properties may be directly measured from present day tactile sensors. Others may have to be indirectly computed from measurements. Others may constitute design objectives for distributed tactile sensors of the future. A parameterization of the surface leads to linear and nonlinear sequential parameter estimation techniques for identification of the surface. Many interesting compromises between measurement and computation are possible.

Cost-Effectiveness and Value of Information Analysis of Brief Interventions to Promote Physical Activity in Primary Care.

PubMed

Gc, Vijay Singh; Suhrcke, Marc; Hardeman, Wendy; Sutton, Stephen; Wilson, Edward C F

2018-01-01

Brief interventions (BIs) delivered in primary care have shown potential to increase physical activity levels and may be cost-effective, at least in the short-term, when compared with usual care. Nevertheless, there is limited evidence on their longer term costs and health benefits. To estimate the cost-effectiveness of BIs to promote physical activity in primary care and to guide future research priorities using value of information analysis. A decision model was used to compare the cost-effectiveness of three classes of BIs that have been used, or could be used, to promote physical activity in primary care: 1) pedometer interventions, 2) advice/counseling on physical activity, and (3) action planning interventions. Published risk equations and data from the available literature or routine data sources were used to inform model parameters. Uncertainty was investigated with probabilistic sensitivity analysis, and value of information analysis was conducted to estimate the value of undertaking further research. In the base-case, pedometer interventions yielded the highest expected net benefit at a willingness to pay of £20,000 per quality-adjusted life-year. There was, however, a great deal of decision uncertainty: the expected value of perfect information surrounding the decision problem for the National Health Service Health Check population was estimated at £1.85 billion. Our analysis suggests that the use of pedometer BIs is the most cost-effective strategy to promote physical activity in primary care, and that there is potential value in further research into the cost-effectiveness of brief (i.e., <30 minutes) and very brief (i.e., <5 minutes) pedometer interventions in this setting. Copyright © 2018 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Observational estimation of radiative feedback to surface air temperature over Northern High Latitudes

NASA Astrophysics Data System (ADS)

Hwang, Jiwon; Choi, Yong-Sang; Kim, WonMoo; Su, Hui; Jiang, Jonathan H.

2018-01-01

The high-latitude climate system contains complicated, but largely veiled physical feedback processes. Climate predictions remain uncertain, especially for the Northern High Latitudes (NHL; north of 60°N), and observational constraint on climate modeling is vital. This study estimates local radiative feedbacks for NHL based on the CERES/Terra satellite observations during March 2000-November 2014. The local shortwave (SW) and longwave (LW) radiative feedback parameters are calculated from linear regression of radiative fluxes at the top of the atmosphere on surface air temperatures. These parameters are estimated by the de-seasonalization and 12-month moving average of the radiative fluxes over NHL. The estimated magnitudes of the SW and the LW radiative feedbacks in NHL are 1.88 ± 0.73 and 2.38 ± 0.59 W m-2 K-1, respectively. The parameters are further decomposed into individual feedback components associated with surface albedo, water vapor, lapse rate, and clouds, as a product of the change in climate variables from ERA-Interim reanalysis estimates and their pre-calculated radiative kernels. The results reveal the significant role of clouds in reducing the surface albedo feedback (1.13 ± 0.44 W m-2 K-1 in the cloud-free condition, and 0.49 ± 0.30 W m-2 K-1 in the all-sky condition), while the lapse rate feedback is predominant in LW radiation (1.33 ± 0.18 W m-2 K-1). However, a large portion of the local SW and LW radiative feedbacks were not simply explained by the sum of these individual feedbacks.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Jared A.; Hacker, Joshua P.; Monache, Luca Delle

A current barrier to greater deployment of offshore wind turbines is the poor quality of numerical weather prediction model wind and turbulence forecasts over open ocean. The bulk of development for atmospheric boundary layer (ABL) parameterization schemes has focused on land, partly due to a scarcity of observations over ocean. The 100-m FINO1 tower in the North Sea is one of the few sources worldwide of atmospheric profile observations from the sea surface to turbine hub height. These observations are crucial to developing a better understanding and modeling of physical processes in the marine ABL. In this paper we usemore » the WRF single column model (SCM), coupled with an ensemble Kalman filter from the Data Assimilation Research Testbed (DART), to create 100-member ensembles at the FINO1 location. The goal of this study is to determine the extent to which model parameter estimation can improve offshore wind forecasts. Combining two datasets that provide lateral forcing for the SCM and two methods for determining z 0, the time-varying sea-surface roughness length, we conduct four WRF-SCM/DART experiments over the October-December 2006 period. The two methods for determining z 0 are the default Fairall-adjusted Charnock formulation in WRF, and using parameter estimation techniques to estimate z 0 in DART. Using DART to estimate z 0 is found to reduce 1-h forecast errors of wind speed over the Charnock-Fairall z 0 ensembles by 4%–22%. Finally, however, parameter estimation of z 0 does not simultaneously reduce turbulent flux forecast errors, indicating limitations of this approach and the need for new marine ABL parameterizations.« less

Recent Advances on INSAR Temporal Decorrelation: Theory and Observations Using UAVSAR

NASA Technical Reports Server (NTRS)

Lavalle, M.; Hensley, S.; Simard, M.

2011-01-01

We review our recent advances in understanding the role of temporal decorrelation in SAR interferometry and polarimetric SAR interferometry. We developed a physical model of temporal decorrelation based on Gaussian-statistic motion that varies along the vertical direction in forest canopies. Temporal decorrelation depends on structural parameters such as forest height, is sensitive to polarization and affects coherence amplitude and phase. A model of temporal-volume decorrelation valid for arbitrary spatial baseline is discussed. We tested the inversion of this model to estimate forest height from model simulations supported by JPL/UAVSAR data and lidar LVIS data. We found a general good agreement between forest height estimated from radar data and forest height estimated from lidar data.

Current Pressure Transducer Application of Model-based Prognostics Using Steady State Conditions

NASA Technical Reports Server (NTRS)

Teubert, Christopher; Daigle, Matthew J.

2014-01-01

Prognostics is the process of predicting a system's future states, health degradation/wear, and remaining useful life (RUL). This information plays an important role in preventing failure, reducing downtime, scheduling maintenance, and improving system utility. Prognostics relies heavily on wear estimation. In some components, the sensors used to estimate wear may not be fast enough to capture brief transient states that are indicative of wear. For this reason it is beneficial to be capable of detecting and estimating the extent of component wear using steady-state measurements. This paper details a method for estimating component wear using steady-state measurements, describes how this is used to predict future states, and presents a case study of a current/pressure (I/P) Transducer. I/P Transducer nominal and off-nominal behaviors are characterized using a physics-based model, and validated against expected and observed component behavior. This model is used to map observed steady-state responses to corresponding fault parameter values in the form of a lookup table. This method was chosen because of its fast, efficient nature, and its ability to be applied to both linear and non-linear systems. Using measurements of the steady state output, and the lookup table, wear is estimated. A regression is used to estimate the wear propagation parameter and characterize the damage progression function, which are used to predict future states and the remaining useful life of the system.

Econometrics of inventory holding and shortage costs: the case of refined gasoline

DOE Office of Scientific and Technical Information (OSTI.GOV)

Krane, S.D.

1985-01-01

This thesis estimates a model of a firm's optimal inventory and production behavior in order to investigate the link between the role of inventories in the business cycle and the microeconomic incentives for holding stocks of finished goods. The goal is to estimate a set of structural cost function parameters that can be used to infer the optimal cyclical response of inventories and production to shocks in demand. To avoid problems associated with the use of value based aggregate inventory data, an industry level physical unit data set for refined motor gasoline is examined. The Euler equations for a refiner'smore » multiperiod decision problem are estimated using restrictions imposed by the rational expectations hypothesis. The model also embodies the fact that, in most periods, the level of shortages will be zero, and even when positive, the shortages are not directly observable in the data set. These two concerns lead us to use a generalized method of moments estimation technique on a functional form that resembles the formulation of a Tobit problem. The estimation results are disappointing; the model and data yield coefficient estimates incongruous with the cost function interpretations of the structural parameters. These is only some superficial evidence that production smoothing is significant and that marginal inventory shortage costs increase at a faster rate than do marginal holding costs.« less

Kalman Filtering with Inequality Constraints for Turbofan Engine Health Estimation

NASA Technical Reports Server (NTRS)

Simon, Dan; Simon, Donald L.

2003-01-01

Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state variable constraints (which may be based on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. This paper develops two analytic methods of incorporating state variable inequality constraints in the Kalman filter. The first method is a general technique of using hard constraints to enforce inequalities on the state variable estimates. The resultant filter is a combination of a standard Kalman filter and a quadratic programming problem. The second method uses soft constraints to estimate state variables that are known to vary slowly with time. (Soft constraints are constraints that are required to be approximately satisfied rather than exactly satisfied.) The incorporation of state variable constraints increases the computational effort of the filter but significantly improves its estimation accuracy. The improvement is proven theoretically and shown via simulation results. The use of the algorithm is demonstrated on a linearized simulation of a turbofan engine to estimate health parameters. The turbofan engine model contains 16 state variables, 12 measurements, and 8 component health parameters. It is shown that the new algorithms provide improved performance in this example over unconstrained Kalman filtering.

Evaporation estimates from the Dead Sea and their implications on its water balance

NASA Astrophysics Data System (ADS)

Oroud, Ibrahim M.

2011-12-01

The Dead Sea (DS) is a terminal hypersaline water body situated in the deepest part of the Jordan Valley. There is a growing interest in linking the DS to the open seas due to severe water shortages in the area and the serious geological and environmental hazards to its vicinity caused by the rapid level drop of the DS. A key issue in linking the DS with the open seas would be an accurate determination of evaporation rates. There exist large uncertainties of evaporation estimates from the DS due to the complex feedback mechanisms between meteorological forcings and thermophysical properties of hypersaline solutions. Numerous methods have been used to estimate current and historical (pre-1960) evaporation rates, with estimates differing by ˜100%. Evaporation from the DS is usually deduced indirectly using energy, water balance, or pan methods with uncertainty in many parameters. Accumulated errors resulting from these uncertainties are usually pooled into the estimates of evaporation rates. In this paper, a physically based method with minimum empirical parameters is used to evaluate historical and current evaporation estimates from the DS. The more likely figures for historical and current evaporation rates from the DS were 1,500-1,600 and 1,200-1,250 mm per annum, respectively. Results obtained are congruent with field observations and with more elaborate procedures.

Identifying sensitive ranges in global warming precipitation change dependence on convective parameters

DOE PAGES

Bernstein, Diana N.; Neelin, J. David

2016-04-28

A branch-run perturbed-physics ensemble in the Community Earth System Model estimates impacts of parameters in the deep convection scheme on current hydroclimate and on end-of-century precipitation change projections under global warming. Regional precipitation change patterns prove highly sensitive to these parameters, especially in the tropics with local changes exceeding 3mm/d, comparable to the magnitude of the predicted change and to differences in global warming predictions among the Coupled Model Intercomparison Project phase 5 models. This sensitivity is distributed nonlinearly across the feasible parameter range, notably in the low-entrainment range of the parameter for turbulent entrainment in the deep convection scheme.more » This suggests that a useful target for parameter sensitivity studies is to identify such disproportionately sensitive dangerous ranges. Here, the low-entrainment range is used to illustrate the reduction in global warming regional precipitation sensitivity that could occur if this dangerous range can be excluded based on evidence from current climate.« less

Identifying sensitive ranges in global warming precipitation change dependence on convective parameters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bernstein, Diana N.; Neelin, J. David

A branch-run perturbed-physics ensemble in the Community Earth System Model estimates impacts of parameters in the deep convection scheme on current hydroclimate and on end-of-century precipitation change projections under global warming. Regional precipitation change patterns prove highly sensitive to these parameters, especially in the tropics with local changes exceeding 3mm/d, comparable to the magnitude of the predicted change and to differences in global warming predictions among the Coupled Model Intercomparison Project phase 5 models. This sensitivity is distributed nonlinearly across the feasible parameter range, notably in the low-entrainment range of the parameter for turbulent entrainment in the deep convection scheme.more » This suggests that a useful target for parameter sensitivity studies is to identify such disproportionately sensitive dangerous ranges. Here, the low-entrainment range is used to illustrate the reduction in global warming regional precipitation sensitivity that could occur if this dangerous range can be excluded based on evidence from current climate.« less

Ensemble-Based Parameter Estimation in a Coupled General Circulation Model

DOE PAGES

Liu, Y.; Liu, Z.; Zhang, S.; ...

2014-09-10

Parameter estimation provides a potentially powerful approach to reduce model bias for complex climate models. Here, in a twin experiment framework, the authors perform the first parameter estimation in a fully coupled ocean–atmosphere general circulation model using an ensemble coupled data assimilation system facilitated with parameter estimation. The authors first perform single-parameter estimation and then multiple-parameter estimation. In the case of the single-parameter estimation, the error of the parameter [solar penetration depth (SPD)] is reduced by over 90% after ~40 years of assimilation of the conventional observations of monthly sea surface temperature (SST) and salinity (SSS). The results of multiple-parametermore » estimation are less reliable than those of single-parameter estimation when only the monthly SST and SSS are assimilated. Assimilating additional observations of atmospheric data of temperature and wind improves the reliability of multiple-parameter estimation. The errors of the parameters are reduced by 90% in ~8 years of assimilation. Finally, the improved parameters also improve the model climatology. With the optimized parameters, the bias of the climatology of SST is reduced by ~90%. Altogether, this study suggests the feasibility of ensemble-based parameter estimation in a fully coupled general circulation model.« less

Soil Erosion as a stochastic process

NASA Astrophysics Data System (ADS)

Casper, Markus C.

2015-04-01

The main tools to provide estimations concerning risk and amount of erosion are different types of soil erosion models: on the one hand, there are empirically based model concepts on the other hand there are more physically based or process based models. However, both types of models have substantial weak points. All empirical model concepts are only capable of providing rough estimates over larger temporal and spatial scales, they do not account for many driving factors that are in the scope of scenario related analysis. In addition, the physically based models contain important empirical parts and hence, the demand for universality and transferability is not given. As a common feature, we find, that all models rely on parameters and input variables, which are to certain, extend spatially and temporally averaged. A central question is whether the apparent heterogeneity of soil properties or the random nature of driving forces needs to be better considered in our modelling concepts. Traditionally, researchers have attempted to remove spatial and temporal variability through homogenization. However, homogenization has been achieved through physical manipulation of the system, or by statistical averaging procedures. The price for obtaining this homogenized (average) model concepts of soils and soil related processes has often been a failure to recognize the profound importance of heterogeneity in many of the properties and processes that we study. Especially soil infiltrability and the resistance (also called "critical shear stress" or "critical stream power") are the most important empirical factors of physically based erosion models. The erosion resistance is theoretically a substrate specific parameter, but in reality, the threshold where soil erosion begins is determined experimentally. The soil infiltrability is often calculated with empirical relationships (e.g. based on grain size distribution). Consequently, to better fit reality, this value needs to be corrected experimentally. To overcome this disadvantage of our actual models, soil erosion models are needed that are able to use stochastic directly variables and parameter distributions. There are only some minor approaches in this direction. The most advanced is the model "STOSEM" proposed by Sidorchuk in 2005. In this model, only a small part of the soil erosion processes is described, the aggregate detachment and the aggregate transport by flowing water. The concept is highly simplified, for example, many parameters are temporally invariant. Nevertheless, the main problem is that our existing measurements and experiments are not geared to provide stochastic parameters (e.g. as probability density functions); in the best case they deliver a statistical validation of the mean values. Again, we get effective parameters, spatially and temporally averaged. There is an urgent need for laboratory and field experiments on overland flow structure, raindrop effects and erosion rate, which deliver information on spatial and temporal structure of soil and surface properties and processes.

A genetic-algorithm approach for assessing the liquefaction potential of sandy soils

NASA Astrophysics Data System (ADS)

Sen, G.; Akyol, E.

2010-04-01

The determination of liquefaction potential is required to take into account a large number of parameters, which creates a complex nonlinear structure of the liquefaction phenomenon. The conventional methods rely on simple statistical and empirical relations or charts. However, they cannot characterise these complexities. Genetic algorithms are suited to solve these types of problems. A genetic algorithm-based model has been developed to determine the liquefaction potential by confirming Cone Penetration Test datasets derived from case studies of sandy soils. Software has been developed that uses genetic algorithms for the parameter selection and assessment of liquefaction potential. Then several estimation functions for the assessment of a Liquefaction Index have been generated from the dataset. The generated Liquefaction Index estimation functions were evaluated by assessing the training and test data. The suggested formulation estimates the liquefaction occurrence with significant accuracy. Besides, the parametric study on the liquefaction index curves shows a good relation with the physical behaviour. The total number of misestimated cases was only 7.8% for the proposed method, which is quite low when compared to another commonly used method.

Effect of Rock Properties on ROP Modeling Using Statistical and Intelligent Methods: A Case Study of an Oil Well in Southwest of Iran

NASA Astrophysics Data System (ADS)

Bezminabadi, Sina Norouzi; Ramezanzadeh, Ahmad; Esmaeil Jalali, Seyed-Mohammad; Tokhmechi, Behzad; Roustaei, Abbas

2017-03-01

Rate of penetration (ROP) is one of the key indicators of drilling operation performance. The estimation of ROP in drilling engineering is very important in terms of more accurate assessment of drilling time which affects operation costs. Hence, estimation of a ROP model using operational and environmental parameters is crucial. For this purpose, firstly physical and mechanical properties of rock were derived from well logs. Correlation between the pair data were determined to find influential parameters on ROP. A new ROP model has been developed in one of the Azadegan oil field wells in southwest of Iran. The model has been simulated using Multiple Nonlinear Regression (MNR) and Artificial Neural Network (ANN). By adding the rock properties, the estimation of the models were precisely improved. The results of simulation using MNR and ANN methods showed correlation coefficients of 0.62 and 0.87, respectively. It was concluded that the performance of ANN model in ROP prediction is fairly better than MNR method.

Simple robust control laws for robot manipulators. Part 2: Adaptive case

NASA Technical Reports Server (NTRS)

Bayard, D. S.; Wen, J. T.

1987-01-01

A new class of asymptotically stable adaptive control laws is introduced for application to the robotic manipulator. Unlike most applications of adaptive control theory to robotic manipulators, this analysis addresses the nonlinear dynamics directly without approximation, linearization, or ad hoc assumptions, and utilizes a parameterization based on physical (time-invariant) quantities. This approach is made possible by using energy-like Lyapunov functions which retain the nonlinear character and structure of the dynamics, rather than simple quadratic forms which are ubiquitous to the adaptive control literature, and which have bound the theory tightly to linear systems with unknown parameters. It is a unique feature of these results that the adaptive forms arise by straightforward certainty equivalence adaptation of their nonadaptive counterparts found in the companion to this paper (i.e., by replacing unknown quantities by their estimates) and that this simple approach leads to asymptotically stable closed-loop adaptive systems. Furthermore, it is emphasized that this approach does not require convergence of the parameter estimates (i.e., via persistent excitation), invertibility of the mass matrix estimate, or measurement of the joint accelerations.

Aerial Refueling Simulator Validation Using Operational Experimentation and Response Surface Methods with Time Series Responses

DTIC Science & Technology

2013-03-21

10 2.3 Time Series Response Data ................................................................................. 12 2.4 Comparison of Response...to 12 evaluating the efficiency of the parameter estimates. In the past, the most popular form of response surface design used the D-optimality...as well. A model can refer to almost anything in math , statistics, or computer science. It can be any “physical, mathematical, or logical

Multivariate generalized hidden Markov regression models with random covariates: Physical exercise in an elderly population.

PubMed

Punzo, Antonio; Ingrassia, Salvatore; Maruotti, Antonello

2018-04-22

A time-varying latent variable model is proposed to jointly analyze multivariate mixed-support longitudinal data. The proposal can be viewed as an extension of hidden Markov regression models with fixed covariates (HMRMFCs), which is the state of the art for modelling longitudinal data, with a special focus on the underlying clustering structure. HMRMFCs are inadequate for applications in which a clustering structure can be identified in the distribution of the covariates, as the clustering is independent from the covariates distribution. Here, hidden Markov regression models with random covariates are introduced by explicitly specifying state-specific distributions for the covariates, with the aim of improving the recovering of the clusters in the data with respect to a fixed covariates paradigm. The hidden Markov regression models with random covariates class is defined focusing on the exponential family, in a generalized linear model framework. Model identifiability conditions are sketched, an expectation-maximization algorithm is outlined for parameter estimation, and various implementation and operational issues are discussed. Properties of the estimators of the regression coefficients, as well as of the hidden path parameters, are evaluated through simulation experiments and compared with those of HMRMFCs. The method is applied to physical activity data. Copyright © 2018 John Wiley & Sons, Ltd.

Estimating the distribution of colored dissolved organic matter during the Southern Ocean Gas Exchange Experiment using four-dimensional variational data assimilation

NASA Astrophysics Data System (ADS)

Del Castillo, C. E.; Dwivedi, S.; Haine, T. W. N.; Ho, D. T.

2017-03-01

We diagnosed the effect of various physical processes on the distribution of mixed-layer colored dissolved organic matter (CDOM) and a sulfur hexafluoride (SF6) tracer during the Southern Ocean Gas Exchange Experiment (SO GasEx). The biochemical upper ocean state estimate uses in situ and satellite biochemical and physical data in the study region, including CDOM (absorption coefficient and spectral slope), SF6, hydrography, and sea level anomaly. Modules for photobleaching of CDOM and surface transport of SF6 were coupled with an ocean circulation model for this purpose. The observed spatial and temporal variations in CDOM were captured by the state estimate without including any new biological source term for CDOM, assuming it to be negligible over the 26 days of the state estimate. Thermocline entrainment and photobleaching acted to diminish the mixed-layer CDOM with time scales of 18 and 16 days, respectively. Lateral advection of CDOM played a dominant role and increased the mixed-layer CDOM with a time scale of 12 days, whereas lateral diffusion of CDOM was negligible. A Lagrangian view on the CDOM variability was demonstrated by using the SF6 as a weighting function to integrate the CDOM fields. This and similar data assimilation methods can be used to provide reasonable estimates of optical properties, and other physical parameters over the short-term duration of a research cruise, and help in the tracking of tracer releases in large-scale oceanographic experiments, and in oceanographic process studies.

Estimating the Distribution of Colored Dissolved Organic Matter During the Southern Ocean Gas Exchange Experiment Using Four-Dimensional Variational Data Assimilation

NASA Technical Reports Server (NTRS)

Del Castillo, C. E.; Dwivedi, S.; Haine, T. W. N.; Ho, D. T.

2017-01-01

We diagnosed the effect of various physical processes on the distribution of mixed-layer colored dissolved organic matter (CDOM) and a sulfur hexauoride (SF6) tracer during the Southern Ocean Gas Exchange Experiment (SO GasEx). The biochemical upper ocean state estimate uses in situ and satellite biochemical and physical data in the study region, including CDOM (absorption coefcient and spectral slope), SF6, hydrography, and sea level anomaly. Modules for photobleaching of CDOM and surface transport of SF6 were coupled with an ocean circulation model for this purpose. The observed spatial and temporal variations in CDOM were captured by the state estimate without including any new biological source term for CDOM, assuming it to be negligible over the 26 days of the state estimate. Thermocline entrainment and photobleaching acted to diminish the mixed-layer CDOM with time scales of 18 and 16 days, respectively. Lateral advection of CDOM played a dominant role and increased the mixed-layer CDOM with a time scale of 12 days, whereas lateral diffusion of CDOM was negligible. A Lagrangian view on the CDOM variability was demonstrated by using the SF6 as a weighting function to integrate the CDOM elds. This and similar data assimilation methods can be used to provide reasonable estimates of optical properties, and other physical parameters over the short-term duration of a research cruise, and help in the tracking of tracer releases in large-scale oceanographic experiments, and in oceanographic process studies.

Fast automated analysis of strong gravitational lenses with convolutional neural networks.

PubMed

Hezaveh, Yashar D; Levasseur, Laurence Perreault; Marshall, Philip J

2017-08-30

Quantifying image distortions caused by strong gravitational lensing-the formation of multiple images of distant sources due to the deflection of their light by the gravity of intervening structures-and estimating the corresponding matter distribution of these structures (the 'gravitational lens') has primarily been performed using maximum likelihood modelling of observations. This procedure is typically time- and resource-consuming, requiring sophisticated lensing codes, several data preparation steps, and finding the maximum likelihood model parameters in a computationally expensive process with downhill optimizers. Accurate analysis of a single gravitational lens can take up to a few weeks and requires expert knowledge of the physical processes and methods involved. Tens of thousands of new lenses are expected to be discovered with the upcoming generation of ground and space surveys. Here we report the use of deep convolutional neural networks to estimate lensing parameters in an extremely fast and automated way, circumventing the difficulties that are faced by maximum likelihood methods. We also show that the removal of lens light can be made fast and automated using independent component analysis of multi-filter imaging data. Our networks can recover the parameters of the 'singular isothermal ellipsoid' density profile, which is commonly used to model strong lensing systems, with an accuracy comparable to the uncertainties of sophisticated models but about ten million times faster: 100 systems in approximately one second on a single graphics processing unit. These networks can provide a way for non-experts to obtain estimates of lensing parameters for large samples of data.

ASTROPHYSICAL PRIOR INFORMATION AND GRAVITATIONAL-WAVE PARAMETER ESTIMATION

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pankow, Chris; Sampson, Laura; Perri, Leah

The detection of electromagnetic counterparts to gravitational waves (GWs) has great promise for the investigation of many scientific questions. While it is well known that certain orientation parameters can reduce uncertainty in other related parameters, it was also hoped that the detection of an electromagnetic signal in conjunction with a GW could augment the measurement precision of the mass and spin from the gravitational signal itself. That is, knowledge of the sky location, inclination, and redshift of a binary could break degeneracies between these extrinsic, coordinate-dependent parameters and the physical parameters that are intrinsic to the binary. In this paper,more » we investigate this issue by assuming perfect knowledge of extrinsic parameters, and assessing the maximal impact of this knowledge on our ability to extract intrinsic parameters. We recover similar gains in extrinsic recovery to earlier work; however, we find only modest improvements in a few intrinsic parameters—namely the primary component’s spin. We thus conclude that, even in the best case, the use of additional information from electromagnetic observations does not improve the measurement of the intrinsic parameters significantly.« less

Etalon (standard) for surface potential distribution produced by electric activity of the heart.

PubMed

Szathmáry, V; Ruttkay-Nedecký, I

1981-01-01

The authors submit etalon (standard) equipotential maps as an aid in the evaluation of maps of surface potential distributions in living subjects. They were obtained by measuring potentials on the surface of an electrolytic tank shaped like the thorax. The individual etalon maps were determined in such a way that the parameters of the physical dipole forming the source of the electric field in the tank corresponded to the mean vectorcardiographic parameters measured in a healthy population sample. The technique also allows a quantitative estimate of the degree of non-dipolarity of the heart as the source of the electric field.

Real-time flutter identification

NASA Technical Reports Server (NTRS)

Roy, R.; Walker, R.

1985-01-01

The techniques and a FORTRAN 77 MOdal Parameter IDentification (MOPID) computer program developed for identification of the frequencies and damping ratios of multiple flutter modes in real time are documented. Physically meaningful model parameterization was combined with state of the art recursive identification techniques and applied to the problem of real time flutter mode monitoring. The performance of the algorithm in terms of convergence speed and parameter estimation error is demonstrated for several simulated data cases, and the results of actual flight data analysis from two different vehicles are presented. It is indicated that the algorithm is capable of real time monitoring of aircraft flutter characteristics with a high degree of reliability.

Dynamical Behavior of Meteor in AN Atmosphere: Theory vs Observations

NASA Astrophysics Data System (ADS)

Gritsevich, Maria

Up to now the only quantities which directly follow from the available meteor observations are its brightness, the height above sea level, the length along the trajectory, and as a consequence its velocity as a function of time. Other important parameters like meteoroid's mass, its shape, bulk and grain density, temperature remain unknown and should be found based on physical theories and special experiments. In this study I will consider modern methods for evaluating meteoroid parameters from observational data, and some of their applications. The study in particular takes an approach in modelling the meteoroids' mass and other properties from the aerodynamical point of view, e.g. from the rate of body deceleration in the atmosphere as opposed to conventionally used luminosity [1]. An analytical model of the atmospheric entry is calculated for registered meteors using published observational data and evaluating parameters describing drag, ablation and rotation rate of meteoroid along the luminous segment of the trajectory. One of the special features of this approach is the possibility of considering a change in body shape during its motion in the atmosphere. The correct mathematical modelling of meteor events is necessary for further studies of consequences for collisions of cosmic bodies with the Earth [2]. It also helps us to estimate the key parameters of the meteoroids, including deceleration, pre-entry mass, terminal mass, ablation coefficient, effective destruction enthalpy, and heat-transfer coefficient. With this information, one can use models for the dust influx onto Earth to estimate the number of meteors detected by a camera of a given sensitivity. References 1. Gritsevich M. I. Determination of Parameters of Meteor Bodies based on Flight Obser-vational Data // Advances in Space Research, 44, p. 323-334, 2009. 2. Gritsevich M. I., Stulov V. P. and Turchak L. I. Classification of Consequences for Col-lisions of Natural Cosmic Bodies with the Earth // Doklady Physics, 54, p. 499-503, 2009.

Geometric Characterization of Multi-Axis Multi-Pinhole SPECT

PubMed Central

DiFilippo, Frank P.

2008-01-01

A geometric model and calibration process are developed for SPECT imaging with multiple pinholes and multiple mechanical axes. Unlike the typical situation where pinhole collimators are mounted directly to rotating gamma ray detectors, this geometric model allows for independent rotation of the detectors and pinholes, for the case where the pinhole collimator is physically detached from the detectors. This geometric model is applied to a prototype small animal SPECT device with a total of 22 pinholes and which uses dual clinical SPECT detectors. All free parameters in the model are estimated from a calibration scan of point sources and without the need for a precision point source phantom. For a full calibration of this device, a scan of four point sources with 360° rotation is suitable for estimating all 95 free parameters of the geometric model. After a full calibration, a rapid calibration scan of two point sources with 180° rotation is suitable for estimating the subset of 22 parameters associated with repositioning the collimation device relative to the detectors. The high accuracy of the calibration process is validated experimentally. Residual differences between predicted and measured coordinates are normally distributed with 0.8 mm full width at half maximum and are estimated to contribute 0.12 mm root mean square to the reconstructed spatial resolution. Since this error is small compared to other contributions arising from the pinhole diameter and the detector, the accuracy of the calibration is sufficient for high resolution small animal SPECT imaging. PMID:18293574

Overcoming equifinality: Leveraging long time series for stream metabolism estimation

USGS Publications Warehouse

Appling, Alison; Hall, Robert O.; Yackulic, Charles B.; Arroita, Maite

2018-01-01

The foundational ecosystem processes of gross primary production (GPP) and ecosystem respiration (ER) cannot be measured directly but can be modeled in aquatic ecosystems from subdaily patterns of oxygen (O2) concentrations. Because rivers and streams constantly exchange O2 with the atmosphere, models must either use empirical estimates of the gas exchange rate coefficient (K600) or solve for all three parameters (GPP, ER, and K600) simultaneously. Empirical measurements of K600 require substantial field work and can still be inaccurate. Three-parameter models have suffered from equifinality, where good fits to O2 data are achieved by many different parameter values, some unrealistic. We developed a new three-parameter, multiday model that ensures similar values for K600 among days with similar physical conditions (e.g., discharge). Our new model overcomes the equifinality problem by (1) flexibly relating K600 to discharge while permitting moderate daily deviations and (2) avoiding the oft-violated assumption that residuals in O2 predictions are uncorrelated. We implemented this hierarchical state-space model and several competitor models in an open-source R package, streamMetabolizer. We then tested the models against both simulated and field data. Our new model reduces error by as much as 70% in daily estimates of K600, GPP, and ER. Further, accuracy benefits of multiday data sets require as few as 3 days of data. This approach facilitates more accurate metabolism estimates for more streams and days, enabling researchers to better quantify carbon fluxes, compare streams by their metabolic regimes, and investigate controls on aquatic activity.

Calibration of the ``Simplified Simple Biosphere Model—SSiB'' for the Brazilian Northeast Caatinga

NASA Astrophysics Data System (ADS)

do Amaral Cunha, Ana Paula Martins; dos Santos Alvalá, Regina Célia; Correia, Francis Wagner Silva; Kubota, Paulo Yoshio

2009-03-01

The Brazilian Northeast region is covered largely by vegetation adapted to the arid conditions and with varied physiognomy, called caatinga. It occupies an extension of about 800.000 km2 that corresponds to 70% of the region. In recent decades, considerable progress in understanding the micrometeorological processes has been reached, with results that were incorporated into soil-vegetation-atmosphere transfer schemes (SVATS) to study the momentum, energy, water vapor, carbon cycle and vegetation dynamics changes of different ecosystems. Notwithstanding, the knowledge of the parameters and physical or physiological characteristics of the vegetation and soil of the caatinga region is very scarce. So, the objective of this work was performing a calibration of the parameters of the SSiB model for the Brazilian Northeast Caatinga. Micrometeorological and hydrological data collected from July 2004 to June 2005, obtained in the Agricultural Research Center of the Semi-Arid Tropic (CPATSA), were used. Preceding the calibration process, a sensibility study of the SSiB model was performed in order to find the parameters that are sensible to the exchange processes between the surface and atmosphere. The results showed that the B parameter, soil moisture potential at saturation (ψs), hydraulic conductivity of saturated soil (ks) and the volumetric moisture at saturation (θs) present high variations on turbulent fluxes. With the initial parameters, the SSiB model showed best results for net radiation, and the latent heat (sensible heat) flux was over-estimated (under-estimated) for all simulation periods. Considering the calibrated parameters, better values of latent flux and sensible flux were obtained. The calibrated parameters were also used for a validation of the surface fluxes considering data from July 2005 to September 2005. The results showed that the model generated better estimations of latent heat and sensible heat fluxes, with low root mean square error. With better estimations of the turbulent fluxes, it was possible to obtain a more representative energy partitioning for the caatinga. Therefore, it is expected that from this calibrated SSiB model, coupled to the meteorological models, it will be possible to obtain more realistic climate and weather forecasts for the Brazilian Northeast region.

Cyber-Physical Attacks With Control Objectives

DOE PAGES

Chen, Yuan; Kar, Soummya; Moura, Jose M. F.

2017-08-18

This study studies attackers with control objectives against cyber-physical systems (CPSs). The goal of the attacker is to counteract the CPS's controller and move the system to a target state while evading detection. We formulate a cost function that reflects the attacker's goals, and, using dynamic programming, we show that the optimal attack strategy reduces to a linear feedback of the attacker's state estimate. By changing the parameters of the cost function, we show how an attacker can design optimal attacks to balance the control objective and the detection avoidance objective. In conclusion, we provide a numerical illustration based onmore » a remotely controlled helicopter under attack.« less

Ion Yields in the Coupled Chemical and Physical Dynamics Model of Matrix-Assisted Laser Desorption/Ionization

NASA Astrophysics Data System (ADS)

Knochenmuss, Richard

2015-08-01

The Coupled Chemical and Physical Dynamics (CPCD) model of matrix assisted laser desorption ionization has been restricted to relative rather than absolute yield comparisons because the rate constant for one step in the model was not accurately known. Recent measurements are used to constrain this constant, leading to good agreement with experimental yield versus fluence data for 2,5-dihydroxybenzoic acid. Parameters for alpha-cyano-4-hydroxycinnamic acid are also estimated, including contributions from a possible triplet state. The results are compared with the polar fluid model, the CPCD is found to give better agreement with the data.

A pore-pressure diffusion model for estimating landslide-inducing rainfall

USGS Publications Warehouse

Reid, M.E.

1994-01-01

Many types of landslide movement are induced by large rainstorms, and empirical rainfall intensity/duration thresholds for initiating movement have been determined for various parts of the world. In this paper, I present a simple pressure diffusion model that provides a physically based hydrologic link between rainfall intensity/duration at the ground surface and destabilizing pore-water pressures at depth. The model approximates rainfall infiltration as a sinusoidally varying flux over time and uses physical parameters that can be determined independently. Using a comprehensive data set from an intensively monitored landslide, I demonstrate that the model is capable of distinguishing movement-inducing rainstorms. -Author

Cyber-Physical Attacks With Control Objectives

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Yuan; Kar, Soummya; Moura, Jose M. F.

This study studies attackers with control objectives against cyber-physical systems (CPSs). The goal of the attacker is to counteract the CPS's controller and move the system to a target state while evading detection. We formulate a cost function that reflects the attacker's goals, and, using dynamic programming, we show that the optimal attack strategy reduces to a linear feedback of the attacker's state estimate. By changing the parameters of the cost function, we show how an attacker can design optimal attacks to balance the control objective and the detection avoidance objective. In conclusion, we provide a numerical illustration based onmore » a remotely controlled helicopter under attack.« less

Understanding identifiability as a crucial step in uncertainty assessment

NASA Astrophysics Data System (ADS)

Jakeman, A. J.; Guillaume, J. H. A.; Hill, M. C.; Seo, L.

2016-12-01

The topic of identifiability analysis offers concepts and approaches to identify why unique model parameter values cannot be identified, and can suggest possible responses that either increase uniqueness or help to understand the effect of non-uniqueness on predictions. Identifiability analysis typically involves evaluation of the model equations and the parameter estimation process. Non-identifiability can have a number of undesirable effects. In terms of model parameters these effects include: parameters not being estimated uniquely even with ideal data; wildly different values being returned for different initialisations of a parameter optimisation algorithm; and parameters not being physically meaningful in a model attempting to represent a process. This presentation illustrates some of the drastic consequences of ignoring model identifiability analysis. It argues for a more cogent framework and use of identifiability analysis as a way of understanding model limitations and systematically learning about sources of uncertainty and their importance. The presentation specifically distinguishes between five sources of parameter non-uniqueness (and hence uncertainty) within the modelling process, pragmatically capturing key distinctions within existing identifiability literature. It enumerates many of the various approaches discussed in the literature. Admittedly, improving identifiability is often non-trivial. It requires thorough understanding of the cause of non-identifiability, and the time, knowledge and resources to collect or select new data, modify model structures or objective functions, or improve conditioning. But ignoring these problems is not a viable solution. Even simple approaches such as fixing parameter values or naively using a different model structure may have significant impacts on results which are too often overlooked because identifiability analysis is neglected.

Bayesian inversion of data from effusive volcanic eruptions using physics-based models: Application to Mount St. Helens 2004--2008

USGS Publications Warehouse

Anderson, Kyle; Segall, Paul

2013-01-01

Physics-based models of volcanic eruptions can directly link magmatic processes with diverse, time-varying geophysical observations, and when used in an inverse procedure make it possible to bring all available information to bear on estimating properties of the volcanic system. We develop a technique for inverting geodetic, extrusive flux, and other types of data using a physics-based model of an effusive silicic volcanic eruption to estimate the geometry, pressure, depth, and volatile content of a magma chamber, and properties of the conduit linking the chamber to the surface. A Bayesian inverse formulation makes it possible to easily incorporate independent information into the inversion, such as petrologic estimates of melt water content, and yields probabilistic estimates for model parameters and other properties of the volcano. Probability distributions are sampled using a Markov-Chain Monte Carlo algorithm. We apply the technique using GPS and extrusion data from the 2004–2008 eruption of Mount St. Helens. In contrast to more traditional inversions such as those involving geodetic data alone in combination with kinematic forward models, this technique is able to provide constraint on properties of the magma, including its volatile content, and on the absolute volume and pressure of the magma chamber. Results suggest a large chamber of >40 km3 with a centroid depth of 11–18 km and a dissolved water content at the top of the chamber of 2.6–4.9 wt%.

Retrieval of effective cloud field parameters from radiometric data

NASA Astrophysics Data System (ADS)

Paulescu, Marius; Badescu, Viorel; Brabec, Marek

2017-06-01

Clouds play a key role in establishing the Earth's climate. Real cloud fields are very different and very complex in both morphological and microphysical senses. Consequently, the numerical description of the cloud field is a critical task for accurate climate modeling. This study explores the feasibility of retrieving the effective cloud field parameters (namely the cloud aspect ratio and cloud factor) from systematic radiometric measurements at high frequency (measurement is taken every 15 s). Two different procedures are proposed, evaluated, and discussed with respect to both physical and numerical restrictions. None of the procedures is classified as best; therefore, the specific advantages and weaknesses are discussed. It is shown that the relationship between the cloud shade and point cloudiness computed using the estimated cloud field parameters recovers the typical relationship derived from measurements.

Francis Perrin's 1939 Analysis of Uranium Criticality

NASA Astrophysics Data System (ADS)

Reed, Cameron

2012-03-01

In May 1939, French physicist Francis Perrin published the first numerical estimate of the fast-neutron critical mass of a uranium compound. While his estimate of about 40 metric tons (12 tons if tamped) pertained to uranium oxide of natural isotopic composition as opposed to the enriched uranium that would be required for a nuclear weapon, it is interesting to examine Perrin's physics and to explore the subsequent impact of his paper. In this presentation I will discuss Perrin's model, the likely provenance of his parameter values, and how his work compared to the approach taken by Robert Serber in his 1943 Los Alamos Primer.

Dynamic Studies of Struve Double Stars: STF4 and STF 236AB Appear Gravitationally Bound

NASA Astrophysics Data System (ADS)

Wiley, E. O.; Rica, F. M.

2015-01-01

Dynamics of two Struve double stars, WDS 00099+0827 (STF 4) and WDS 02556+2652 (STF 326 AB) are analyzed using astrometric criteria to determine their natures as gravitationally bound or unbound systems. If gravitationally bound, then observed relative velocity will be within limits according to the orbital energy conservation equation. Full implementation of this criterion was possible because the relative radial velocities as well as proper motions have been estimated. Other physical parameters were taken from literature or estimated using published protocols. Monte Carlo analysis indicates that both pairs have a high probability of being gravitationally bound and thus are long-period binaries.

A shallow water table fluctuation model in response to precipitation with consideration of unsaturated gravitational flow

NASA Astrophysics Data System (ADS)

Park, E.; Jeong, J.

2017-12-01

A precise estimation of groundwater fluctuation is studied by considering delayed recharge flux (DRF) and unsaturated zone drainage (UZD). Both DRF and UZD are due to gravitational flow impeded in the unsaturated zone, which may nonnegligibly affect groundwater level changes. In the validation, a previous model without the consideration of unsaturated flow is benchmarked where the actual groundwater level and precipitation data are divided into three periods based on the climatic condition. The estimation capability of the new model is superior to the benchmarked model as indicated by the significantly improved representation of groundwater level with physically interpretable model parameters.

Using multiplicity as a fractional cross-section estimation for centrality in PHOBOS

NASA Astrophysics Data System (ADS)

Hollis, Richard S.; Back, B. B.; Baker, M. D.; Ballintijn, M.; Barton, D. S.; Betts, R. R.; Bickley, A. A.; Bindel, R.; Busza, W.; Carroll, A.; Chai, Z.; Decowski, M. P.; García, E.; Gburek, T.; George, N.; Gulbrandsen, K.; Halliwell, C.; Hamblen, J.; Hauer, M.; Henderson, C.; Hofman, D. J.; Hollis, R. S.; Holylnski, R.; Holzman, B.; Iordanova, A.; Johnson, E.; Kane, J. L.; Khan, N.; Kulinich, P.; Kuo, C. M.; Lin, W. T.; Manly, S.; Mignerey, A. C.; Nouicer, R.; Olszewski, A.; Pak, R.; Reed, C.; Roland, C.; Roland, G.; Sagerer, J.; Seals, H.; Sedykh, I.; Smith, C. E.; Stankiewicz, M. A.; Steinberg, P.; Stephans, G. S. F.; Sukhanov, A.; Tonjes, M. B.; Trzupek, A.; Vale, C.; van Nieuwenhuizen, G. J.; Vaurynovich, S. S.; Verdier, R.; Veres, G. I.; Wenger, E.; Wolfs, F. L. H.; Wosiek, B.; Wozniak, K.; Wyslouch, B.; PHOBOS Collaboration

2005-01-01

Collision centrality is a valuable parameter used in relativistic nuclear physics which relates to geometrical quantities such as the number of participating nucleons. PHOBOS utilizes a multiplicity measurement as a means to estimate fractional cross-section of a collision event-by-event. From this, the centrality of this collision can be deduced. The details of the centrality determination depend both on the collision system and collision energy. Presented here are the techniques developed over the course of the RHIC program that are used by PHOBOS to extract the centrality. Possible biases that have to be overcome before a final measurement can be interpreted are discussed.

Addressing the impact of environmental uncertainty in plankton model calibration with a dedicated software system: the Marine Model Optimization Testbed (MarMOT 1.1 alpha)

NASA Astrophysics Data System (ADS)

Hemmings, J. C. P.; Challenor, P. G.

2012-04-01

A wide variety of different plankton system models have been coupled with ocean circulation models, with the aim of understanding and predicting aspects of environmental change. However, an ability to make reliable inferences about real-world processes from the model behaviour demands a quantitative understanding of model error that remains elusive. Assessment of coupled model output is inhibited by relatively limited observing system coverage of biogeochemical components. Any direct assessment of the plankton model is further inhibited by uncertainty in the physical state. Furthermore, comparative evaluation of plankton models on the basis of their design is inhibited by the sensitivity of their dynamics to many adjustable parameters. Parameter uncertainty has been widely addressed by calibrating models at data-rich ocean sites. However, relatively little attention has been given to quantifying uncertainty in the physical fields required by the plankton models at these sites, and tendencies in the biogeochemical properties due to the effects of horizontal processes are often neglected. Here we use model twin experiments, in which synthetic data are assimilated to estimate a system's known "true" parameters, to investigate the impact of error in a plankton model's environmental input data. The experiments are supported by a new software tool, the Marine Model Optimization Testbed, designed for rigorous analysis of plankton models in a multi-site 1-D framework. Simulated errors are derived from statistical characterizations of the mixed layer depth, the horizontal flux divergence tendencies of the biogeochemical tracers and the initial state. Plausible patterns of uncertainty in these data are shown to produce strong temporal and spatial variability in the expected simulation error variance over an annual cycle, indicating variation in the significance attributable to individual model-data differences. An inverse scheme using ensemble-based estimates of the simulation error variance to allow for this environment error performs well compared with weighting schemes used in previous calibration studies, giving improved estimates of the known parameters. The efficacy of the new scheme in real-world applications will depend on the quality of statistical characterizations of the input data. Practical approaches towards developing reliable characterizations are discussed.

Application of a time-magnitude prediction model for earthquakes

NASA Astrophysics Data System (ADS)

An, Weiping; Jin, Xueshen; Yang, Jialiang; Dong, Peng; Zhao, Jun; Zhang, He

2007-06-01

In this paper we discuss the physical meaning of the magnitude-time model parameters for earthquake prediction. The gestation process for strong earthquake in all eleven seismic zones in China can be described by the magnitude-time prediction model using the computations of the parameters of the model. The average model parameter values for China are: b = 0.383, c=0.154, d = 0.035, B = 0.844, C = -0.209, and D = 0.188. The robustness of the model parameters is estimated from the variation in the minimum magnitude of the transformed data, the spatial extent, and the temporal period. Analysis of the spatial and temporal suitability of the model indicates that the computation unit size should be at least 4° × 4° for seismic zones in North China, at least 3° × 3° in Southwest and Northwest China, and the time period should be as long as possible.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Y.; Liu, Z.; Zhang, S.

Parameter estimation provides a potentially powerful approach to reduce model bias for complex climate models. Here, in a twin experiment framework, the authors perform the first parameter estimation in a fully coupled ocean–atmosphere general circulation model using an ensemble coupled data assimilation system facilitated with parameter estimation. The authors first perform single-parameter estimation and then multiple-parameter estimation. In the case of the single-parameter estimation, the error of the parameter [solar penetration depth (SPD)] is reduced by over 90% after ~40 years of assimilation of the conventional observations of monthly sea surface temperature (SST) and salinity (SSS). The results of multiple-parametermore » estimation are less reliable than those of single-parameter estimation when only the monthly SST and SSS are assimilated. Assimilating additional observations of atmospheric data of temperature and wind improves the reliability of multiple-parameter estimation. The errors of the parameters are reduced by 90% in ~8 years of assimilation. Finally, the improved parameters also improve the model climatology. With the optimized parameters, the bias of the climatology of SST is reduced by ~90%. Altogether, this study suggests the feasibility of ensemble-based parameter estimation in a fully coupled general circulation model.« less

The physical and biological basis of quantitative parameters derived from diffusion MRI

PubMed Central

2012-01-01

Diffusion magnetic resonance imaging is a quantitative imaging technique that measures the underlying molecular diffusion of protons. Diffusion-weighted imaging (DWI) quantifies the apparent diffusion coefficient (ADC) which was first used to detect early ischemic stroke. However this does not take account of the directional dependence of diffusion seen in biological systems (anisotropy). Diffusion tensor imaging (DTI) provides a mathematical model of diffusion anisotropy and is widely used. Parameters, including fractional anisotropy (FA), mean diffusivity (MD), parallel and perpendicular diffusivity can be derived to provide sensitive, but non-specific, measures of altered tissue structure. They are typically assessed in clinical studies by voxel-based or region-of-interest based analyses. The increasing recognition of the limitations of the diffusion tensor model has led to more complex multi-compartment models such as CHARMED, AxCaliber or NODDI being developed to estimate microstructural parameters including axonal diameter, axonal density and fiber orientations. However these are not yet in routine clinical use due to lengthy acquisition times. In this review, I discuss how molecular diffusion may be measured using diffusion MRI, the biological and physical bases for the parameters derived from DWI and DTI, how these are used in clinical studies and the prospect of more complex tissue models providing helpful micro-structural information. PMID:23289085

Evaluation of Specific Absorption Rate as a Dosimetric Quantity for Electromagnetic Fields Bioeffects

PubMed Central

Panagopoulos, Dimitris J.; Johansson, Olle; Carlo, George L.

2013-01-01

Purpose To evaluate SAR as a dosimetric quantity for EMF bioeffects, and identify ways for increasing the precision in EMF dosimetry and bioactivity assessment. Methods We discuss the interaction of man-made electromagnetic waves with biological matter and calculate the energy transferred to a single free ion within a cell. We analyze the physics and biology of SAR and evaluate the methods of its estimation. We discuss the experimentally observed non-linearity between electromagnetic exposure and biological effect. Results We find that: a) The energy absorbed by living matter during exposure to environmentally accounted EMFs is normally well below the thermal level. b) All existing methods for SAR estimation, especially those based upon tissue conductivity and internal electric field, have serious deficiencies. c) The only method to estimate SAR without large error is by measuring temperature increases within biological tissue, which normally are negligible for environmental EMF intensities, and thus cannot be measured. Conclusions SAR actually refers to thermal effects, while the vast majority of the recorded biological effects from man-made non-ionizing environmental radiation are non-thermal. Even if SAR could be accurately estimated for a whole tissue, organ, or body, the biological/health effect is determined by tiny amounts of energy/power absorbed by specific biomolecules, which cannot be calculated. Moreover, it depends upon field parameters not taken into account in SAR calculation. Thus, SAR should not be used as the primary dosimetric quantity, but used only as a complementary measure, always reporting the estimating method and the corresponding error. Radiation/field intensity along with additional physical parameters (such as frequency, modulation etc) which can be directly and in any case more accurately measured on the surface of biological tissues, should constitute the primary measure for EMF exposures, in spite of similar uncertainty to predict the biological effect due to non-linearity. PMID:23750202

A combination of routine blood analytes predicts fitness decrement in elderly endurance athletes.

PubMed

Haslacher, Helmuth; Ratzinger, Franz; Perkmann, Thomas; Batmyagmar, Delgerdalai; Nistler, Sonja; Scherzer, Thomas M; Ponocny-Seliger, Elisabeth; Pilger, Alexander; Gerner, Marlene; Scheichenberger, Vanessa; Kundi, Michael; Endler, Georg; Wagner, Oswald F; Winker, Robert

2017-01-01

Endurance sports are enjoying greater popularity, particularly among new target groups such as the elderly. Predictors of future physical capacities providing a basis for training adaptations are in high demand. We therefore aimed to estimate the future physical performance of elderly marathoners (runners/bicyclists) using a set of easily accessible standard laboratory parameters. To this end, 47 elderly marathon athletes underwent physical examinations including bicycle ergometry and a blood draw at baseline and after a three-year follow-up period. In order to compile a statistical model containing baseline laboratory results allowing prediction of follow-up ergometry performance, the cohort was subgrouped into a model training (n = 25) and a test sample (n = 22). The model containing significant predictors in univariate analysis (alanine aminotransferase, urea, folic acid, myeloperoxidase and total cholesterol) presented with high statistical significance and excellent goodness of fit (R2 = 0.789, ROC-AUC = 0.951±0.050) in the model training sample and was validated in the test sample (ROC-AUC = 0.786±0.098). Our results suggest that standard laboratory parameters could be particularly useful for predicting future physical capacity in elderly marathoners. It hence merits further research whether these conclusions can be translated to other disciplines or age groups.

A combination of routine blood analytes predicts fitness decrement in elderly endurance athletes

PubMed Central

Ratzinger, Franz; Perkmann, Thomas; Batmyagmar, Delgerdalai; Nistler, Sonja; Scherzer, Thomas M.; Ponocny-Seliger, Elisabeth; Pilger, Alexander; Gerner, Marlene; Scheichenberger, Vanessa; Kundi, Michael; Endler, Georg; Wagner, Oswald F.; Winker, Robert

2017-01-01

Endurance sports are enjoying greater popularity, particularly among new target groups such as the elderly. Predictors of future physical capacities providing a basis for training adaptations are in high demand. We therefore aimed to estimate the future physical performance of elderly marathoners (runners/bicyclists) using a set of easily accessible standard laboratory parameters. To this end, 47 elderly marathon athletes underwent physical examinations including bicycle ergometry and a blood draw at baseline and after a three-year follow-up period. In order to compile a statistical model containing baseline laboratory results allowing prediction of follow-up ergometry performance, the cohort was subgrouped into a model training (n = 25) and a test sample (n = 22). The model containing significant predictors in univariate analysis (alanine aminotransferase, urea, folic acid, myeloperoxidase and total cholesterol) presented with high statistical significance and excellent goodness of fit (R2 = 0.789, ROC-AUC = 0.951±0.050) in the model training sample and was validated in the test sample (ROC-AUC = 0.786±0.098). Our results suggest that standard laboratory parameters could be particularly useful for predicting future physical capacity in elderly marathoners. It hence merits further research whether these conclusions can be translated to other disciplines or age groups. PMID:28475643

PERSEUS QC: preparing statistic data sets

NASA Astrophysics Data System (ADS)

Belokopytov, Vladimir; Khaliulin, Alexey; Ingerov, Andrey; Zhuk, Elena; Gertman, Isaac; Zodiatis, George; Nikolaidis, Marios; Nikolaidis, Andreas; Stylianou, Stavros

2017-09-01

The Desktop Oceanographic Data Processing Module was developed for visual analysis of interdisciplinary cruise measurements. The program provides the possibility of data selection based on different criteria, map plotting, sea horizontal sections, and sea depth vertical profiles. The data selection in the area of interest can be specified according to a set of different physical and chemical parameters complimented by additional parameters, such as the cruise number, ship name, and time period. The visual analysis of a set of vertical profiles in the selected area allows to determine the quality of the data, their location and the time of the in-situ measurements and to exclude any questionable data from the statistical analysis. For each selected set of profiles, the average vertical profile, the minimal and maximal values of the parameter under examination and the root mean square (r.m.s.) are estimated. These estimates are compared with the parameter ranges, set for each sub-region by MEDAR/MEDATLAS-II and SeaDataNet2 projects. In the framework of the PERSEUS project, certain parameters which lacked a range were calculated from scratch, while some of the previously used ranges were re-defined using more comprehensive data sets based on SeaDataNet2, SESAME and PERSEUS projects. In some cases we have used additional sub- regions to redefine the ranges ore precisely. The recalculated ranges are used to improve the PERSEUS Data Quality Control.

Estimating Forest Vertical Structure from Multialtitude, Fixed-Baseline Radar Interferometric and Polarimetric Data

NASA Technical Reports Server (NTRS)

Treuhaft, Robert N.; Law, Beverly E.; Siqueira, Paul R.

2000-01-01

Parameters describing the vertical structure of forests, for example tree height, height-to-base-of-live-crown, underlying topography, and leaf area density, bear on land-surface, biogeochemical, and climate modeling efforts. Single, fixed-baseline interferometric synthetic aperture radar (INSAR) normalized cross-correlations constitute two observations from which to estimate forest vertical structure parameters: Cross-correlation amplitude and phase. Multialtitude INSAR observations increase the effective number of baselines potentially enabling the estimation of a larger set of vertical-structure parameters. Polarimetry and polarimetric interferometry can further extend the observation set. This paper describes the first acquisition of multialtitude INSAR for the purpose of estimating the parameters describing a vegetated land surface. These data were collected over ponderosa pine in central Oregon near longitude and latitude -121 37 25 and 44 29 56. The JPL interferometric TOPSAR system was flown at the standard 8-km altitude, and also at 4-km and 2-km altitudes, in a race track. A reference line including the above coordinates was maintained at 35 deg for both the north-east heading and the return southwest heading, at all altitudes. In addition to the three altitudes for interferometry, one line was flown with full zero-baseline polarimetry at the 8-km altitude. A preliminary analysis of part of the data collected suggests that they are consistent with one of two physical models describing the vegetation: 1) a single-layer, randomly oriented forest volume with a very strong ground return or 2) a multilayered randomly oriented volume; a homogeneous, single-layer model with no ground return cannot account for the multialtitude correlation amplitudes. Below the inconsistency of the data with a single-layer model is followed by analysis scenarios which include either the ground or a layered structure. The ground returns suggested by this preliminary analysis seem too strong to be plausible, but parameters describing a two-layer compare reasonably well to a field-measured probability distribution of tree heights in the area.

Propagation of a channelized debris-flow: experimental investigation and parameters identification for numerical modelling

NASA Astrophysics Data System (ADS)

Termini, Donatella

2013-04-01

Recent catastrophic events due to intense rainfalls have mobilized large amount of sediments causing extensive damages in vast areas. These events have highlighted how debris-flows runout estimations are of crucial importance to delineate the potentially hazardous areas and to make reliable assessment of the level of risk of the territory. Especially in recent years, several researches have been conducted in order to define predicitive models. But, existing runout estimation methods need input parameters that can be difficult to estimate. Recent experimental researches have also allowed the assessment of the physics of the debris flows. But, the major part of the experimental studies analyze the basic kinematic conditions which determine the phenomenon evolution. Experimental program has been recently conducted at the Hydraulic laboratory of the Department of Civil, Environmental, Aerospatial and of Materials (DICAM) - University of Palermo (Italy). The experiments, carried out in a laboratory flume appositely constructed, were planned in order to evaluate the influence of different geometrical parameters (such as the slope and the geometrical characteristics of the confluences to the main channel) on the propagation phenomenon of the debris flow and its deposition. Thus, the aim of the present work is to give a contribution to defining input parameters in runout estimation by numerical modeling. The propagation phenomenon is analyzed for different concentrations of solid materials. Particular attention is devoted to the identification of the stopping distance of the debris flow and of the involved parameters (volume, angle of depositions, type of material) in the empirical predictive equations available in literature (Rickenmanm, 1999; Bethurst et al. 1997). Bethurst J.C., Burton A., Ward T.J. 1997. Debris flow run-out and landslide sediment delivery model tests. Journal of hydraulic Engineering, ASCE, 123(5), 419-429 Rickenmann D. 1999. Empirical relationships fro debris flow. Natural hazards, 19, pp. 47-77

[Required manpower for health care and nursing services for the aged at home public health nurses, visiting nurses, dental hygienists, dietitians, physical therapists, and occupational therapists].

PubMed

Ojima, T; Saito, E; Kanagawa, K; Sakata, K; Yanagawa, H

1997-04-01

The purpose of this study was to estimate the manpower required for the health care and nursing services for the aged at home. For prefectural health care and welfare planning for the aged, data such as the proportion of the aged who need help, service demand, and required frequency of services were obtained. The means and "mean +/- 2 x standard deviations" were calculated to obtain various parameters. Calculated figures were those which can be obtained with some effort. The results are as follows (middle level estimation (low level estimation-high level estimation)): requirements are 1.9 (0.61-5.7) public health nurses, 2.6 (0.63-14) visiting nurses, 0.20 (0.084-0.42) dental hygienists, 0.35 (0.17-0.66) dietitians, and 0.25 (0.014-1.27) physical and occupational therapists per population 10,000. For the national total, requirements are 23 (7.3-67) thousand public health nurses, 31 (7.5-160) thousand visiting nurses, 2.4 (1.0-5.0) thousand dental hygienists, 3.9 (2.0-7.8) thousand dietitians, and 3.0 (0.17-15) thousand physical and occupational therapists. By population sizes, for example in the municipalities which has 10-30 thousand people, required are 4.2 (1.7-11) public health nurses, 5.3 (1.3-27) visiting nurses, 0.4 (0.2-0.8) dental hygienists, 0.5 (0.3-0.9) dietitians, and 0.5 (0.0-2.5) physical and occupational therapists. Comparison of the present numbers with estimated manpower needs show that, the present number of public health personnel is almost the same as the low level estimation. But the present numbers of other manpower is lower than the low level estimation. Considering other services such as maternal and child health, it seems that the municipalities which has 10+ thousand population should employ full-time dietitians and dental hygienists. For policy making in a municipality, the policies of other municipalities should be considered. Because it is based on means for municipalities, the results of this study should be useful for application by other municipalities.

[Effects of exercise and physical activity on vital age].

PubMed

Tanaka, Kiyoji; Matsuo, Tomoaki

2009-07-01

Advances in medical care have enabled many middle-aged and older adults to live for long periods of time. However, considerable variability is present among those people with regards to both longevity and physical health status. Physical inactivity is a significant risk factor for many chronic diseases, while exercise habituation is beneficial for the maintenance of good health and high vitality. The authors have developed the concept of so-called vital age for the assessment of health and functional status in middle-aged and older adults. The vital age is estimated using a variety of bio-medical, primarily cardiovascular risk factor parameters. Previous research has compared vital age between sedentary persons and those with obesity and chronic diseases and between sedentary persons and those with exercise habituation, and found that exercise habituation can certainly contribute to better physical vitality in previously sedentary persons as well as diseased persons.

Constraints on CDM cosmology from galaxy power spectrum, CMB and SNIa evolution

NASA Astrophysics Data System (ADS)

Ferramacho, L. D.; Blanchard, A.; Zolnierowski, Y.

2009-05-01

Aims: We examine the constraints that can be obtained on standard cold dark matter models from the most currently used data set: CMB anisotropies, type Ia supernovae and the SDSS luminous red galaxies. We also examine how these constraints are widened when the equation of state parameter w and the curvature parameter Ωk are left as free parameters. Finally, we investigate the impact on these constraints of a possible form of evolution in SNIa intrinsic luminosity. Methods: We obtained our results from MCMC analysis using the full likelihood of each data set. Results: For the ΛCDM model, our “vanilla” model, cosmological parameters are tightly constrained and consistent with current estimates from various methods. When the dark energy parameter w is free we find that the constraints remain mostly unchanged, i.e. changes are smaller than the 1 sigma uncertainties. Similarly, relaxing the assumption of a flat universe leads to nearly identical constraints on the dark energy density parameter of the universe Ω_Λ , baryon density of the universe Ω_b, the optical depth τ, the index of the power spectrum of primordial fluctuations n_S, with most one sigma uncertainties better than 5%. More significant changes appear on other parameters: while preferred values are almost unchanged, uncertainties for the physical dark matter density Ω_ch^2, Hubble constant H0 and σ8 are typically twice as large. The constraint on the age of the Universe, which is very accurate for the vanilla model, is the most degraded. We found that different methodological approaches on large scale structure estimates lead to appreciable differences in preferred values and uncertainty widths. We found that possible evolution in SNIa intrinsic luminosity does not alter these constraints by much, except for w, for which the uncertainty is twice as large. At the same time, this possible evolution is severely constrained. Conclusions: We conclude that systematic uncertainties for some estimated quantities are similar or larger than statistical ones.

Impact of Water Use by Utility-Scale Solar on Groundwater Resources of the Chuckwalla Basin, CA: Final Modeling Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shen, Chaopeng; Fang, Kuai; Ludwig, Noel

The DOE and BLM identified 285,000 acres of desert land in the Chuckwalla valley in the western U.S., for solar energy development. In addition to several approved solar projects, a pumped storage project was recently proposed to pump nearly 8000 acre-ft-yr of groundwater to store and stabilize solar energy output. This study aims at providing estimates of the amount of naturally-occurring recharge, and to estimate the impact of the pumping on the water table. To better provide the locations and intensity of natural recharge, this study employs an integrated, physically-based hydrologic model, PAWS+CLM, to calculate recharge. Then, the simulated rechargemore » is used in a parameter estimation package to calibrate spatially-distributed K field. This design is to incorporate all available observational data, including soil moisture monitoring stations, groundwater head, and estimates of groundwater conductivity, to constrain the modeling. To address the uncertainty of the soil parameters, an ensemble of simulations are conducted, and the resulting recharges are either rejected or accepted based on calibrated groundwater head and local variation of the K field. The results indicate that the natural total inflow to the study domain is between 7107 and 12,772 afy. During the initial-fill phase of pumped storage project, the total outflow exceeds the upper bound estimate of the inflow. If the initial-fill is annualized to 20 years, the average pumping is more than the lower bound of inflows. The results indicate after adding the pumped storage project, the system will nearing, if not exceeding, its maximum renewable pumping capacity. The accepted recharges lead to a drawdown range of 24 to 45 ft for an assumed specific yield of 0.05. However, the drawdown is sensitive to this parameter, whereas there is insufficient data to adequately constrain this parameter.« less

Estimation of settling velocity of sediment particles in estuarine and coastal waters

NASA Astrophysics Data System (ADS)

Nasiha, Hussain J.; Shanmugam, Palanisamy

2018-04-01

A model for estimating the settling velocity of sediment particles (spherical and non-spherical) in estuarine and coastal waters is developed and validated using experimental data. The model combines the physical, optical and hydrodynamic properties of the particles and medium to estimate the sediment settling velocity. The well-known Stokes law is broadened to account for the influencing factors of settling velocity such as particle size, shape and density. To derive the model parameters, laboratory experiments were conducted using natural flaky seashells, spherical beach sands and ball-milled seashell powders. Spectral light backscattering measurements of settling particles in a water tank were made showing a distinct optical feature with a peak shifting from 470-490 nm to 500-520 nm for particle populations from spherical to flaky grains. This significant optical feature was used as a proxy to make a shape determination in the present model. Other parameters experimentally determined included specific gravity (ΔSG) , Corey shape factor (CSF) , median grain diameter (D50) , drag coefficient (Cd) and Reynolds number (Re) . The CSF values considered ranged from 0.2 for flaky to 1.0 for perfectly spherical grains and Reynolds numbers from 2.0 to 105 for the laminar to turbulent flow regimes. The specific gravity of submerged particles was optically derived and used along with these parameters to estimate the sediment settling velocity. Comparison with the experiment data showed that the present model estimated settling velocities of spherical and non-spherical particles that were closely consistent with the measured values. Findings revealed that for a given D50, the flaky particles caused a greater decrease in settling velocity than the spherical particles which suggests that the particle shape factor has a profound role in influencing the sediment settling velocity and drag coefficients, especially in transitional and turbulent flow regimes. The present model can be easily adopted for various scientific and operational applications since the required parameters are readily measurable with the commercially available instrumentations.

Nested Sampling for Bayesian Model Comparison in the Context of Salmonella Disease Dynamics

PubMed Central

Dybowski, Richard; McKinley, Trevelyan J.; Mastroeni, Pietro; Restif, Olivier

2013-01-01

Understanding the mechanisms underlying the observed dynamics of complex biological systems requires the statistical assessment and comparison of multiple alternative models. Although this has traditionally been done using maximum likelihood-based methods such as Akaike's Information Criterion (AIC), Bayesian methods have gained in popularity because they provide more informative output in the form of posterior probability distributions. However, comparison between multiple models in a Bayesian framework is made difficult by the computational cost of numerical integration over large parameter spaces. A new, efficient method for the computation of posterior probabilities has recently been proposed and applied to complex problems from the physical sciences. Here we demonstrate how nested sampling can be used for inference and model comparison in biological sciences. We present a reanalysis of data from experimental infection of mice with Salmonella enterica showing the distribution of bacteria in liver cells. In addition to confirming the main finding of the original analysis, which relied on AIC, our approach provides: (a) integration across the parameter space, (b) estimation of the posterior parameter distributions (with visualisations of parameter correlations), and (c) estimation of the posterior predictive distributions for goodness-of-fit assessments of the models. The goodness-of-fit results suggest that alternative mechanistic models and a relaxation of the quasi-stationary assumption should be considered. PMID:24376528

Dynamic modeling, property investigation, and adaptive controller design of serial robotic manipulators modeled with structural compliance

NASA Technical Reports Server (NTRS)

Tesar, Delbert; Tosunoglu, Sabri; Lin, Shyng-Her

1990-01-01

Research results on general serial robotic manipulators modeled with structural compliances are presented. Two compliant manipulator modeling approaches, distributed and lumped parameter models, are used in this study. System dynamic equations for both compliant models are derived by using the first and second order influence coefficients. Also, the properties of compliant manipulator system dynamics are investigated. One of the properties, which is defined as inaccessibility of vibratory modes, is shown to display a distinct character associated with compliant manipulators. This property indicates the impact of robot geometry on the control of structural oscillations. Example studies are provided to illustrate the physical interpretation of inaccessibility of vibratory modes. Two types of controllers are designed for compliant manipulators modeled by either lumped or distributed parameter techniques. In order to maintain the generality of the results, neither linearization is introduced. Example simulations are given to demonstrate the controller performance. The second type controller is also built for general serial robot arms and is adaptive in nature which can estimate uncertain payload parameters on-line and simultaneously maintain trajectory tracking properties. The relation between manipulator motion tracking capability and convergence of parameter estimation properties is discussed through example case studies. The effect of control input update delays on adaptive controller performance is also studied.

Assessing locomotor skills development in childhood using wearable inertial sensor devices: the running paradigm.

PubMed

Masci, Ilaria; Vannozzi, Giuseppe; Bergamini, Elena; Pesce, Caterina; Getchell, Nancy; Cappozzo, Aurelio

2013-04-01

Objective quantitative evaluation of motor skill development is of increasing importance to carefully drive physical exercise programs in childhood. Running is a fundamental motor skill humans adopt to accomplish locomotion, which is linked to physical activity levels, although the assessment is traditionally carried out using qualitative evaluation tests. The present study aimed at investigating the feasibility of using inertial sensors to quantify developmental differences in the running pattern of young children. Qualitative and quantitative assessment tools were adopted to identify a skill-sensitive set of biomechanical parameters for running and to further our understanding of the factors that determine progression to skilled running performance. Running performances of 54 children between the ages of 2 and 12 years were submitted to both qualitative and quantitative analysis, the former using sequences of developmental level, the latter estimating temporal and kinematic parameters from inertial sensor measurements. Discriminant analysis with running developmental level as dependent variable allowed to identify a set of temporal and kinematic parameters, within those obtained with the sensor, that best classified children into the qualitative developmental levels (accuracy higher than 67%). Multivariate analysis of variance with the quantitative parameters as dependent variables allowed to identify whether and which specific parameters or parameter subsets were differentially sensitive to specific transitions between contiguous developmental levels. The findings showed that different sets of temporal and kinematic parameters are able to tap all steps of the transitional process in running skill described through qualitative observation and can be prospectively used for applied diagnostic and sport training purposes. Copyright © 2012 Elsevier B.V. All rights reserved.

A numerical scheme to calculate temperature and salinity dependent air-water transfer velocities for any gas

NASA Astrophysics Data System (ADS)

Johnson, M. T.

2010-10-01

The ocean-atmosphere flux of a gas can be calculated from its measured or estimated concentration gradient across the air-sea interface and the transfer velocity (a term representing the conductivity of the layers either side of the interface with respect to the gas of interest). Traditionally the transfer velocity has been estimated from empirical relationships with wind speed, and then scaled by the Schmidt number of the gas being transferred. Complex, physically based models of transfer velocity (based on more physical forcings than wind speed alone), such as the NOAA COARE algorithm, have more recently been applied to well-studied gases such as carbon dioxide and DMS (although many studies still use the simpler approach for these gases), but there is a lack of validation of such schemes for other, more poorly studied gases. The aim of this paper is to provide a flexible numerical scheme which will allow the estimation of transfer velocity for any gas as a function of wind speed, temperature and salinity, given data on the solubility and liquid molar volume of the particular gas. New and existing parameterizations (including a novel empirical parameterization of the salinity-dependence of Henry's law solubility) are brought together into a scheme implemented as a modular, extensible program in the R computing environment which is available in the supplementary online material accompanying this paper; along with input files containing solubility and structural data for ~90 gases of general interest, enabling the calculation of their total transfer velocities and component parameters. Comparison of the scheme presented here with alternative schemes and methods for calculating air-sea flux parameters shows good agreement in general. It is intended that the various components of this numerical scheme should be applied only in the absence of experimental data providing robust values for parameters for a particular gas of interest.

Revision of the Phenomenological Characteristics of the Algol-Type Stars Using the Nav Algorithm

NASA Astrophysics Data System (ADS)

Tkachenko, M. G.; Andronov, I. L.; Chinarova, L. L.

Phenomenological characteristics of the sample of the Algol-type stars are revised using a recently developed NAV ("New Algol Variable") algorithm (2012Ap.....55..536A, 2012arXiv 1212.6707A) and compared to that obtained using common methods of Trigonometric Polynomial Fit (TP) or local Algebraic Polynomial (A) fit of a fixed or (alternately) statistically optimal degree (1994OAP.....7...49A, 2003ASPC..292..391A). The computer program NAV is introduced, which allows to determine the best fit with 7 "linear" and 5 "nonlinear" parameters and their error estimates. The number of parameters is much smaller than for the TP fit (typically 20-40, depending on the width of the eclipse, and is much smaller (5-20) for the W UMa and β Lyrae-type stars. This causes more smooth approximation taking into account the reflection and ellipsoidal effects (TP2) and generally different shapes of the primary and secondary eclipses. An application of the method to two-color CCD photometry to the recently discovered eclipsing variable 2MASS J18024395 + 4003309 = VSX J180243.9 +400331 (2015JASS...32..101A) allowed to make estimates of the physical parameters of the binary system based on the phenomenological parameters of the light curve. The phenomenological parameters of the light curves were determined for the sample of newly discovered EA and EW-type stars (VSX J223429.3+552903, VSX J223421.4+553013, VSX J223416.2+553424, USNO-B1.0 1347-0483658, UCAC3-191-085589, VSX J180755.6+074711= UCAC3 196-166827). Despite we have used original observations published by the discoverers, the accuracy estimates of the period using the NAV method are typically better than the original ones.

A Hydrological Modeling Framework for Flood Risk Assessment for Japan

NASA Astrophysics Data System (ADS)

Ashouri, H.; Chinnayakanahalli, K.; Chowdhary, H.; Sen Gupta, A.

2016-12-01

Flooding has been the most frequent natural disaster that claims lives and imposes significant economic losses to human societies worldwide. Japan, with an annual rainfall of up to approximately 4000 mm is extremely vulnerable to flooding. The focus of this research is to develop a macroscale hydrologic model for simulating flooding toward an improved understanding and assessment of flood risk across Japan. The framework employs a conceptual hydrological model, known as the Probability Distributed Model (PDM), as well as the Muskingum-Cunge flood routing procedure for simulating streamflow. In addition, a Temperature-Index model is incorporated to account for snowmelt and its contribution to streamflow. For an efficient calibration of the model, in terms of computational timing and convergence of the parameters, a set of A Priori parameters is obtained based on the relationships between the model parameters and the physical properties of watersheds. In this regard, we have implemented a particle tracking algorithm and a statistical model which use high resolution Digital Terrain Models to estimate different time related parameters of the model such as time to peak of the unit hydrograph. In addition, global soil moisture and depth data are used to generate A Priori estimation of maximum soil moisture capacity, an important parameter of the PDM model. Once the model is calibrated, its performance is examined during the Typhoon Nabi which struck Japan in September 2005 and caused severe flooding throughout the country. The model is also validated for the extreme precipitation event in 2012 which affected Kyushu. In both cases, quantitative measures show that simulated streamflow depicts good agreement with gauge-based observations. The model is employed to simulate thousands of possible flood events for the entire Japan which makes a basis for a comprehensive flood risk assessment and loss estimation for the flood insurance industry.

Chemical and isotopic methods for quantifying ground-water recharge in a regional, semiarid environment

USGS Publications Warehouse

Wood, Warren W.; Sanford, Ward E.

1995-01-01

The High Plains aquifer underlying the semiarid Southern High Plains of Texas and New Mexico, USA was used to illustrate solute and isotopic methods for evaluating recharge fluxes, runoff, and spatial and temporal distribution of recharge. The chloride mass-balance method can provide, under certain conditions, a time-integrated technique for evaluation of recharge flux to regional aquifers that is independent of physical parameters. Applying this method to the High Plains aquifer of the Southern High Plains suggests that recharge flux is approximately 2% of precipitation, or approximately 11 ± 2 mm/y, consistent with previous estimates based on a variety of physically based measurements. The method is useful because long-term average precipitation and chloride concentrations in rain and ground water have less uncertainty and are generally less expensive to acquire than physically based parameters commonly used in analyzing recharge. Spatial and temporal distribution of recharge was evaluated by use of δ2H, δ18O, and tritium concentrations in both ground water and the unsaturated zone. Analyses suggest that nearly half of the recharge to the Southern High Plains occurs as piston flow through playa basin floors that occupy approximately 6% of the area, and that macropore recharge may be important in the remaining recharge. Tritium and chloride concentrations in the unsaturated zone were used in a new equation developed to quantify runoff. Using this equation and data from a representative basin, runoff was found to be 24 ± 3 mm/y; that is in close agreement with values obtained from water-balance measurements on experimental watersheds in the area. Such geochemical estimates are possible because tritium is used to calculate a recharge flux that is independent of precipitation and runoff, whereas recharge flux based on chloride concentration in the unsaturated zone is dependent upon the amount of runoff. The difference between these two estimates yields the amount of runoff to the basin.

Benchmarking test of empirical root water uptake models

NASA Astrophysics Data System (ADS)

dos Santos, Marcos Alex; de Jong van Lier, Quirijn; van Dam, Jos C.; Freire Bezerra, Andre Herman

2017-01-01

Detailed physical models describing root water uptake (RWU) are an important tool for the prediction of RWU and crop transpiration, but the hydraulic parameters involved are hardly ever available, making them less attractive for many studies. Empirical models are more readily used because of their simplicity and the associated lower data requirements. The purpose of this study is to evaluate the capability of some empirical models to mimic the RWU distribution under varying environmental conditions predicted from numerical simulations with a detailed physical model. A review of some empirical models used as sub-models in ecohydrological models is presented, and alternative empirical RWU models are proposed. All these empirical models are analogous to the standard Feddes model, but differ in how RWU is partitioned over depth or how the transpiration reduction function is defined. The parameters of the empirical models are determined by inverse modelling of simulated depth-dependent RWU. The performance of the empirical models and their optimized empirical parameters depends on the scenario. The standard empirical Feddes model only performs well in scenarios with low root length density R, i.e. for scenarios with low RWU compensation. For medium and high R, the Feddes RWU model cannot mimic properly the root uptake dynamics as predicted by the physical model. The Jarvis RWU model in combination with the Feddes reduction function (JMf) only provides good predictions for low and medium R scenarios. For high R, it cannot mimic the uptake patterns predicted by the physical model. Incorporating a newly proposed reduction function into the Jarvis model improved RWU predictions. Regarding the ability of the models to predict plant transpiration, all models accounting for compensation show good performance. The Akaike information criterion (AIC) indicates that the Jarvis (2010) model (JMII), with no empirical parameters to be estimated, is the best model. The proposed models are better in predicting RWU patterns similar to the physical model. The statistical indices point to them as the best alternatives for mimicking RWU predictions of the physical model.

Optimal Model-Based Fault Estimation and Correction for Particle Accelerators and Industrial Plants Using Combined Support Vector Machines and First Principles Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sayyar-Rodsari, Bijan; Schweiger, Carl; /SLAC /Pavilion Technologies, Inc., Austin, TX

2010-08-25

Timely estimation of deviations from optimal performance in complex systems and the ability to identify corrective measures in response to the estimated parameter deviations has been the subject of extensive research over the past four decades. The implications in terms of lost revenue from costly industrial processes, operation of large-scale public works projects and the volume of the published literature on this topic clearly indicates the significance of the problem. Applications range from manufacturing industries (integrated circuits, automotive, etc.), to large-scale chemical plants, pharmaceutical production, power distribution grids, and avionics. In this project we investigated a new framework for buildingmore » parsimonious models that are suited for diagnosis and fault estimation of complex technical systems. We used Support Vector Machines (SVMs) to model potentially time-varying parameters of a First-Principles (FP) description of the process. The combined SVM & FP model was built (i.e. model parameters were trained) using constrained optimization techniques. We used the trained models to estimate faults affecting simulated beam lifetime. In the case where a large number of process inputs are required for model-based fault estimation, the proposed framework performs an optimal nonlinear principal component analysis of the large-scale input space, and creates a lower dimension feature space in which fault estimation results can be effectively presented to the operation personnel. To fulfill the main technical objectives of the Phase I research, our Phase I efforts have focused on: (1) SVM Training in a Combined Model Structure - We developed the software for the constrained training of the SVMs in a combined model structure, and successfully modeled the parameters of a first-principles model for beam lifetime with support vectors. (2) Higher-order Fidelity of the Combined Model - We used constrained training to ensure that the output of the SVM (i.e. the parameters of the beam lifetime model) are physically meaningful. (3) Numerical Efficiency of the Training - We investigated the numerical efficiency of the SVM training. More specifically, for the primal formulation of the training, we have developed a problem formulation that avoids the linear increase in the number of the constraints as a function of the number of data points. (4) Flexibility of Software Architecture - The software framework for the training of the support vector machines was designed to enable experimentation with different solvers. We experimented with two commonly used nonlinear solvers for our simulations. The primary application of interest for this project has been the sustained optimal operation of particle accelerators at the Stanford Linear Accelerator Center (SLAC). Particle storage rings are used for a variety of applications ranging from 'colliding beam' systems for high-energy physics research to highly collimated x-ray generators for synchrotron radiation science. Linear accelerators are also used for collider research such as International Linear Collider (ILC), as well as for free electron lasers, such as the Linear Coherent Light Source (LCLS) at SLAC. One common theme in the operation of storage rings and linear accelerators is the need to precisely control the particle beams over long periods of time with minimum beam loss and stable, yet challenging, beam parameters. We strongly believe that beyond applications in particle accelerators, the high fidelity and cost benefits of a combined model-based fault estimation/correction system will attract customers from a wide variety of commercial and scientific industries. Even though the acquisition of Pavilion Technologies, Inc. by Rockwell Automation Inc. in 2007 has altered the small business status of the Pavilion and it no longer qualifies for a Phase II funding, our findings in the course of the Phase I research have convinced us that further research will render a workable model-based fault estimation and correction for particle accelerators and industrial plants feasible.« less

Uncertainty and the Social Cost of Methane Using Bayesian Constrained Climate Models

NASA Astrophysics Data System (ADS)

Errickson, F. C.; Anthoff, D.; Keller, K.

2016-12-01

Social cost estimates of greenhouse gases are important for the design of sound climate policies and are also plagued by uncertainty. One major source of uncertainty stems from the simplified representation of the climate system used in the integrated assessment models that provide these social cost estimates. We explore how uncertainty over the social cost of methane varies with the way physical processes and feedbacks in the methane cycle are modeled by (i) coupling three different methane models to a simple climate model, (ii) using MCMC to perform a Bayesian calibration of the three coupled climate models that simulates direct sampling from the joint posterior probability density function (pdf) of model parameters, and (iii) producing probabilistic climate projections that are then used to calculate the Social Cost of Methane (SCM) with the DICE and FUND integrated assessment models. We find that including a temperature feedback in the methane cycle acts as an additional constraint during the calibration process and results in a correlation between the tropospheric lifetime of methane and several climate model parameters. This correlation is not seen in the models lacking this feedback. Several of the estimated marginal pdfs of the model parameters also exhibit different distributional shapes and expected values depending on the methane model used. As a result, probabilistic projections of the climate system out to the year 2300 exhibit different levels of uncertainty and magnitudes of warming for each of the three models under an RCP8.5 scenario. We find these differences in climate projections result in differences in the distributions and expected values for our estimates of the SCM. We also examine uncertainty about the SCM by performing a Monte Carlo analysis using a distribution for the climate sensitivity while holding all other climate model parameters constant. Our SCM estimates using the Bayesian calibration are lower and exhibit less uncertainty about extremely high values in the right tail of the distribution compared to the Monte Carlo approach. This finding has important climate policy implications and suggests previous work that accounts for climate model uncertainty by only varying the climate sensitivity parameter may overestimate the SCM.

Parametric soil water retention models: a critical evaluation of expressions for the full moisture range

NASA Astrophysics Data System (ADS)

Madi, Raneem; Huibert de Rooij, Gerrit; Mielenz, Henrike; Mai, Juliane

2018-02-01

Few parametric expressions for the soil water retention curve are suitable for dry conditions. Furthermore, expressions for the soil hydraulic conductivity curves associated with parametric retention functions can behave unrealistically near saturation. We developed a general criterion for water retention parameterizations that ensures physically plausible conductivity curves. Only 3 of the 18 tested parameterizations met this criterion without restrictions on the parameters of a popular conductivity curve parameterization. A fourth required one parameter to be fixed. We estimated parameters by shuffled complex evolution (SCE) with the objective function tailored to various observation methods used to obtain retention curve data. We fitted the four parameterizations with physically plausible conductivities as well as the most widely used parameterization. The performance of the resulting 12 combinations of retention and conductivity curves was assessed in a numerical study with 751 days of semiarid atmospheric forcing applied to unvegetated, uniform, 1 m freely draining columns for four textures. Choosing different parameterizations had a minor effect on evaporation, but cumulative bottom fluxes varied by up to an order of magnitude between them. This highlights the need for a careful selection of the soil hydraulic parameterization that ideally does not only rely on goodness of fit to static soil water retention data but also on hydraulic conductivity measurements. Parameter fits for 21 soils showed that extrapolations into the dry range of the retention curve often became physically more realistic when the parameterization had a logarithmic dry branch, particularly in fine-textured soils where high residual water contents would otherwise be fitted.

Characterizing Woody Vegetation Spectral and Structural Parameters with a 3-D Scene Model

NASA Astrophysics Data System (ADS)

Qin, W.; Yang, L.

2004-05-01

Quantification of structural and biophysical parameters of woody vegetation is of great significance in understanding vegetation condition, dynamics and functionality. Such information over a landscape scale is crucial for global and regional land cover characterization, global carbon-cycle research, forest resource inventories, and fire fuel estimation. While great efforts and progress have been made in mapping general land cover types over large area, at present, the ability to quantify regional woody vegetation structural and biophysical parameters is limited. One approach to address this research issue is through an integration of physically based 3-D scene model with multiangle and multispectral remote sensing data and in-situ measurements. The first step of this work is to model woody vegetation structure and its radiation regime using a physically based 3-D scene model and field data, before a robust operational algorithm can be developed for retrieval of important woody vegetation structural/biophysical parameters. In this study, we use an advanced 3-D scene model recently developed by Qin and Gerstl (2000), based on L-systems and radiosity theories. This 3-D scene model has been successfully applied to semi-arid shrubland to study structure and radiation regime at a regional scale. We apply this 3-D scene model to a more complicated and heterogeneous forest environment dominated by deciduous and coniferous trees. The data used in this study are from a field campaign conducted by NASA in a portion of the Superior National Forest (SNF) near Ely, Minnesota during the summers of 1983 and 1984, and supplement data collected during our revisit to the same area of SNF in summer of 2003. The model is first validated with reflectance measurements at different scales (ground observations, helicopter, aircraft, and satellite). Then its ability to characterize the structural and spectral parameters of the forest scene is evaluated. Based on the results from this study and the current multi-spectral and multi-angular satellite data (MODIS, MISR), a robust retrieval system to estimate woody vegetation structural/biophysical parameters is proposed.

Multi-physics and multi-scale characterization of shale anisotropy

NASA Astrophysics Data System (ADS)

Sarout, J.; Nadri, D.; Delle Piane, C.; Esteban, L.; Dewhurst, D.; Clennell, M. B.

2012-12-01

Shales are the most abundant sedimentary rock type in the Earth's shallow crust. In the past decade or so, they have attracted increased attention from the petroleum industry as reservoirs, as well as more traditionally for their sealing capacity for hydrocarbon/CO2 traps or underground waste repositories. The effectiveness of both fundamental and applied shale research is currently limited by (i) the extreme variability of physical, mechanical and chemical properties observed for these rocks, and by (ii) the scarce data currently available. The variability in observed properties is poorly understood due to many factors that are often irrelevant for other sedimentary rocks. The relationships between these properties and the petrophysical measurements performed at the field and laboratory scales are not straightforward, translating to a scale dependency typical of shale behaviour. In addition, the complex and often anisotropic micro-/meso-structures of shales give rise to a directional dependency of some of the measured physical properties that are tensorial by nature such as permeability or elastic stiffness. Currently, fundamental understanding of the parameters controlling the directional and scale dependency of shale properties is far from complete. Selected results of a multi-physics laboratory investigation of the directional and scale dependency of some critical shale properties are reported. In particular, anisotropic features of shale micro-/meso-structures are related to the directional-dependency of elastic and fluid transport properties: - Micro-/meso-structure (μm to cm scale) characterization by electron microscopy and X-ray tomography; - Estimation of elastic anisotropy parameters on a single specimen using elastic wave propagation (cm scale); - Estimation of the permeability tensor using the steady-state method on orthogonal specimens (cm scale); - Estimation of the low-frequency diffusivity tensor using NMR method on orthogonal specimens (<μm scale). For each of the above properties, leading-edge experimental techniques have been associated with novel interpretation tools. In this contribution, these experimental and interpretation methods are described. Relationships between the measured properties and the corresponding micro-/meso-structural features are discussed. For example, P-wave velocity was measured along 100 different propagation paths on a single cylindrical shale specimen using miniature ultrasonic transducers. Assuming that (i) the elastic tensor of this shale is transversely isotropic; and (i) the sample has been cored perfectly perpendicular to the bedding plane (symmetry plane is horizontal), Thomsen's anisotropy parameters inverted from the measured velocities are: - P-wave velocity along the symmetry axis (perpendicular to the bedding plane) αo=3.45km/s; - P-wave anisotropy ɛ=0.12; - Parameter controlling the wave front geometry δ=0.058. A novel inversion algorithm allows for recovering these parameters without assuming a priori a horizontal bedding (symmetry) plane. The inversion of the same data set using this algorithm yields (i) αo=3.23km/s, ɛ=0.25 and δ=0.18, and (ii) the elastic symmetry axis is inclined of ω=30° with respect to the specimen's axis. Such difference can have strong impact on field applications (AVO, ray tracing, tomography).

Estimating network effect in geocenter motion: Applications

NASA Astrophysics Data System (ADS)

Zannat, Umma Jamila; Tregoning, Paul

2017-10-01

The network effect is the error associated with the subsampling of the Earth surface by space geodetic networks. It is an obstacle toward the precise measurement of geocenter motion, that is, the relative motion between the center of mass of the Earth system and the center of figure of the Earth surface. In a complementary paper, we proposed a theoretical approach to estimate the magnitude of this effect from the displacement fields predicted by geophysical models. Here we evaluate the effectiveness of our estimate for two illustrative physical processes: coseismic displacements inducing instantaneous changes in the Helmert parameters and elastic deformation due to surface water movements causing secular drifts in those parameters. For the first, we consider simplified models of the 2004 Sumatra-Andaman and the 2011 Tōhoku-Oki earthquakes, and for the second, we use the observations of the Gravity Recovery and Climate Experiment, complemented by an ocean model. In both case studies, it is found that the magnitude of the network effect, even for a large global network, is often as large as the magnitude of the changes in the Helmert parameters themselves. However, we also show that our proposed modification to the definition of the center of network frame to include weights proportional to the area of the Earth surface that the stations represent can significantly reduce the network effect in most cases.

Isotherm, kinetic, and thermodynamic study of ciprofloxacin sorption on sediments.

PubMed

Mutavdžić Pavlović, Dragana; Ćurković, Lidija; Grčić, Ivana; Šimić, Iva; Župan, Josip

2017-04-01

In this study, equilibrium isotherms, kinetics and thermodynamics of ciprofloxacin on seven sediments in a batch sorption process were examined. The effects of contact time, initial ciprofloxacin concentration, temperature and ionic strength on the sorption process were studied. The K d parameter from linear sorption model was determined by linear regression analysis, while the Freundlich and Dubinin-Radushkevich (D-R) sorption models were applied to describe the equilibrium isotherms by linear and nonlinear methods. The estimated K d values varied from 171 to 37,347 mL/g. The obtained values of E (free energy estimated from D-R isotherm model) were between 3.51 and 8.64 kJ/mol, which indicated a physical nature of ciprofloxacin sorption on studied sediments. According to obtained n values as measure of intensity of sorption estimate from Freundlich isotherm model (from 0.69 to 1.442), ciprofloxacin sorption on sediments can be categorized from poor to moderately difficult sorption characteristics. Kinetics data were best fitted by the pseudo-second-order model (R 2  > 0.999). Thermodynamic parameters including the Gibbs free energy (ΔG°), enthalpy (ΔH°) and entropy (ΔS°) were calculated to estimate the nature of ciprofloxacin sorption. Results suggested that sorption on sediments was a spontaneous exothermic process.

Simultaneous Estimation of Microphysical Parameters and Atmospheric State Variables With Radar Data and Ensemble Square-root Kalman Filter

NASA Astrophysics Data System (ADS)

Tong, M.; Xue, M.

2006-12-01

An important source of model error for convective-scale data assimilation and prediction is microphysical parameterization. This study investigates the possibility of estimating up to five fundamental microphysical parameters, which are closely involved in the definition of drop size distribution of microphysical species in a commonly used single-moment ice microphysics scheme, using radar observations and the ensemble Kalman filter method. The five parameters include the intercept parameters for rain, snow and hail/graupel, and the bulk densities of hail/graupel and snow. Parameter sensitivity and identifiability are first examined. The ensemble square-root Kalman filter (EnSRF) is employed for simultaneous state and parameter estimation. OSS experiments are performed for a model-simulated supercell storm, in which the five microphysical parameters are estimated individually or in different combinations starting from different initial guesses. When error exists in only one of the microphysical parameters, the parameter can be successfully estimated without exception. The estimation of multiple parameters is found to be less robust, with end results of estimation being sensitive to the realization of the initial parameter perturbation. This is believed to be because of the reduced parameter identifiability and the existence of non-unique solutions. The results of state estimation are, however, always improved when simultaneous parameter estimation is performed, even when the estimated parameters values are not accurate.

A posteriori noise estimation in variable data sets. With applications to spectra and light curves

NASA Astrophysics Data System (ADS)

Czesla, S.; Molle, T.; Schmitt, J. H. M. M.

2018-01-01

Most physical data sets contain a stochastic contribution produced by measurement noise or other random sources along with the signal. Usually, neither the signal nor the noise are accurately known prior to the measurement so that both have to be estimated a posteriori. We have studied a procedure to estimate the standard deviation of the stochastic contribution assuming normality and independence, requiring a sufficiently well-sampled data set to yield reliable results. This procedure is based on estimating the standard deviation in a sample of weighted sums of arbitrarily sampled data points and is identical to the so-called DER_SNR algorithm for specific parameter settings. To demonstrate the applicability of our procedure, we present applications to synthetic data, high-resolution spectra, and a large sample of space-based light curves and, finally, give guidelines to apply the procedure in situation not explicitly considered here to promote its adoption in data analysis.

Robust Characterization of Loss Rates

NASA Astrophysics Data System (ADS)

Wallman, Joel J.; Barnhill, Marie; Emerson, Joseph

2015-08-01

Many physical implementations of qubits—including ion traps, optical lattices and linear optics—suffer from loss. A nonzero probability of irretrievably losing a qubit can be a substantial obstacle to fault-tolerant methods of processing quantum information, requiring new techniques to safeguard against loss that introduce an additional overhead that depends upon the loss rate. Here we present a scalable and platform-independent protocol for estimating the average loss rate (averaged over all input states) resulting from an arbitrary Markovian noise process, as well as an independent estimate of detector efficiency. Moreover, we show that our protocol gives an additional constraint on estimated parameters from randomized benchmarking that improves the reliability of the estimated error rate and provides a new indicator for non-Markovian signatures in the experimental data. We also derive a bound for the state-dependent loss rate in terms of the average loss rate.

Enhancing PTFs with remotely sensed data for multi-scale soil water retention estimation

NASA Astrophysics Data System (ADS)

Jana, Raghavendra B.; Mohanty, Binayak P.

2011-03-01

SummaryUse of remotely sensed data products in the earth science and water resources fields is growing due to increasingly easy availability of the data. Traditionally, pedotransfer functions (PTFs) employed for soil hydraulic parameter estimation from other easily available data have used basic soil texture and structure information as inputs. Inclusion of surrogate/supplementary data such as topography and vegetation information has shown some improvement in the PTF's ability to estimate more accurate soil hydraulic parameters. Artificial neural networks (ANNs) are a popular tool for PTF development, and are usually applied across matching spatial scales of inputs and outputs. However, different hydrologic, hydro-climatic, and contaminant transport models require input data at different scales, all of which may not be easily available from existing databases. In such a scenario, it becomes necessary to scale the soil hydraulic parameter values estimated by PTFs to suit the model requirements. Also, uncertainties in the predictions need to be quantified to enable users to gauge the suitability of a particular dataset in their applications. Bayesian Neural Networks (BNNs) inherently provide uncertainty estimates for their outputs due to their utilization of Markov Chain Monte Carlo (MCMC) techniques. In this paper, we present a PTF methodology to estimate soil water retention characteristics built on a Bayesian framework for training of neural networks and utilizing several in situ and remotely sensed datasets jointly. The BNN is also applied across spatial scales to provide fine scale outputs when trained with coarse scale data. Our training data inputs include ground/remotely sensed soil texture, bulk density, elevation, and Leaf Area Index (LAI) at 1 km resolutions, while similar properties measured at a point scale are used as fine scale inputs. The methodology was tested at two different hydro-climatic regions. We also tested the effect of varying the support scale of the training data for the BNNs by sequentially aggregating finer resolution training data to coarser resolutions, and the applicability of the technique to upscaling problems. The BNN outputs are corrected for bias using a non-linear CDF-matching technique. Final results show good promise of the suitability of this Bayesian Neural Network approach for soil hydraulic parameter estimation across spatial scales using ground-, air-, or space-based remotely sensed geophysical parameters. Inclusion of remotely sensed data such as elevation and LAI in addition to in situ soil physical properties improved the estimation capabilities of the BNN-based PTF in certain conditions.

Structural Reliability Using Probability Density Estimation Methods Within NESSUS

NASA Technical Reports Server (NTRS)

Chamis, Chrisos C. (Technical Monitor); Godines, Cody Ric

2003-01-01

A reliability analysis studies a mathematical model of a physical system taking into account uncertainties of design variables and common results are estimations of a response density, which also implies estimations of its parameters. Some common density parameters include the mean value, the standard deviation, and specific percentile(s) of the response, which are measures of central tendency, variation, and probability regions, respectively. Reliability analyses are important since the results can lead to different designs by calculating the probability of observing safe responses in each of the proposed designs. All of this is done at the expense of added computational time as compared to a single deterministic analysis which will result in one value of the response out of many that make up the density of the response. Sampling methods, such as monte carlo (MC) and latin hypercube sampling (LHS), can be used to perform reliability analyses and can compute nonlinear response density parameters even if the response is dependent on many random variables. Hence, both methods are very robust; however, they are computationally expensive to use in the estimation of the response density parameters. Both methods are 2 of 13 stochastic methods that are contained within the Numerical Evaluation of Stochastic Structures Under Stress (NESSUS) program. NESSUS is a probabilistic finite element analysis (FEA) program that was developed through funding from NASA Glenn Research Center (GRC). It has the additional capability of being linked to other analysis programs; therefore, probabilistic fluid dynamics, fracture mechanics, and heat transfer are only a few of what is possible with this software. The LHS method is the newest addition to the stochastic methods within NESSUS. Part of this work was to enhance NESSUS with the LHS method. The new LHS module is complete, has been successfully integrated with NESSUS, and been used to study four different test cases that have been proposed by the Society of Automotive Engineers (SAE). The test cases compare different probabilistic methods within NESSUS because it is important that a user can have confidence that estimates of stochastic parameters of a response will be within an acceptable error limit. For each response, the mean, standard deviation, and 0.99 percentile, are repeatedly estimated which allows confidence statements to be made for each parameter estimated, and for each method. Thus, the ability of several stochastic methods to efficiently and accurately estimate density parameters is compared using four valid test cases. While all of the reliability methods used performed quite well, for the new LHS module within NESSUS it was found that it had a lower estimation error than MC when they were used to estimate the mean, standard deviation, and 0.99 percentile of the four different stochastic responses. Also, LHS required a smaller amount of calculations to obtain low error answers with a high amount of confidence than MC. It can therefore be stated that NESSUS is an important reliability tool that has a variety of sound probabilistic methods a user can employ and the newest LHS module is a valuable new enhancement of the program.

WE-G-204-01: BEST IN PHYSICS (IMAGING): Effect of Image Processing Parameters On Nodule Detectability in Chest Radiography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Little, K; Lu, Z; MacMahon, H

Purpose: To investigate the effect of varying system image processing parameters on lung nodule detectability in digital radiography. Methods: An anthropomorphic chest phantom was imaged in the posterior-anterior position using a GE Discovery XR656 digital radiography system. To simulate lung nodules, a polystyrene board with 6.35mm diameter PMMA spheres was placed adjacent to the phantom (into the x-ray path). Due to magnification, the projected simulated nodules had a diameter in the radiographs of approximately 7.5 mm. The images were processed using one of GE’s default chest settings (Factory3) and reprocessed by varying the “Edge” and “Tissue Contrast” processing parameters, whichmore » were the two user-configurable parameters for a single edge and contrast enhancement algorithm. For each parameter setting, the nodule signals were calculated by subtracting the chest-only image from the image with simulated nodules. Twenty nodule signals were averaged, Gaussian filtered, and radially averaged in order to generate an approximately noiseless signal. For each processing parameter setting, this noise-free signal and 180 background samples from across the lung were used to estimate ideal observer performance in a signal-known-exactly detection task. Performance was estimated using a channelized Hotelling observer with 10 Laguerre-Gauss channel functions. Results: The “Edge” and “Tissue Contrast” parameters each had an effect on the detectability as calculated by the model observer. The CHO-estimated signal detectability ranged from 2.36 to 2.93 and was highest for “Edge” = 4 and “Tissue Contrast” = −0.15. In general, detectability tended to decrease as “Edge” was increased and as “Tissue Contrast” was increased. A human observer study should be performed to validate the relation to human detection performance. Conclusion: Image processing parameters can affect lung nodule detection performance in radiography. While validation with a human observer study is needed, model observer detectability for common tasks could provide a means for optimizing image processing parameters.« less

A Software Toolkit to Study Systematic Uncertainties of the Physics Models of the Geant4 Simulation Package

DOE Office of Scientific and Technical Information (OSTI.GOV)

Genser, Krzysztof; Hatcher, Robert; Perdue, Gabriel

2016-11-10

The Geant4 toolkit is used to model interactions between particles and matter. Geant4 employs a set of validated physics models that span a wide range of interaction energies. These models are tuned to cover a large variety of possible applications. This raises the critical question of what uncertainties are associated with the Geant4 physics model, or group of models, involved in a simulation project. To address the challenge, we have designed and implemented a comprehen- sive, modular, user-friendly software toolkit that allows the variation of one or more parameters of one or more Geant4 physics models involved in simulation studies.more » It also enables analysis of multiple variants of the resulting physics observables of interest in order to estimate the uncertain- ties associated with the simulation model choices. Key functionalities of the toolkit are presented in this paper and are illustrated with selected results.« less

Bounded diffusion impedance characterization of battery electrodes using fractional modeling

NASA Astrophysics Data System (ADS)

Gabano, Jean-Denis; Poinot, Thierry; Huard, Benoît

2017-06-01

This article deals with the ability of fractional modeling to describe the bounded diffusion behavior encountered in modern thin film and nanoparticles lithium battery electrodes. Indeed, the diffusion impedance of such batteries behaves as a half order integrator characterized by the Warburg impedance at high frequencies and becomes a classical integrator described by a capacitor at low frequencies. The transition between these two behaviors depends on the particles geometry. Three of them will be considered in this paper: planar, cylindrical and spherical ones. The fractional representation proposed is a gray box model able to perfectly fit the low and high frequency diffusive impedance behaviors while optimizing the frequency response transition. Identification results are provided using frequential simulation data considering the three electrochemical diffusion models based on the particles geometry. Furthermore, knowing this geometry allows to estimate the diffusion ionic resistance and time constant using the relationships linking these physical parameters to the structural fractional model parameters. Finally, other simulations using Randles impedance models including the charge transfer impedance and the external resistance demonstrate the interest of fractional modeling in order to identify properly not only the charge transfer impedance but also the diffusion physical parameters whatever the particles geometry.

Detection of damage in welded structure using experimental modal data

NASA Astrophysics Data System (ADS)

Abu Husain, N.; Ouyang, H.

2011-07-01

A typical automotive structure could contain thousands of spot weld joints that contribute significantly to the vehicle's structural stiffness and dynamic characteristics. However, some of these joints may be imperfect or even absent during the manufacturing process and they are also highly susceptible to damage due to operational and environmental conditions during the vehicle lifetime. Therefore, early detection and estimation of damage are important so necessary actions can be taken to avoid further problems. Changes in physical parameters due to existence of damage in a structure often leads to alteration of vibration modes; thus demonstrating the dependency between the vibration characteristics and the physical properties of structures. A sensitivity-based model updating method, performed using a combination of MATLAB and NASTRAN, has been selected for the purpose of this work. The updating procedure is regarded as parameter identification which aims to bring the numerical prediction to be as closely as possible to the measured natural frequencies and mode shapes data of the damaged structure in order to identify the damage parameters (characterised by the reductions in the Young's modulus of the weld patches to indicate the loss of material/stiffness at the damage region).

Bayesian Inference of Physics Parameters in the DIII-D Charge-Exchange Recombination Spectroscopy System

NASA Astrophysics Data System (ADS)

Bowman, C.; Gibson, K. J.; La Haye, R. J.; Groebner, R. J.; Taylor, N. Z.; Grierson, B. A.

2014-10-01

A Bayesian inference framework has been developed for the DIII-D charge-exchange recombination (CER) system, capable of computing probability distribution functions (PDFs) for desired parameters. CER is a key diagnostic system at DIII-D, measuring important physics parameters such as plasma rotation and impurity ion temperature. This work is motivated by a case in which the CER system was used to probe the plasma rotation radial profile around an m/n = 2/1 tearing mode island rotating at ~ 1 kHz. Due to limited resolution in the tearing mode phase and short integration time, it has proven challenging to observe the structure of the rotation profile across the island. We seek to solve this problem by using the Bayesian framework to improve the estimation accuracy of the plasma rotation, helping to reveal details of how it is perturbed in the magnetic island vicinity. Examples of the PDFs obtained through the Bayesian framework will be presented, and compared with results from a conventional least-squares analysis of the CER data. Work supported by the US DOE under DE-FC02-04ER54698 and DE-AC02-09CH11466.

Investigation of heat and mass transfer under the influence of variable diffusion coefficient and thermal conductivity

NASA Astrophysics Data System (ADS)

Mohyud Din, S. T.; Zubair, T.; Usman, M.; Hamid, M.; Rafiq, M.; Mohsin, S.

2018-04-01

This study is devoted to analyze the influence of variable diffusion coefficient and variable thermal conductivity on heat and mass transfer in Casson fluid flow. The behavior of concentration and temperature profiles in the presence of Joule heating and viscous dissipation is also studied. The dimensionless conversation laws with suitable BCs are solved via Modified Gegenbauer Wavelets Method (MGWM). It has been observed that increase in Casson fluid parameter (β ) and parameter ɛ enhances the Nusselt number. Moreover, Nusselt number of Newtonian fluid is less than that of the Casson fluid. The phenomenon of mass transport can be increased by solute of variable diffusion coefficient rather than solute of constant diffusion coefficient. A detailed analysis of results is appropriately highlighted. The obtained results, error estimates, and convergence analysis reconfirm the credibility of proposed algorithm. It is concluded that MGWM is an appropriate tool to tackle nonlinear physical models and hence may be extended to some other nonlinear problems of diversified physical nature also.

Leakage current and capacitance characteristics of Si/SiO2/Si single-barrier varactor

NASA Astrophysics Data System (ADS)

Mamor, M.; Fu, Y.; Nur, O.; Willander, M.; Bengtsson, S.

We investigate, both experimentally and theoretically, current and capacitance (I-V/C-V) characteristics and the device performance of Si/SiO2/Si single-barrier varactor diodes (SBVs). Two diodes were fabricated with different SiO2 layer thicknesses using the state-of-the-art wafer bonding technique. The devices have very low leakage currents (about 5×10-2 and 1.8×10-2 mA/mm2) and intrinsic capacitance levels of typically 1.5 and 50 nF/mm2 for diodes with 5-nm and 20-nm oxide layers, respectively. With the present device physical parameters (25-mm2 device area, 760-μm modulation layer thickness and 1015-cm-3 doping level), the estimated cut-off frequency is about 5×107 Hz. With the physical parameters of the present existing III-V triplers, the cut-off frequency of our Si-based SBV can be as high as 0.5 THz.

Delayed neutron spectral data for Hansen-Roach energy group structure

DOE Office of Scientific and Technical Information (OSTI.GOV)

Campbell, J.M.; Spriggs, G.D.

A detailed knowledge of delayed neutron spectra is important in reactor physics. It not only allows for an accurate estimate of the effective delayed neutron fraction {beta}{sub eff} but also is essential to calculating important reactor kinetic parameters, such as effective group abundances and the ratio of {beta}{sub eff} to the prompt neutron generation time. Numerous measurements of delayed neutron spectra for various delayed neutron precursors have been performed and reported in the literature. However, for application in reactor physics calculations, these spectra are usually lumped into one of the traditional six groups of delayed neutrons in accordance to theirmore » half-lives. Subsequently, these six-group spectra are binned into energy intervals corresponding to the energy intervals of a chosen nuclear cross-section set. In this work, the authors present a set of delayed neutron spectra that were formulated specifically to match Keepin`s six-group parameters and the 16-energy-group Hansen-Roach cross sections.« less

Life as an Ex-Physicist on Wall Street

NASA Astrophysics Data System (ADS)

Derman, Emanuel

2001-06-01

Financial theory looks deceptively like physics because of the techniques it uses. But physics models deal with the relatively unchanging parameters of the external world; in contrast, the parameters of financial theory are people's current estimates of, and sentiments about, future behavior. Life as a Ph.D. on Wall St is therefore very different from the way most people in academic life imagine it to be. For most of us, most of the time, it involves neither finding miraculous and previously undiscovered arbitrages, nor doing boring mindless programming. Instead, it demands an interdisciplinary mix of inventive computation, applied mathematics, financial understanding, self-education and educating others, applied in a hectic environment, to help build and maintain a business. Financial modeling as a practitioner means suffering many interruptions when you want to think quietly, and making many pragmatic compromises dictated by constraints on time and resources. It also means a stimulating and lively environment, using your imagination, avoiding complexity, and always trying to bridge the gap between analysis and intuition (yours and others).

Parameter estimation in Cox models with missing failure indicators and the OPPERA study.

PubMed

Brownstein, Naomi C; Cai, Jianwen; Slade, Gary D; Bair, Eric

2015-12-30

In a prospective cohort study, examining all participants for incidence of the condition of interest may be prohibitively expensive. For example, the "gold standard" for diagnosing temporomandibular disorder (TMD) is a physical examination by a trained clinician. In large studies, examining all participants in this manner is infeasible. Instead, it is common to use questionnaires to screen for incidence of TMD and perform the "gold standard" examination only on participants who screen positively. Unfortunately, some participants may leave the study before receiving the "gold standard" examination. Within the framework of survival analysis, this results in missing failure indicators. Motivated by the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) study, a large cohort study of TMD, we propose a method for parameter estimation in survival models with missing failure indicators. We estimate the probability of being an incident case for those lacking a "gold standard" examination using logistic regression. These estimated probabilities are used to generate multiple imputations of case status for each missing examination that are combined with observed data in appropriate regression models. The variance introduced by the procedure is estimated using multiple imputation. The method can be used to estimate both regression coefficients in Cox proportional hazard models as well as incidence rates using Poisson regression. We simulate data with missing failure indicators and show that our method performs as well as or better than competing methods. Finally, we apply the proposed method to data from the OPPERA study. Copyright © 2015 John Wiley & Sons, Ltd.

Development of a Predictive Model for the Long-Term Stability Assessment of Drug-In-Adhesive Transdermal Films Using Polar Pressure-Sensitive Adhesives as Carrier/Matrix.

PubMed

Chenevas-Paule, Clémence; Wolff, Hans-Michael; Ashton, Mark; Schubert, Martin; Dodou, Kalliopi

2017-05-01

Drug crystallization in transdermal drug delivery systems is a critical quality defect. The impact of drug load and hydration on the physical stability of polar (acrylic) drug-in-adhesive (DIA) films was investigated with the objective to identify predictive formulation parameters with respect to drug solubility and long-term stability. Medicated acrylic films were prepared over a range of drug concentrations below and above saturation solubility and were characterized by Fourier transform infrared spectroscopy, differential scanning calorimetry, polarized microscopy, and dynamic vapor sorption (DVS) analysis. Physical stability of medicated films was monitored over 4 months under different storage conditions and was dependent on solubility parameters, Gibbs free energy for drug phase transition from the amorphous to the crystalline state, and relative humidity. DVS data, for assessing H-bonding capacity experimentally, were essential to predict physical stability at different humidities and were used together with Gibbs free energy change and the Hoffman equation to develop a new predictive thermodynamic model to estimate drug solubility and stability in DIA films taking into account relative humidity. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Fast automated analysis of strong gravitational lenses with convolutional neural networks

NASA Astrophysics Data System (ADS)

Hezaveh, Yashar D.; Levasseur, Laurence Perreault; Marshall, Philip J.

2017-08-01

Quantifying image distortions caused by strong gravitational lensing—the formation of multiple images of distant sources due to the deflection of their light by the gravity of intervening structures—and estimating the corresponding matter distribution of these structures (the ‘gravitational lens’) has primarily been performed using maximum likelihood modelling of observations. This procedure is typically time- and resource-consuming, requiring sophisticated lensing codes, several data preparation steps, and finding the maximum likelihood model parameters in a computationally expensive process with downhill optimizers. Accurate analysis of a single gravitational lens can take up to a few weeks and requires expert knowledge of the physical processes and methods involved. Tens of thousands of new lenses are expected to be discovered with the upcoming generation of ground and space surveys. Here we report the use of deep convolutional neural networks to estimate lensing parameters in an extremely fast and automated way, circumventing the difficulties that are faced by maximum likelihood methods. We also show that the removal of lens light can be made fast and automated using independent component analysis of multi-filter imaging data. Our networks can recover the parameters of the ‘singular isothermal ellipsoid’ density profile, which is commonly used to model strong lensing systems, with an accuracy comparable to the uncertainties of sophisticated models but about ten million times faster: 100 systems in approximately one second on a single graphics processing unit. These networks can provide a way for non-experts to obtain estimates of lensing parameters for large samples of data.

EnKF with closed-eye period - bridging intermittent model structural errors in soil hydrology

NASA Astrophysics Data System (ADS)

Bauser, Hannes H.; Jaumann, Stefan; Berg, Daniel; Roth, Kurt

2017-04-01

The representation of soil water movement exposes uncertainties in all model components, namely dynamics, forcing, subscale physics and the state itself. Especially model structural errors in the description of the dynamics are difficult to represent and can lead to an inconsistent estimation of the other components. We address the challenge of a consistent aggregation of information for a manageable specific hydraulic situation: a 1D soil profile with TDR-measured water contents during a time period of less than 2 months. We assess the uncertainties for this situation and detect initial condition, soil hydraulic parameters, small-scale heterogeneity, upper boundary condition, and (during rain events) the local equilibrium assumption by the Richards equation as the most important ones. We employ an iterative Ensemble Kalman Filter (EnKF) with an augmented state. Based on a single rain event, we are able to reduce all uncertainties directly, except for the intermittent violation of the local equilibrium assumption. We detect these times by analyzing the temporal evolution of estimated parameters. By introducing a closed-eye period - during which we do not estimate parameters, but only guide the state based on measurements - we can bridge these times. The introduced closed-eye period ensured constant parameters, suggesting that they resemble the believed true material properties. The closed-eye period improves predictions during periods when the local equilibrium assumption is met, but consequently worsens predictions when the assumption is violated. Such a prediction requires a description of the dynamics during local non-equilibrium phases, which remains an open challenge.

Probabilistic treatment of the uncertainty from the finite size of weighted Monte Carlo data

NASA Astrophysics Data System (ADS)

Glüsenkamp, Thorsten

2018-06-01

Parameter estimation in HEP experiments often involves Monte Carlo simulation to model the experimental response function. A typical application are forward-folding likelihood analyses with re-weighting, or time-consuming minimization schemes with a new simulation set for each parameter value. Problematically, the finite size of such Monte Carlo samples carries intrinsic uncertainty that can lead to a substantial bias in parameter estimation if it is neglected and the sample size is small. We introduce a probabilistic treatment of this problem by replacing the usual likelihood functions with novel generalized probability distributions that incorporate the finite statistics via suitable marginalization. These new PDFs are analytic, and can be used to replace the Poisson, multinomial, and sample-based unbinned likelihoods, which covers many use cases in high-energy physics. In the limit of infinite statistics, they reduce to the respective standard probability distributions. In the general case of arbitrary Monte Carlo weights, the expressions involve the fourth Lauricella function FD, for which we find a new finite-sum representation in a certain parameter setting. The result also represents an exact form for Carlson's Dirichlet average Rn with n > 0, and thereby an efficient way to calculate the probability generating function of the Dirichlet-multinomial distribution, the extended divided difference of a monomial, or arbitrary moments of univariate B-splines. We demonstrate the bias reduction of our approach with a typical toy Monte Carlo problem, estimating the normalization of a peak in a falling energy spectrum, and compare the results with previously published methods from the literature.

Combining Approach in Stages with Least Squares for fits of data in hyperelasticity

NASA Astrophysics Data System (ADS)

Beda, Tibi

2006-10-01

The present work concerns a method of continuous approximation by block of a continuous function; a method of approximation combining the Approach in Stages with the finite domains Least Squares. An identification procedure by sub-domains: basic generating functions are determined step-by-step permitting their weighting effects to be felt. This procedure allows one to be in control of the signs and to some extent of the optimal values of the parameters estimated, and consequently it provides a unique set of solutions that should represent the real physical parameters. Illustrations and comparisons are developed in rubber hyperelastic modeling. To cite this article: T. Beda, C. R. Mecanique 334 (2006).

Analysis and prediction of Doppler noise during solar conjunctions

NASA Technical Reports Server (NTRS)

Berman, A. L.; Rockwell, S. T.

1975-01-01

The results of a study of Doppler data noise during solar conjunctions were presented. During the first half of 1975, a sizeable data base of Doppler data noise (estimates) for the Pioneer 10, Pioneer 11, and Helios 1 solar conjunctions was accumulated. To analyze this data, certain physical assumptions are made, leading to the development of a geometric parameter ("ISI") which correlates strongly with Doppler data noise under varying sun-earth-spacecraft geometries. Doppler noise models are then constructed from this parameter, resulting in the newfound ability to predict Doppler data noise during solar conjunctions, and hence to additionally be in a position to validate Doppler data acquired during solar conjunctions.