Explicit simulation of ice particle habits in a Numerical Weather Prediction Model

NASA Astrophysics Data System (ADS)

Hashino, Tempei

2007-05-01

This study developed a scheme for explicit simulation of ice particle habits in Numerical Weather Prediction (NWP) Models. The scheme is called Spectral Ice Habit Prediction System (SHIPS), and the goal is to retain growth history of ice particles in the Eulerian dynamics framework. It diagnoses characteristics of ice particles based on a series of particle property variables (PPVs) that reflect history of microphysieal processes and the transport between mass bins and air parcels in space. Therefore, categorization of ice particles typically used in bulk microphysical parameterization and traditional bin models is not necessary, so that errors that stem from the categorization can be avoided. SHIPS predicts polycrystals as well as hexagonal monocrystals based on empirically derived habit frequency and growth rate, and simulates the habit-dependent aggregation and riming processes by use of the stochastic collection equation with predicted PPVs. Idealized two dimensional simulations were performed with SHIPS in a NWP model. The predicted spatial distribution of ice particle habits and types, and evolution of particle size distributions showed good quantitative agreement with observation This comprehensive model of ice particle properties, distributions, and evolution in clouds can be used to better understand problems facing wide range of research disciplines, including microphysics processes, radiative transfer in a cloudy atmosphere, data assimilation, and weather modification.

A Modified Isotropic-Kinematic Hardening Model to Predict the Defects in Tube Hydroforming Process

NASA Astrophysics Data System (ADS)

Jin, Kai; Guo, Qun; Tao, Jie; Guo, Xun-zhong

2017-11-01

Numerical simulations of tube hydroforming process of hollow crankshafts were conducted by using finite element analysis method. Moreover, the modified model involving the integration of isotropic-kinematic hardening model with ductile criteria model was used to more accurately optimize the process parameters such as internal pressure, feed distance and friction coefficient. Subsequently, hydroforming experiments were performed based on the simulation results. The comparison between experimental and simulation results indicated that the prediction of tube deformation, crack and wrinkle was quite accurate for the tube hydroforming process. Finally, hollow crankshafts with high thickness uniformity were obtained and the thickness distribution between numerical and experimental results was well consistent.

Through-process modelling of texture and anisotropy in AA5182

NASA Astrophysics Data System (ADS)

Crumbach, M.; Neumann, L.; Goerdeler, M.; Aretz, H.; Gottstein, G.; Kopp, R.

2006-07-01

A through-process texture and anisotropy prediction for AA5182 sheet production from hot rolling through cold rolling and annealing is reported. Thermo-mechanical process data predicted by the finite element method (FEM) package T-Pack based on the software LARSTRAN were fed into a combination of physics based microstructure models for deformation texture (GIA), work hardening (3IVM), nucleation texture (ReNuc), and recrystallization texture (StaRT). The final simulated sheet texture was fed into a FEM simulation of cup drawing employing a new concept of interactively updated texture based yield locus predictions. The modelling results of texture development and anisotropy were compared to experimental data. The applicability to other alloys and processes is discussed.

Predictive Simulation of Process Windows for Powder Bed Fusion Additive Manufacturing: Influence of the Powder Bulk Density.

PubMed

Rausch, Alexander M; Küng, Vera E; Pobel, Christoph; Markl, Matthias; Körner, Carolin

2017-09-22

The resulting properties of parts fabricated by powder bed fusion additive manufacturing processes are determined by their porosity, local composition, and microstructure. The objective of this work is to examine the influence of the stochastic powder bed on the process window for dense parts by means of numerical simulation. The investigations demonstrate the unique capability of simulating macroscopic domains in the range of millimeters with a mesoscopic approach, which resolves the powder bed and the hydrodynamics of the melt pool. A simulated process window reveals the influence of the stochastic powder layer. The numerical results are verified with an experimental process window for selective electron beam-melted Ti-6Al-4V. Furthermore, the influence of the powder bulk density is investigated numerically. The simulations predict an increase in porosity and surface roughness for samples produced with lower powder bulk densities. Due to its higher probability for unfavorable powder arrangements, the process stability is also decreased. This shrinks the actual parameter range in a process window for producing dense parts.

Predictive Simulation of Process Windows for Powder Bed Fusion Additive Manufacturing: Influence of the Powder Bulk Density

PubMed Central

Rausch, Alexander M.; Küng, Vera E.; Pobel, Christoph; Körner, Carolin

2017-01-01

The resulting properties of parts fabricated by powder bed fusion additive manufacturing processes are determined by their porosity, local composition, and microstructure. The objective of this work is to examine the influence of the stochastic powder bed on the process window for dense parts by means of numerical simulation. The investigations demonstrate the unique capability of simulating macroscopic domains in the range of millimeters with a mesoscopic approach, which resolves the powder bed and the hydrodynamics of the melt pool. A simulated process window reveals the influence of the stochastic powder layer. The numerical results are verified with an experimental process window for selective electron beam-melted Ti-6Al-4V. Furthermore, the influence of the powder bulk density is investigated numerically. The simulations predict an increase in porosity and surface roughness for samples produced with lower powder bulk densities. Due to its higher probability for unfavorable powder arrangements, the process stability is also decreased. This shrinks the actual parameter range in a process window for producing dense parts. PMID:28937633

SIM_ADJUST -- A computer code that adjusts simulated equivalents for observations or predictions

USGS Publications Warehouse

Poeter, Eileen P.; Hill, Mary C.

2008-01-01

This report documents the SIM_ADJUST computer code. SIM_ADJUST surmounts an obstacle that is sometimes encountered when using universal model analysis computer codes such as UCODE_2005 (Poeter and others, 2005), PEST (Doherty, 2004), and OSTRICH (Matott, 2005; Fredrick and others (2007). These codes often read simulated equivalents from a list in a file produced by a process model such as MODFLOW that represents a system of interest. At times values needed by the universal code are missing or assigned default values because the process model could not produce a useful solution. SIM_ADJUST can be used to (1) read a file that lists expected observation or prediction names and possible alternatives for the simulated values; (2) read a file produced by a process model that contains space or tab delimited columns, including a column of simulated values and a column of related observation or prediction names; (3) identify observations or predictions that have been omitted or assigned a default value by the process model; and (4) produce an adjusted file that contains a column of simulated values and a column of associated observation or prediction names. The user may provide alternatives that are constant values or that are alternative simulated values. The user may also provide a sequence of alternatives. For example, the heads from a series of cells may be specified to ensure that a meaningful value is available to compare with an observation located in a cell that may become dry. SIM_ADJUST is constructed using modules from the JUPITER API, and is intended for use on any computer operating system. SIM_ADJUST consists of algorithms programmed in Fortran90, which efficiently performs numerical calculations.

The Use of Particle/Substrate Material Models in Simulation of Cold-Gas Dynamic-Spray Process

NASA Astrophysics Data System (ADS)

Rahmati, Saeed; Ghaei, Abbas

2014-02-01

Cold spray is a coating deposition method in which the solid particles are accelerated to the substrate using a low temperature supersonic gas flow. Many numerical studies have been carried out in the literature in order to study this process in more depth. Despite the inability of Johnson-Cook plasticity model in prediction of material behavior at high strain rates, it is the model that has been frequently used in simulation of cold spray. Therefore, this research was devoted to compare the performance of different material models in the simulation of cold spray process. Six different material models, appropriate for high strain-rate plasticity, were employed in finite element simulation of cold spray process for copper. The results showed that the material model had a considerable effect on the predicted deformed shapes.

Multivariable Time Series Prediction for the Icing Process on Overhead Power Transmission Line

PubMed Central

Li, Peng; Zhao, Na; Zhou, Donghua; Cao, Min; Li, Jingjie; Shi, Xinling

2014-01-01

The design of monitoring and predictive alarm systems is necessary for successful overhead power transmission line icing. Given the characteristics of complexity, nonlinearity, and fitfulness in the line icing process, a model based on a multivariable time series is presented here to predict the icing load of a transmission line. In this model, the time effects of micrometeorology parameters for the icing process have been analyzed. The phase-space reconstruction theory and machine learning method were then applied to establish the prediction model, which fully utilized the history of multivariable time series data in local monitoring systems to represent the mapping relationship between icing load and micrometeorology factors. Relevant to the characteristic of fitfulness in line icing, the simulations were carried out during the same icing process or different process to test the model's prediction precision and robustness. According to the simulation results for the Tao-Luo-Xiong Transmission Line, this model demonstrates a good accuracy of prediction in different process, if the prediction length is less than two hours, and would be helpful for power grid departments when deciding to take action in advance to address potential icing disasters. PMID:25136653

Predicting the excess solubility of acetanilide, acetaminophen, phenacetin, benzocaine, and caffeine in binary water/ethanol mixtures via molecular simulation.

PubMed

Paluch, Andrew S; Parameswaran, Sreeja; Liu, Shuai; Kolavennu, Anasuya; Mobley, David L

2015-01-28

We present a general framework to predict the excess solubility of small molecular solids (such as pharmaceutical solids) in binary solvents via molecular simulation free energy calculations at infinite dilution with conventional molecular models. The present study used molecular dynamics with the General AMBER Force Field to predict the excess solubility of acetanilide, acetaminophen, phenacetin, benzocaine, and caffeine in binary water/ethanol solvents. The simulations are able to predict the existence of solubility enhancement and the results are in good agreement with available experimental data. The accuracy of the predictions in addition to the generality of the method suggests that molecular simulations may be a valuable design tool for solvent selection in drug development processes.

Predicting the excess solubility of acetanilide, acetaminophen, phenacetin, benzocaine, and caffeine in binary water/ethanol mixtures via molecular simulation

NASA Astrophysics Data System (ADS)

Paluch, Andrew S.; Parameswaran, Sreeja; Liu, Shuai; Kolavennu, Anasuya; Mobley, David L.

2015-01-01

We present a general framework to predict the excess solubility of small molecular solids (such as pharmaceutical solids) in binary solvents via molecular simulation free energy calculations at infinite dilution with conventional molecular models. The present study used molecular dynamics with the General AMBER Force Field to predict the excess solubility of acetanilide, acetaminophen, phenacetin, benzocaine, and caffeine in binary water/ethanol solvents. The simulations are able to predict the existence of solubility enhancement and the results are in good agreement with available experimental data. The accuracy of the predictions in addition to the generality of the method suggests that molecular simulations may be a valuable design tool for solvent selection in drug development processes.

The role of bias in simulation of the Indian monsoon and its relationship to predictability

NASA Astrophysics Data System (ADS)

Kelly, P.

2016-12-01

Confidence in future projections of how climate change will affect the Indian monsoon is currently limited by- among other things-model biases. That is, the systematic error in simulating the mean present day climate. An important priority question in seamless prediction involves the role of the mean state. How much of the prediction error in imperfect models stems from a biased mean state (itself a result of many interacting process errors), and how much stems from the flow dependence of processes during an oscillation or variation we are trying to predict? Using simple but effective nudging techniques, we are able to address this question in a clean and incisive framework that teases apart the roles of the mean state vs. transient flow dependence in constraining predictability. The role of bias in model fidelity of simulations of the Indian monsoon is investigated in CAM5, and the relationship to predictability in remote regions in the "free" (non-nudged) domain is explored.

Orchestrating TRANSP Simulations for Interpretative and Predictive Tokamak Modeling with OMFIT

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

Grierson, B. A.; Yuan, X.; Gorelenkova, M.

TRANSP simulations are being used in the OMFIT work- flow manager to enable a machine independent means of experimental analysis, postdictive validation, and predictive time dependent simulations on the DIII-D, NSTX, JET and C-MOD tokamaks. The procedures for preparing the input data from plasma profile diagnostics and equilibrium reconstruction, as well as processing of the time-dependent heating and current drive sources and assumptions about the neutral recycling, vary across machines, but are streamlined by using a common workflow manager. Settings for TRANSP simulation fidelity are incorporated into the OMFIT framework, contrasting between-shot analysis, power balance, and fast-particle simulations. A previouslymore » established series of data consistency metrics are computed such as comparison of experimental vs. calculated neutron rate, equilibrium stored energy vs. total stored energy from profile and fast-ion pressure, and experimental vs. computed surface loop voltage. Discrepancies between data consistency metrics can indicate errors in input quantities such as electron density profile or Zeff, or indicate anomalous fast-particle transport. Measures to assess the sensitivity of the verification metrics to input quantities are provided by OMFIT, including scans of the input profiles and standardized post-processing visualizations. For predictive simulations, TRANSP uses GLF23 or TGLF to predict core plasma profiles, with user defined boundary conditions in the outer region of the plasma. ITPA validation metrics are provided in post-processing to assess the transport model validity. By using OMFIT to orchestrate the steps for experimental data preparation, selection of operating mode, submission, post-processing and visualization, we have streamlined and standardized the usage of TRANSP.« less

Orchestrating TRANSP Simulations for Interpretative and Predictive Tokamak Modeling with OMFIT

DOE PAGES

Grierson, B. A.; Yuan, X.; Gorelenkova, M.; ...

2018-02-21

TRANSP simulations are being used in the OMFIT work- flow manager to enable a machine independent means of experimental analysis, postdictive validation, and predictive time dependent simulations on the DIII-D, NSTX, JET and C-MOD tokamaks. The procedures for preparing the input data from plasma profile diagnostics and equilibrium reconstruction, as well as processing of the time-dependent heating and current drive sources and assumptions about the neutral recycling, vary across machines, but are streamlined by using a common workflow manager. Settings for TRANSP simulation fidelity are incorporated into the OMFIT framework, contrasting between-shot analysis, power balance, and fast-particle simulations. A previouslymore » established series of data consistency metrics are computed such as comparison of experimental vs. calculated neutron rate, equilibrium stored energy vs. total stored energy from profile and fast-ion pressure, and experimental vs. computed surface loop voltage. Discrepancies between data consistency metrics can indicate errors in input quantities such as electron density profile or Zeff, or indicate anomalous fast-particle transport. Measures to assess the sensitivity of the verification metrics to input quantities are provided by OMFIT, including scans of the input profiles and standardized post-processing visualizations. For predictive simulations, TRANSP uses GLF23 or TGLF to predict core plasma profiles, with user defined boundary conditions in the outer region of the plasma. ITPA validation metrics are provided in post-processing to assess the transport model validity. By using OMFIT to orchestrate the steps for experimental data preparation, selection of operating mode, submission, post-processing and visualization, we have streamlined and standardized the usage of TRANSP.« less

Simulation for Carbon Nanotube Dispersion and Microstructure Formation in CNTs/AZ91D Composite Fabricated by Ultrasonic Processing

NASA Astrophysics Data System (ADS)

Yang, Yuansheng; Zhao, Fuze; Feng, Xiaohui

2017-10-01

The dispersion of carbon nanotubes (CNTs) in AZ91D melt by ultrasonic processing and microstructure formation of CNTs/AZ91D composite were studied using numerical and physical simulations. The sound field and acoustic streaming were predicted using finite element method. Meanwhile, optimal immersion depth of the ultrasonic probe and suitable ultrasonic power were obtained. Single-bubble model was used to predict ultrasonic cavitation in AZ91D melt. The relationship between sound pressure amplitude and ultrasonic cavitation was established. Physical simulations of acoustic streaming and ultrasonic cavitation agreed well with the numerical simulations. It was confirmed that the dispersion of carbon nanotubes was remarkably improved by ultrasonic processing. Microstructure formation of CNTs/AZ91D composite was numerically simulated using cellular automation method. In addition, grain refinement was achieved and the growth of dendrites was changed due to the uniform dispersion of CNTs.

Defense Waste Processing Facility Nitric- Glycolic Flowsheet Chemical Process Cell Chemistry: Part 2

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

Zamecnik, J.; Edwards, T.

The conversions of nitrite to nitrate, the destruction of glycolate, and the conversion of glycolate to formate and oxalate were modeled for the Nitric-Glycolic flowsheet using data from Chemical Process Cell (CPC) simulant runs conducted by Savannah River National Laboratory (SRNL) from 2011 to 2016. The goal of this work was to develop empirical correlation models to predict these values from measureable variables from the chemical process so that these quantities could be predicted a-priori from the sludge or simulant composition and measurable processing variables. The need for these predictions arises from the need to predict the REDuction/OXidation (REDOX) statemore » of the glass from the Defense Waste Processing Facility (DWPF) melter. This report summarizes the work on these correlations based on the aforementioned data. Previous work on these correlations was documented in a technical report covering data from 2011-2015. This current report supersedes this previous report. Further refinement of the models as additional data are collected is recommended.« less

DEVELOPMENT AND USE OF COMPUTER-AIDED PROCESS ENGINEERING TOOLS FOR POLLUTION PREVENTION

EPA Science Inventory

The use of Computer-Aided Process Engineering (CAPE) and process simulation tools has become established industry practice to predict simulation software, new opportunities are available for the creation of a wide range of ancillary tools that can be used from within multiple sim...

Computational Simulation of Thermal and Spattering Phenomena and Microstructure in Selective Laser Melting of Inconel 625

NASA Astrophysics Data System (ADS)

Özel, Tuğrul; Arısoy, Yiğit M.; Criales, Luis E.

Computational modelling of Laser Powder Bed Fusion (L-PBF) processes such as Selective laser Melting (SLM) can reveal information that is hard to obtain or unobtainable by in-situ experimental measurements. A 3D thermal field that is not visible by the thermal camera can be obtained by solving the 3D heat transfer problem. Furthermore, microstructural modelling can be used to predict the quality and mechanical properties of the product. In this paper, a nonlinear 3D Finite Element Method based computational code is developed to simulate the SLM process with different process parameters such as laser power and scan velocity. The code is further improved by utilizing an in-situ thermal camera recording to predict spattering which is in turn included as a stochastic heat loss. Then, thermal gradients extracted from the simulations applied to predict growth directions in the resulting microstructure.

Prediction of Indian Summer-Monsoon Onset Variability: A Season in Advance.

PubMed

Pradhan, Maheswar; Rao, A Suryachandra; Srivastava, Ankur; Dakate, Ashish; Salunke, Kiran; Shameera, K S

2017-10-27

Monsoon onset is an inherent transient phenomenon of Indian Summer Monsoon and it was never envisaged that this transience can be predicted at long lead times. Though onset is precipitous, its variability exhibits strong teleconnections with large scale forcing such as ENSO and IOD and hence may be predictable. Despite of the tremendous skill achieved by the state-of-the-art models in predicting such large scale processes, the prediction of monsoon onset variability by the models is still limited to just 2-3 weeks in advance. Using an objective definition of onset in a global coupled ocean-atmosphere model, it is shown that the skillful prediction of onset variability is feasible under seasonal prediction framework. The better representations/simulations of not only the large scale processes but also the synoptic and intraseasonal features during the evolution of monsoon onset are the comprehensions behind skillful simulation of monsoon onset variability. The changes observed in convection, tropospheric circulation and moisture availability prior to and after the onset are evidenced in model simulations, which resulted in high hit rate of early/delay in monsoon onset in the high resolution model.

The impact of (n, γ) reaction rate uncertainties of unstable isotopes near N = 50 on the i-process nucleosynthesis in He-shell flash white dwarfs

NASA Astrophysics Data System (ADS)

Denissenkov, Pavel; Perdikakis, Georgios; Herwig, Falk; Schatz, Hendrik; Ritter, Christian; Pignatari, Marco; Jones, Samuel; Nikas, Stylianos; Spyrou, Artemis

2018-05-01

The first-peak s-process elements Rb, Sr, Y and Zr in the post-AGB star Sakurai's object (V4334 Sagittarii) have been proposed to be the result of i-process nucleosynthesis in a post-AGB very-late thermal pulse event. We estimate the nuclear physics uncertainties in the i-process model predictions to determine whether the remaining discrepancies with observations are significant and point to potential issues with the underlying astrophysical model. We find that the dominant source in the nuclear physics uncertainties are predictions of neutron capture rates on unstable neutron rich nuclei, which can have uncertainties of more than a factor 20 in the band of the i-process. We use a Monte Carlo variation of 52 neutron capture rates and a 1D multi-zone post-processing model for the i-process in Sakurai's object to determine the cumulative effect of these uncertainties on the final elemental abundance predictions. We find that the nuclear physics uncertainties are large and comparable to observational errors. Within these uncertainties the model predictions are consistent with observations. A correlation analysis of the results of our MC simulations reveals that the strongest impact on the predicted abundances of Rb, Sr, Y and Zr is made by the uncertainties in the (n, γ) reaction rates of 85Br, 86Br, 87Kr, 88Kr, 89Kr, 89Rb, 89Sr, and 92Sr. This conclusion is supported by a series of multi-zone simulations in which we increased and decreased to their maximum and minimum limits one or two reaction rates per run. We also show that simple and fast one-zone simulations should not be used instead of more realistic multi-zone stellar simulations for nuclear sensitivity and uncertainty studies of convective–reactive processes. Our findings apply more generally to any i-process site with similar neutron exposure, such as rapidly accreting white dwarfs with near-solar metallicities.

Computational prediction of kink properties of helices in membrane proteins

NASA Astrophysics Data System (ADS)

Mai, T.-L.; Chen, C.-M.

2014-02-01

We have combined molecular dynamics simulations and fold identification procedures to investigate the structure of 696 kinked and 120 unkinked transmembrane (TM) helices in the PDBTM database. Our main aim of this study is to understand the formation of helical kinks by simulating their quasi-equilibrium heating processes, which might be relevant to the prediction of their structural features. The simulated structural features of these TM helices, including the position and the angle of helical kinks, were analyzed and compared with statistical data from PDBTM. From quasi-equilibrium heating processes of TM helices with four very different relaxation time constants, we found that these processes gave comparable predictions of the structural features of TM helices. Overall, 95 % of our best kink position predictions have an error of no more than two residues and 75 % of our best angle predictions have an error of less than 15°. Various structure assessments have been carried out to assess our predicted models of TM helices in PDBTM. Our results show that, in 696 predicted kinked helices, 70 % have a RMSD less than 2 Å, 71 % have a TM-score greater than 0.5, 69 % have a MaxSub score greater than 0.8, 60 % have a GDT-TS score greater than 85, and 58 % have a GDT-HA score greater than 70. For unkinked helices, our predicted models are also highly consistent with their crystal structure. These results provide strong supports for our assumption that kink formation of TM helices in quasi-equilibrium heating processes is relevant to predicting the structure of TM helices.

Practical Unitary Simulator for Non-Markovian Complex Processes

NASA Astrophysics Data System (ADS)

Binder, Felix C.; Thompson, Jayne; Gu, Mile

2018-06-01

Stochastic processes are as ubiquitous throughout the quantitative sciences as they are notorious for being difficult to simulate and predict. In this Letter, we propose a unitary quantum simulator for discrete-time stochastic processes which requires less internal memory than any classical analogue throughout the simulation. The simulator's internal memory requirements equal those of the best previous quantum models. However, in contrast to previous models, it only requires a (small) finite-dimensional Hilbert space. Moreover, since the simulator operates unitarily throughout, it avoids any unnecessary information loss. We provide a stepwise construction for simulators for a large class of stochastic processes hence directly opening the possibility for experimental implementations with current platforms for quantum computation. The results are illustrated for an example process.

Predicting the excess solubility of acetanilide, acetaminophen, phenacetin, benzocaine, and caffeine in binary water/ethanol mixtures via molecular simulation

PubMed Central

Paluch, Andrew S.; Parameswaran, Sreeja; Liu, Shuai; Kolavennu, Anasuya; Mobley, David L.

2015-01-01

We present a general framework to predict the excess solubility of small molecular solids (such as pharmaceutical solids) in binary solvents via molecular simulation free energy calculations at infinite dilution with conventional molecular models. The present study used molecular dynamics with the General AMBER Force Field to predict the excess solubility of acetanilide, acetaminophen, phenacetin, benzocaine, and caffeine in binary water/ethanol solvents. The simulations are able to predict the existence of solubility enhancement and the results are in good agreement with available experimental data. The accuracy of the predictions in addition to the generality of the method suggests that molecular simulations may be a valuable design tool for solvent selection in drug development processes. PMID:25637996

The useful field of view assessment predicts simulated commercial motor vehicle driving safety.

PubMed

McManus, Benjamin; Heaton, Karen; Vance, David E; Stavrinos, Despina

2016-10-02

The Useful Field of View (UFOV) assessment, a measure of visual speed of processing, has been shown to be a predictive measure of motor vehicle collision (MVC) involvement in an older adult population, but it remains unknown whether UFOV predicts commercial motor vehicle (CMV) driving safety during secondary task engagement. The purpose of this study is to determine whether the UFOV assessment predicts simulated MVCs in long-haul CMV drivers. Fifty licensed CMV drivers (Mage = 39.80, SD = 8.38, 98% male, 56% Caucasian) were administered the 3-subtest version of the UFOV assessment, where lower scores measured in milliseconds indicated better performance. CMV drivers completed 4 simulated drives, each spanning approximately a 22.50-mile distance. Four secondary tasks were presented to participants in a counterbalanced order during the drives: (a) no secondary task, (b) cell phone conversation, (c) text messaging interaction, and (d) e-mailing interaction with an on-board dispatch device. The selective attention subtest significantly predicted simulated MVCs regardless of secondary task. Each 20 ms slower on subtest 3 was associated with a 25% increase in the risk of an MVC in the simulated drive. The e-mail interaction secondary task significantly predicted simulated MVCs with a 4.14 times greater risk of an MVC compared to the no secondary task condition. Subtest 3, a measure of visual speed of processing, significantly predicted MVCs in the email interaction task. Each 20 ms slower on subtest 3 was associated with a 25% increase in the risk of an MVC during the email interaction task. The UFOV subtest 3 may be a promising measure to identify CMV drivers who may be at risk for MVCs or in need of cognitive training aimed at improving speed of processing. Subtest 3 may also identify CMV drivers who are particularly at risk when engaged in secondary tasks while driving.

Evaluation of ceramics for stator application: Gas turbine engine report

NASA Technical Reports Server (NTRS)

Trela, W.; Havstad, P. H.

1978-01-01

Current ceramic materials, component fabrication processes, and reliability prediction capability for ceramic stators in an automotive gas turbine engine environment are assessed. Simulated engine duty cycle testing of stators conducted at temperatures up to 1093 C is discussed. Materials evaluated are SiC and Si3N4 fabricated from two near-net-shape processes: slip casting and injection molding. Stators for durability cycle evaluation and test specimens for material property characterization, and reliability prediction model prepared to predict stator performance in the simulated engine environment are considered. The status and description of the work performed for the reliability prediction modeling, stator fabrication, material property characterization, and ceramic stator evaluation efforts are reported.

Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do.

PubMed

Zhao, Linlin; Wang, Wenyi; Sedykh, Alexander; Zhu, Hao

2017-06-30

Numerous chemical data sets have become available for quantitative structure-activity relationship (QSAR) modeling studies. However, the quality of different data sources may be different based on the nature of experimental protocols. Therefore, potential experimental errors in the modeling sets may lead to the development of poor QSAR models and further affect the predictions of new compounds. In this study, we explored the relationship between the ratio of questionable data in the modeling sets, which was obtained by simulating experimental errors, and the QSAR modeling performance. To this end, we used eight data sets (four continuous endpoints and four categorical endpoints) that have been extensively curated both in-house and by our collaborators to create over 1800 various QSAR models. Each data set was duplicated to create several new modeling sets with different ratios of simulated experimental errors (i.e., randomizing the activities of part of the compounds) in the modeling process. A fivefold cross-validation process was used to evaluate the modeling performance, which deteriorates when the ratio of experimental errors increases. All of the resulting models were also used to predict external sets of new compounds, which were excluded at the beginning of the modeling process. The modeling results showed that the compounds with relatively large prediction errors in cross-validation processes are likely to be those with simulated experimental errors. However, after removing a certain number of compounds with large prediction errors in the cross-validation process, the external predictions of new compounds did not show improvement. Our conclusion is that the QSAR predictions, especially consensus predictions, can identify compounds with potential experimental errors. But removing those compounds by the cross-validation procedure is not a reasonable means to improve model predictivity due to overfitting.

Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do

PubMed Central

2017-01-01

Numerous chemical data sets have become available for quantitative structure–activity relationship (QSAR) modeling studies. However, the quality of different data sources may be different based on the nature of experimental protocols. Therefore, potential experimental errors in the modeling sets may lead to the development of poor QSAR models and further affect the predictions of new compounds. In this study, we explored the relationship between the ratio of questionable data in the modeling sets, which was obtained by simulating experimental errors, and the QSAR modeling performance. To this end, we used eight data sets (four continuous endpoints and four categorical endpoints) that have been extensively curated both in-house and by our collaborators to create over 1800 various QSAR models. Each data set was duplicated to create several new modeling sets with different ratios of simulated experimental errors (i.e., randomizing the activities of part of the compounds) in the modeling process. A fivefold cross-validation process was used to evaluate the modeling performance, which deteriorates when the ratio of experimental errors increases. All of the resulting models were also used to predict external sets of new compounds, which were excluded at the beginning of the modeling process. The modeling results showed that the compounds with relatively large prediction errors in cross-validation processes are likely to be those with simulated experimental errors. However, after removing a certain number of compounds with large prediction errors in the cross-validation process, the external predictions of new compounds did not show improvement. Our conclusion is that the QSAR predictions, especially consensus predictions, can identify compounds with potential experimental errors. But removing those compounds by the cross-validation procedure is not a reasonable means to improve model predictivity due to overfitting. PMID:28691113

Predicting mesoscale microstructural evolution in electron beam welding

DOE PAGES

Rodgers, Theron M.; Madison, Jonathan D.; Tikare, Veena; ...

2016-03-16

Using the kinetic Monte Carlo simulator, Stochastic Parallel PARticle Kinetic Simulator, from Sandia National Laboratories, a user routine has been developed to simulate mesoscale predictions of a grain structure near a moving heat source. Here, we demonstrate the use of this user routine to produce voxelized, synthetic, three-dimensional microstructures for electron-beam welding by comparing them with experimentally produced microstructures. When simulation input parameters are matched to experimental process parameters, qualitative and quantitative agreement for both grain size and grain morphology are achieved. The method is capable of simulating both single- and multipass welds. As a result, the simulations provide anmore » opportunity for not only accelerated design but also the integration of simulation and experiments in design such that simulations can receive parameter bounds from experiments and, in turn, provide predictions of a resultant microstructure.« less

A Microstructure-Based Constitutive Model for Superplastic Forming

NASA Astrophysics Data System (ADS)

Jafari Nedoushan, Reza; Farzin, Mahmoud; Mashayekhi, Mohammad; Banabic, Dorel

2012-11-01

A constitutive model is proposed for simulations of hot metal forming processes. This model is constructed based on dominant mechanisms that take part in hot forming and includes intergranular deformation, grain boundary sliding, and grain boundary diffusion. A Taylor type polycrystalline model is used to predict intergranular deformation. Previous works on grain boundary sliding and grain boundary diffusion are extended to drive three-dimensional macro stress-strain rate relationships for each mechanism. In these relationships, the effect of grain size is also taken into account. The proposed model is first used to simulate step strain-rate tests and the results are compared with experimental data. It is shown that the model can be used to predict flow stresses for various grain sizes and strain rates. The yield locus is then predicted for multiaxial stress states, and it is observed that it is very close to the von Mises yield criterion. It is also shown that the proposed model can be directly used to simulate hot forming processes. Bulge forming process and gas pressure tray forming are simulated, and the results are compared with experimental data.

Fast Quantum Algorithm for Predicting Descriptive Statistics of Stochastic Processes

NASA Technical Reports Server (NTRS)

Williams Colin P.

1999-01-01

Stochastic processes are used as a modeling tool in several sub-fields of physics, biology, and finance. Analytic understanding of the long term behavior of such processes is only tractable for very simple types of stochastic processes such as Markovian processes. However, in real world applications more complex stochastic processes often arise. In physics, the complicating factor might be nonlinearities; in biology it might be memory effects; and in finance is might be the non-random intentional behavior of participants in a market. In the absence of analytic insight, one is forced to understand these more complex stochastic processes via numerical simulation techniques. In this paper we present a quantum algorithm for performing such simulations. In particular, we show how a quantum algorithm can predict arbitrary descriptive statistics (moments) of N-step stochastic processes in just O(square root of N) time. That is, the quantum complexity is the square root of the classical complexity for performing such simulations. This is a significant speedup in comparison to the current state of the art.

Finite element simulations and experimental investigations on ductile fracture in cold forging of aluminum alloy

NASA Astrophysics Data System (ADS)

Amiri, Amir; Nikpour, Amin; Saraeian, Payam

2018-05-01

Forging is one of the manufacturing processes of aluminium parts which has two major categories: called hot and cold forging. In the cold forging, the dimensional and geometrical accuracy of final part is high. However, fracture may occur in some aluminium alloys during the process because of less workability. Fracture in cold forging can be in the form of ductile, brittle or combination of both depending on the alloy type. There are several criteria for predicting fracture in cold forging. In this study, cold forging process of 6063 aluminium alloy for three different parts is simulated in order to predict fracture. The results of numerical simulations of Freudenthal criterion is in conformity with experimental tests.

Predicting Protein Structure Using Parallel Genetic Algorithms.

DTIC Science & Technology

1994-12-01

Molecular dynamics attempts to simulate the protein folding process. However, the time steps required for this simulation are on the order of one...harmonics. These two factors have limited molecular dynamics simulations to less than a few nanoseconds (10-9 sec), even on today’s fastest supercomputers...By " Predicting rotein Structure D istribticfiar.. ................ Using Parallel Genetic Algorithms ,Avaiu " ’ •"... Dist THESIS I IGeorge H

A system identification approach for developing model predictive controllers of antibody quality attributes in cell culture processes

PubMed Central

Schmitt, John; Beller, Justin; Russell, Brian; Quach, Anthony; Hermann, Elizabeth; Lyon, David; Breit, Jeffrey

2017-01-01

As the biopharmaceutical industry evolves to include more diverse protein formats and processes, more robust control of Critical Quality Attributes (CQAs) is needed to maintain processing flexibility without compromising quality. Active control of CQAs has been demonstrated using model predictive control techniques, which allow development of processes which are robust against disturbances associated with raw material variability and other potentially flexible operating conditions. Wide adoption of model predictive control in biopharmaceutical cell culture processes has been hampered, however, in part due to the large amount of data and expertise required to make a predictive model of controlled CQAs, a requirement for model predictive control. Here we developed a highly automated, perfusion apparatus to systematically and efficiently generate predictive models using application of system identification approaches. We successfully created a predictive model of %galactosylation using data obtained by manipulating galactose concentration in the perfusion apparatus in serialized step change experiments. We then demonstrated the use of the model in a model predictive controller in a simulated control scenario to successfully achieve a %galactosylation set point in a simulated fed‐batch culture. The automated model identification approach demonstrated here can potentially be generalized to many CQAs, and could be a more efficient, faster, and highly automated alternative to batch experiments for developing predictive models in cell culture processes, and allow the wider adoption of model predictive control in biopharmaceutical processes. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 33:1647–1661, 2017 PMID:28786215

An approach to value-based simulator selection: The creation and evaluation of the simulator value index tool.

PubMed

Rooney, Deborah M; Hananel, David M; Covington, Benjamin J; Dionise, Patrick L; Nykamp, Michael T; Pederson, Melvin; Sahloul, Jamal M; Vasquez, Rachael; Seagull, F Jacob; Pinsky, Harold M; Sweier, Domenica G; Cooke, James M

2018-04-01

Currently there is no reliable, standardized mechanism to support health care professionals during the evaluation of and procurement processes for simulators. A tool founded on best practices could facilitate simulator purchase processes. In a 3-phase process, we identified top factors considered during the simulator purchase process through expert consensus (n = 127), created the Simulator Value Index (SVI) tool, evaluated targeted validity evidence, and evaluated the practical value of this SVI. A web-based survey was sent to simulation professionals. Participants (n = 79) used the SVI and provided feedback. We evaluated the practical value of 4 tool variations by calculating their sensitivity to predict a preferred simulator. Seventeen top factors were identified and ranked. The top 2 were technical stability/reliability of the simulator and customer service, with no practical differences in rank across institution or stakeholder role. Full SVI variations predicted successfully the preferred simulator with good (87%) sensitivity, whereas the sensitivity of variations in cost and customer service and cost and technical stability decreased (≤54%). The majority (73%) of participants agreed that the SVI was helpful at guiding simulator purchase decisions, and 88% agreed the SVI tool would help facilitate discussion with peers and leadership. Our findings indicate the SVI supports the process of simulator purchase using a standardized framework. Sensitivity of the tool improved when factors extend beyond traditionally targeted factors. We propose the tool will facilitate discussion amongst simulation professionals dealing with simulation, provide essential information for finance and procurement professionals, and improve the long-term value of simulation solutions. Limitations and application of the tool are discussed. Copyright © 2017 Elsevier Inc. All rights reserved.

Visualization of the Invisible, Explanation of the Unknown, Ruggedization of the Unstable: Sensitivity Analysis, Virtual Tryout and Robust Design through Systematic Stochastic Simulation

NASA Astrophysics Data System (ADS)

Zwickl, Titus; Carleer, Bart; Kubli, Waldemar

2005-08-01

In the past decade, sheet metal forming simulation became a well established tool to predict the formability of parts. In the automotive industry, this has enabled significant reduction in the cost and time for vehicle design and development, and has helped to improve the quality and performance of vehicle parts. However, production stoppages for troubleshooting and unplanned die maintenance, as well as production quality fluctuations continue to plague manufacturing cost and time. The focus therefore has shifted in recent times beyond mere feasibility to robustness of the product and process being engineered. Ensuring robustness is the next big challenge for the virtual tryout / simulation technology. We introduce new methods, based on systematic stochastic simulations, to visualize the behavior of the part during the whole forming process — in simulation as well as in production. Sensitivity analysis explains the response of the part to changes in influencing parameters. Virtual tryout allows quick exploration of changed designs and conditions. Robust design and manufacturing guarantees quality and process capability for the production process. While conventional simulations helped to reduce development time and cost by ensuring feasible processes, robustness engineering tools have the potential for far greater cost and time savings. Through examples we illustrate how expected and unexpected behavior of deep drawing parts may be tracked down, identified and assigned to the influential parameters. With this knowledge, defects can be eliminated or springback can be compensated e.g.; the response of the part to uncontrollable noise can be predicted and minimized. The newly introduced methods enable more reliable and predictable stamping processes in general.

Optimization of Biomathematical Model Predictions for Cognitive Performance Impairment in Individuals: Accounting for Unknown Traits and Uncertain States in Homeostatic and Circadian Processes

PubMed Central

Van Dongen, Hans P. A.; Mott, Christopher G.; Huang, Jen-Kuang; Mollicone, Daniel J.; McKenzie, Frederic D.; Dinges, David F.

2007-01-01

Current biomathematical models of fatigue and performance do not accurately predict cognitive performance for individuals with a priori unknown degrees of trait vulnerability to sleep loss, do not predict performance reliably when initial conditions are uncertain, and do not yield statistically valid estimates of prediction accuracy. These limitations diminish their usefulness for predicting the performance of individuals in operational environments. To overcome these 3 limitations, a novel modeling approach was developed, based on the expansion of a statistical technique called Bayesian forecasting. The expanded Bayesian forecasting procedure was implemented in the two-process model of sleep regulation, which has been used to predict performance on the basis of the combination of a sleep homeostatic process and a circadian process. Employing the two-process model with the Bayesian forecasting procedure to predict performance for individual subjects in the face of unknown traits and uncertain states entailed subject-specific optimization of 3 trait parameters (homeostatic build-up rate, circadian amplitude, and basal performance level) and 2 initial state parameters (initial homeostatic state and circadian phase angle). Prior information about the distribution of the trait parameters in the population at large was extracted from psychomotor vigilance test (PVT) performance measurements in 10 subjects who had participated in a laboratory experiment with 88 h of total sleep deprivation. The PVT performance data of 3 additional subjects in this experiment were set aside beforehand for use in prospective computer simulations. The simulations involved updating the subject-specific model parameters every time the next performance measurement became available, and then predicting performance 24 h ahead. Comparison of the predictions to the subjects' actual data revealed that as more data became available for the individuals at hand, the performance predictions became increasingly more accurate and had progressively smaller 95% confidence intervals, as the model parameters converged efficiently to those that best characterized each individual. Even when more challenging simulations were run (mimicking a change in the initial homeostatic state; simulating the data to be sparse), the predictions were still considerably more accurate than would have been achieved by the two-process model alone. Although the work described here is still limited to periods of consolidated wakefulness with stable circadian rhythms, the results obtained thus far indicate that the Bayesian forecasting procedure can successfully overcome some of the major outstanding challenges for biomathematical prediction of cognitive performance in operational settings. Citation: Van Dongen HPA; Mott CG; Huang JK; Mollicone DJ; McKenzie FD; Dinges DF. Optimization of biomathematical model predictions for cognitive performance impairment in individuals: accounting for unknown traits and uncertain states in homeostatic and circadian processes. SLEEP 2007;30(9):1129-1143. PMID:17910385

Computer simulation to predict energy use, greenhouse gas emissions and costs for production of fluid milk using alternative processing methods

USDA-ARS?s Scientific Manuscript database

Computer simulation is a useful tool for benchmarking the electrical and fuel energy consumption and water use in a fluid milk plant. In this study, a computer simulation model of the fluid milk process based on high temperature short time (HTST) pasteurization was extended to include models for pr...

Numerical Simulation of Molten Flow in Directed Energy Deposition Using an Iterative Geometry Technique

NASA Astrophysics Data System (ADS)

Vincent, Timothy J.; Rumpfkeil, Markus P.; Chaudhary, Anil

2018-03-01

The complex, multi-faceted physics of laser-based additive metals processing tends to demand high-fidelity models and costly simulation tools to provide predictions accurate enough to aid in selecting process parameters. Of particular difficulty is the accurate determination of melt pool shape and size, which are useful for predicting lack-of-fusion, as this typically requires an adequate treatment of thermal and fluid flow. In this article we describe a novel numerical simulation tool which aims to achieve a balance between accuracy and cost. This is accomplished by making simplifying assumptions regarding the behavior of the gas-liquid interface for processes with a moderate energy density, such as Laser Engineered Net Shaping (LENS). The details of the implementation, which is based on the solver simpleFoam of the well-known software suite OpenFOAM, are given here and the tool is verified and validated for a LENS process involving Ti-6Al-4V. The results indicate that the new tool predicts width and height of a deposited track to engineering accuracy levels.

Numerical Simulation of Molten Flow in Directed Energy Deposition Using an Iterative Geometry Technique

NASA Astrophysics Data System (ADS)

Vincent, Timothy J.; Rumpfkeil, Markus P.; Chaudhary, Anil

2018-06-01

The complex, multi-faceted physics of laser-based additive metals processing tends to demand high-fidelity models and costly simulation tools to provide predictions accurate enough to aid in selecting process parameters. Of particular difficulty is the accurate determination of melt pool shape and size, which are useful for predicting lack-of-fusion, as this typically requires an adequate treatment of thermal and fluid flow. In this article we describe a novel numerical simulation tool which aims to achieve a balance between accuracy and cost. This is accomplished by making simplifying assumptions regarding the behavior of the gas-liquid interface for processes with a moderate energy density, such as Laser Engineered Net Shaping (LENS). The details of the implementation, which is based on the solver simpleFoam of the well-known software suite OpenFOAM, are given here and the tool is verified and validated for a LENS process involving Ti-6Al-4V. The results indicate that the new tool predicts width and height of a deposited track to engineering accuracy levels.

The integrated simulation and assessment of the impacts of process change in biotherapeutic antibody production.

PubMed

Chhatre, Sunil; Jones, Carl; Francis, Richard; O'Donovan, Kieran; Titchener-Hooker, Nigel; Newcombe, Anthony; Keshavarz-Moore, Eli

2006-01-01

Growing commercial pressures in the pharmaceutical industry are establishing a need for robust computer simulations of whole bioprocesses to allow rapid prediction of the effects of changes made to manufacturing operations. This paper presents an integrated process simulation that models the cGMP manufacture of the FDA-approved biotherapeutic CroFab, an IgG fragment used to treat rattlesnake envenomation (Protherics U.K. Limited, Blaenwaun, Ffostrasol, Llandysul, Wales, U.K.). Initially, the product is isolated from ovine serum by precipitation and centrifugation, before enzymatic digestion of the IgG to produce FAB and FC fragments. These are purified by ion exchange and affinity chromatography to remove the FC and non-specific FAB fragments from the final venom-specific FAB product. The model was constructed in a discrete event simulation environment and used to determine the potential impact of a series of changes to the process, such as increasing the step efficiencies or volumes of chromatographic matrices, upon product yields and process times. The study indicated that the overall FAB yield was particularly sensitive to changes in the digestive and affinity chromatographic step efficiencies, which have a predicted 30% greater impact on process FAB yield than do the precipitation or centrifugation stages. The study showed that increasing the volume of affinity matrix has a negligible impact upon total process time. Although results such as these would require experimental verification within the physical constraints of the process and the facility, the model predictions are still useful in allowing rapid "what-if" scenario analysis of the likely impacts of process changes within such an integrated production process.

Rolling Process Modeling Report: Finite-Element Prediction of Roll Separating Force and Rolling Defects

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

Soulami, Ayoub; Lavender, Curt A.; Paxton, Dean M.

2014-04-23

Pacific Northwest National Laboratory (PNNL) has been investigating manufacturing processes for the uranium-10% molybdenum (U-10Mo) alloy plate-type fuel for the U.S. high-performance research reactors. This work supports the Convert Program of the U.S. Department of Energy’s National Nuclear Security Administration (DOE/NNSA) Global Threat Reduction Initiative. This report documents modeling results of PNNL’s efforts to perform finite-element simulations to predict roll separating forces and rolling defects. Simulations were performed using a finite-element model developed using the commercial code LS-Dyna. Simulations of the hot rolling of U-10Mo coupons encapsulated in low-carbon steel have been conducted following two different schedules. Model predictions ofmore » the roll-separation force and roll-pack thicknesses at different stages of the rolling process were compared with experimental measurements. This report discusses various attributes of the rolled coupons revealed by the model (e.g., dog-boning and thickness non-uniformity).« less

Analyst-to-Analyst Variability in Simulation-Based Prediction

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

Glickman, Matthew R.; Romero, Vicente J.

This report describes findings from the culminating experiment of the LDRD project entitled, "Analyst-to-Analyst Variability in Simulation-Based Prediction". For this experiment, volunteer participants solving a given test problem in engineering and statistics were interviewed at different points in their solution process. These interviews are used to trace differing solutions to differing solution processes, and differing processes to differences in reasoning, assumptions, and judgments. The issue that the experiment was designed to illuminate -- our paucity of understanding of the ways in which humans themselves have an impact on predictions derived from complex computational simulations -- is a challenging and openmore » one. Although solution of the test problem by analyst participants in this experiment has taken much more time than originally anticipated, and is continuing past the end of this LDRD, this project has provided a rare opportunity to explore analyst-to-analyst variability in significant depth, from which we derive evidence-based insights to guide further explorations in this important area.« less

Numerical Simulation of Sintering Process in Ceramic Powder Injection Moulded Components

NASA Astrophysics Data System (ADS)

Song, J.; Barriere, T.; Liu, B.; Gelin, J. C.

2007-05-01

A phenomenological model based on viscoplastic constitutive law is presented to describe the sintering process of ceramic components obtained by powder injection moulding. The parameters entering in the model are identified through sintering experiments in dilatometer with the proposed optimization method. The finite element simulations are carried out to predict the density variations and dimensional changes of the components during sintering. A simulation example on the sintering process of hip implant in alumina has been conducted. The simulation results have been compared with the experimental ones. A good agreement is obtained.

The development of an industrial-scale fed-batch fermentation simulation.

PubMed

Goldrick, Stephen; Ştefan, Andrei; Lovett, David; Montague, Gary; Lennox, Barry

2015-01-10

This paper describes a simulation of an industrial-scale fed-batch fermentation that can be used as a benchmark in process systems analysis and control studies. The simulation was developed using a mechanistic model and validated using historical data collected from an industrial-scale penicillin fermentation process. Each batch was carried out in a 100,000 L bioreactor that used an industrial strain of Penicillium chrysogenum. The manipulated variables recorded during each batch were used as inputs to the simulator and the predicted outputs were then compared with the on-line and off-line measurements recorded in the real process. The simulator adapted a previously published structured model to describe the penicillin fermentation and extended it to include the main environmental effects of dissolved oxygen, viscosity, temperature, pH and dissolved carbon dioxide. In addition the effects of nitrogen and phenylacetic acid concentrations on the biomass and penicillin production rates were also included. The simulated model predictions of all the on-line and off-line process measurements, including the off-gas analysis, were in good agreement with the batch records. The simulator and industrial process data are available to download at www.industrialpenicillinsimulation.com and can be used to evaluate, study and improve on the current control strategy implemented on this facility. Crown Copyright © 2014. Published by Elsevier B.V. All rights reserved.

Mechanism of Void Prediction in Flip Chip Packages with Molded Underfill

NASA Astrophysics Data System (ADS)

Wu, Kuo-Tsai; Hwang, Sheng-Jye; Lee, Huei-Huang

2017-08-01

Voids have always been present using the molded underfill (MUF) package process, which is a problem that needs further investigation. In this study, the process was studied using the Moldex3D numerical analysis software. The effects of gas (air vent effect) on the overall melt front were also considered. In this isothermal process containing two fluids, the gas and melt colloid interact in the mold cavity. Simulation enabled an appropriate understanding of the actual situation to be gained, and, through analysis, the void region and exact location of voids were predicted. First, the global flow end area was observed to predict the void movement trend, and then the local flow ends were observed to predict the location and size of voids. In the MUF 518 case study, simulations predicted the void region as well as the location and size of the voids. The void phenomenon in a flip chip ball grid array underfill is discussed as part of the study.

Implementation of channel-routing routines in the Water Erosion Prediction Project (WEPP) model

Treesearch

Li Wang; Joan Q. Wu; William J. Elliott; Shuhui Dun; Sergey Lapin; Fritz R. Fiedler; Dennis C. Flanagan

2010-01-01

The Water Erosion Prediction Project (WEPP) model is a process-based, continuous-simulation, watershed hydrology and erosion model. It is an important tool for water erosion simulation owing to its unique functionality in representing diverse landuse and management conditions. Its applicability is limited to relatively small watersheds since its current version does...

A system identification approach for developing model predictive controllers of antibody quality attributes in cell culture processes.

PubMed

Downey, Brandon; Schmitt, John; Beller, Justin; Russell, Brian; Quach, Anthony; Hermann, Elizabeth; Lyon, David; Breit, Jeffrey

2017-11-01

As the biopharmaceutical industry evolves to include more diverse protein formats and processes, more robust control of Critical Quality Attributes (CQAs) is needed to maintain processing flexibility without compromising quality. Active control of CQAs has been demonstrated using model predictive control techniques, which allow development of processes which are robust against disturbances associated with raw material variability and other potentially flexible operating conditions. Wide adoption of model predictive control in biopharmaceutical cell culture processes has been hampered, however, in part due to the large amount of data and expertise required to make a predictive model of controlled CQAs, a requirement for model predictive control. Here we developed a highly automated, perfusion apparatus to systematically and efficiently generate predictive models using application of system identification approaches. We successfully created a predictive model of %galactosylation using data obtained by manipulating galactose concentration in the perfusion apparatus in serialized step change experiments. We then demonstrated the use of the model in a model predictive controller in a simulated control scenario to successfully achieve a %galactosylation set point in a simulated fed-batch culture. The automated model identification approach demonstrated here can potentially be generalized to many CQAs, and could be a more efficient, faster, and highly automated alternative to batch experiments for developing predictive models in cell culture processes, and allow the wider adoption of model predictive control in biopharmaceutical processes. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 33:1647-1661, 2017. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers.

Numerical simulation of the change characteristics of power dissipation coefficient of Ti-24Al-15Nb alloy in hot deformation

NASA Astrophysics Data System (ADS)

Wang, Kelu; Li, Xin; Zhang, Xiaobo

2018-03-01

The power dissipation maps of Ti-25Al-15Nb alloy were constructed by using the compression test data. A method is proposed to predict the distribution and variation of power dissipation coefficient in hot forging process using both the dynamic material model and finite element simulation. Using the proposed method, the change characteristics of the power dissipation coefficient are simulated and predicted. The effectiveness of the proposed method was verified by comparing the simulation results with the physical experimental results.

Comparison of RF spectrum prediction methods for dynamic spectrum access

NASA Astrophysics Data System (ADS)

Kovarskiy, Jacob A.; Martone, Anthony F.; Gallagher, Kyle A.; Sherbondy, Kelly D.; Narayanan, Ram M.

2017-05-01

Dynamic spectrum access (DSA) refers to the adaptive utilization of today's busy electromagnetic spectrum. Cognitive radio/radar technologies require DSA to intelligently transmit and receive information in changing environments. Predicting radio frequency (RF) activity reduces sensing time and energy consumption for identifying usable spectrum. Typical spectrum prediction methods involve modeling spectral statistics with Hidden Markov Models (HMM) or various neural network structures. HMMs describe the time-varying state probabilities of Markov processes as a dynamic Bayesian network. Neural Networks model biological brain neuron connections to perform a wide range of complex and often non-linear computations. This work compares HMM, Multilayer Perceptron (MLP), and Recurrent Neural Network (RNN) algorithms and their ability to perform RF channel state prediction. Monte Carlo simulations on both measured and simulated spectrum data evaluate the performance of these algorithms. Generalizing spectrum occupancy as an alternating renewal process allows Poisson random variables to generate simulated data while energy detection determines the occupancy state of measured RF spectrum data for testing. The results suggest that neural networks achieve better prediction accuracy and prove more adaptable to changing spectral statistics than HMMs given sufficient training data.

The role of effort in moderating the anxiety-performance relationship: Testing the prediction of processing efficiency theory in simulated rally driving.

PubMed

Wilson, Mark; Smith, Nickolas C; Chattington, Mark; Ford, Mike; Marple-Horvat, Dilwyn E

2006-11-01

We tested some of the key predictions of processing efficiency theory using a simulated rally driving task. Two groups of participants were classified as either dispositionally high or low anxious based on trait anxiety scores and trained on a simulated driving task. Participants then raced individually on two similar courses under counterbalanced experimental conditions designed to manipulate the level of anxiety experienced. The effort exerted on the driving tasks was assessed though self-report (RSME), psychophysiological measures (pupil dilation) and visual gaze data. Efficiency was measured in terms of efficiency of visual processing (search rate) and driving control (variability of wheel and accelerator pedal) indices. Driving performance was measured as the time taken to complete the course. As predicted, increased anxiety had a negative effect on processing efficiency as indexed by the self-report, pupillary response and variability of gaze data. Predicted differences due to dispositional levels of anxiety were also found in the driving control and effort data. Although both groups of drivers performed worse under the threatening condition, the performance of the high trait anxious individuals was affected to a greater extent by the anxiety manipulation than the performance of the low trait anxious drivers. The findings suggest that processing efficiency theory holds promise as a theoretical framework for examining the relationship between anxiety and performance in sport.

Process optimization of rolling for zincked sheet technology using response surface methodology and genetic algorithm

NASA Astrophysics Data System (ADS)

Ji, Liang-Bo; Chen, Fang

2017-07-01

Numerical simulation and intelligent optimization technology were adopted for rolling and extrusion of zincked sheet. By response surface methodology (RSM), genetic algorithm (GA) and data processing technology, an efficient optimization of process parameters for rolling of zincked sheet was investigated. The influence trend of roller gap, rolling speed and friction factor effects on reduction rate and plate shortening rate were analyzed firstly. Then a predictive response surface model for comprehensive quality index of part was created using RSM. Simulated and predicted values were compared. Through genetic algorithm method, the optimal process parameters for the forming of rolling were solved. They were verified and the optimum process parameters of rolling were obtained. It is feasible and effective.

Mechatronics technology in predictive maintenance method

NASA Astrophysics Data System (ADS)

Majid, Nurul Afiqah A.; Muthalif, Asan G. A.

2017-11-01

This paper presents recent mechatronics technology that can help to implement predictive maintenance by combining intelligent and predictive maintenance instrument. Vibration Fault Simulation System (VFSS) is an example of mechatronics system. The focus of this study is the prediction on the use of critical machines to detect vibration. Vibration measurement is often used as the key indicator of the state of the machine. This paper shows the choice of the appropriate strategy in the vibration of diagnostic process of the mechanical system, especially rotating machines, in recognition of the failure during the working process. In this paper, the vibration signature analysis is implemented to detect faults in rotary machining that includes imbalance, mechanical looseness, bent shaft, misalignment, missing blade bearing fault, balancing mass and critical speed. In order to perform vibration signature analysis for rotating machinery faults, studies have been made on how mechatronics technology is used as predictive maintenance methods. Vibration Faults Simulation Rig (VFSR) is designed to simulate and understand faults signatures. These techniques are based on the processing of vibrational data in frequency-domain. The LabVIEW-based spectrum analyzer software is developed to acquire and extract frequency contents of faults signals. This system is successfully tested based on the unique vibration fault signatures that always occur in a rotating machinery.

On the Validity of Continuum Computational Fluid Dynamics Approach Under Very Low-Pressure Plasma Spray Conditions

NASA Astrophysics Data System (ADS)

Ivchenko, Dmitrii; Zhang, Tao; Mariaux, Gilles; Vardelle, Armelle; Goutier, Simon; Itina, Tatiana E.

2018-01-01

Plasma spray physical vapor deposition aims to substantially evaporate powders in order to produce coatings with various microstructures. This is achieved by powder vapor condensation onto the substrate and/or by deposition of fine melted powder particles and nanoclusters. The deposition process typically operates at pressures ranging between 10 and 200 Pa. In addition to the experimental works, numerical simulations are performed to better understand the process and optimize the experimental conditions. However, the combination of high temperatures and low pressure with shock waves initiated by supersonic expansion of the hot gas in the low-pressure medium makes doubtful the applicability of the continuum approach for the simulation of such a process. This work investigates (1) effects of the pressure dependence of thermodynamic and transport properties on computational fluid dynamics (CFD) predictions and (2) the validity of the continuum approach for thermal plasma flow simulation under very low-pressure conditions. The study compares the flow fields predicted with a continuum approach using CFD software with those obtained by a kinetic-based approach using a direct simulation Monte Carlo method (DSMC). It also shows how the presence of high gradients can contribute to prediction errors for typical PS-PVD conditions.

Simulation and prediction of the thuringiensin abiotic degradation processes in aqueous solution by a radius basis function neural network model.

PubMed

Zhou, Jingwen; Xu, Zhenghong; Chen, Shouwen

2013-04-01

The thuringiensin abiotic degradation processes in aqueous solution under different conditions, with a pH range of 5.0-9.0 and a temperature range of 10-40°C, were systematically investigated by an exponential decay model and a radius basis function (RBF) neural network model, respectively. The half-lives of thuringiensin calculated by the exponential decay model ranged from 2.72 d to 16.19 d under the different conditions mentioned above. Furthermore, an RBF model with accuracy of 0.1 and SPREAD value 5 was employed to model the degradation processes. The results showed that the model could simulate and predict the degradation processes well. Both the half-lives and the prediction data showed that thuringiensin was an easily degradable antibiotic, which could be an important factor in the evaluation of its safety. Copyright © 2012 Elsevier Ltd. All rights reserved.

Local mechanical properties of LFT injection molded parts: Numerical simulations versus experiments

NASA Astrophysics Data System (ADS)

Desplentere, F.; Soete, K.; Bonte, H.; Debrabandere, E.

2014-05-01

In predictive engineering for polymer processes, the proper prediction of material microstructure from known processing conditions and constituent material properties is a critical step forward properly predicting bulk properties in the finished composite. Operating within the context of long-fiber thermoplastics (LFT, length < 15mm) this investigation concentrates on the prediction of the local mechanical properties of an injection molded part. To realize this, the Autodesk Simulation Moldflow Insight 2014 software has been used. In this software, a fiber breakage algorithm for the polymer flow inside the mold is available. Using well known micro mechanic formulas allow to combine the local fiber length with the local orientation into local mechanical properties. Different experiments were performed using a commercially available glass fiber filled compound to compare the measured data with the numerical simulation results. In this investigation, tensile tests and 3 point bending tests are considered. To characterize the fiber length distribution of the polymer melt entering the mold (necessary for the numerical simulations), air shots were performed. For those air shots, similar homogenization conditions were used as during the injection molding tests. The fiber length distribution is characterized using automated optical method on samples for which the matrix material is burned away. Using the appropriate settings for the different experiments, good predictions of the local mechanical properties are obtained.

System dynamic simulation: A new method in social impact assessment (SIA)

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

Karami, Shobeir, E-mail: shobeirkarami@gmail.com; Karami, Ezatollah, E-mail: ekarami@shirazu.ac.ir; Buys, Laurie, E-mail: l.buys@qut.edu.au

Many complex social questions are difficult to address adequately with conventional methods and techniques, due to the complicated dynamics, and hard to quantify social processes. Despite these difficulties researchers and practitioners have attempted to use conventional methods not only in evaluative modes but also in predictive modes to inform decision making. The effectiveness of SIAs would be increased if they were used to support the project design processes. This requires deliberate use of lessons from retrospective assessments to inform predictive assessments. Social simulations may be a useful tool for developing a predictive SIA method. There have been limited attempts tomore » develop computer simulations that allow social impacts to be explored and understood before implementing development projects. In light of this argument, this paper aims to introduce system dynamic (SD) simulation as a new predictive SIA method in large development projects. We propose the potential value of the SD approach to simulate social impacts of development projects. We use data from the SIA of Gareh-Bygone floodwater spreading project to illustrate the potential of SD simulation in SIA. It was concluded that in comparison to traditional SIA methods SD simulation can integrate quantitative and qualitative inputs from different sources and methods and provides a more effective and dynamic assessment of social impacts for development projects. We recommend future research to investigate the full potential of SD in SIA in comparing different situations and scenarios.« less

Process fault detection and nonlinear time series analysis for anomaly detection in safeguards

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

Burr, T.L.; Mullen, M.F.; Wangen, L.E.

In this paper we discuss two advanced techniques, process fault detection and nonlinear time series analysis, and apply them to the analysis of vector-valued and single-valued time-series data. We investigate model-based process fault detection methods for analyzing simulated, multivariate, time-series data from a three-tank system. The model-predictions are compared with simulated measurements of the same variables to form residual vectors that are tested for the presence of faults (possible diversions in safeguards terminology). We evaluate two methods, testing all individual residuals with a univariate z-score and testing all variables simultaneously with the Mahalanobis distance, for their ability to detect lossmore » of material from two different leak scenarios from the three-tank system: a leak without and with replacement of the lost volume. Nonlinear time-series analysis tools were compared with the linear methods popularized by Box and Jenkins. We compare prediction results using three nonlinear and two linear modeling methods on each of six simulated time series: two nonlinear and four linear. The nonlinear methods performed better at predicting the nonlinear time series and did as well as the linear methods at predicting the linear values.« less

Toward Process-resolving Synthesis and Prediction of Arctic Climate Change Using the Regional Arctic System Model

NASA Astrophysics Data System (ADS)

Maslowski, W.

2017-12-01

The Regional Arctic System Model (RASM) has been developed to better understand the operation of Arctic System at process scale and to improve prediction of its change at a spectrum of time scales. RASM is a pan-Arctic, fully coupled ice-ocean-atmosphere-land model with marine biogeochemistry extension to the ocean and sea ice models. The main goal of our research is to advance a system-level understanding of critical processes and feedbacks in the Arctic and their links with the Earth System. The secondary, an equally important objective, is to identify model needs for new or additional observations to better understand such processes and to help constrain models. Finally, RASM has been used to produce sea ice forecasts for September 2016 and 2017, in contribution to the Sea Ice Outlook of the Sea Ice Prediction Network. Future RASM forecasts, are likely to include increased resolution for model components and ecosystem predictions. Such research is in direct support of the US environmental assessment and prediction needs, including those of the U.S. Navy, Department of Defense, and the recent IARPC Arctic Research Plan 2017-2021. In addition to an overview of RASM technical details, selected model results are presented from a hierarchy of climate models together with available observations in the region to better understand potential oceanic contributions to polar amplification. RASM simulations are analyzed to evaluate model skill in representing seasonal climatology as well as interannual and multi-decadal climate variability and predictions. Selected physical processes and resulting feedbacks are discussed to emphasize the need for fully coupled climate model simulations, high model resolution and sensitivity of simulated sea ice states to scale dependent model parameterizations controlling ice dynamics, thermodynamics and coupling with the atmosphere and ocean.

Simulation Modeling of Software Development Processes

NASA Technical Reports Server (NTRS)

Calavaro, G. F.; Basili, V. R.; Iazeolla, G.

1996-01-01

A simulation modeling approach is proposed for the prediction of software process productivity indices, such as cost and time-to-market, and the sensitivity analysis of such indices to changes in the organization parameters and user requirements. The approach uses a timed Petri Net and Object Oriented top-down model specification. Results demonstrate the model representativeness, and its usefulness in verifying process conformance to expectations, and in performing continuous process improvement and optimization.

Dependence of Snowmelt Simulations on Scaling of the Forcing Processes (Invited)

NASA Astrophysics Data System (ADS)

Winstral, A. H.; Marks, D. G.; Gurney, R. J.

2009-12-01

The spatial organization and scaling relationships of snow distribution in mountain environs is ultimately dependent on the controlling processes. These processes include interactions between weather, topography, vegetation, snow state, and seasonally-dependent radiation inputs. In large scale snow modeling it is vital to know these dependencies to obtain accurate predictions while reducing computational costs. This study examined the scaling characteristics of the forcing processes and the dependency of distributed snowmelt simulations to their scaling. A base model simulation characterized these processes with 10m resolution over a 14.0 km2 basin with an elevation range of 1474 - 2244 masl. Each of the major processes affecting snow accumulation and melt - precipitation, wind speed, solar radiation, thermal radiation, temperature, and vapor pressure - were independently degraded to 1 km resolution. Seasonal and event-specific results were analyzed. Results indicated that scale effects on melt vary by process and weather conditions. The dependence of melt simulations on the scaling of solar radiation fluxes also had a seasonal component. These process-based scaling characteristics should remain static through time as they are based on physical considerations. As such, these results not only provide guidance for current modeling efforts, but are also well suited to predicting how potential climate changes will affect the heterogeneity of mountain snow distributions.

FY10 Report on Multi-scale Simulation of Solvent Extraction Processes: Molecular-scale and Continuum-scale Studies

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

Wardle, Kent E.; Frey, Kurt; Pereira, Candido

2014-02-02

This task is aimed at predictive modeling of solvent extraction processes in typical extraction equipment through multiple simulation methods at various scales of resolution. We have conducted detailed continuum fluid dynamics simulation on the process unit level as well as simulations of the molecular-level physical interactions which govern extraction chemistry. Through combination of information gained through simulations at each of these two tiers along with advanced techniques such as the Lattice Boltzmann Method (LBM) which can bridge these two scales, we can develop the tools to work towards predictive simulation for solvent extraction on the equipment scale (Figure 1). Themore » goal of such a tool-along with enabling optimized design and operation of extraction units-would be to allow prediction of stage extraction effrciency under specified conditions. Simulation efforts on each of the two scales will be described below. As the initial application of FELBM in the work performed during FYl0 has been on annular mixing it will be discussed in context of the continuum-scale. In the future, however, it is anticipated that the real value of FELBM will be in its use as a tool for sub-grid model development through highly refined DNS-like multiphase simulations facilitating exploration and development of droplet models including breakup and coalescence which will be needed for the large-scale simulations where droplet level physics cannot be resolved. In this area, it can have a significant advantage over traditional CFD methods as its high computational efficiency allows exploration of significantly greater physical detail especially as computational resources increase in the future.« less

75 FR 75961 - Notice of Implementation of the Wind Erosion Prediction System for Soil Erodibility System...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-12-07

... Wind Erosion Prediction System for Soil Erodibility System Calculations for the Natural Resources... Erosion Prediction System (WEPS) for soil erodibility system calculations scheduled for implementation for... computer model is a process-based, daily time-step computer model that predicts soil erosion via simulation...

Simulating the Current Water Cycle with the NASA Ames Mars Global Climate Model

NASA Astrophysics Data System (ADS)

Kahre, M. A.; Haberle, R. M.; Hollingsworth, J. L.; Brecht, A. S.; Urata, R. A.; Montmessin, F.

2017-12-01

The water cycle is a critical component of the current Mars climate system, and it is now widely recognized that water ice clouds significantly affect the nature of the simulated water cycle. Two processes are key to implementing clouds in a Mars global climate model (GCM): the microphysical processes of formation and dissipation, and their radiative effects on atmospheric heating/cooling rates. Together, these processes alter the thermal structure, change the atmospheric dynamics, and regulate inter-hemispheric transport. We have made considerable progress using the NASA Ames Mars GCM to simulate the current-day water cycle with radiatively active clouds. Cloud fields from our baseline simulation are in generally good agreement with observations. The predicted seasonal extent and peak IR optical depths are consistent MGS/TES observations. Additionally, the thermal response to the clouds in the aphelion cloud belt (ACB) is generally consistent with observations and other climate model predictions. Notably, there is a distinct gap in the predicted clouds over the North Residual Cap (NRC) during local summer, but the clouds reappear in this simulation over the NRC earlier than the observations indicate. Polar clouds are predicted near the seasonal CO2 ice caps, but the column thicknesses of these clouds are generally too thick compared to observations. Our baseline simulation is dry compared to MGS/TES-observed water vapor abundances, particularly in the tropics and subtropics. These areas of disagreement appear to be a consistent with other current water cycle GCMs. Future avenues of investigation will target improving our understanding of what controls the vertical extent of clouds and the apparent seasonal evolution of cloud particle sizes within the ACB.

Application of two hydrologic models with different runoff mechanisms to a hillslope dominated watershed in the northeastern US: A comparison of HSPF and SMR

USGS Publications Warehouse

Johnson, M.S.; Coon, W.F.; Mehta, V.K.; Steenhuis, T.S.; Brooks, E.S.; Boll, J.

2003-01-01

Differences in the simulation of hydrologic processes by watershed models directly affect the accuracy of results. Surface runoff generation can be simulated as either: (1) infiltration-excess (or Hortonian) overland flow, or (2) saturation-excess overland flow. This study compared the Hydrological Simulation Program - FORTRAN (HSPF) and the Soil Moisture Routing (SMR) models, each representing one of these mechanisms. These two models were applied to a 102 km2 watershed in the upper part of the Irondequoit Creek basin in central New York State over a seven-year simulation period. The models differed in both the complexity of simulating snowmelt and baseflow processes as well as the detail in which the geographic information was preserved by each model. Despite their differences in structure and representation of hydrologic processes, the two models simulated streamflow with almost equal accuracy. Since streamflow is an integral response and depends mainly on the watershed water balance, this was not unexpected. Model efficiency values for the seven-year simulation period were 0.67 and 0.65 for SMR and HSPF, respectively. HSPF simulated winter streamflow slightly better than SMR as a result of its complex snowmelt routine, whereas SMR simulated summer flows better than HSPF as a result of its runoff and baseflow processes. An important difference between model results was the ability to predict the spatial distribution of soil moisture content. HSPF aggregates soil moisture content, which is generally related to a specific pervious land unit across the entire watershed, whereas SMR predictions of moisture content distribution are geographically specific and matched field observations reasonably well. Important is that the saturated area was predicted well by SMR and confirmed the validity of using saturation-excess mechanisms for this hillslope dominated watershed. ?? 2003 Elsevier B.V. All rights reserved.

Integration of Tuyere, Raceway and Shaft Models for Predicting Blast Furnace Process

NASA Astrophysics Data System (ADS)

Fu, Dong; Tang, Guangwu; Zhao, Yongfu; D'Alessio, John; Zhou, Chenn Q.

2018-06-01

A novel modeling strategy is presented for simulating the blast furnace iron making process. Such physical and chemical phenomena are taking place across a wide range of length and time scales, and three models are developed to simulate different regions of the blast furnace, i.e., the tuyere model, the raceway model and the shaft model. This paper focuses on the integration of the three models to predict the entire blast furnace process. Mapping output and input between models and an iterative scheme are developed to establish communications between models. The effects of tuyere operation and burden distribution on blast furnace fuel efficiency are investigated numerically. The integration of different models provides a way to realistically simulate the blast furnace by improving the modeling resolution on local phenomena and minimizing the model assumptions.

Numerical simulation of the laser welding process for the prediction of temperature distribution on welded aluminium aircraft components

NASA Astrophysics Data System (ADS)

Tsirkas, S. A.

2018-03-01

The present investigation is focused to the modelling of the temperature field in aluminium aircraft components welded by a CO2 laser. A three-dimensional finite element model has been developed to simulate the laser welding process and predict the temperature distribution in T-joint laser welded plates with fillet material. The simulation of the laser beam welding process was performed using a nonlinear heat transfer analysis, based on a keyhole formation model analysis. The model employs the technique of element ;birth and death; in order to simulate the weld fillet. Various phenomena associated with welding like temperature dependent material properties and heat losses through convection and radiation were accounted for in the model. The materials considered were 6056-T78 and 6013-T4 aluminium alloys, commonly used for aircraft components. The temperature distribution during laser welding process has been calculated numerically and validated by experimental measurements on different locations of the welded structure. The numerical results are in good agreement with the experimental measurements.

A Method for Combining Experimentation and Molecular Dynamics Simulation to Improve Cohesive Zone Models for Metallic Microstructures

NASA Technical Reports Server (NTRS)

Hochhalter, J. D.; Glaessgen, E. H.; Ingraffea, A. R.; Aquino, W. A.

2009-01-01

Fracture processes within a material begin at the nanometer length scale at which the formation, propagation, and interaction of fundamental damage mechanisms occur. Physics-based modeling of these atomic processes quickly becomes computationally intractable as the system size increases. Thus, a multiscale modeling method, based on the aggregation of fundamental damage processes occurring at the nanoscale within a cohesive zone model, is under development and will enable computationally feasible and physically meaningful microscale fracture simulation in polycrystalline metals. This method employs atomistic simulation to provide an optimization loop with an initial prediction of a cohesive zone model (CZM). This initial CZM is then applied at the crack front region within a finite element model. The optimization procedure iterates upon the CZM until the finite element model acceptably reproduces the near-crack-front displacement fields obtained from experimental observation. With this approach, a comparison can be made between the original CZM predicted by atomistic simulation and the converged CZM that is based on experimental observation. Comparison of the two CZMs gives insight into how atomistic simulation scales.

FE-simulation of hot forging with an integrated heat treatment with the objective of residual stress prediction

NASA Astrophysics Data System (ADS)

Behrens, Bernd-Arno; Chugreeva, Anna; Chugreev, Alexander

2018-05-01

Hot forming as a coupled thermo-mechanical process comprises numerous material phenomena with a corresponding impact on the material behavior during and after the forming process as well as on the final component performance. In this context, a realistic FE-simulation requires reliable mathematical models as well as detailed thermo-mechanical material data. This paper presents experimental and numerical results focused on the FE-based simulation of a hot forging process with a subsequent heat treatment step aiming at the prediction of the final mechanical properties and residual stress state in the forged component made of low alloy CrMo-steel DIN 42CrMo4. For this purpose, hot forging experiments of connecting rod geometry with a corresponding metallographic analysis and x-ray residual stress measurements have been carried out. For the coupled thermo-mechanical-metallurgical FE-simulations, a special user-defined material model based on the additive strain decomposition method and implemented in Simufact Forming via MSC.Marc solver features has been used.

Deficits in Attention and Visual Processing but not Global Cognition Predict Simulated Driving Errors in Drivers Diagnosed With Mild Alzheimer's Disease.

PubMed

Yamin, Stephanie; Stinchcombe, Arne; Gagnon, Sylvain

2016-06-01

This study sought to predict driving performance of drivers with Alzheimer's disease (AD) using measures of attention, visual processing, and global cognition. Simulated driving performance of individuals with mild AD (n = 20) was contrasted with performance of a group of healthy controls (n = 21). Performance on measures of global cognitive function and specific tests of attention and visual processing were examined in relation to simulated driving performance. Strong associations were observed between measures of attention, notably the Test of Everyday Attention (sustained attention; r = -.651, P = .002) and the Useful Field of View (r = .563, P = .010), and driving performance among drivers with mild AD. The Visual Object and Space Perception Test-object was significantly correlated with the occurrence of crashes (r = .652, P = .002). Tests of global cognition did not correlate with simulated driving outcomes. The results suggest that professionals exercise caution when extrapolating driving performance based on global cognitive indicators. © The Author(s) 2015.

Use of simulated data sets to evaluate the fidelity of metagenomic processing methods

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

Mavromatis, K; Ivanova, N; Barry, Kerrie

2007-01-01

Metagenomics is a rapidly emerging field of research for studying microbial communities. To evaluate methods presently used to process metagenomic sequences, we constructed three simulated data sets of varying complexity by combining sequencing reads randomly selected from 113 isolate genomes. These data sets were designed to model real metagenomes in terms of complexity and phylogenetic composition. We assembled sampled reads using three commonly used genome assemblers (Phrap, Arachne and JAZZ), and predicted genes using two popular gene-finding pipelines (fgenesb and CRITICA/GLIMMER). The phylogenetic origins of the assembled contigs were predicted using one sequence similarity-based ( blast hit distribution) and twomore » sequence composition-based (PhyloPythia, oligonucleotide frequencies) binning methods. We explored the effects of the simulated community structure and method combinations on the fidelity of each processing step by comparison to the corresponding isolate genomes. The simulated data sets are available online to facilitate standardized benchmarking of tools for metagenomic analysis.« less

Improved Dynamic Modeling of the Cascade Distillation Subsystem and Analysis of Factors Affecting Its Performance

NASA Technical Reports Server (NTRS)

Perry, Bruce A.; Anderson, Molly S.

2015-01-01

The Cascade Distillation Subsystem (CDS) is a rotary multistage distiller being developed to serve as the primary processor for wastewater recovery during long-duration space missions. The CDS could be integrated with a system similar to the International Space Station Water Processor Assembly to form a complete water recovery system for future missions. A preliminary chemical process simulation was previously developed using Aspen Custom Modeler® (ACM), but it could not simulate thermal startup and lacked detailed analysis of several key internal processes, including heat transfer between stages. This paper describes modifications to the ACM simulation of the CDS that improve its capabilities and the accuracy of its predictions. Notably, the modified version can be used to model thermal startup and predicts the total energy consumption of the CDS. The simulation has been validated for both NaC1 solution and pretreated urine feeds and no longer requires retuning when operating parameters change. The simulation was also used to predict how internal processes and operating conditions of the CDS affect its performance. In particular, it is shown that the coefficient of performance of the thermoelectric heat pump used to provide heating and cooling for the CDS is the largest factor in determining CDS efficiency. Intrastage heat transfer affects CDS performance indirectly through effects on the coefficient of performance.

Computer Simulation in Predicting Biochemical Processes and Energy Balance at WWTPs

NASA Astrophysics Data System (ADS)

Drewnowski, Jakub; Zaborowska, Ewa; Hernandez De Vega, Carmen

2018-02-01

Nowadays, the use of mathematical models and computer simulation allow analysis of many different technological solutions as well as testing various scenarios in a short time and at low financial budget in order to simulate the scenario under typical conditions for the real system and help to find the best solution in design or operation process. The aim of the study was to evaluate different concepts of biochemical processes and energy balance modelling using a simulation platform GPS-x and a comprehensive model Mantis2. The paper presents the example of calibration and validation processes in the biological reactor as well as scenarios showing an influence of operational parameters on the WWTP energy balance. The results of batch tests and full-scale campaign obtained in the former work were used to predict biochemical and operational parameters in a newly developed plant model. The model was extended with sludge treatment devices, including anaerobic digester. Primary sludge removal efficiency was found as a significant factor determining biogas production and further renewable energy production in cogeneration. Water and wastewater utilities, which run and control WWTP, are interested in optimizing the process in order to save environment, their budget and decrease the pollutant emissions to water and air. In this context, computer simulation can be the easiest and very useful tool to improve the efficiency without interfering in the actual process performance.

Polar Processes in a 50-year Simulation of Stratospheric Chemistry and Transport

NASA Technical Reports Server (NTRS)

Kawa, S.R.; Douglass, A. R.; Patrick, L. C.; Allen, D. R.; Randall, C. E.

2004-01-01

The unique chemical, dynamical, and microphysical processes that occur in the winter polar lower stratosphere are expected to interact strongly with changing climate and trace gas abundances. Significant changes in ozone have been observed and prediction of future ozone and climate interactions depends on modeling these processes successfully. We have conducted an off-line model simulation of the stratosphere for trace gas conditions representative of 1975-2025 using meteorology from the NASA finite-volume general circulation model. The objective of this simulation is to examine the sensitivity of stratospheric ozone and chemical change to varying meteorology and trace gas inputs. This presentation will examine the dependence of ozone and related processes in polar regions on the climatological and trace gas changes in the model. The model past performance is base-lined against available observations, and a future ozone recovery scenario is forecast. Overall the model ozone simulation is quite realistic, but initial analysis of the detailed evolution of some observable processes suggests systematic shortcomings in our description of the polar chemical rates and/or mechanisms. Model sensitivities, strengths, and weaknesses will be discussed with implications for uncertainty and confidence in coupled climate chemistry predictions.

WEPP Model applications for evaluations of best management practices

Treesearch

D. C. Flanagan; W. J. Elliott; J. R. Frankenberger; C. Huang

2010-01-01

The Water Erosion Prediction Project (WEPP) model is a process-based erosion prediction technology for application to small watersheds and hillslope profiles, under agricultural, forested, rangeland, and other land management conditions. Developed by the United States Department of Agriculture (USDA) over the past 25 years, WEPP simulates many of the physical processes...

Use of simulated satellite radiances from a mesoscale numerical model to understand kinematic and dynamic processes

NASA Technical Reports Server (NTRS)

Kalb, Michael; Robertson, Franklin; Jedlovec, Gary; Perkey, Donald

1987-01-01

Techniques by which mesoscale numerical weather prediction model output and radiative transfer codes are combined to simulate the radiance fields that a given passive temperature/moisture satellite sensor would see if viewing the evolving model atmosphere are introduced. The goals are to diagnose the dynamical atmospheric processes responsible for recurring patterns in observed satellite radiance fields, and to develop techniques to anticipate the ability of satellite sensor systems to depict atmospheric structures and provide information useful for numerical weather prediction (NWP). The concept of linking radiative transfer and dynamical NWP codes is demonstrated with time sequences of simulated radiance imagery in the 24 TIROS vertical sounder channels derived from model integrations for March 6, 1982.

Predicting Formation Damage in Aquifer Thermal Energy Storage Systems Utilizing a Coupled Hydraulic-Thermal-Chemical Reservoir Model

NASA Astrophysics Data System (ADS)

Müller, Daniel; Regenspurg, Simona; Milsch, Harald; Blöcher, Guido; Kranz, Stefan; Saadat, Ali

2014-05-01

In aquifer thermal energy storage (ATES) systems, large amounts of energy can be stored by injecting hot water into deep or intermediate aquifers. In a seasonal production-injection cycle, water is circulated through a system comprising the porous aquifer, a production well, a heat exchanger and an injection well. This process involves large temperature and pressure differences, which shift chemical equilibria and introduce or amplify mechanical processes. Rock-fluid interaction such as dissolution and precipitation or migration and deposition of fine particles will affect the hydraulic properties of the porous medium and may lead to irreversible formation damage. In consequence, these processes determine the long-term performance of the ATES system and need to be predicted to ensure the reliability of the system. However, high temperature and pressure gradients and dynamic feedback cycles pose challenges on predicting the influence of the relevant processes. Within this study, a reservoir model comprising a coupled hydraulic-thermal-chemical simulation was developed based on an ATES demonstration project located in the city of Berlin, Germany. The structural model was created with Petrel, based on data available from seismic cross-sections and wellbores. The reservoir simulation was realized by combining the capabilities of multiple simulation tools. For the reactive transport model, COMSOL Multiphysics (hydraulic-thermal) and PHREEQC (chemical) were combined using the novel interface COMSOL_PHREEQC, developed by Wissmeier & Barry (2011). It provides a MATLAB-based coupling interface between both programs. Compared to using COMSOL's built-in reactive transport simulator, PHREEQC additionally calculates adsorption and reaction kinetics and allows the selection of different activity coefficient models in the database. The presented simulation tool will be able to predict the most important aspects of hydraulic, thermal and chemical transport processes relevant to formation damage in ATES systems. We would like to present preliminary results of the structural reservoir model and the hydraulic-thermal-chemical coupling for the demonstration site. Literature: Wissmeier, L. and Barry, D.A., 2011. Simulation tool for variably saturated flow with comprehensive geochemical reactions in two- and three-dimensional domains. Environmental Modelling & Software 26, 210-218.

Simulation and Validation of Injection-Compression Filling Stage of Liquid Moulding with Fast Curing Resins

NASA Astrophysics Data System (ADS)

Martin, Ffion A.; Warrior, Nicholas A.; Simacek, Pavel; Advani, Suresh; Hughes, Adrian; Darlington, Roger; Senan, Eissa

2018-03-01

Very short manufacture cycle times are required if continuous carbon fibre and epoxy composite components are to be economically viable solutions for high volume composite production for the automotive industry. Here, a manufacturing process variant of resin transfer moulding (RTM), targets a reduction of in-mould manufacture time by reducing the time to inject and cure components. The process involves two stages; resin injection followed by compression. A flow simulation methodology using an RTM solver for the process has been developed. This paper compares the simulation prediction to experiments performed using industrial equipment. The issues encountered during the manufacturing are included in the simulation and their sensitivity to the process is explored.

An empirical analysis of the distribution of the duration of overshoots in a stationary gaussian stochastic process

NASA Technical Reports Server (NTRS)

Parrish, R. S.; Carter, M. C.

1974-01-01

This analysis utilizes computer simulation and statistical estimation. Realizations of stationary gaussian stochastic processes with selected autocorrelation functions are computer simulated. Analysis of the simulated data revealed that the mean and the variance of a process were functionally dependent upon the autocorrelation parameter and crossing level. Using predicted values for the mean and standard deviation, by the method of moments, the distribution parameters was estimated. Thus, given the autocorrelation parameter, crossing level, mean, and standard deviation of a process, the probability of exceeding the crossing level for a particular length of time was calculated.

Numerical simulation of the casting process of titanium tooth crowns and bridges.

PubMed

Wu, M; Augthun, M; Wagner, I; Sahm, P R; Spiekermann, H

2001-06-01

The objectives of this paper were to simulate the casting process of titanium tooth crowns and bridges; to predict and control porosity defect. A casting simulation software, MAGMASOFT, was used. The geometry of the crowns with fine details of the occlusal surface were digitized by means of laser measuring technique, then converted and read in the simulation software. Both mold filling and solidification were simulated, the shrinkage porosity was predicted by a "feeding criterion", and the gas pore sensitivity was studied based on the mold filling and solidification simulations. Two types of dental prostheses (a single-crown casting and a three-unit-bridge) with various sprue designs were numerically "poured", and only one optimal design for each prosthesis was recommended for real casting trial. With the numerically optimized design, real titanium dental prostheses (five replicas for each) were made on a centrifugal casting machine. All the castings endured radiographic examination, and no porosity was detected in the cast prostheses. It indicates that the numerical simulation is an efficient tool for dental casting design and porosity control. Copyright 2001 Kluwer Academic Publishers

A general software reliability process simulation technique

NASA Technical Reports Server (NTRS)

Tausworthe, Robert C.

1991-01-01

The structure and rationale of the generalized software reliability process, together with the design and implementation of a computer program that simulates this process are described. Given assumed parameters of a particular project, the users of this program are able to generate simulated status timelines of work products, numbers of injected anomalies, and the progress of testing, fault isolation, repair, validation, and retest. Such timelines are useful in comparison with actual timeline data, for validating the project input parameters, and for providing data for researchers in reliability prediction modeling.

Predicting Silk Fiber Mechanical Properties through Multiscale Simulation and Protein Design.

PubMed

Rim, Nae-Gyune; Roberts, Erin G; Ebrahimi, Davoud; Dinjaski, Nina; Jacobsen, Matthew M; Martín-Moldes, Zaira; Buehler, Markus J; Kaplan, David L; Wong, Joyce Y

2017-08-14

Silk is a promising material for biomedical applications, and much research is focused on how application-specific, mechanical properties of silk can be designed synthetically through proper amino acid sequences and processing parameters. This protocol describes an iterative process between research disciplines that combines simulation, genetic synthesis, and fiber analysis to better design silk fibers with specific mechanical properties. Computational methods are used to assess the protein polymer structure as it forms an interconnected fiber network through shearing and how this process affects fiber mechanical properties. Model outcomes are validated experimentally with the genetic design of protein polymers that match the simulation structures, fiber fabrication from these polymers, and mechanical testing of these fibers. Through iterative feedback between computation, genetic synthesis, and fiber mechanical testing, this protocol will enable a priori prediction capability of recombinant material mechanical properties via insights from the resulting molecular architecture of the fiber network based entirely on the initial protein monomer composition. This style of protocol may be applied to other fields where a research team seeks to design a biomaterial with biomedical application-specific properties. This protocol highlights when and how the three research groups (simulation, synthesis, and engineering) should be interacting to arrive at the most effective method for predictive design of their material.

Evaluating crown fire rate of spread predictions from physics-based models

Treesearch

C. M. Hoffman; J. Ziegler; J. Canfield; R. R. Linn; W. Mell; C. H. Sieg; F. Pimont

2015-01-01

Modeling the behavior of crown fires is challenging due to the complex set of coupled processes that drive the characteristics of a spreading wildfire and the large range of spatial and temporal scales over which these processes occur. Detailed physics-based modeling approaches such as FIRETEC and the Wildland Urban Interface Fire Dynamics Simulator (WFDS) simulate...

Computational approach on PEB process in EUV resist: multi-scale simulation

NASA Astrophysics Data System (ADS)

Kim, Muyoung; Moon, Junghwan; Choi, Joonmyung; Lee, Byunghoon; Jeong, Changyoung; Kim, Heebom; Cho, Maenghyo

2017-03-01

For decades, downsizing has been a key issue for high performance and low cost of semiconductor, and extreme ultraviolet lithography is one of the promising candidates to achieve the goal. As a predominant process in extreme ultraviolet lithography on determining resolution and sensitivity, post exposure bake has been mainly studied by experimental groups, but development of its photoresist is at the breaking point because of the lack of unveiled mechanism during the process. Herein, we provide theoretical approach to investigate underlying mechanism on the post exposure bake process in chemically amplified resist, and it covers three important reactions during the process: acid generation by photo-acid generator dissociation, acid diffusion, and deprotection. Density functional theory calculation (quantum mechanical simulation) was conducted to quantitatively predict activation energy and probability of the chemical reactions, and they were applied to molecular dynamics simulation for constructing reliable computational model. Then, overall chemical reactions were simulated in the molecular dynamics unit cell, and final configuration of the photoresist was used to predict the line edge roughness. The presented multiscale model unifies the phenomena of both quantum and atomic scales during the post exposure bake process, and it will be helpful to understand critical factors affecting the performance of the resulting photoresist and design the next-generation material.

Surrogate modelling for the prediction of spatial fields based on simultaneous dimensionality reduction of high-dimensional input/output spaces.

PubMed

Crevillén-García, D

2018-04-01

Time-consuming numerical simulators for solving groundwater flow and dissolution models of physico-chemical processes in deep aquifers normally require some of the model inputs to be defined in high-dimensional spaces in order to return realistic results. Sometimes, the outputs of interest are spatial fields leading to high-dimensional output spaces. Although Gaussian process emulation has been satisfactorily used for computing faithful and inexpensive approximations of complex simulators, these have been mostly applied to problems defined in low-dimensional input spaces. In this paper, we propose a method for simultaneously reducing the dimensionality of very high-dimensional input and output spaces in Gaussian process emulators for stochastic partial differential equation models while retaining the qualitative features of the original models. This allows us to build a surrogate model for the prediction of spatial fields in such time-consuming simulators. We apply the methodology to a model of convection and dissolution processes occurring during carbon capture and storage.

Embodied simulation as part of affective evaluation processes: task dependence of valence concordant EMG activity.

PubMed

Weinreich, André; Funcke, Jakob Maria

2014-01-01

Drawing on recent findings, this study examines whether valence concordant electromyography (EMG) responses can be explained as an unconditional effect of mere stimulus processing or as somatosensory simulation driven by task-dependent processing strategies. While facial EMG over the Corrugator supercilii and the Zygomaticus major was measured, each participant performed two tasks with pictures of album covers. One task was an affective evaluation task and the other was to attribute the album covers to one of five decades. The Embodied Emotion Account predicts that valence concordant EMG is more likely to occur if the task necessitates a somatosensory simulation of the evaluative meaning of stimuli. Results support this prediction with regard to Corrugator supercilii in that valence concordant EMG activity was only present in the affective evaluation task but not in the non-evaluative task. Results for the Zygomaticus major were ambiguous. Our findings are in line with the view that EMG activity is an embodied part of the evaluation process and not a mere physical outcome.

Simulation software: engineer processes before reengineering.

PubMed

Lepley, C J

2001-01-01

People make decisions all the time using intuition. But what happens when you are asked: "Are you sure your predictions are accurate? How much will a mistake cost? What are the risks associated with this change?" Once a new process is engineered, it is difficult to analyze what would have been different if other options had been chosen. Simulating a process can help senior clinical officers solve complex patient flow problems and avoid wasted efforts. Simulation software can give you the data you need to make decisions. The author introduces concepts, methodologies, and applications of computer aided simulation to illustrate their use in making decisions to improve workflow design.

Uncertainty quantification's role in modeling and simulation planning, and credibility assessment through the predictive capability maturity model

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

Rider, William J.; Witkowski, Walter R.; Mousseau, Vincent Andrew

2016-04-13

The importance of credible, trustworthy numerical simulations is obvious especially when using the results for making high-consequence decisions. Determining the credibility of such numerical predictions is much more difficult and requires a systematic approach to assessing predictive capability, associated uncertainties and overall confidence in the computational simulation process for the intended use of the model. This process begins with an evaluation of the computational modeling of the identified, important physics of the simulation for its intended use. This is commonly done through a Phenomena Identification Ranking Table (PIRT). Then an assessment of the evidence basis supporting the ability to computationallymore » simulate these physics can be performed using various frameworks such as the Predictive Capability Maturity Model (PCMM). There were several critical activities that follow in the areas of code and solution verification, validation and uncertainty quantification, which will be described in detail in the following sections. Here, we introduce the subject matter for general applications but specifics are given for the failure prediction project. In addition, the first task that must be completed in the verification & validation procedure is to perform a credibility assessment to fully understand the requirements and limitations of the current computational simulation capability for the specific application intended use. The PIRT and PCMM are tools used at Sandia National Laboratories (SNL) to provide a consistent manner to perform such an assessment. Ideally, all stakeholders should be represented and contribute to perform an accurate credibility assessment. PIRTs and PCMMs are both described in brief detail below and the resulting assessments for an example project are given.« less

Expanding Regional Airport Usage to Accommodate Increased Air Traffic Demand

NASA Technical Reports Server (NTRS)

Russell, Carl R.

2009-01-01

Small regional airports present an underutilized source of capacity in the national air transportation system. This study sought to determine whether a 50 percent increase in national operations could be achieved by limiting demand growth at large hub airports and instead growing traffic levels at the surrounding regional airports. This demand scenario for future air traffic in the United States was generated and used as input to a 24-hour simulation of the national airspace system. Results of the demand generation process and metrics predicting the simulation results are presented, in addition to the actual simulation results. The demand generation process showed that sufficient runway capacity exists at regional airports to offload a significant portion of traffic from hub airports. Predictive metrics forecast a large reduction of delays at most major airports when demand is shifted. The simulation results then show that offloading hub traffic can significantly reduce nationwide delays.

Simulating Picosecond X-ray Diffraction from shocked crystals by Post-processing Molecular Dynamics Calculations

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

Kimminau, G; Nagler, B; Higginbotham, A

2008-06-19

Calculations of the x-ray diffraction patterns from shocked crystals derived from the results of Non-Equilibrium-Molecular-Dynamics (NEMD) simulations are presented. The atomic coordinates predicted by the NEMD simulations combined with atomic form factors are used to generate a discrete distribution of electron density. A Fast-Fourier-Transform (FFT) of this distribution provides an image of the crystal in reciprocal space, which can be further processed to produce quantitative simulated data for direct comparison with experiments that employ picosecond x-ray diffraction from laser-irradiated crystalline targets.

Monte Carlo simulations of particle acceleration at oblique shocks: Including cross-field diffusion

NASA Technical Reports Server (NTRS)

Baring, M. G.; Ellison, D. C.; Jones, F. C.

1995-01-01

The Monte Carlo technique of simulating diffusive particle acceleration at shocks has made spectral predictions that compare extremely well with particle distributions observed at the quasi-parallel region of the earth's bow shock. The current extension of this work to compare simulation predictions with particle spectra at oblique interplanetary shocks has required the inclusion of significant cross-field diffusion (strong scattering) in the simulation technique, since oblique shocks are intrinsically inefficient in the limit of weak scattering. In this paper, we present results from the method we have developed for the inclusion of cross-field diffusion in our simulations, namely model predictions of particle spectra downstream of oblique subluminal shocks. While the high-energy spectral index is independent of the shock obliquity and the strength of the scattering, the latter is observed to profoundly influence the efficiency of injection of cosmic rays into the acceleration process.

In-situ biogas upgrading process: Modeling and simulations aspects.

PubMed

Lovato, Giovanna; Alvarado-Morales, Merlin; Kovalovszki, Adam; Peprah, Maria; Kougias, Panagiotis G; Rodrigues, José Alberto Domingues; Angelidaki, Irini

2017-12-01

Biogas upgrading processes by in-situ hydrogen (H 2 ) injection are still challenging and could benefit from a mathematical model to predict system performance. Therefore, a previous model on anaerobic digestion was updated and expanded to include the effect of H 2 injection into the liquid phase of a fermenter with the aim of modeling and simulating these processes. This was done by including hydrogenotrophic methanogen kinetics for H 2 consumption and inhibition effect on the acetogenic steps. Special attention was paid to gas to liquid transfer of H 2 . The final model was successfully validated considering a set of Case Studies. Biogas composition and H 2 utilization were correctly predicted, with overall deviation below 10% compared to experimental measurements. Parameter sensitivity analysis revealed that the model is highly sensitive to the H 2 injection rate and mass transfer coefficient. The model developed is an effective tool for predicting process performance in scenarios with biogas upgrading. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predictive modeling of infrared radiative heating in tomato dry-peeling process: Part II. Model validation and sensitivity analysis

USDA-ARS?s Scientific Manuscript database

A predictive mathematical model was developed to simulate heat transfer in a tomato undergoing double sided infrared (IR) heating in a dry-peeling process. The aims of this study were to validate the developed model using experimental data and to investigate different engineering parameters that mos...

Numerical simulations of an advection fog event over Shanghai Pudong International Airport with the WRF model

NASA Astrophysics Data System (ADS)

Lin, Caiyan; Zhang, Zhongfeng; Pu, Zhaoxia; Wang, Fengyun

2017-10-01

A series of numerical simulations is conducted to understand the formation, evolution, and dissipation of an advection fog event over Shanghai Pudong International Airport (ZSPD) with the Weather Research and Forecasting (WRF) model. Using the current operational settings at the Meteorological Center of East China Air Traffic Management Bureau, the WRF model successfully predicts the fog event at ZSPD. Additional numerical experiments are performed to examine the physical processes associated with the fog event. The results indicate that prediction of this particular fog event is sensitive to microphysical schemes for the time of fog dissipation but not for the time of fog onset. The simulated timing of the arrival and dissipation of the fog, as well as the cloud distribution, is substantially sensitive to the planetary boundary layer and radiation (both longwave and shortwave) processes. Moreover, varying forecast lead times also produces different simulation results for the fog event regarding its onset and duration, suggesting a trade-off between more accurate initial conditions and a proper forecast lead time that allows model physical processes to spin up adequately during the fog simulation. The overall outcomes from this study imply that the complexity of physical processes and their interactions within the WRF model during fog evolution and dissipation is a key area of future research.

Predicting protein concentrations with ELISA microarray assays, monotonic splines and Monte Carlo simulation

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

Daly, Don S.; Anderson, Kevin K.; White, Amanda M.

Background: A microarray of enzyme-linked immunosorbent assays, or ELISA microarray, predicts simultaneously the concentrations of numerous proteins in a small sample. These predictions, however, are uncertain due to processing error and biological variability. Making sound biological inferences as well as improving the ELISA microarray process require require both concentration predictions and creditable estimates of their errors. Methods: We present a statistical method based on monotonic spline statistical models, penalized constrained least squares fitting (PCLS) and Monte Carlo simulation (MC) to predict concentrations and estimate prediction errors in ELISA microarray. PCLS restrains the flexible spline to a fit of assay intensitymore » that is a monotone function of protein concentration. With MC, both modeling and measurement errors are combined to estimate prediction error. The spline/PCLS/MC method is compared to a common method using simulated and real ELISA microarray data sets. Results: In contrast to the rigid logistic model, the flexible spline model gave credible fits in almost all test cases including troublesome cases with left and/or right censoring, or other asymmetries. For the real data sets, 61% of the spline predictions were more accurate than their comparable logistic predictions; especially the spline predictions at the extremes of the prediction curve. The relative errors of 50% of comparable spline and logistic predictions differed by less than 20%. Monte Carlo simulation rendered acceptable asymmetric prediction intervals for both spline and logistic models while propagation of error produced symmetric intervals that diverged unrealistically as the standard curves approached horizontal asymptotes. Conclusions: The spline/PCLS/MC method is a flexible, robust alternative to a logistic/NLS/propagation-of-error method to reliably predict protein concentrations and estimate their errors. The spline method simplifies model selection and fitting, and reliably estimates believable prediction errors. For the 50% of the real data sets fit well by both methods, spline and logistic predictions are practically indistinguishable, varying in accuracy by less than 15%. The spline method may be useful when automated prediction across simultaneous assays of numerous proteins must be applied routinely with minimal user intervention.« less

Prediction of La0.6Sr0.4Co0.2Fe0.8O3 cathode microstructures during sintering: Kinetic Monte Carlo (KMC) simulations calibrated by artificial neural networks

NASA Astrophysics Data System (ADS)

Yan, Zilin; Kim, Yongtae; Hara, Shotaro; Shikazono, Naoki

2017-04-01

The Potts Kinetic Monte Carlo (KMC) model, proven to be a robust tool to study all stages of sintering process, is an ideal tool to analyze the microstructure evolution of electrodes in solid oxide fuel cells (SOFCs). Due to the nature of this model, the input parameters of KMC simulations such as simulation temperatures and attempt frequencies are difficult to identify. We propose a rigorous and efficient approach to facilitate the input parameter calibration process using artificial neural networks (ANNs). The trained ANN reduces drastically the number of trial-and-error of KMC simulations. The KMC simulation using the calibrated input parameters predicts the microstructures of a La0.6Sr0.4Co0.2Fe0.8O3 cathode material during sintering, showing both qualitative and quantitative congruence with real 3D microstructures obtained by focused ion beam scanning electron microscopy (FIB-SEM) reconstruction.

Development of the ARISTOTLE webware for cloud-based rarefied gas flow modeling

NASA Astrophysics Data System (ADS)

Deschenes, Timothy R.; Grot, Jonathan; Cline, Jason A.

2016-11-01

Rarefied gas dynamics are important for a wide variety of applications. An improvement in the ability of general users to predict these gas flows will enable optimization of current, and discovery of future processes. Despite this potential, most rarefied simulation software is designed by and for experts in the community. This has resulted in low adoption of the methods outside of the immediate RGD community. This paper outlines an ongoing effort to create a rarefied gas dynamics simulation tool that can be used by a general audience. The tool leverages a direct simulation Monte Carlo (DSMC) library that is available to the entire community and a web-based simulation process that will enable all users to take advantage of high performance computing capabilities. First, the DSMC library and simulation architecture are described. Then the DSMC library is used to predict a number of representative transient gas flows that are applicable to the rarefied gas dynamics community. The paper closes with a summary and future direction.

Fatigue Life Variability in Large Aluminum Forgings with Residual Stress

DTIC Science & Technology

2011-07-01

been conducted. A detailed finite element analysis of the forge/ quench /coldwork/machine process was performed in order to predict the bulk residual...forge/ quench /coldwork/machine process was performed in order to predict the bulk residual stresses in a fictitious aluminum bulkhead. The residual...continues to develop the capability for computational simulation of the forge, quench , cold work and machining processes. In order to handle the

Extending BPM Environments of Your Choice with Performance Related Decision Support

NASA Astrophysics Data System (ADS)

Fritzsche, Mathias; Picht, Michael; Gilani, Wasif; Spence, Ivor; Brown, John; Kilpatrick, Peter

What-if Simulations have been identified as one solution for business performance related decision support. Such support is especially useful in cases where it can be automatically generated out of Business Process Management (BPM) Environments from the existing business process models and performance parameters monitored from the executed business process instances. Currently, some of the available BPM Environments offer basic-level performance prediction capabilities. However, these functionalities are normally too limited to be generally useful for performance related decision support at business process level. In this paper, an approach is presented which allows the non-intrusive integration of sophisticated tooling for what-if simulations, analytic performance prediction tools, process optimizations or a combination of such solutions into already existing BPM environments. The approach abstracts from process modelling techniques which enable automatic decision support spanning processes across numerous BPM Environments. For instance, this enables end-to-end decision support for composite processes modelled with the Business Process Modelling Notation (BPMN) on top of existing Enterprise Resource Planning (ERP) processes modelled with proprietary languages.

Evaluation of global climate model on performances of precipitation simulation and prediction in the Huaihe River basin

NASA Astrophysics Data System (ADS)

Wu, Yenan; Zhong, Ping-an; Xu, Bin; Zhu, Feilin; Fu, Jisi

2017-06-01

Using climate models with high performance to predict the future climate changes can increase the reliability of results. In this paper, six kinds of global climate models that selected from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under Representative Concentration Path (RCP) 4.5 scenarios were compared to the measured data during baseline period (1960-2000) and evaluate the simulation performance on precipitation. Since the results of single climate models are often biased and highly uncertain, we examine the back propagation (BP) neural network and arithmetic mean method in assembling the precipitation of multi models. The delta method was used to calibrate the result of single model and multimodel ensembles by arithmetic mean method (MME-AM) during the validation period (2001-2010) and the predicting period (2011-2100). We then use the single models and multimodel ensembles to predict the future precipitation process and spatial distribution. The result shows that BNU-ESM model has the highest simulation effect among all the single models. The multimodel assembled by BP neural network (MME-BP) has a good simulation performance on the annual average precipitation process and the deterministic coefficient during the validation period is 0.814. The simulation capability on spatial distribution of precipitation is: calibrated MME-AM > MME-BP > calibrated BNU-ESM. The future precipitation predicted by all models tends to increase as the time period increases. The order of average increase amplitude of each season is: winter > spring > summer > autumn. These findings can provide useful information for decision makers to make climate-related disaster mitigation plans.

Consistent Simulation Framework for Efficient Mass Discharge and Source Depletion Time Predictions of DNAPL Contaminants in Heterogeneous Aquifers Under Uncertainty

NASA Astrophysics Data System (ADS)

Nowak, W.; Koch, J.

2014-12-01

Predicting DNAPL fate and transport in heterogeneous aquifers is challenging and subject to an uncertainty that needs to be quantified. Models for this task needs to be equipped with an accurate source zone description, i.e., the distribution of mass of all partitioning phases (DNAPL, water, and soil) in all possible states ((im)mobile, dissolved, and sorbed), mass-transfer algorithms, and the simulation of transport processes in the groundwater. Such detailed models tend to be computationally cumbersome when used for uncertainty quantification. Therefore, a selective choice of the relevant model states, processes, and scales are both sensitive and indispensable. We investigate the questions: what is a meaningful level of model complexity and how to obtain an efficient model framework that is still physically and statistically consistent. In our proposed model, aquifer parameters and the contaminant source architecture are conceptualized jointly as random space functions. The governing processes are simulated in a three-dimensional, highly-resolved, stochastic, and coupled model that can predict probability density functions of mass discharge and source depletion times. We apply a stochastic percolation approach as an emulator to simulate the contaminant source formation, a random walk particle tracking method to simulate DNAPL dissolution and solute transport within the aqueous phase, and a quasi-steady-state approach to solve for DNAPL depletion times. Using this novel model framework, we test whether and to which degree the desired model predictions are sensitive to simplifications often found in the literature. With this we identify that aquifer heterogeneity, groundwater flow irregularity, uncertain and physically-based contaminant source zones, and their mutual interlinkages are indispensable components of a sound model framework.

Demonstration of the Water Erosion Prediction Project (WEPP) internet interface and services

USDA-ARS?s Scientific Manuscript database

The Water Erosion Prediction Project (WEPP) model is a process-based FORTRAN computer simulation program for prediction of runoff and soil erosion by water at hillslope profile, field, and small watershed scales. To effectively run the WEPP model and interpret results additional software has been de...

Evaluation of protein-ligand affinity prediction using steered molecular dynamics simulations.

PubMed

Okimoto, Noriaki; Suenaga, Atsushi; Taiji, Makoto

2017-11-01

In computational drug design, ranking a series of compound analogs in a manner that is consistent with experimental affinities remains a challenge. In this study, we evaluated the prediction of protein-ligand binding affinities using steered molecular dynamics simulations. First, we investigated the appropriate conditions for accurate predictions in these simulations. A conic harmonic restraint was applied to the system for efficient sampling of work values on the ligand unbinding pathway. We found that pulling velocity significantly influenced affinity predictions, but that the number of collectable trajectories was less influential. We identified the appropriate pulling velocity and collectable trajectories for binding affinity predictions as 1.25 Å/ns and 100, respectively, and these parameters were used to evaluate three target proteins (FK506 binding protein, trypsin, and cyclin-dependent kinase 2). For these proteins using our parameters, the accuracy of affinity prediction was higher and more stable when Jarzynski's equality was employed compared with the second-order cumulant expansion equation of Jarzynski's equality. Our results showed that steered molecular dynamics simulations are effective for predicting the rank order of ligands; thus, they are a potential tool for compound selection in hit-to-lead and lead optimization processes.

Finite-element model to predict roll-separation force and defects during rolling of U-10Mo alloys

NASA Astrophysics Data System (ADS)

Soulami, Ayoub; Burkes, Douglas E.; Joshi, Vineet V.; Lavender, Curt A.; Paxton, Dean

2017-10-01

A major goal of the Convert Program of the U.S. Department of Energy's National Nuclear Security Administration (DOE/NNSA) is to enable high-performance research reactors to operate with low-enriched uranium rather than the high-enriched uranium currently used. To this end, uranium alloyed with 10 wt% molybdenum (U-10Mo) represents an ideal candidate because of its stable gamma phase, low neutron caption cross section, acceptable swelling response, and predictable irradiation behavior. However, because of the complexities of the fuel design and the need for rolled monolithic U-10Mo foils, new developments in processing and fabrication are necessary. This study used a finite-element code, LS-DYNA, as a predictive tool to optimize the rolling process. Simulations of the hot rolling of U-10Mo coupons encapsulated in low-carbon steel were conducted following two different schedules. Model predictions of the roll-separation force and roll pack thicknesses at different stages of the rolling process were compared with experimental measurements. The study reported here discussed various attributes of the rolled coupons revealed by the model (e.g., waviness and thickness non-uniformity like dog-boning). To investigate the influence of the cladding material on these rolling defects, other cases were simulated: hot rolling with alternative can materials, namely, 304 stainless steel and Zircaloy-2, and bare-rolling. Simulation results demonstrated that reducing the mismatch in strength between the coupon and can material improves the quality of the rolled sheet. Bare-rolling simulation results showed a defect-free rolled coupon. The finite-element model developed and presented in this study can be used to conduct parametric studies of several process parameters (e.g., rolling speed, roll diameter, can material, and reduction).

Numerical study of vortex rope during load rejection of a prototype pump-turbine

NASA Astrophysics Data System (ADS)

Liu, J. T.; Liu, S. H.; Sun, Y. K.; Wu, Y. L.; Wang, L. Q.

2012-11-01

A transient process of load rejection of a prototype pump-turbine was studied by three dimensional, unsteady simulations, as well as steady calculations.Dynamic mesh (DM) method and remeshing method were used to simulate the rotation of guide vanes and runner. The rotational speed of the runner was predicted by fluid couplingmethod. Both the transient calculation and steady calculation were performed based on turbulence model. Results show that steady calculation results have large error in the prediction of the external characteristics of the transient process. The runaway speed can reach 1.15 times the initial rotational speed during the transient process. The vortex rope occurs before the pump-turbine runs at zero moment point. Vortex rope has the same rotating direction with the runner. The vortex rope is separated into two parts as the flow rate decreases to 0. Pressure level decreases during the whole transient process.The transient simulation result were also compared and verified by experimental results. This computational method could be used in the fault diagnosis of transient operation, as well as the optimization of a transient process.

Diffusion Coefficients from Molecular Dynamics Simulations in Binary and Ternary Mixtures

NASA Astrophysics Data System (ADS)

Liu, Xin; Schnell, Sondre K.; Simon, Jean-Marc; Krüger, Peter; Bedeaux, Dick; Kjelstrup, Signe; Bardow, André; Vlugt, Thijs J. H.

2013-07-01

Multicomponent diffusion in liquids is ubiquitous in (bio)chemical processes. It has gained considerable and increasing interest as it is often the rate limiting step in a process. In this paper, we review methods for calculating diffusion coefficients from molecular simulation and predictive engineering models. The main achievements of our research during the past years can be summarized as follows: (1) we introduced a consistent method for computing Fick diffusion coefficients using equilibrium molecular dynamics simulations; (2) we developed a multicomponent Darken equation for the description of the concentration dependence of Maxwell-Stefan diffusivities. In the case of infinite dilution, the multicomponent Darken equation provides an expression for [InlineEquation not available: see fulltext.] which can be used to parametrize the generalized Vignes equation; and (3) a predictive model for self-diffusivities was proposed for the parametrization of the multicomponent Darken equation. This equation accurately describes the concentration dependence of self-diffusivities in weakly associating systems. With these methods, a sound framework for the prediction of mutual diffusion in liquids is achieved.

An application of sedimentation simulation in Tahe oilfield

NASA Astrophysics Data System (ADS)

Tingting, He; Lei, Zhao; Xin, Tan; Dongxu, He

2017-12-01

The braided river delta develops in Triassic low oil formation in the block 9 of Tahe oilfield, but its sedimentation evolution process is unclear. By using sedimentation simulation technology, sedimentation process and distribution of braided river delta are studied based on the geological parameters including sequence stratigraphic division, initial sedimentation environment, relative lake level change and accommodation change, source supply and sedimentary transport pattern. The simulation result shows that the error rate between strata thickness of simulation and actual strata thickness is small, and the single well analysis result of simulation is highly consistent with the actual analysis, which can prove that the model is reliable. The study area belongs to braided river delta retrogradation evolution process, which provides favorable basis for fine reservoir description and prediction.

Simulation of SiO2 etching in an inductively coupled CF4 plasma

NASA Astrophysics Data System (ADS)

Xu, Qing; Li, Yu-Xing; Li, Xiao-Ning; Wang, Jia-Bin; Yang, Fan; Yang, Yi; Ren, Tian-Ling

2017-02-01

Plasma etching technology is an indispensable processing method in the manufacturing process of semiconductor devices. Because of the high fluorine/carbon ratio of CF4, the CF4 gas is often used for etching SiO2. A commercial software ESI-CFD is used to simulate the process of plasma etching with an inductively coupled plasma model. For the simulation part, CFD-ACE is used to simulate the chamber, and CFD-TOPO is used to simulate the surface of the sample. The effects of chamber pressure, bias voltage and ICP power on the reactant particles were investigated, and the etching profiles of SiO2 were obtained. Simulation can be used to predict the effects of reaction conditions on the density, energy and angular distributions of reactant particles, which can play a good role in guiding the etching process.

Perceptual Processing Affects Conceptual Processing

ERIC Educational Resources Information Center

van Dantzig, Saskia; Pecher, Diane; Zeelenberg, Rene; Barsalou, Lawrence W.

2008-01-01

According to the Perceptual Symbols Theory of cognition (Barsalou, 1999), modality-specific simulations underlie the representation of concepts. A strong prediction of this view is that perceptual processing affects conceptual processing. In this study, participants performed a perceptual detection task and a conceptual property-verification task…

Web-Based Predictive Analytics to Improve Patient Flow in the Emergency Department

NASA Technical Reports Server (NTRS)

Buckler, David L.

2012-01-01

The Emergency Department (ED) simulation project was established to demonstrate how requirements-driven analysis and process simulation can help improve the quality of patient care for the Veterans Health Administration's (VHA) Veterans Affairs Medical Centers (VAMC). This project developed a web-based simulation prototype of patient flow in EDs, validated the performance of the simulation against operational data, and documented IT requirements for the ED simulation.

Error-growth dynamics and predictability of surface thermally induced atmospheric flow

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

Zeng, X.; Pielke, R.A.

1993-09-01

Using the CSU Regional Atmospheric Modeling System (RAMS) in its nonhydrostatic and compressible configuration, over 200 two-dimensional simulations with [Delta]x = 2 km and [Delta]x = 100 m are performed to study in detail the initial adjustment process and the error-growth dynamics of surface thermally induced circulation including the sensitivity to initial conditions, boundary conditions, and model parameters, and to study the predictability as a function of the size of surface heat patches under a calm mean wind. It is found that the error growth is not sensitive to the characterisitics of the initial perturbations. The numerical smoothing has amore » strong impact on the initial adjustment process and on the error-growth dynamics. The predictability and flow structures, it is found that the vertical velocity field is strongly affected by the mean wind, and the flow structures are quite sensitive to the initial soil water content. The transition from organized flow to the situation in which fluxes are dominated by noncoherent turbulent eddies under a calm mean wind is quantitatively evaluated and this transition is different for different variables. The relationship between the predictability of a realization and of an ensemble average is discussed. The predictability and the coherent circulations modulated by the surface inhomogeneities are also studied by computing the autocorrelations and the power spectra. The three-dimensional mesoscale and large-eddy simulations are performed to verify the above results. It is found that the two-dimensional mesoscale (or fine resolution) simulation yields very close or similar results regarding the predictability as those from the three-dimensional mesoscale (or large eddy) simulation. The horizontally averaged quantities based on two-dimensional fine-resolution simulations are insensitive to initial perturbations and agree with those based on three-dimensional large-eddy simulations. 87 refs., 25 figs.« less

Development and validation of a predictive model for the influences of selected product and process variables on ascorbic acid degradation in simulated fruit juice.

PubMed

Gabriel, Alonzo A; Cayabyab, Jochelle Elysse C; Tan, Athalie Kaye L; Corook, Mark Lester F; Ables, Errol John O; Tiangson-Bayaga, Cecile Leah P

2015-06-15

A predictive response surface model for the influences of product (soluble solids and titratable acidity) and process (temperature and heating time) parameters on the degradation of ascorbic acid (AA) in heated simulated fruit juices (SFJs) was established. Physicochemical property ranges of freshly squeezed and processed juices, and a previously established decimal reduction times of Escherichiacoli O157:H7 at different heating temperatures were used in establishing a Central Composite Design of Experiment that determined the combinations of product and process variable used in the model building. Only the individual linear effects of temperature and heating time significantly (P<0.05) affected AA reduction (%AAr). Validating systems either over- or underestimated actual %AAr with bias factors 0.80-1.20. However, all validating systems still resulted in acceptable predictive efficacy, with accuracy factor 1.00-1.26. The model may be useful in establishing unique process schedules for specific products, for the simultaneous control and improvement of food safety and quality. Copyright © 2015 Elsevier Ltd. All rights reserved.

Miscellaneous Topics in Computer-Aided Drug Design: Synthetic Accessibility and GPU Computing, and Other Topics.

PubMed

Fukunishi, Yoshifumi; Mashimo, Tadaaki; Misoo, Kiyotaka; Wakabayashi, Yoshinori; Miyaki, Toshiaki; Ohta, Seiji; Nakamura, Mayu; Ikeda, Kazuyoshi

2016-01-01

Computer-aided drug design is still a state-of-the-art process in medicinal chemistry, and the main topics in this field have been extensively studied and well reviewed. These topics include compound databases, ligand-binding pocket prediction, protein-compound docking, virtual screening, target/off-target prediction, physical property prediction, molecular simulation and pharmacokinetics/pharmacodynamics (PK/PD) prediction. Message and Conclusion: However, there are also a number of secondary or miscellaneous topics that have been less well covered. For example, methods for synthesizing and predicting the synthetic accessibility (SA) of designed compounds are important in practical drug development, and hardware/software resources for performing the computations in computer-aided drug design are crucial. Cloud computing and general purpose graphics processing unit (GPGPU) computing have been used in virtual screening and molecular dynamics simulations. Not surprisingly, there is a growing demand for computer systems which combine these resources. In the present review, we summarize and discuss these various topics of drug design.

Miscellaneous Topics in Computer-Aided Drug Design: Synthetic Accessibility and GPU Computing, and Other Topics

PubMed Central

Fukunishi, Yoshifumi; Mashimo, Tadaaki; Misoo, Kiyotaka; Wakabayashi, Yoshinori; Miyaki, Toshiaki; Ohta, Seiji; Nakamura, Mayu; Ikeda, Kazuyoshi

2016-01-01

Abstract: Background Computer-aided drug design is still a state-of-the-art process in medicinal chemistry, and the main topics in this field have been extensively studied and well reviewed. These topics include compound databases, ligand-binding pocket prediction, protein-compound docking, virtual screening, target/off-target prediction, physical property prediction, molecular simulation and pharmacokinetics/pharmacodynamics (PK/PD) prediction. Message and Conclusion: However, there are also a number of secondary or miscellaneous topics that have been less well covered. For example, methods for synthesizing and predicting the synthetic accessibility (SA) of designed compounds are important in practical drug development, and hardware/software resources for performing the computations in computer-aided drug design are crucial. Cloud computing and general purpose graphics processing unit (GPGPU) computing have been used in virtual screening and molecular dynamics simulations. Not surprisingly, there is a growing demand for computer systems which combine these resources. In the present review, we summarize and discuss these various topics of drug design. PMID:27075578

Dispersal and fallout simulations for urban consequences management (u)

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

Grinstein, Fernando F; Wachtor, Adam J; Nelson, Matt

2010-01-01

Hazardous chemical, biological, or radioactive releases from leaks, spills, fires, or blasts, may occur (intentionally or accidentally) in urban environments during warfare or as part of terrorist attacks on military bases or other facilities. The associated contaminant dispersion is complex and semi-chaotic. Urban predictive simulation capabilities can have direct impact in many threat-reduction areas of interest, including, urban sensor placement and threat analysis, contaminant transport (CT) effects on surrounding civilian population (dosages, evacuation, shelter-in-place), education and training of rescue teams and services. Detailed simulations for the various processes involved are in principle possible, but generally not fast. Predicting urban airflowmore » accompanied by CT presents extremely challenging requirements. Crucial technical issues include, simulating turbulent fluid and particulate transport, initial and boundary condition modeling incorporating a consistent stratified urban boundary layer with realistic wind fluctuations, and post-processing of the simulation results for practical consequences management. Relevant fluid dynamic processes to be simulated include, detailed energetic and contaminant sources, complex building vortex shedding and flows in recirculation zones, and modeling of particle distributions, including particulate fallout, as well as deposition, re-suspension and evaporation. Other issues include, modeling building damage effects due to eventual blasts, addressing appropriate regional and atmospheric data reduction.« less

Prediction of 3D chip formation in the facing cutting with lathe machine using FEM

NASA Astrophysics Data System (ADS)

Prasetyo, Yudhi; Tauviqirrahman, Mohamad; Rusnaldy

2016-04-01

This paper presents the prediction of the chip formation at the machining process using a lathe machine in a more specific way focusing on facing cutting (face turning). The main purpose is to propose a new approach to predict the chip formation with the variation of the cutting directions i.e., the backward and forward direction. In addition, the interaction between stress analysis and chip formation on cutting process was also investigated. The simulations were conducted using three dimensional (3D) finite element method based on ABAQUS software with aluminum and high speed steel (HSS) as the workpiece and the tool materials, respectively. The simulation result showed that the chip resulted using a backward direction depicts a better formation than that using a conventional (forward) direction.

Mathematical modelling and numerical simulation of forces in milling process

NASA Astrophysics Data System (ADS)

Turai, Bhanu Murthy; Satish, Cherukuvada; Prakash Marimuthu, K.

2018-04-01

Machining of the material by milling induces forces, which act on the work piece material, tool and which in turn act on the machining tool. The forces involved in milling process can be quantified, mathematical models help to predict these forces. A lot of research has been carried out in this area in the past few decades. The current research aims at developing a mathematical model to predict forces at different levels which arise machining of Aluminium6061 alloy. Finite element analysis was used to develop a FE model to predict the cutting forces. Simulation was done for varying cutting conditions. Different experiments was designed using Taguchi method. A L9 orthogonal array was designed and the output was measure for the different experiments. The same was used to develop the mathematical model.

The Australian Computational Earth Systems Simulator

NASA Astrophysics Data System (ADS)

Mora, P.; Muhlhaus, H.; Lister, G.; Dyskin, A.; Place, D.; Appelbe, B.; Nimmervoll, N.; Abramson, D.

2001-12-01

Numerical simulation of the physics and dynamics of the entire earth system offers an outstanding opportunity for advancing earth system science and technology but represents a major challenge due to the range of scales and physical processes involved, as well as the magnitude of the software engineering effort required. However, new simulation and computer technologies are bringing this objective within reach. Under a special competitive national funding scheme to establish new Major National Research Facilities (MNRF), the Australian government together with a consortium of Universities and research institutions have funded construction of the Australian Computational Earth Systems Simulator (ACcESS). The Simulator or computational virtual earth will provide the research infrastructure to the Australian earth systems science community required for simulations of dynamical earth processes at scales ranging from microscopic to global. It will consist of thematic supercomputer infrastructure and an earth systems simulation software system. The Simulator models and software will be constructed over a five year period by a multi-disciplinary team of computational scientists, mathematicians, earth scientists, civil engineers and software engineers. The construction team will integrate numerical simulation models (3D discrete elements/lattice solid model, particle-in-cell large deformation finite-element method, stress reconstruction models, multi-scale continuum models etc) with geophysical, geological and tectonic models, through advanced software engineering and visualization technologies. When fully constructed, the Simulator aims to provide the software and hardware infrastructure needed to model solid earth phenomena including global scale dynamics and mineralisation processes, crustal scale processes including plate tectonics, mountain building, interacting fault system dynamics, and micro-scale processes that control the geological, physical and dynamic behaviour of earth systems. ACcESS represents a part of Australia's contribution to the APEC Cooperation for Earthquake Simulation (ACES) international initiative. Together with other national earth systems science initiatives including the Japanese Earth Simulator and US General Earthquake Model projects, ACcESS aims to provide a driver for scientific advancement and technological breakthroughs including: quantum leaps in understanding of earth evolution at global, crustal, regional and microscopic scales; new knowledge of the physics of crustal fault systems required to underpin the grand challenge of earthquake prediction; new understanding and predictive capabilities of geological processes such as tectonics and mineralisation.

In Silico Dynamics: computer simulation in a Virtual Embryo (SOT)

EPA Science Inventory

Abstract: Utilizing cell biological information to predict higher order biological processes is a significant challenge in predictive toxicology. This is especially true for highly dynamical systems such as the embryo where morphogenesis, growth and differentiation require preci...

Measurement with microscopic MRI and simulation of flow in different aneurysm models.

PubMed

Edelhoff, Daniel; Walczak, Lars; Frank, Frauke; Heil, Marvin; Schmitz, Inge; Weichert, Frank; Suter, Dieter

2015-10-01

The impact and the development of aneurysms depend to a significant degree on the exchange of liquid between the regular vessel and the pathological extension. A better understanding of this process will lead to improved prediction capabilities. The aim of the current study was to investigate fluid-exchange in aneurysm models of different complexities by combining microscopic magnetic resonance measurements with numerical simulations. In order to evaluate the accuracy and applicability of these methods, the fluid-exchange process between the unaltered vessel lumen and the aneurysm phantoms was analyzed quantitatively using high spatial resolution. Magnetic resonance flow imaging was used to visualize fluid-exchange in two different models produced with a 3D printer. One model of an aneurysm was based on histological findings. The flow distribution in the different models was measured on a microscopic scale using time of flight magnetic resonance imaging. The whole experiment was simulated using fast graphics processing unit-based numerical simulations. The obtained simulation results were compared qualitatively and quantitatively with the magnetic resonance imaging measurements, taking into account flow and spin-lattice relaxation. The results of both presented methods compared well for the used aneurysm models and the chosen flow distributions. The results from the fluid-exchange analysis showed comparable characteristics concerning measurement and simulation. Similar symmetry behavior was observed. Based on these results, the amount of fluid-exchange was calculated. Depending on the geometry of the models, 7% to 45% of the liquid was exchanged per second. The result of the numerical simulations coincides well with the experimentally determined velocity field. The rate of fluid-exchange between vessel and aneurysm was well-predicted. Hence, the results obtained by simulation could be validated by the experiment. The observed deviations can be caused by the noise in the measurement and by the limited resolution of the simulation. The resulting differences are small enough to allow reliable predictions of the flow distribution in vessels with stents and for pulsed blood flow.

Optimizing the Hydrological and Biogeochemical Simulations on a Hillslope with Stony Soil

NASA Astrophysics Data System (ADS)

Zhu, Q.

2017-12-01

Stony soils are widely distributed in the hilly area. However, traditional pedotransfer functions are not reliable in predicting the soil hydraulic parameters for these soils due to the impacts of rock fragments. Therefore, large uncertainties and errors may exist in the hillslope hydrological and biogeochemical simulations in stony soils due to poor estimations of soil hydraulic parameters. In addition, homogenous soil hydraulic parameters are usually used in traditional hillslope simulations. However, soil hydraulic parameters are spatially heterogeneous on the hillslope. This may also cause the unreliable simulations. In this study, we obtained soil hydraulic parameters using five different approaches on a tea hillslope in Taihu Lake basin, China. These five approaches included (1) Rossetta predicted and spatially homogenous, (2) Rossetta predicted and spatially heterogeneous), (3) Rossetta predicted, rock fragment corrected and spatially homogenous, (4) Rossetta predicted, rock fragment corrected and spatially heterogeneous, and (5) extracted from observed soil-water retention curves fitted by dual-pore function and spatially heterogeneous (observed). These five sets of soil hydraulic properties were then input into Hydrus-3D and DNDC to simulate the soil hydrological and biogeochemical processes. The aim of this study is testing two hypotheses. First, considering the spatial heterogeneity of soil hydraulic parameters will improve the simulations. Second, considering the impact of rock fragment on soil hydraulic parameters will improve the simulations.

Prediction of SFL Interruption Performance from the Results of Arc Simulation during High-Current Phase

NASA Astrophysics Data System (ADS)

Lee, Jong-Chul; Lee, Won-Ho; Kim, Woun-Jea

2015-09-01

The design and development procedures of SF6 gas circuit breakers are still largely based on trial and error through testing although the development costs go higher every year. The computation cannot cover the testing satisfactorily because all the real processes arc not taken into account. But the knowledge of the arc behavior and the prediction of the thermal-flow inside the interrupters by numerical simulations are more useful than those by experiments due to the difficulties to obtain physical quantities experimentally and the reduction of computational costs in recent years. In this paper, in order to get further information into the interruption process of a SF6 self-blast interrupter, which is based on a combination of thermal expansion and the arc rotation principle, gas flow simulations with a CFD-arc modeling are performed during the whole switching process such as high-current period, pre-current zero period, and current-zero period. Through the complete work, the pressure-rise and the ramp of the pressure inside the chamber before current zero as well as the post-arc current after current zero should be a good criterion to predict the short-line fault interruption performance of interrupters.

Predictive Validation of an Influenza Spread Model

PubMed Central

Hyder, Ayaz; Buckeridge, David L.; Leung, Brian

2013-01-01

Background Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. Methods and Findings We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998–1999). Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type). Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. Conclusions Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers) with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve their predictive ability. PMID:23755236

Prediction of Shrinkage Porosity Defect in Sand Casting Process of LM25

NASA Astrophysics Data System (ADS)

Rathod, Hardik; Dhulia, Jay K.; Maniar, Nirav P.

2017-08-01

In the present worldwide and aggressive environment, foundry commercial enterprises need to perform productively with least number of rejections and create casting parts in shortest lead time. It has become extremely difficult for foundry industries to meet demands of defects free casting and meet strict delivery schedules. The process of casting solidification is complex in nature. Prediction of shrinkage defect in metal casting is one of the critical concern in foundries and is one of the potential research areas in casting. Due to increasing pressure to improve quality and to reduce cost, it is very essential to upgrade the level of current methodology used in foundries. In the present research work, prediction methodology of shrinkage porosity defect in sand casting process of LM25 using experimentation and ANSYS is proposed. The objectives successfully achieved are prediction of shrinkage porosity distribution in Al-Si casting and determining effectiveness of investigated function for predicting shrinkage porosity by correlating results of simulating studies to those obtained experimentally. The real-time application of the research reflects from the fact that experimentation is performed on 9 different Y junctions at foundry industry and practical data obtained from experimentation are used for simulation.

A Modified Johnson-Cook Model for Sheet Metal Forming at Elevated Temperatures and Its Application for Cooled Stress-Strain Curve and Spring-Back Prediction

NASA Astrophysics Data System (ADS)

Duc-Toan, Nguyen; Tien-Long, Banh; Young-Suk, Kim; Dong-Won, Jung

2011-08-01

In this study, a modified Johnson-Cook (J-C) model and an innovated method to determine (J-C) material parameters are proposed to predict more correctly stress-strain curve for tensile tests in elevated temperatures. A MATLAB tool is used to determine material parameters by fitting a curve to follow Ludwick's hardening law at various elevated temperatures. Those hardening law parameters are then utilized to determine modified (J-C) model material parameters. The modified (J-C) model shows the better prediction compared to the conventional one. As the first verification, an FEM tensile test simulation based on the isotropic hardening model for boron sheet steel at elevated temperatures was carried out via a user-material subroutine, using an explicit finite element code, and compared with the measurements. The temperature decrease of all elements due to the air cooling process was then calculated when considering the modified (J-C) model and coded to VUMAT subroutine for tensile test simulation of cooling process. The modified (J-C) model showed the good agreement between the simulation results and the corresponding experiments. The second investigation was applied for V-bending spring-back prediction of magnesium alloy sheets at elevated temperatures. Here, the combination of proposed J-C model with modified hardening law considering the unusual plastic behaviour for magnesium alloy sheet was adopted for FEM simulation of V-bending spring-back prediction and shown the good comparability with corresponding experiments.

Application of Anaerobic Digestion Model No. 1 for simulating anaerobic mesophilic sludge digestion

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

Mendes, Carlos, E-mail: carllosmendez@gmail.com; Esquerre, Karla, E-mail: karlaesquerre@ufba.br; Matos Queiroz, Luciano, E-mail: lmqueiroz@ufba.br

2015-01-15

Highlights: • The behavior of a anaerobic reactor was evaluated through modeling. • Parametric sensitivity analysis was used to select most sensitive of the ADM1. • The results indicate that the ADM1 was able to predict the experimental results. • Organic load rate above of 35 kg/m{sup 3} day affects the performance of the process. - Abstract: Improving anaerobic digestion of sewage sludge by monitoring common indicators such as volatile fatty acids (VFAs), gas composition and pH is a suitable solution for better sludge management. Modeling is an important tool to assess and to predict process performance. The present studymore » focuses on the application of the Anaerobic Digestion Model No. 1 (ADM1) to simulate the dynamic behavior of a reactor fed with sewage sludge under mesophilic conditions. Parametric sensitivity analysis is used to select the most sensitive ADM1 parameters for estimation using a numerical procedure while other parameters are applied without any modification to the original values presented in the ADM1 report. The results indicate that the ADM1 model after parameter estimation was able to predict the experimental results of effluent acetate, propionate, composites and biogas flows and pH with reasonable accuracy. The simulation of the effect of organic shock loading clearly showed that an organic shock loading rate above of 35 kg/m{sup 3} day affects the performance of the reactor. The results demonstrate that simulations can be helpful to support decisions on predicting the anaerobic digestion process of sewage sludge.« less

Development and Experimental Validation of Large Eddy Simulation Techniques for the Prediction of Combustion-Dynamic Process in Syngas Combustion: Characterization of Autoignition, Flashback, and Flame-Liftoff at Gas-Turbine Relevant Operating Conditions

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

Ihme, Matthias; Driscoll, James

2015-08-31

The objective of this closely coordinated experimental and computational research effort is the development of simulation techniques for the prediction of combustion processes, relevant to the oxidation of syngas and high hydrogen content (HHC) fuels at gas-turbine relevant operating conditions. Specifically, the research goals are (i) the characterization of the sensitivity of syngas ignition processes to hydrodynamic processes and perturbations in temperature and mixture composition in rapid compression machines and ow-reactors and (ii) to conduct comprehensive experimental investigations in a swirl-stabilized gas turbine (GT) combustor under realistic high-pressure operating conditions in order (iii) to obtain fundamental understanding about mechanisms controllingmore » unstable flame regimes in HHC-combustion.« less

Alloy Shrinkage factors for the investment casting of 17-4PH stainless steel parts

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

Sabau, Adrian S; Porter, Wallace D

2008-01-01

In this study, the alloy shrinkage factors were obtained for the investment casting of 17-4PH stainless steel parts. For the investment casting process, unfilled wax and fused silica with a zircon prime coat were used for patterns and shell molds, respectively. Dimensions of the die tooling, wax pattern, and casting were measured using a Coordinate Measurement Machine. For all the properties, the experimental data available in the literature did not cover the entire temperature range necessary for process simulation. A comparison between the predicted material property data measured property data is made. It was found that most material properties weremore » accurately predicted over the most of the temperature range of the process. Several assumptions were made in order to obtain a complete set of mechanical property data at high temperatures. Thermal expansion measurements for the 17-4PH alloy were conducted at heating and cooling. As a function of temperature, the thermal expansion for both the alloy and shell mold materials showed different evolution at heating and cooling. Thus, one generic simulation were performed with thermal expansion obtained at heating and another one with thermal expansion obtained at cooling. The alloy dimensions were obtained from numerical simulation results of solidification, heat transfer, and deformation phenomena. As compared with experimental results, the numerical simulation results for the shrinkage factors were slightly over-predicted.« less

Bacterial transformation and biodegradation processes simulation in horizontal subsurface flow constructed wetlands using CWM1-RETRASO.

PubMed

Llorens, Esther; Saaltink, Maarten W; Poch, Manel; García, Joan

2011-01-01

The performance and reliability of the CWM1-RETRASO model for simulating processes in horizontal subsurface flow constructed wetlands (HSSF CWs) and the relative contribution of different microbial reactions to organic matter (COD) removal in a HSSF CW treating urban wastewater were evaluated. Various different approaches with diverse influent configurations were simulated. According to the simulations, anaerobic processes were more widespread in the simulated wetland and contributed to a higher COD removal rate [72-79%] than anoxic [0-1%] and aerobic reactions [20-27%] did. In all the cases tested, the reaction that most contributed to COD removal was methanogenesis [58-73%]. All results provided by the model were in consonance with literature and experimental field observations, suggesting a good performance and reliability of CWM1-RETRASO. According to the good simulation predictions, CWM1-RETRASO is the first mechanistic model able to successfully simulate the processes described by the CWM1 model in HSSF CWs. Copyright © 2010 Elsevier Ltd. All rights reserved.

Collaborative Project: Improving the Representation of Coastal and Estuarine Processes in Earth System Models

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

Bryan, Frank; Dennis, John; MacCready, Parker

This project aimed to improve long term global climate simulations by resolving and enhancing the representation of the processes involved in the cycling of freshwater through estuaries and coastal regions. This was a collaborative multi-institution project consisting of physical oceanographers, climate model developers, and computational scientists. It specifically targeted the DOE objectives of advancing simulation and predictive capability of climate models through improvements in resolution and physical process representation.

Final Report Collaborative Project: Improving the Representation of Coastal and Estuarine Processes in Earth System Models

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

Bryan, Frank; Dennis, John; MacCready, Parker

This project aimed to improve long term global climate simulations by resolving and enhancing the representation of the processes involved in the cycling of freshwater through estuaries and coastal regions. This was a collaborative multi-institution project consisting of physical oceanographers, climate model developers, and computational scientists. It specifically targeted the DOE objectives of advancing simulation and predictive capability of climate models through improvements in resolution and physical process representation.

Mathematical simulation of the drying of suspensions and colloidal solutions by their depressurization

NASA Astrophysics Data System (ADS)

Lashkov, V. A.; Levashko, E. I.; Safin, R. G.

2006-05-01

The heat and mass transfer in the process of drying of high-humidity materials by their depressurization has been investigated. The results of experimental investigation and mathematical simulation of the indicated process are presented. They allow one to determine the regularities of this process and predict the quality of the finished product. A technological scheme and an engineering procedure for calculating the drying of the liquid base of a soap are presented.

The Neural Basis of Event Simulation: An fMRI Study

PubMed Central

Yomogida, Yukihito; Sugiura, Motoaki; Akimoto, Yoritaka; Miyauchi, Carlos Makoto; Kawashima, Ryuta

2014-01-01

Event simulation (ES) is the situational inference process in which perceived event features such as objects, agents, and actions are associated in the brain to represent the whole situation. ES provides a common basis for various cognitive processes, such as perceptual prediction, situational understanding/prediction, and social cognition (such as mentalizing/trait inference). Here, functional magnetic resonance imaging was used to elucidate the neural substrates underlying important subdivisions within ES. First, the study investigated whether ES depends on different neural substrates when it is conducted explicitly and implicitly. Second, the existence of neural substrates specific to the future-prediction component of ES was assessed. Subjects were shown contextually related object pictures implying a situation and performed several picture–word-matching tasks. By varying task goals, subjects were made to infer the implied situation implicitly/explicitly or predict the future consequence of that situation. The results indicate that, whereas implicit ES activated the lateral prefrontal cortex and medial/lateral parietal cortex, explicit ES activated the medial prefrontal cortex, posterior cingulate cortex, and medial/lateral temporal cortex. Additionally, the left temporoparietal junction plays an important role in the future-prediction component of ES. These findings enrich our understanding of the neural substrates of the implicit/explicit/predictive aspects of ES-related cognitive processes. PMID:24789353

Predicting ecological effects of pollutants: A role for marine mesocosms

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

Ward, T.J.

1994-12-31

The major uncertainty in predicting the ecological effects of a pollutant is the relationship between dose and the ecological response. Mesocosms may be used to simulate population-level biological processes and to estimate the nature and shape of dose-related responses to pollutants, for use in predictive evaluations of pollutant impacts. To ensure that responses observed in mesocosm tests are representative it is necessary to confirm that the simulated processes operate at rates similar to those found in the field. Pilot experiments were conducted in small marine mesocosms simulating major processes in two local habitat types: unvegetated sand and sand colonized bymore » the brown macroalga Sargassum. The results showed that for a range of variates (such as the % of egg-bearing harpacticoid copepods, or the chlorophyll a concentration in surface sediments) the mean values for measurements in the tanks over a 9 week period did not consistently converge or diverge from those in the field. Also, for a number of the variates, a modelled decrease of more than about 60% in the mean could be detected with greater than 80% statistical power. This indicates that the effects of a pollutant could be detected with acceptable power. Use of a combination of such variates based on different functional or taxonomic groups for pollutant effects testing could greatly decrease uncertainty about the predicted effects of pollutants discharged to these habitats.« less

The Role of Moist Processes in the Intrinsic Predictability of Indian Ocean Cyclones

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

Taraphdar, Sourav; Mukhopadhyay, P.; Leung, Lai-Yung R.

The role of moist processes and the possibility of error cascade from cloud scale processes affecting the intrinsic predictable time scale of a high resolution convection permitting model within the environment of tropical cyclones (TCs) over the Indian region are investigated. Consistent with past studies of extra-tropical cyclones, it is demonstrated that moist processes play a major role in forecast error growth which may ultimately limit the intrinsic predictability of the TCs. Small errors in the initial conditions may grow rapidly and cascades from smaller scales to the larger scales through strong diabatic heating and nonlinearities associated with moist convection.more » Results from a suite of twin perturbation experiments for four tropical cyclones suggest that the error growth is significantly higher in cloud permitting simulation at 3.3 km resolutions compared to simulations at 3.3 km and 10 km resolution with parameterized convection. Convective parameterizations with prescribed convective time scales typically longer than the model time step allows the effects of microphysical tendencies to average out so convection responds to a smoother dynamical forcing. Without convective parameterizations, the finer-scale instabilities resolved at 3.3 km resolution and stronger vertical motion that results from the cloud microphysical parameterizations removing super-saturation at each model time step can ultimately feed the error growth in convection permitting simulations. This implies that careful considerations and/or improvements in cloud parameterizations are needed if numerical predictions are to be improved through increased model resolution. Rapid upscale error growth from convective scales may ultimately limit the intrinsic mesoscale predictability of the TCs, which further supports the needs for probabilistic forecasts of these events, even at the mesoscales.« less

Numerical simulation study on rolling-chemical milling process of aluminum-lithium alloy skin panel

NASA Astrophysics Data System (ADS)

Huang, Z. B.; Sun, Z. G.; Sun, X. F.; Li, X. Q.

2017-09-01

Single curvature parts such as aircraft fuselage skin panels are usually manufactured by rolling-chemical milling process, which is usually faced with the problem of geometric accuracy caused by springback. In most cases, the methods of manual adjustment and multiple roll bending are used to control or eliminate the springback. However, these methods can cause the increase of product cost and cycle, and lead to material performance degradation. Therefore, it is of significance to precisely control the springback of rolling-chemical milling process. In this paper, using the method of experiment and numerical simulation on rolling-chemical milling process, the simulation model for rolling-chemical milling process of 2060-T8 aluminum-lithium alloy skin was established and testified by the comparison between numerical simulation and experiment results for the validity. Then, based on the numerical simulation model, the relative technological parameters which influence on the curvature of the skin panel were analyzed. Finally, the prediction of springback and the compensation can be realized by controlling the process parameters.

Study of a Compression-Molding Process for Ultraviolet Light-Emitting Diode Exposure Systems via Finite-Element Analysis

PubMed Central

Wu, Kuo-Tsai; Hwang, Sheng-Jye; Lee, Huei-Huang

2017-01-01

Although wafer-level camera lenses are a very promising technology, problems such as warpage with time and non-uniform thickness of products still exist. In this study, finite element simulation was performed to simulate the compression molding process for acquiring the pressure distribution on the product on completion of the process and predicting the deformation with respect to the pressure distribution. Results show that the single-gate compression molding process significantly increases the pressure at the center of the product, whereas the multi-gate compressing molding process can effectively distribute the pressure. This study evaluated the non-uniform thickness of product and changes in the process parameters through computer simulations, which could help to improve the compression molding process. PMID:28617315

Assessment of predictive capabilities for aerodynamic heating in hypersonic flow

NASA Astrophysics Data System (ADS)

Knight, Doyle; Chazot, Olivier; Austin, Joanna; Badr, Mohammad Ali; Candler, Graham; Celik, Bayram; Rosa, Donato de; Donelli, Raffaele; Komives, Jeffrey; Lani, Andrea; Levin, Deborah; Nompelis, Ioannis; Panesi, Marco; Pezzella, Giuseppe; Reimann, Bodo; Tumuklu, Ozgur; Yuceil, Kemal

2017-04-01

The capability for CFD prediction of hypersonic shock wave laminar boundary layer interaction was assessed for a double wedge model at Mach 7.1 in air and nitrogen at 2.1 MJ/kg and 8 MJ/kg. Simulations were performed by seven research organizations encompassing both Navier-Stokes and Direct Simulation Monte Carlo (DSMC) methods as part of the NATO STO AVT Task Group 205 activity. Comparison of the CFD simulations with experimental heat transfer and schlieren visualization suggest the need for accurate modeling of the tunnel startup process in short-duration hypersonic test facilities, and the importance of fully 3-D simulations of nominally 2-D (i.e., non-axisymmmetric) experimental geometries.

Coupled thermal stress simulations of ductile tearing

DOE PAGES

Neilsen, Michael K.; Dion, Kristin

2016-03-01

Predictions for ductile tearing of a geometrically complex Ti-6Al-4V plate were generated using a Unified Creep Plasticity Damage model in fully coupled thermal stress simulations. Uniaxial tension and butterfly shear tests performed at displacement rates of 0.0254 and 25.4 mm/s were also simulated. Results from these simulations revealed that the material temperature increase due to plastic work can have a dramatic effect on material ductility predictions in materials that exhibit little strain hardening. Furthermore, this occurs because the temperature increase causes the apparent hardening of the material to decrease which leads to the initiation of deformation localization and subsequent ductilemore » tearing earlier in the loading process.« less

Predictive images of postoperative levator resection outcome using image processing software.

PubMed

Mawatari, Yuki; Fukushima, Mikiko

2016-01-01

This study aims to evaluate the efficacy of processed images to predict postoperative appearance following levator resection. Analysis involved 109 eyes from 65 patients with blepharoptosis who underwent advancement of levator aponeurosis and Müller's muscle complex (levator resection). Predictive images were prepared from preoperative photographs using the image processing software (Adobe Photoshop ® ). Images of selected eyes were digitally enlarged in an appropriate manner and shown to patients prior to surgery. Approximately 1 month postoperatively, we surveyed our patients using questionnaires. Fifty-six patients (89.2%) were satisfied with their postoperative appearances, and 55 patients (84.8%) positively responded to the usefulness of processed images to predict postoperative appearance. Showing processed images that predict postoperative appearance to patients prior to blepharoptosis surgery can be useful for those patients concerned with their postoperative appearance. This approach may serve as a useful tool to simulate blepharoptosis surgery.

Predictive images of postoperative levator resection outcome using image processing software

PubMed Central

Mawatari, Yuki; Fukushima, Mikiko

2016-01-01

Purpose This study aims to evaluate the efficacy of processed images to predict postoperative appearance following levator resection. Methods Analysis involved 109 eyes from 65 patients with blepharoptosis who underwent advancement of levator aponeurosis and Müller’s muscle complex (levator resection). Predictive images were prepared from preoperative photographs using the image processing software (Adobe Photoshop®). Images of selected eyes were digitally enlarged in an appropriate manner and shown to patients prior to surgery. Results Approximately 1 month postoperatively, we surveyed our patients using questionnaires. Fifty-six patients (89.2%) were satisfied with their postoperative appearances, and 55 patients (84.8%) positively responded to the usefulness of processed images to predict postoperative appearance. Conclusion Showing processed images that predict postoperative appearance to patients prior to blepharoptosis surgery can be useful for those patients concerned with their postoperative appearance. This approach may serve as a useful tool to simulate blepharoptosis surgery. PMID:27757008

Roughness Versus Charge Contributions to Representative Discrete Heterogeneity Underlying Mechanistic Prediction of Colloid Attachment, Detachment and Breakthrough-Elution Behavior Under Environmental Conditions.

NASA Astrophysics Data System (ADS)

Johnson, William; Farnsworth, Anna; Vanness, Kurt; Hilpert, Markus

2017-04-01

The key element of a mechanistic theory to predict colloid attachment in porous media under environmental conditions where colloid-collector repulsion exists (unfavorable conditions for attachment) is representation of the nano-scale surface heterogeneity (herein called discrete heterogeneity) that drives colloid attachment under unfavorable conditions. The observed modes of colloid attachment under unfavorable conditions emerge from simulations that incorporate discrete heterogeneity. Quantitative prediction of attachment (and detachment) requires capturing the sizes, spatial frequencies, and other properties of roughness asperities and charge heterodomains in discrete heterogeneity representations of different surfaces. The fact that a given discrete heterogeneity representation will interact differently with different-sized colloids as well as different ionic strengths for a given sized colloid allows backing out representative discrete heterogeneity via comparison of simulations to experiments performed across a range of colloid size, solution IS, and fluid velocity. This has been achieved on unfavorable smooth surfaces yielding quantitative prediction of attachment, and qualitative prediction of detachment in response to ionic strength or flow perturbations. Extending this treatment to rough surfaces, and representing the contributions of nanoscale roughness as well as charge heterogeneity is a focus of this talk. Another focus of this talk is the upscaling the pore scale simulations to produce contrasting breakthrough-elution behaviors at the continuum (column) scale that are observed, for example, for different-sized colloids, or same-sized colloids under different ionic strength conditions. The outcome of mechanistic pore scale simulations incorporating discrete heterogeneity and subsequent upscaling is that temporal processes such as blocking and ripening will emerge organically from these simulations, since these processes fundamentally stem from the limited sites available for attachment as represented in discrete heterogeneity.

Performance of protein-structure predictions with the physics-based UNRES force field in CASP11.

PubMed

Krupa, Paweł; Mozolewska, Magdalena A; Wiśniewska, Marta; Yin, Yanping; He, Yi; Sieradzan, Adam K; Ganzynkowicz, Robert; Lipska, Agnieszka G; Karczyńska, Agnieszka; Ślusarz, Magdalena; Ślusarz, Rafał; Giełdoń, Artur; Czaplewski, Cezary; Jagieła, Dawid; Zaborowski, Bartłomiej; Scheraga, Harold A; Liwo, Adam

2016-11-01

Participating as the Cornell-Gdansk group, we have used our physics-based coarse-grained UNited RESidue (UNRES) force field to predict protein structure in the 11th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP11). Our methodology involved extensive multiplexed replica exchange simulations of the target proteins with a recently improved UNRES force field to provide better reproductions of the local structures of polypeptide chains. All simulations were started from fully extended polypeptide chains, and no external information was included in the simulation process except for weak restraints on secondary structure to enable us to finish each prediction within the allowed 3-week time window. Because of simplified UNRES representation of polypeptide chains, use of enhanced sampling methods, code optimization and parallelization and sufficient computational resources, we were able to treat, for the first time, all 55 human prediction targets with sizes from 44 to 595 amino acid residues, the average size being 251 residues. Complete structures of six single-domain proteins were predicted accurately, with the highest accuracy being attained for the T0769, for which the CαRMSD was 3.8 Å for 97 residues of the experimental structure. Correct structures were also predicted for 13 domains of multi-domain proteins with accuracy comparable to that of the best template-based modeling methods. With further improvements of the UNRES force field that are now underway, our physics-based coarse-grained approach to protein-structure prediction will eventually reach global prediction capacity and, consequently, reliability in simulating protein structure and dynamics that are important in biochemical processes. Freely available on the web at http://www.unres.pl/ CONTACT: has5@cornell.edu. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Modeling and Simulation of Voids in Composite Tape Winding Process Based on Domain Superposition Technique

NASA Astrophysics Data System (ADS)

Deng, Bo; Shi, Yaoyao

2017-11-01

The tape winding technology is an effective way to fabricate rotationally composite materials. Nevertheless, some inevitable defects will seriously influence the performance of winding products. One of the crucial ways to identify the quality of fiber-reinforced composite material products is examining its void content. Significant improvement in products' mechanical properties can be achieved by minimizing the void defect. Two methods were applied in this study, finite element analysis and experimental testing, respectively, to investigate the mechanism of how void forming in composite tape winding processing. Based on the theories of interlayer intimate contact and Domain Superposition Technique (DST), a three-dimensional model of prepreg tape void with SolidWorks has been modeled in this paper. Whereafter, ABAQUS simulation software was used to simulate the void content change with pressure and temperature. Finally, a series of experiments were performed to determine the accuracy of the model-based predictions. The results showed that the model is effective for predicting the void content in the composite tape winding process.

Prediction of the Grain-Microstructure Evolution Within a Friction Stir Welding (FSW) Joint via the Use of the Monte Carlo Simulation Method

NASA Astrophysics Data System (ADS)

Grujicic, M.; Ramaswami, S.; Snipes, J. S.; Avuthu, V.; Galgalikar, R.; Zhang, Z.

2015-09-01

A thermo-mechanical finite element analysis of the friction stir welding (FSW) process is carried out and the evolution of the material state (e.g., temperature, the extent of plastic deformation, etc.) monitored. Subsequently, the finite-element results are used as input to a Monte-Carlo simulation algorithm in order to predict the evolution of the grain microstructure within different weld zones, during the FSW process and the subsequent cooling of the material within the weld to room temperature. To help delineate different weld zones, (a) temperature and deformation fields during the welding process, and during the subsequent cooling, are monitored; and (b) competition between the grain growth (driven by the reduction in the total grain-boundary surface area) and dynamic-recrystallization grain refinement (driven by the replacement of highly deformed material with an effectively "dislocation-free" material) is simulated. The results obtained clearly revealed that different weld zones form as a result of different outcomes of the competition between the grain growth and grain refinement processes.

Simulation of Triple Oxidation Ditch Wastewater Treatment Process

NASA Astrophysics Data System (ADS)

Yang, Yue; Zhang, Jinsong; Liu, Lixiang; Hu, Yongfeng; Xu, Ziming

2010-11-01

This paper presented the modeling mechanism and method of a sewage treatment system. A triple oxidation ditch process of a WWTP was simulated based on activated sludge model ASM2D with GPS-X software. In order to identify the adequate model structure to be implemented into the GPS-X environment, the oxidation ditch was divided into several completely stirred tank reactors depended on the distribution of aeration devices and dissolved oxygen concentration. The removal efficiency of COD, ammonia nitrogen, total nitrogen, total phosphorus and SS were simulated by GPS-X software with influent quality data of this WWTP from June to August 2009, to investigate the differences between the simulated results and the actual results. The results showed that, the simulated values could well reflect the actual condition of the triple oxidation ditch process. Mathematical modeling method was appropriate in effluent quality predicting and process optimizing.

Theory of quantized systems: formal basis for DEVS/HLA distributed simulation environment

NASA Astrophysics Data System (ADS)

Zeigler, Bernard P.; Lee, J. S.

1998-08-01

In the context of a DARPA ASTT project, we are developing an HLA-compliant distributed simulation environment based on the DEVS formalism. This environment will provide a user- friendly, high-level tool-set for developing interoperable discrete and continuous simulation models. One application is the study of contract-based predictive filtering. This paper presents a new approach to predictive filtering based on a process called 'quantization' to reduce state update transmission. Quantization, which generates state updates only at quantum level crossings, abstracts a sender model into a DEVS representation. This affords an alternative, efficient approach to embedding continuous models within distributed discrete event simulations. Applications of quantization to message traffic reduction are discussed. The theory has been validated by DEVSJAVA simulations of test cases. It will be subject to further test in actual distributed simulations using the DEVS/HLA modeling and simulation environment.

CFD simulation of local and global mixing time in an agitated tank

NASA Astrophysics Data System (ADS)

Li, Liangchao; Xu, Bin

2017-01-01

The Issue of mixing efficiency in agitated tanks has drawn serious concern in many industrial processes. The turbulence model is very critical to predicting mixing process in agitated tanks. On the basis of computational fluid dynamics(CFD) software package Fluent 6.2, the mixing characteristics in a tank agitated by dual six-blade-Rushton-turbines(6-DT) are predicted using the detached eddy simulation(DES) method. A sliding mesh(SM) approach is adopted to solve the rotation of the impeller. The simulated flow patterns and liquid velocities in the agitated tank are verified by experimental data in the literature. The simulation results indicate that the DES method can obtain more flow details than Reynolds-averaged Navier-Stokes(RANS) model. Local and global mixing time in the agitated tank is predicted by solving a tracer concentration scalar transport equation. The simulated results show that feeding points have great influence on mixing process and mixing time. Mixing efficiency is the highest for the feeding point at location of midway of the two impellers. Two methods are used to determine global mixing time and get close result. Dimensionless global mixing time remains unchanged with increasing of impeller speed. Parallel, merging and diverging flow pattern form in the agitated tank, respectively, by changing the impeller spacing and clearance of lower impeller from the bottom of the tank. The global mixing time is the shortest for the merging flow, followed by diverging flow, and the longest for parallel flow. The research presents helpful references for design, optimization and scale-up of agitated tanks with multi-impeller.

Towards inverse modeling of turbidity currents: The inverse lock-exchange problem

NASA Astrophysics Data System (ADS)

Lesshafft, Lutz; Meiburg, Eckart; Kneller, Ben; Marsden, Alison

2011-04-01

A new approach is introduced for turbidite modeling, leveraging the potential of computational fluid dynamics methods to simulate the flow processes that led to turbidite formation. The practical use of numerical flow simulation for the purpose of turbidite modeling so far is hindered by the need to specify parameters and initial flow conditions that are a priori unknown. The present study proposes a method to determine optimal simulation parameters via an automated optimization process. An iterative procedure matches deposit predictions from successive flow simulations against available localized reference data, as in practice may be obtained from well logs, and aims at convergence towards the best-fit scenario. The final result is a prediction of the entire deposit thickness and local grain size distribution. The optimization strategy is based on a derivative-free, surrogate-based technique. Direct numerical simulations are performed to compute the flow dynamics. A proof of concept is successfully conducted for the simple test case of a two-dimensional lock-exchange turbidity current. The optimization approach is demonstrated to accurately retrieve the initial conditions used in a reference calculation.

Predicting Flows of Rarefied Gases

NASA Technical Reports Server (NTRS)

LeBeau, Gerald J.; Wilmoth, Richard G.

2005-01-01

DSMC Analysis Code (DAC) is a flexible, highly automated, easy-to-use computer program for predicting flows of rarefied gases -- especially flows of upper-atmospheric, propulsion, and vented gases impinging on spacecraft surfaces. DAC implements the direct simulation Monte Carlo (DSMC) method, which is widely recognized as standard for simulating flows at densities so low that the continuum-based equations of computational fluid dynamics are invalid. DAC enables users to model complex surface shapes and boundary conditions quickly and easily. The discretization of a flow field into computational grids is automated, thereby relieving the user of a traditionally time-consuming task while ensuring (1) appropriate refinement of grids throughout the computational domain, (2) determination of optimal settings for temporal discretization and other simulation parameters, and (3) satisfaction of the fundamental constraints of the method. In so doing, DAC ensures an accurate and efficient simulation. In addition, DAC can utilize parallel processing to reduce computation time. The domain decomposition needed for parallel processing is completely automated, and the software employs a dynamic load-balancing mechanism to ensure optimal parallel efficiency throughout the simulation.

Estimation of state and material properties during heat-curing molding of composite materials using data assimilation: A numerical study.

PubMed

Matsuzaki, Ryosuke; Tachikawa, Takeshi; Ishizuka, Junya

2018-03-01

Accurate simulations of carbon fiber-reinforced plastic (CFRP) molding are vital for the development of high-quality products. However, such simulations are challenging and previous attempts to improve the accuracy of simulations by incorporating the data acquired from mold monitoring have not been completely successful. Therefore, in the present study, we developed a method to accurately predict various CFRP thermoset molding characteristics based on data assimilation, a process that combines theoretical and experimental values. The degree of cure as well as temperature and thermal conductivity distributions during the molding process were estimated using both temperature data and numerical simulations. An initial numerical experiment demonstrated that the internal mold state could be determined solely from the surface temperature values. A subsequent numerical experiment to validate this method showed that estimations based on surface temperatures were highly accurate in the case of degree of cure and internal temperature, although predictions of thermal conductivity were more difficult.

Use of simulated data sets to evaluate the fidelity of Metagenomicprocessing methods

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

Mavromatis, Konstantinos; Ivanova, Natalia; Barry, Kerri

2006-12-01

Metagenomics is a rapidly emerging field of research for studying microbial communities. To evaluate methods presently used to process metagenomic sequences, we constructed three simulated data sets of varying complexity by combining sequencing reads randomly selected from 113 isolate genomes. These data sets were designed to model real metagenomes in terms of complexity and phylogenetic composition. We assembled sampled reads using three commonly used genome assemblers (Phrap, Arachne and JAZZ), and predicted genes using two popular gene finding pipelines (fgenesb and CRITICA/GLIMMER). The phylogenetic origins of the assembled contigs were predicted using one sequence similarity--based (blast hit distribution) and twomore » sequence composition--based (PhyloPythia, oligonucleotide frequencies) binning methods. We explored the effects of the simulated community structure and method combinations on the fidelity of each processing step by comparison to the corresponding isolate genomes. The simulated data sets are available online to facilitate standardized benchmarking of tools for metagenomic analysis.« less

Static and transient performance prediction for CFB boilers using a Bayesian-Gaussian Neural Network

NASA Astrophysics Data System (ADS)

Ye, Haiwen; Ni, Weidou

1997-06-01

A Bayesian-Gaussian Neural Network (BGNN) is put forward in this paper to predict the static and transient performance of Circulating Fluidized Bed (CFB) boilers. The advantages of this network over Back-Propagation Neural Networks (BPNNs), easier determination of topology, simpler and time saving in training process as well as self-organizing ability, make this network more practical in on-line performance prediction for complicated processes. Simulation shows that this network is comparable to the BPNNs in predicting the performance of CFB boilers. Good and practical on-line performance predictions are essential for operation guide and model predictive control of CFB boilers, which are under research by the authors.

Physically representative atomistic modeling of atomic-scale friction

NASA Astrophysics Data System (ADS)

Dong, Yalin

Nanotribology is a research field to study friction, adhesion, wear and lubrication occurred between two sliding interfaces at nano scale. This study is motivated by the demanding need of miniaturization mechanical components in Micro Electro Mechanical Systems (MEMS), improvement of durability in magnetic storage system, and other industrial applications. Overcoming tribological failure and finding ways to control friction at small scale have become keys to commercialize MEMS with sliding components as well as to stimulate the technological innovation associated with the development of MEMS. In addition to the industrial applications, such research is also scientifically fascinating because it opens a door to understand macroscopic friction from the most bottom atomic level, and therefore serves as a bridge between science and engineering. This thesis focuses on solid/solid atomic friction and its associated energy dissipation through theoretical analysis, atomistic simulation, transition state theory, and close collaboration with experimentalists. Reduced-order models have many advantages for its simplification and capacity to simulating long-time event. We will apply Prandtl-Tomlinson models and their extensions to interpret dry atomic-scale friction. We begin with the fundamental equations and build on them step-by-step from the simple quasistatic one-spring, one-mass model for predicting transitions between friction regimes to the two-dimensional and multi-atom models for describing the effect of contact area. Theoretical analysis, numerical implementation, and predicted physical phenomena are all discussed. In the process, we demonstrate the significant potential for this approach to yield new fundamental understanding of atomic-scale friction. Atomistic modeling can never be overemphasized in the investigation of atomic friction, in which each single atom could play a significant role, but is hard to be captured experimentally. In atomic friction, the interesting physical process is buried between the two contact interfaces, thus makes a direct measurement more difficult. Atomistic simulation is able to simulate the process with the dynamic information of each single atom, and therefore provides valuable interpretations for experiments. In this, we will systematically to apply Molecular Dynamics (MD) simulation to optimally model the Atomic Force Microscopy (AFM) measurement of atomic friction. Furthermore, we also employed molecular dynamics simulation to correlate the atomic dynamics with the friction behavior observed in experiments. For instance, ParRep dynamics (an accelerated molecular dynamic technique) is introduced to investigate velocity dependence of atomic friction; we also employ MD simulation to "see" how the reconstruction of gold surface modulates the friction, and the friction enhancement mechanism at a graphite step edge. Atomic stick-slip friction can be treated as a rate process. Instead of running a direction simulation of the process, we can apply transition state theory to predict its property. We will have a rigorous derivation of velocity and temperature dependence of friction based on the Prandtl-Tomlinson model as well as transition theory. A more accurate relation to prediction velocity and temperature dependence is obtained. Furthermore, we have included instrumental noise inherent in AFM measurement to interpret two discoveries in experiments, suppression of friction at low temperature and the attempt frequency discrepancy between AFM measurement and theoretical prediction. We also discuss the possibility to treat wear as a rate process.

Slug sizing/slug volume prediction, state of the art review and simulation

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

Burke, N.E.; Kashou, S.F.

1995-12-01

Slug flow is a flow pattern commonly encountered in offshore multiphase flowlines. It is characterized by an alternate flow of liquid slugs and gas pockets, resulting in an unsteady hydrodynamic behavior. All important design variables, such as slug length and slug frequency, liquid holdup, and pressure drop, vary with time and this makes the prediction of slug flow characteristics both difficult and challenging. This paper reviews the state of the art methods in slug catcher sizing and slug volume predictions. In addition, history matching of measured slug flow data is performed using the OLGA transient simulator. This paper reviews themore » design factors that impact slug catcher sizing during steady state, during transient, during pigging, and during operations under a process control system. The slug tracking option of the OLGA simulator is applied to predict the slug length and the slug volume during a field operation. This paper will also comment on the performance of common empirical slug prediction correlations.« less

Slug-sizing/slug-volume prediction: State of the art review and simulation

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

Burke, N.E.; Kashou, S.F.

1996-08-01

Slug flow is a flow pattern commonly encountered in offshore multiphase flowlines. It is characterized by an alternate flow of liquid slugs and gas pockets, resulting in an unsteady hydrodynamic behavior. All important design variables, such as slug length and slug frequency, liquid holdup, and pressure drop, vary with time and this makes the prediction of slug flow characteristics both difficult and challenging. This paper reviews the state of the art methods in slug-catcher sizing and slug-volume predictions. In addition, history matching of measured slug flow data is performed using the OLGA transient simulator. This paper reviews the design factorsmore » that impact slug-catcher sizing during steady state, during transient, during pigging, and during operations under a process-control system. The slug-tracking option of the simulator is applied to predict the slug length and the slug volume during a field operation. This paper will also comment on the performance of common empirical slug-prediction correlations.« less

FINAL REPORT FOR VERIFICATION OF THE METAL FINISHING FACILITY POLLUTION PREVENTION TOOL (MFFPPT)

EPA Science Inventory

The United States Environmental Protection Agency (USEPA) has prepared a computer process simulation package for the metal finishing industry that enables users to predict process outputs based upon process inputs and other operating conditions. This report documents the developm...

Simulation based optimization on automated fibre placement process

NASA Astrophysics Data System (ADS)

Lei, Shi

2018-02-01

In this paper, a software simulation (Autodesk TruPlan & TruFiber) based method is proposed to optimize the automate fibre placement (AFP) process. Different types of manufacturability analysis are introduced to predict potential defects. Advanced fibre path generation algorithms are compared with respect to geometrically different parts. Major manufacturing data have been taken into consideration prior to the tool paths generation to achieve high success rate of manufacturing.

A New Numerical Simulation technology of Multistage Fracturing in Horizontal Well

NASA Astrophysics Data System (ADS)

Cheng, Ning; Kang, Kaifeng; Li, Jianming; Liu, Tao; Ding, Kun

2017-11-01

Horizontal multi-stage fracturing is recognized the effective development technology of unconventional oil resources. Geological mechanics in the numerical simulation of hydraulic fracturing technology occupies very important position, compared with the conventional numerical simulation technology, because of considering the influence of geological mechanics. New numerical simulation of hydraulic fracturing can more effectively optimize the design of fracturing and evaluate the production after fracturing. This paper studies is based on the three-dimensional stress and rock physics parameters model, using the latest fluid-solid coupling numerical simulation technology to engrave the extension process of fracture and describes the change of stress field in fracturing process, finally predict the production situation.

Statistical learning and probabilistic prediction in music cognition: mechanisms of stylistic enculturation.

PubMed

Pearce, Marcus T

2018-05-11

Music perception depends on internal psychological models derived through exposure to a musical culture. It is hypothesized that this musical enculturation depends on two cognitive processes: (1) statistical learning, in which listeners acquire internal cognitive models of statistical regularities present in the music to which they are exposed; and (2) probabilistic prediction based on these learned models that enables listeners to organize and process their mental representations of music. To corroborate these hypotheses, I review research that uses a computational model of probabilistic prediction based on statistical learning (the information dynamics of music (IDyOM) model) to simulate data from empirical studies of human listeners. The results show that a broad range of psychological processes involved in music perception-expectation, emotion, memory, similarity, segmentation, and meter-can be understood in terms of a single, underlying process of probabilistic prediction using learned statistical models. Furthermore, IDyOM simulations of listeners from different musical cultures demonstrate that statistical learning can plausibly predict causal effects of differential cultural exposure to musical styles, providing a quantitative model of cultural distance. Understanding the neural basis of musical enculturation will benefit from close coordination between empirical neuroimaging and computational modeling of underlying mechanisms, as outlined here. © 2018 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals, Inc. on behalf of New York Academy of Sciences.

A systems approach to college drinking: development of a deterministic model for testing alcohol control policies.

PubMed

Scribner, Richard; Ackleh, Azmy S; Fitzpatrick, Ben G; Jacquez, Geoffrey; Thibodeaux, Jeremy J; Rommel, Robert; Simonsen, Neal

2009-09-01

The misuse and abuse of alcohol among college students remain persistent problems. Using a systems approach to understand the dynamics of student drinking behavior and thus forecasting the impact of campus policy to address the problem represents a novel approach. Toward this end, the successful development of a predictive mathematical model of college drinking would represent a significant advance for prevention efforts. A deterministic, compartmental model of college drinking was developed, incorporating three processes: (1) individual factors, (2) social interactions, and (3) social norms. The model quantifies these processes in terms of the movement of students between drinking compartments characterized by five styles of college drinking: abstainers, light drinkers, moderate drinkers, problem drinkers, and heavy episodic drinkers. Predictions from the model were first compared with actual campus-level data and then used to predict the effects of several simulated interventions to address heavy episodic drinking. First, the model provides a reasonable fit of actual drinking styles of students attending Social Norms Marketing Research Project campuses varying by "wetness" and by drinking styles of matriculating students. Second, the model predicts that a combination of simulated interventions targeting heavy episodic drinkers at a moderately "dry" campus would extinguish heavy episodic drinkers, replacing them with light and moderate drinkers. Instituting the same combination of simulated interventions at a moderately "wet" campus would result in only a moderate reduction in heavy episodic drinkers (i.e., 50% to 35%). A simple, five-state compartmental model adequately predicted the actual drinking patterns of students from a variety of campuses surveyed in the Social Norms Marketing Research Project study. The model predicted the impact on drinking patterns of several simulated interventions to address heavy episodic drinking on various types of campuses.

A Systems Approach to College Drinking: Development of a Deterministic Model for Testing Alcohol Control Policies*

PubMed Central

Scribner, Richard; Ackleh, Azmy S.; Fitzpatrick, Ben G.; Jacquez, Geoffrey; Thibodeaux, Jeremy J.; Rommel, Robert; Simonsen, Neal

2009-01-01

Objective: The misuse and abuse of alcohol among college students remain persistent problems. Using a systems approach to understand the dynamics of student drinking behavior and thus forecasting the impact of campus policy to address the problem represents a novel approach. Toward this end, the successful development of a predictive mathematical model of college drinking would represent a significant advance for prevention efforts. Method: A deterministic, compartmental model of college drinking was developed, incorporating three processes: (1) individual factors, (2) social interactions, and (3) social norms. The model quantifies these processes in terms of the movement of students between drinking compartments characterized by five styles of college drinking: abstainers, light drinkers, moderate drinkers, problem drinkers, and heavy episodic drinkers. Predictions from the model were first compared with actual campus-level data and then used to predict the effects of several simulated interventions to address heavy episodic drinking. Results: First, the model provides a reasonable fit of actual drinking styles of students attending Social Norms Marketing Research Project campuses varying by “wetness” and by drinking styles of matriculating students. Second, the model predicts that a combination of simulated interventions targeting heavy episodic drinkers at a moderately “dry” campus would extinguish heavy episodic drinkers, replacing them with light and moderate drinkers. Instituting the same combination of simulated interventions at a moderately “wet” campus would result in only a moderate reduction in heavy episodic drinkers (i.e., 50% to 35%). Conclusions: A simple, five-state compartmental model adequately predicted the actual drinking patterns of students from a variety of campuses surveyed in the Social Norms Marketing Research Project study. The model predicted the impact on drinking patterns of several simulated interventions to address heavy episodic drinking on various types of campuses. PMID:19737506

An adaptive approach to the physical annealing strategy for simulated annealing

NASA Astrophysics Data System (ADS)

Hasegawa, M.

2013-02-01

A new and reasonable method for adaptive implementation of simulated annealing (SA) is studied on two types of random traveling salesman problems. The idea is based on the previous finding on the search characteristics of the threshold algorithms, that is, the primary role of the relaxation dynamics in their finite-time optimization process. It is shown that the effective temperature for optimization can be predicted from the system's behavior analogous to the stabilization phenomenon occurring in the heating process starting from a quenched solution. The subsequent slow cooling near the predicted point draws out the inherent optimizing ability of finite-time SA in more straightforward manner than the conventional adaptive approach.

Mechanistic simulation of batch acetone-butanol-ethanol (ABE) fermentation with in situ gas stripping using Aspen Plus™.

PubMed

Darkwah, Kwabena; Nokes, Sue E; Seay, Jeffrey R; Knutson, Barbara L

2018-05-22

Process simulations of batch fermentations with in situ product separation traditionally decouple these interdependent steps by simulating a separate "steady state" continuous fermentation and separation units. In this study, an integrated batch fermentation and separation process was simulated for a model system of acetone-butanol-ethanol (ABE) fermentation with in situ gas stripping, such that the fermentation kinetics are linked in real-time to the gas stripping process. A time-dependent cell growth, substrate utilization, and product production is translated to an Aspen Plus batch reactor. This approach capitalizes on the phase equilibria calculations of Aspen Plus to predict the effect of stripping on the ABE fermentation kinetics. The product profiles of the integrated fermentation and separation are shown to be sensitive to gas flow rate, unlike separate steady state fermentation and separation simulations. This study demonstrates the importance of coupled fermentation and separation simulation approaches for the systematic analyses of unsteady state processes.

Simulation and flavor compound analysis of dealcoholized beer via one-step vacuum distillation.

PubMed

Andrés-Iglesias, Cristina; García-Serna, Juan; Montero, Olimpio; Blanco, Carlos A

2015-10-01

The coupled operation of vacuum distillation process to produce alcohol free beer at laboratory scale and Aspen HYSYS simulation software was studied to define the chemical changes during the dealcoholization process in the aroma profiles of 2 different lager beers. At the lab-scale process, 2 different parameters were chosen to dealcoholize beer samples, 102mbar at 50°C and 200mbar at 67°C. Samples taken at different steps of the process were analyzed by HS-SPME-GC-MS focusing on the concentration of 7 flavor compounds, 5 alcohols and 2 esters. For simulation process, the EoS parameters of the Wilson-2 property package were adjusted to the experimental data and one more pressure was tested (60mbar). Simulation methods represent a viable alternative to predict results of the volatile compound composition of a final dealcoholized beer. Copyright © 2015 Elsevier Ltd. All rights reserved.

Calibration and prediction of removal function in magnetorheological finishing.

PubMed

Dai, Yifan; Song, Ci; Peng, Xiaoqiang; Shi, Feng

2010-01-20

A calibrated and predictive model of the removal function has been established based on the analysis of a magnetorheological finishing (MRF) process. By introducing an efficiency coefficient of the removal function, the model can be used to calibrate the removal function in a MRF figuring process and to accurately predict the removal function of a workpiece to be polished whose material is different from the spot part. Its correctness and feasibility have been validated by simulations. Furthermore, applying this model to the MRF figuring experiments, the efficiency coefficient of the removal function can be identified accurately to make the MRF figuring process deterministic and controllable. Therefore, all the results indicate that the calibrated and predictive model of the removal function can improve the finishing determinacy and increase the model applicability in a MRF process.

A chemical EOR benchmark study of different reservoir simulators

NASA Astrophysics Data System (ADS)

Goudarzi, Ali; Delshad, Mojdeh; Sepehrnoori, Kamy

2016-09-01

Interest in chemical EOR processes has intensified in recent years due to the advancements in chemical formulations and injection techniques. Injecting Polymer (P), surfactant/polymer (SP), and alkaline/surfactant/polymer (ASP) are techniques for improving sweep and displacement efficiencies with the aim of improving oil production in both secondary and tertiary floods. There has been great interest in chemical flooding recently for different challenging situations. These include high temperature reservoirs, formations with extreme salinity and hardness, naturally fractured carbonates, and sandstone reservoirs with heavy and viscous crude oils. More oil reservoirs are reaching maturity where secondary polymer floods and tertiary surfactant methods have become increasingly important. This significance has added to the industry's interest in using reservoir simulators as tools for reservoir evaluation and management to minimize costs and increase the process efficiency. Reservoir simulators with special features are needed to represent coupled chemical and physical processes present in chemical EOR processes. The simulators need to be first validated against well controlled lab and pilot scale experiments to reliably predict the full field implementations. The available data from laboratory scale include 1) phase behavior and rheological data; and 2) results of secondary and tertiary coreflood experiments for P, SP, and ASP floods under reservoir conditions, i.e. chemical retentions, pressure drop, and oil recovery. Data collected from corefloods are used as benchmark tests comparing numerical reservoir simulators with chemical EOR modeling capabilities such as STARS of CMG, ECLIPSE-100 of Schlumberger, REVEAL of Petroleum Experts. The research UTCHEM simulator from The University of Texas at Austin is also included since it has been the benchmark for chemical flooding simulation for over 25 years. The results of this benchmark comparison will be utilized to improve chemical design for field-scale studies using commercial simulators. The benchmark tests illustrate the potential of commercial simulators for chemical flooding projects and provide a comprehensive table of strengths and limitations of each simulator for a given chemical EOR process. Mechanistic simulations of chemical EOR processes will provide predictive capability and can aid in optimization of the field injection projects. The objective of this paper is not to compare the computational efficiency and solution algorithms; it only focuses on the process modeling comparison.

A Simulation Model of Carbon Cycling and Methane Emissions in Amazon Wetlands

NASA Technical Reports Server (NTRS)

Potter, Christopher; Melack, John; Hess, Laura; Forsberg, Bruce; Novo, Evlyn Moraes; Klooster, Steven

2004-01-01

An integrative carbon study is investigating the hypothesis that measured fluxes of methane from wetlands in the Amazon region can be predicted accurately using a combination of process modeling of ecosystem carbon cycles and remote sensing of regional floodplain dynamics. A new simulation model has been build using the NASA- CASA concept for predicting methane production and emission fluxes in Amazon river and floodplain ecosystems. Numerous innovations area being made to model Amazon wetland ecosystems, including: (1) prediction of wetland net primary production (NPP) as the source for plant litter decomposition and accumulation of sediment organic matter in two major vegetation classes - flooded forests (varzea or igapo) and floating macrophytes, (2) representation of controls on carbon processing and methane evasion at the diffusive boundary layer, through the lake water column, and in wetland sediments as a function of changes in floodplain water level, (3) inclusion of surface emissions controls on wetland methane fluxes, including variations in daily surface temperature and of hydrostatic pressure linked to water level fluctuations. A model design overview and early simulation results are presented.

A Case Study Using Modeling and Simulation to Predict Logistics Supply Chain Issues

NASA Technical Reports Server (NTRS)

Tucker, David A.

2007-01-01

Optimization of critical supply chains to deliver thousands of parts, materials, sub-assemblies, and vehicle structures as needed is vital to the success of the Constellation Program. Thorough analysis needs to be performed on the integrated supply chain processes to plan, source, make, deliver, and return critical items efficiently. Process modeling provides simulation technology-based, predictive solutions for supply chain problems which enable decision makers to reduce costs, accelerate cycle time and improve business performance. For example, United Space Alliance, LLC utilized this approach in late 2006 to build simulation models that recreated shuttle orbiter thruster failures and predicted the potential impact of thruster removals on logistics spare assets. The main objective was the early identification of possible problems in providing thruster spares for the remainder of the Shuttle Flight Manifest. After extensive analysis the model results were used to quantify potential problems and led to improvement actions in the supply chain. Similarly the proper modeling and analysis of Constellation parts, materials, operations, and information flows will help ensure the efficiency of the critical logistics supply chains and the overall success of the program.

Putting mechanisms into crop production models

USDA-ARS?s Scientific Manuscript database

Crop simulation models dynamically predict processes of carbon, nitrogen, and water balance on daily or hourly time-steps to the point of predicting yield and production at crop maturity. A brief history of these models is reviewed, and their level of mechanism for assimilation and respiration, ran...

Numerical tools to predict the environmental loads for offshore structures under extreme weather conditions

NASA Astrophysics Data System (ADS)

Wu, Yanling

2018-05-01

In this paper, the extreme waves were generated using the open source computational fluid dynamic (CFD) tools — OpenFOAM and Waves2FOAM — using linear and nonlinear NewWave input. They were used to conduct the numerical simulation of the wave impact process. Numerical tools based on first-order (with and without stretching) and second-order NewWave are investigated. The simulation to predict force loading for the offshore platform under the extreme weather condition is implemented and compared.

Prediction of forming limit in hydro-mechanical deep drawing of steel sheets using ductile fracture criterion

NASA Astrophysics Data System (ADS)

Oh, S.-T.; Chang, H.-J.; Oh, K. H.; Han, H. N.

2006-04-01

It has been observed that the forming limit curve at fracture (FLCF) of steel sheets, with a relatively higher ductility limit have linear shapes, similar to those of a bulk forming process. In contrast, the FLCF of sheets with a relatively lower ductility limit have rather complex shapes approaching the forming limit curve at neck (FLCN) towards the equi-biaxial strain paths. In this study, the FLCFs of steel sheets were measured and compared with the fracture strains predicted from specific ductile fracture criteria, including a criterion suggested by the authors, which can accurately describe FLCFs with both linear and complex shapes. To predict the forming limit for hydro-mechanical deep drawing of steel sheets, the ductile fracture criteria were integrated into a finite element simulation. The simulation, results based on the criterion suggested by authors accurately predicted the experimetal, fracture limits of steel sheets for the hydro-mechanical deep drawing process.

Short-term Climate Simulations of African Easterly Waves with a Global Mesoscale Model

NASA Astrophysics Data System (ADS)

Shen, B. W.

2015-12-01

Recent high-resolution global model simulations ( Shen et al., 2010a, 2010b, 2012; 2013), which were conducted to examine the role of multiscale processes associated with tropical waves in the predictability of mesoscale tropical cyclones (TCs), suggested that a large-scale system (e.g., tropical waves) can provide determinism on the prediction of TC genesis, making it possible to extend the lead time of genesis predictions. Selected cases include the relationship between (i) TC Nargis (2008) and an Equatorial Rossby wave; (ii) Hurricane Helene (2006) and an intensifying African Easterly Wave (AEW); (iii) Twin TCs (2002) and a mixed Rossby-gravity wave during an active phase of the Madden Julian Oscillation (MJO); (iv) Hurricane Sandy (2012) and tropical waves during an active phase of the MJO. In this talk, thirty-day simulations with different model configurations are presented to examine the model's ability to simulate AEWs and MJOs and their association with tropical cyclogenesis. I will first discuss the simulations of the initiation and propagation of 6 consecutive AEWs in late August 2006 and the mean state of the African easterly jet (AEJ) over both Africa and downstream in the tropical Atlantic. By comparing our simulations with NCEP analysis and satellite data (e.g., TRMM), it is shown that the statistical characteristics of individual AEWs are realistically simulated with larger errors in the 5th and th AEWs. Results from the sensitivity experiments suggest the following: 1) accurate representations of non-linear interactions between the atmosphere and land processes are crucial for improving the simulations of the AEWs and the AEJ; 2) improved simulations of an individual AEW and its interaction with local environments (e.g., the Guinea Highlands) could provide determinism for hurricane formation downstream. Of interest is the potential to extend the lead time for predicting hurricane formation (e.g., a lead time of up to 22 days) as the 4th AEW is realistically simulated; 3) however, the dependence of AEW simulations on accurate dynamic and surface initial conditions and boundary conditions poses a challenge in simulating their modulation on hurricane activity. In addition to the simulations of AEWs, I will also present the 30-day simulations of selected MJO cases.

Injection-Molded Long-Fiber Thermoplastic Composites: From Process Modeling to Prediction of Mechanical Properties

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

Nguyen, Ba Nghiep; Kunc, Vlastimil; Jin, Xiaoshi

2013-12-18

This article illustrates the predictive capabilities for long-fiber thermoplastic (LFT) composites that first simulate the injection molding of LFT structures by Autodesk® Simulation Moldflow® Insight (ASMI) to accurately predict fiber orientation and length distributions in these structures. After validating fiber orientation and length predictions against the experimental data, the predicted results are used by ASMI to compute distributions of elastic properties in the molded structures. In addition, local stress-strain responses and damage accumulation under tensile loading are predicted by an elastic-plastic damage model of EMTA-NLA, a nonlinear analysis tool implemented in ABAQUS® via user-subroutines using an incremental Eshelby-Mori-Tanaka approach. Predictedmore » stress-strain responses up to failure and damage accumulations are compared to the experimental results to validate the model.« less

A Simulation Model Articulation of the REA Ontology

NASA Astrophysics Data System (ADS)

Laurier, Wim; Poels, Geert

This paper demonstrates how the REA enterprise ontology can be used to construct simulation models for business processes, value chains and collaboration spaces in supply chains. These models support various high-level and operational management simulation applications, e.g. the analysis of enterprise sustainability and day-to-day planning. First, the basic constructs of the REA ontology and the ExSpect modelling language for simulation are introduced. Second, collaboration space, value chain and business process models and their conceptual dependencies are shown, using the ExSpect language. Third, an exhibit demonstrates the use of value chain models in predicting the financial performance of an enterprise.

Is social projection based on simulation or theory? Why new methods are needed for differentiating

PubMed Central

Bazinger, Claudia; Kühberger, Anton

2012-01-01

The literature on social cognition reports many instances of a phenomenon titled ‘social projection’ or ‘egocentric bias’. These terms indicate egocentric predictions, i.e., an over-reliance on the self when predicting the cognition, emotion, or behavior of other people. The classic method to diagnose egocentric prediction is to establish high correlations between our own and other people's cognition, emotion, or behavior. We argue that this method is incorrect because there is a different way to come to a correlation between own and predicted states, namely, through the use of theoretical knowledge. Thus, the use of correlational measures is not sufficient to identify the source of social predictions. Based on the distinction between simulation theory and theory theory, we propose the following alternative methods for inferring prediction strategies: independent vs. juxtaposed predictions, the use of ‘hot’ mental processes, and the use of participants’ self-reports. PMID:23209342

Stochastic Earthquake Rupture Modeling Using Nonparametric Co-Regionalization

NASA Astrophysics Data System (ADS)

Lee, Kyungbook; Song, Seok Goo

2017-09-01

Accurate predictions of the intensity and variability of ground motions are essential in simulation-based seismic hazard assessment. Advanced simulation-based ground motion prediction methods have been proposed to complement the empirical approach, which suffers from the lack of observed ground motion data, especially in the near-source region for large events. It is important to quantify the variability of the earthquake rupture process for future events and to produce a number of rupture scenario models to capture the variability in simulation-based ground motion predictions. In this study, we improved the previously developed stochastic earthquake rupture modeling method by applying the nonparametric co-regionalization, which was proposed in geostatistics, to the correlation models estimated from dynamically derived earthquake rupture models. The nonparametric approach adopted in this study is computationally efficient and, therefore, enables us to simulate numerous rupture scenarios, including large events ( M > 7.0). It also gives us an opportunity to check the shape of true input correlation models in stochastic modeling after being deformed for permissibility. We expect that this type of modeling will improve our ability to simulate a wide range of rupture scenario models and thereby predict ground motions and perform seismic hazard assessment more accurately.

Determination of melt pool dimensions using DOE-FEM and RSM with process window during SLM of Ti6Al4V powder

NASA Astrophysics Data System (ADS)

Zhuang, Jyun-Rong; Lee, Yee-Ting; Hsieh, Wen-Hsin; Yang, An-Shik

2018-07-01

Selective laser melting (SLM) shows a positive prospect as an additive manufacturing (AM) technique for fabrication of 3D parts with complicated structures. A transient thermal model was developed by the finite element method (FEM) to simulate the thermal behavior for predicting the time evolution of temperature field and melt pool dimensions of Ti6Al4V powder during SLM. The FEM predictions were then compared with published experimental measurements and calculation results for model validation. This study applied the design of experiment (DOE) scheme together with the response surface method (RSM) to conduct the regression analysis based on four processing parameters (exactly, the laser power, scanning speed, preheating temperature and hatch space) for predicting the dimensions of the melt pool in SLM. The preliminary RSM results were used to quantify the effects of those parameters on the melt pool size. The process window was further implemented via two criteria of the width and depth of the molten pool to screen impractical conditions of four parameters for including the practical ranges of processing parameters. The FEM simulations confirmed the good accuracy of the critical RSM models in the predictions of melt pool dimensions for three typical SLM working scenarios.

Thermo-Mechanical Calculations of Hybrid Rotary Friction Welding at Equal Diameter Copper Bars and Effects of Essential Parameters on Dependent Special Variables

NASA Astrophysics Data System (ADS)

Parsa, M. H.; Davari, H.; Hadian, A. M.; Ahmadabadi, M. Nili

2007-05-01

Hybrid Rotary Friction Welding is a modified type of common rotary friction welding processes. In this welding method parameters such as pressure, angular velocity and time of welding control temperature, stress, strain and their variations. These dependent factors play an important rule in defining optimum process parameters combinations in order to improve the design and manufacturing of welding machines and quality of welded parts. Thermo-mechanical simulation of friction welding has been carried out and it has been shown that, simulation is an important tool for prediction of generated heat and strain at the weld interface and can be used for prediction of microstructure and evaluation of quality of welds. For simulation of Hybrid Rotary Friction Welding, a commercial finite element program has been used and the effects of pressure and rotary velocity of rotary part on temperature and strain variations have been investigated.

Acoustic Analysis and Design of the E-STA MSA Simulator

NASA Technical Reports Server (NTRS)

Bittinger, Samantha A.

2016-01-01

The Orion European Service Module Structural Test Article (E-STA) Acoustic Test was completed in May 2016 to verify that the European Service Module (ESM) can withstand qualification acoustic environments. The test article required an aft closeout to simulate the Multi-Purpose Crew Vehicle (MPCV) Stage Adapter (MSA) cavity, however, the flight MSA design was too cost-prohibitive to build. NASA Glenn Research Center (GRC) had 6 months to design an MSA Simulator that could recreate the qualification prediction MSA cavity sound pressure level to within a reasonable tolerance. This paper summarizes the design and analysis process to arrive at a design for the MSA Simulator, and then compares its performance to the final prediction models created prior to test.

Prediction of shock initiation thresholds and ignition probability of polymer-bonded explosives using mesoscale simulations

NASA Astrophysics Data System (ADS)

Kim, Seokpum; Wei, Yaochi; Horie, Yasuyuki; Zhou, Min

2018-05-01

The design of new materials requires establishment of macroscopic measures of material performance as functions of microstructure. Traditionally, this process has been an empirical endeavor. An approach to computationally predict the probabilistic ignition thresholds of polymer-bonded explosives (PBXs) using mesoscale simulations is developed. The simulations explicitly account for microstructure, constituent properties, and interfacial responses and capture processes responsible for the development of hotspots and damage. The specific mechanisms tracked include viscoelasticity, viscoplasticity, fracture, post-fracture contact, frictional heating, and heat conduction. The probabilistic analysis uses sets of statistically similar microstructure samples to directly mimic relevant experiments for quantification of statistical variations of material behavior due to inherent material heterogeneities. The particular thresholds and ignition probabilities predicted are expressed in James type and Walker-Wasley type relations, leading to the establishment of explicit analytical expressions for the ignition probability as function of loading. Specifically, the ignition thresholds corresponding to any given level of ignition probability and ignition probability maps are predicted for PBX 9404 for the loading regime of Up = 200-1200 m/s where Up is the particle speed. The predicted results are in good agreement with available experimental measurements. A parametric study also shows that binder properties can significantly affect the macroscopic ignition behavior of PBXs. The capability to computationally predict the macroscopic engineering material response relations out of material microstructures and basic constituent and interfacial properties lends itself to the design of new materials as well as the analysis of existing materials.

A numerical model to simulate foams during devolatilization of polymers

NASA Astrophysics Data System (ADS)

Khan, Irfan; Dixit, Ravindra

2014-11-01

Customers often demand that the polymers sold in the market have low levels of volatile organic compounds (VOC). Some of the processes for making polymers involve the removal of volatiles to the levels of parts per million (devolatilization). During this step the volatiles are phase separated out of the polymer through a combination of heating and applying lower pressure, creating foam with the pure polymer in liquid phase and the volatiles in the gas phase. The efficiency of the devolatilization process depends on predicting the onset of solvent phase change in the polymer and volatiles mixture accurately based on the processing conditions. However due to the complex relationship between the polymer properties and the processing conditions this is not trivial. In this work, a bubble scale model is coupled with a bulk scale transport model to simulate the processing conditions of polymer devolatilization. The bubble scale model simulates the nucleation and bubble growth based on the classical nucleation theory and the popular ``influence volume approach.'' As such it provides the information of bubble size distribution and number density inside the polymer at any given time and position. This information is used to predict the bulk properties of the polymer and its behavior under the applied processing conditions. Initial results of this modeling approach will be presented.

A Statistics-Based Cracking Criterion of Resin-Bonded Silica Sand for Casting Process Simulation

NASA Astrophysics Data System (ADS)

Wang, Huimin; Lu, Yan; Ripplinger, Keith; Detwiler, Duane; Luo, Alan A.

2017-02-01

Cracking of sand molds/cores can result in many casting defects such as veining. A robust cracking criterion is needed in casting process simulation for predicting/controlling such defects. A cracking probability map, relating to fracture stress and effective volume, was proposed for resin-bonded silica sand based on Weibull statistics. Three-point bending test results of sand samples were used to generate the cracking map and set up a safety line for cracking criterion. Tensile test results confirmed the accuracy of the safety line for cracking prediction. A laboratory casting experiment was designed and carried out to predict cracking of a cup mold during aluminum casting. The stress-strain behavior and the effective volume of the cup molds were calculated using a finite element analysis code ProCAST®. Furthermore, an energy dispersive spectroscopy fractographic examination of the sand samples confirmed the binder cracking in resin-bonded silica sand.

Concepts Within Reach: Action Performance Predicts Action Language Processing in Stroke

PubMed Central

Desai, Rutvik H.; Herter, Troy; Riccardi, Nicholas; Rorden, Chris; Fridriksson, Julius

2015-01-01

The relationship between the brain’s conceptual or semantic and sensory-motor systems remains controversial. Here, we tested manual and conceptual abilities of 41 chronic stroke patients in order to examine their relationship. Manual abilities were assed through a reaching task using an exoskeleton robot. Semantic abilities were assessed with implicit as well as explicit semantic tasks, for both verbs and nouns. The results show that that the degree of selective impairment for action word processing was predicted by the degree of impairment in reaching performance. Moreover, the implicit semantic measures showed a correlation with a global reaching parameter, while the explicit semantic similarity judgment task predicted performance in action initiation. These results indicate that action concepts are dynamically grounded through motoric simulations, and that more details are simulated for more explicit semantic tasks. This is evidence for a close and causal relationship between sensory-motor and conceptual systems of the brain. PMID:25858602

Critical length scale controls adhesive wear mechanisms

PubMed Central

Aghababaei, Ramin; Warner, Derek H.; Molinari, Jean-Francois

2016-01-01

The adhesive wear process remains one of the least understood areas of mechanics. While it has long been established that adhesive wear is a direct result of contacting surface asperities, an agreed upon understanding of how contacting asperities lead to wear debris particle has remained elusive. This has restricted adhesive wear prediction to empirical models with limited transferability. Here we show that discrepant observations and predictions of two distinct adhesive wear mechanisms can be reconciled into a unified framework. Using atomistic simulations with model interatomic potentials, we reveal a transition in the asperity wear mechanism when contact junctions fall below a critical length scale. A simple analytic model is formulated to predict the transition in both the simulation results and experiments. This new understanding may help expand use of computer modelling to explore adhesive wear processes and to advance physics-based wear laws without empirical coefficients. PMID:27264270

The numerical modelling and process simulation for the fault diagnosis of rotary kiln incinerator.

PubMed

Roh, S D; Kim, S W; Cho, W S

2001-10-01

The numerical modelling and process simulation for the fault diagnosis of rotary kiln incinerator were accomplished. In the numerical modelling, two models applied to the modelling within the kiln are the combustion chamber model including the mass and energy balance equations for two combustion chambers and 3D thermal model. The combustion chamber model predicts temperature within the kiln, flue gas composition, flux and heat of combustion. Using the combustion chamber model and 3D thermal model, the production-rules for the process simulation can be obtained through interrelation analysis between control and operation variables. The process simulation of the kiln is operated with the production-rules for automatic operation. The process simulation aims to provide fundamental solutions to the problems in incineration process by introducing an online expert control system to provide an integrity in process control and management. Knowledge-based expert control systems use symbolic logic and heuristic rules to find solutions for various types of problems. It was implemented to be a hybrid intelligent expert control system by mutually connecting with the process control systems which has the capability of process diagnosis, analysis and control.

High fidelity studies of exploding foil initiator bridges, Part 3: ALEGRA MHD simulations

NASA Astrophysics Data System (ADS)

Neal, William; Garasi, Christopher

2017-01-01

Simulations of high voltage detonators, such as Exploding Bridgewire (EBW) and Exploding Foil Initiators (EFI), have historically been simple, often empirical, one-dimensional models capable of predicting parameters such as current, voltage, and in the case of EFIs, flyer velocity. Experimental methods have correspondingly generally been limited to the same parameters. With the advent of complex, first principles magnetohydrodynamic codes such as ALEGRA and ALE-MHD, it is now possible to simulate these components in three dimensions, and predict a much greater range of parameters than before. A significant improvement in experimental capability was therefore required to ensure these simulations could be adequately verified. In this third paper of a three part study, the experimental results presented in part 2 are compared against 3-dimensional MHD simulations. This improved experimental capability, along with advanced simulations, offer an opportunity to gain a greater understanding of the processes behind the functioning of EBW and EFI detonators.

Simulation of beam-induced plasma in gas-filled rf cavities

DOE PAGES

Yu, Kwangmin; Samulyak, Roman; Yonehara, Katsuya; ...

2017-03-07

Processes occurring in a radio-frequency (rf) cavity, filled with high pressure gas and interacting with proton beams, have been studied via advanced numerical simulations. Simulations support the experimental program on the hydrogen gas-filled rf cavity in the Mucool Test Area (MTA) at Fermilab, and broader research on the design of muon cooling devices. space, a 3D electromagnetic particle-in-cell (EM-PIC) code with atomic physics support, was used in simulation studies. Plasma dynamics in the rf cavity, including the process of neutral gas ionization by proton beams, plasma loading of the rf cavity, and atomic processes in plasma such as electron-ion andmore » ion-ion recombination and electron attachment to dopant molecules, have been studied. Here, through comparison with experiments in the MTA, simulations quantified several uncertain values of plasma properties such as effective recombination rates and the attachment time of electrons to dopant molecules. Simulations have achieved very good agreement with experiments on plasma loading and related processes. Lastly, the experimentally validated code space is capable of predictive simulations of muon cooling devices.« less

Trajectory optimization for an asymmetric launch vehicle. M.S. Thesis - MIT

NASA Technical Reports Server (NTRS)

Sullivan, Jeanne Marie

1990-01-01

A numerical optimization technique is used to fully automate the trajectory design process for an symmetric configuration of the proposed Advanced Launch System (ALS). The objective of the ALS trajectory design process is the maximization of the vehicle mass when it reaches the desired orbit. The trajectories used were based on a simple shape that could be described by a small set of parameters. The use of a simple trajectory model can significantly reduce the computation time required for trajectory optimization. A predictive simulation was developed to determine the on-orbit mass given an initial vehicle state, wind information, and a set of trajectory parameters. This simulation utilizes an idealized control system to speed computation by increasing the integration time step. The conjugate gradient method is used for the numerical optimization of on-orbit mass. The method requires only the evaluation of the on-orbit mass function using the predictive simulation, and the gradient of the on-orbit mass function with respect to the trajectory parameters. The gradient is approximated with finite differencing. Prelaunch trajectory designs were carried out using the optimization procedure. The predictive simulation is used in flight to redesign the trajectory to account for trajectory deviations produced by off-nominal conditions, e.g., stronger than expected head winds.

Estimating plant available water for general crop simulations in ALMANAC/APEX/EPIC/SWAT

USDA-ARS?s Scientific Manuscript database

Process-based simulation models ALMANAC/APEX/EPIC/SWAT contain generalized plant growth subroutines to predict biomass and crop yield. Environmental constraints typically restrict plant growth and yield. Water stress is often an important limiting factor; it is calculated as the sum of water use f...

Prediction of coagulation and flocculation processes using ANN models and fuzzy regression.

PubMed

Zangooei, Hossein; Delnavaz, Mohammad; Asadollahfardi, Gholamreza

2016-09-01

Coagulation and flocculation are two main processes used to integrate colloidal particles into larger particles and are two main stages of primary water treatment. Coagulation and flocculation processes are only needed when colloidal particles are a significant part of the total suspended solid fraction. Our objective was to predict turbidity of water after the coagulation and flocculation process while other parameters such as types and concentrations of coagulants, pH, and influent turbidity of raw water were known. We used a multilayer perceptron (MLP), a radial basis function (RBF) of artificial neural networks (ANNs) and various kinds of fuzzy regression analysis to predict turbidity after the coagulation and flocculation processes. The coagulant used in the pilot plant, which was located in water treatment plant, was poly aluminum chloride. We used existing data, including the type and concentrations of coagulant, pH and influent turbidity, of the raw water because these types of data were available from the pilot plant for simulation and data was collected by the Tehran water authority. The results indicated that ANNs had more ability in simulating the coagulation and flocculation process and predicting turbidity removal with different experimental data than did the fuzzy regression analysis, and may have the ability to reduce the number of jar tests, which are time-consuming and expensive. The MLP neural network proved to be the best network compared to the RBF neural network and fuzzy regression analysis in this study. The MLP neural network can predict the effluent turbidity of the coagulation and the flocculation process with a coefficient of determination (R 2 ) of 0.96 and root mean square error of 0.0106.

Multiphysics Modeling and Simulations of Mil A46100 Armor-Grade Martensitic Steel Gas Metal Arc Welding Process

DTIC Science & Technology

2013-05-23

simulation of the conventional Gas Metal Arc Welding (GMAW) process, and the application of the developed methods and tools for prediction of the...technology in many industries such as chemical, oil , aerospace, and shipbuilding construction. In fact, within the metal fabrication industry as a...Mechanical Properties of Low Alloy Steel Products. Hardenability Concepts with Applications to Steel, The Metallurgical Society of AIME, Chicago, 1978, p

Experimental and Numerical Simulations of Phase Transformations Occurring During Continuous Annealing of DP Steel Strips

NASA Astrophysics Data System (ADS)

Wrożyna, Andrzej; Pernach, Monika; Kuziak, Roman; Pietrzyk, Maciej

2016-04-01

Due to their exceptional strength properties combined with good workability the Advanced High-Strength Steels (AHSS) are commonly used in automotive industry. Manufacturing of these steels is a complex process which requires precise control of technological parameters during thermo-mechanical treatment. Design of these processes can be significantly improved by the numerical models of phase transformations. Evaluation of predictive capabilities of models, as far as their applicability in simulation of thermal cycles thermal cycles for AHSS is considered, was the objective of the paper. Two models were considered. The former was upgrade of the JMAK equation while the latter was an upgrade of the Leblond model. The models can be applied to any AHSS though the examples quoted in the paper refer to the Dual Phase (DP) steel. Three series of experimental simulations were performed. The first included various thermal cycles going beyond limitations of the continuous annealing lines. The objective was to validate models behavior in more complex cooling conditions. The second set of tests included experimental simulations of the thermal cycle characteristic for the continuous annealing lines. Capability of the models to describe properly phase transformations in this process was evaluated. The third set included data from the industrial continuous annealing line. Validation and verification of models confirmed their good predictive capabilities. Since it does not require application of the additivity rule, the upgrade of the Leblond model was selected as the better one for simulation of industrial processes in AHSS production.

Microstructure based simulations for prediction of flow curves and selection of process parameters for inter-critical annealing in DP steel

NASA Astrophysics Data System (ADS)

Deepu, M. J.; Farivar, H.; Prahl, U.; Phanikumar, G.

2017-04-01

Dual phase steels are versatile advanced high strength steels that are being used for sheet metal applications in automotive industry. It also has the potential for application in bulk components like gear. The inter-critical annealing in dual phase steels is one of the crucial steps that determine the mechanical properties of the material. Selection of the process parameters for inter-critical annealing, in particular, the inter-critical annealing temperature and time is important as it plays a major role in determining the volume fractions of ferrite and martensite, which in turn determines the mechanical properties. Selection of these process parameters to obtain a particular required mechanical property requires large number of experimental trials. Simulation of microstructure evolution and virtual compression/tensile testing can help in reducing the number of such experimental trials. In the present work, phase field modeling implemented in the commercial software Micress® is used to predict the microstructure evolution during inter-critical annealing. Virtual compression tests are performed on the simulated microstructure using finite element method implemented in the commercial software, to obtain the effective flow curve of the macroscopic material. The flow curves obtained by simulation are experimentally validated with physical simulation in Gleeble® and compared with that obtained using linear rule of mixture. The methodology could be used in determining the inter-critical annealing process parameters required for achieving a particular flow curve.

A brief peripheral motion contrast threshold test predicts older drivers' hazardous behaviors in simulated driving.

PubMed

Henderson, Steven; Woods-Fry, Heather; Collin, Charles A; Gagnon, Sylvain; Voloaca, Misha; Grant, John; Rosenthal, Ted; Allen, Wade

2015-05-01

Our research group has previously demonstrated that the peripheral motion contrast threshold (PMCT) test predicts older drivers' self-report accident risk, as well as simulated driving performance. However, the PMCT is too lengthy to be a part of a battery of tests to assess fitness to drive. Therefore, we have developed a new version of this test, which takes under two minutes to administer. We assessed the motion contrast thresholds of 24 younger drivers (19-32) and 25 older drivers (65-83) with both the PMCT-10min and the PMCT-2min test and investigated if thresholds were associated with measures of simulated driving performance. Younger participants had significantly lower motion contrast thresholds than older participants and there were no significant correlations between younger participants' thresholds and any measures of driving performance. The PMCT-10min and the PMCT-2min thresholds of older drivers' predicted simulated crash risk, as well as the minimum distance of approach to all hazards. This suggests that our tests of motion processing can help predict the risk of collision or near collision in older drivers. Thresholds were also correlated with the total lane deviation time, suggesting a deficiency in processing of peripheral flow and delayed detection of adjacent cars. The PMCT-2min is an improved version of a previously validated test, and it has the potential to help assess older drivers' fitness to drive. Copyright © 2015 Elsevier Ltd. All rights reserved.

Measurement with microscopic MRI and simulation of flow in different aneurysm models

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

Edelhoff, Daniel, E-mail: daniel.edelhoff@tu-dortmund.de; Frank, Frauke; Heil, Marvin

2015-10-15

Purpose: The impact and the development of aneurysms depend to a significant degree on the exchange of liquid between the regular vessel and the pathological extension. A better understanding of this process will lead to improved prediction capabilities. The aim of the current study was to investigate fluid-exchange in aneurysm models of different complexities by combining microscopic magnetic resonance measurements with numerical simulations. In order to evaluate the accuracy and applicability of these methods, the fluid-exchange process between the unaltered vessel lumen and the aneurysm phantoms was analyzed quantitatively using high spatial resolution. Methods: Magnetic resonance flow imaging was usedmore » to visualize fluid-exchange in two different models produced with a 3D printer. One model of an aneurysm was based on histological findings. The flow distribution in the different models was measured on a microscopic scale using time of flight magnetic resonance imaging. The whole experiment was simulated using fast graphics processing unit-based numerical simulations. The obtained simulation results were compared qualitatively and quantitatively with the magnetic resonance imaging measurements, taking into account flow and spin–lattice relaxation. Results: The results of both presented methods compared well for the used aneurysm models and the chosen flow distributions. The results from the fluid-exchange analysis showed comparable characteristics concerning measurement and simulation. Similar symmetry behavior was observed. Based on these results, the amount of fluid-exchange was calculated. Depending on the geometry of the models, 7% to 45% of the liquid was exchanged per second. Conclusions: The result of the numerical simulations coincides well with the experimentally determined velocity field. The rate of fluid-exchange between vessel and aneurysm was well-predicted. Hence, the results obtained by simulation could be validated by the experiment. The observed deviations can be caused by the noise in the measurement and by the limited resolution of the simulation. The resulting differences are small enough to allow reliable predictions of the flow distribution in vessels with stents and for pulsed blood flow.« less

Analytical Modeling and Performance Prediction of Remanufactured Gearbox Components

NASA Astrophysics Data System (ADS)

Pulikollu, Raja V.; Bolander, Nathan; Vijayakar, Sandeep; Spies, Matthew D.

Gearbox components operate in extreme environments, often leading to premature removal or overhaul. Though worn or damaged, these components still have the ability to function given the appropriate remanufacturing processes are deployed. Doing so reduces a significant amount of resources (time, materials, energy, manpower) otherwise required to produce a replacement part. Unfortunately, current design and analysis approaches require extensive testing and evaluation to validate the effectiveness and safety of a component that has been used in the field then processed outside of original OEM specification. To test all possible combination of component coupled with various levels of potential damage repaired through various options of processing would be an expensive and time consuming feat, thus prohibiting a broad deployment of remanufacturing processes across industry. However, such evaluation and validation can occur through Integrated Computational Materials Engineering (ICME) modeling and simulation. Sentient developed a microstructure-based component life prediction (CLP) tool to quantify and assist gearbox components remanufacturing process. This was achieved by modeling the design-manufacturing-microstructure-property relationship. The CLP tool assists in remanufacturing of high value, high demand rotorcraft, automotive and wind turbine gears and bearings. This paper summarizes the CLP models development, and validation efforts by comparing the simulation results with rotorcraft spiral bevel gear physical test data. CLP analyzes gear components and systems for safety, longevity, reliability and cost by predicting (1) New gearbox component performance, and optimal time-to-remanufacture (2) Qualification of used gearbox components for remanufacturing process (3) Predicting the remanufactured component performance.

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.

Atomistic simulations of dislocation pileup: Grain boundaries interaction

DOE PAGES

Wang, Jian

2015-05-27

Here, using molecular dynamics (MD) simulations, we studied the dislocation pileup–grain boundary (GB) interactions. Two Σ11 asymmetrical tilt grain boundaries in Al are studied to explore the influence of orientation relationship and interface structure on dislocation activities at grain boundaries. To mimic the reality of a dislocation pileup in a coarse-grained polycrystalline, we optimized the dislocation population in MD simulations and developed a predict-correct method to create a dislocation pileup in MD simulations. MD simulations explored several kinetic processes of dislocations–GB reactions: grain boundary sliding, grain boundary migration, slip transmission, dislocation reflection, reconstruction of grain boundary, and the correlation ofmore » these kinetic processes with the available slip systems across the GB and atomic structures of the GB.« less

Modeling streamflow in a snow-dominated forest watershed using the Water Erosion Prediction Project (WEPP) model

USDA-ARS?s Scientific Manuscript database

The Water Erosion Prediction Project (WEPP) model was originally developed for hillslope and small watershed applications. The model simulates complex interactive processes influencing erosion, such as surface runoff, soil-water changes, vegetation growth and senescence, and snow accumulation and me...

Soliton triads ensemble in frequency conversion: from inverse scattering theory to experimental observation.

PubMed

Baronio, Fabio; Andreana, Marco; Conforti, Matteo; Manili, Gabriele; Couderc, Vincent; De Angelis, Costantino; Barthélémy, Alain

2011-07-04

We consider the spectral theory of three-wave interactions to predict the initiation, formation and dynamics of an ensemble of bright-dark-bright soliton triads in frequency conversion processes. Spatial observation of non-interacting triads ensemble in a KTP crystal confirms theoretical prediction and numerical simulations.

The Comparative Accuracy of Two Hydrologic Models in Simulating Warm-Season Runoff for Two Small, Hillslope Catchments

EPA Science Inventory

Runoff prediction is a cornerstone of water resources planning, and therefore modeling performance is a key issue. This paper investigates the comparative advantages of conceptual versus process- based models in predicting warm season runoff for upland, low-yield micro-catchments...

Effects of soil moisture on the diurnal pattern of pesticide emission: Numerical simulation and sensitivity analysis

USDA-ARS?s Scientific Manuscript database

Accurate prediction of pesticide volatilization is important for the protection of human and environmental health. Due to the complexity of the volatilization process, sophisticated predictive models are needed, especially for dry soil conditions. A mathematical model was developed to allow simulati...

Failure prediction for the optimization of stretch forming aluminium-polymer laminate foils used for pharmaceutical packaging

NASA Astrophysics Data System (ADS)

Müller, Simon; Weygand, Sabine M.

2018-05-01

Axisymmetric stretch forming processes of aluminium-polymer laminate foils (e.g. consisting of PA-Al-PVC layers) are analyzed numerically by finite element modeling of the multi-layer material as well as experimentally in order to identify a suitable damage initiation criterion. A simple ductile fracture criterion is proposed to predict the forming limits. The corresponding material constants are determined from tensile tests and then applied in forming simulations with different punch geometries. A comparison between the simulations and the experimental results shows that the determined failure constants are not applicable. Therefore, one forming experiment was selected and in the corresponding simulation the failure constant was fitted to its measured maximum stretch. With this approach it is possible to predict the forming limit of the laminate foil with satisfying accuracy for different punch geometries.

Quantitative modeling of soil genesis processes

NASA Technical Reports Server (NTRS)

Levine, E. R.; Knox, R. G.; Kerber, A. G.

1992-01-01

For fine spatial scale simulation, a model is being developed to predict changes in properties over short-, meso-, and long-term time scales within horizons of a given soil profile. Processes that control these changes can be grouped into five major process clusters: (1) abiotic chemical reactions; (2) activities of organisms; (3) energy balance and water phase transitions; (4) hydrologic flows; and (5) particle redistribution. Landscape modeling of soil development is possible using digitized soil maps associated with quantitative soil attribute data in a geographic information system (GIS) framework to which simulation models are applied.

Rayleigh instability at small length scales.

PubMed

Gopan, Nandu; Sathian, Sarith P

2014-09-01

The Rayleigh instability (also called the Plateau-Rayleigh instability) of a nanosized liquid propane thread is investigated using molecular dynamics (MD). The validity of classical predictions at small length scales is verified by comparing the temporal evolution of liquid thread simulated by MD against classical predictions. Previous works have shown that thermal fluctuations become dominant at small length scales. The role and influence of the stochastic nature of thermal fluctuations in determining the instability at small length scale is also investigated. Thermal fluctuations are seen to dominate and accelerate the breakup process only during the last stages of breakup. The simulations also reveal that the breakup profile of nanoscale threads undergo modification due to reorganization of molecules by the evaporation-condensation process.

Qualitative simulation for process modeling and control

NASA Technical Reports Server (NTRS)

Dalle Molle, D. T.; Edgar, T. F.

1989-01-01

A qualitative model is developed for a first-order system with a proportional-integral controller without precise knowledge of the process or controller parameters. Simulation of the qualitative model yields all of the solutions to the system equations. In developing the qualitative model, a necessary condition for the occurrence of oscillatory behavior is identified. Initializations that cannot exhibit oscillatory behavior produce a finite set of behaviors. When the phase-space behavior of the oscillatory behavior is properly constrained, these initializations produce an infinite but comprehensible set of asymptotically stable behaviors. While the predictions include all possible behaviors of the real system, a class of spurious behaviors has been identified. When limited numerical information is included in the model, the number of predictions is significantly reduced.

An Integrated Modeling Framework Forecasting Ecosystem Exposure-- A Systems Approach to the Cumulative Impacts of Multiple Stressors

NASA Astrophysics Data System (ADS)

Johnston, J. M.

2013-12-01

Freshwater habitats provide fishable, swimmable and drinkable resources and are a nexus of geophysical and biological processes. These processes in turn influence the persistence and sustainability of populations, communities and ecosystems. Climate change and landuse change encompass numerous stressors of potential exposure, including the introduction of toxic contaminants, invasive species, and disease in addition to physical drivers such as temperature and hydrologic regime. A systems approach that includes the scientific and technologic basis of assessing the health of ecosystems is needed to effectively protect human health and the environment. The Integrated Environmental Modeling Framework 'iemWatersheds' has been developed as a consistent and coherent means of forecasting the cumulative impact of co-occurring stressors. The Framework consists of three facilitating technologies: Data for Environmental Modeling (D4EM) that automates the collection and standardization of input data; the Framework for Risk Assessment of Multimedia Environmental Systems (FRAMES) that manages the flow of information between linked models; and the Supercomputer for Model Uncertainty and Sensitivity Evaluation (SuperMUSE) that provides post-processing and analysis of model outputs, including uncertainty and sensitivity analysis. Five models are linked within the Framework to provide multimedia simulation capabilities for hydrology and water quality processes: the Soil Water Assessment Tool (SWAT) predicts surface water and sediment runoff and associated contaminants; the Watershed Mercury Model (WMM) predicts mercury runoff and loading to streams; the Water quality Analysis and Simulation Program (WASP) predicts water quality within the stream channel; the Habitat Suitability Index (HSI) model scores physicochemical habitat quality for individual fish species; and the Bioaccumulation and Aquatic System Simulator (BASS) predicts fish growth, population dynamics and bioaccumulation of toxic substances. The capability of the Framework to address cumulative impacts will be demonstrated for freshwater ecosystem services and mountaintop mining.

Improving Permafrost Hydrology Prediction Through Data-Model Integration

NASA Astrophysics Data System (ADS)

Wilson, C. J.; Andresen, C. G.; Atchley, A. L.; Bolton, W. R.; Busey, R.; Coon, E.; Charsley-Groffman, L.

2017-12-01

The CMIP5 Earth System Models were unable to adequately predict the fate of the 16GT of permafrost carbon in a warming climate due to poor representation of Arctic ecosystem processes. The DOE Office of Science Next Generation Ecosystem Experiment, NGEE-Arctic project aims to reduce uncertainty in the Arctic carbon cycle and its impact on the Earth's climate system by improved representation of the coupled physical, chemical and biological processes that drive how much buried carbon will be converted to CO2 and CH4, how fast this will happen, which form will dominate, and the degree to which increased plant productivity will offset increased soil carbon emissions. These processes fundamentally depend on permafrost thaw rate and its influence on surface and subsurface hydrology through thermal erosion, land subsidence and changes to groundwater flow pathways as soil, bedrock and alluvial pore ice and massive ground ice melts. LANL and its NGEE colleagues are co-developing data and models to better understand controls on permafrost degradation and improve prediction of the evolution of permafrost and its impact on Arctic hydrology. The LANL Advanced Terrestrial Simulator was built using a state of the art HPC software framework to enable the first fully coupled 3-dimensional surface-subsurface thermal-hydrology and land surface deformation simulations to simulate the evolution of the physical Arctic environment. Here we show how field data including hydrology, snow, vegetation, geochemistry and soil properties, are informing the development and application of the ATS to improve understanding of controls on permafrost stability and permafrost hydrology. The ATS is being used to inform parameterizations of complex coupled physical, ecological and biogeochemical processes for implementation in the DOE ACME land model, to better predict the role of changing Arctic hydrology on the global climate system. LA-UR-17-26566.

Analysis of the lightning-attractive radius for wind turbines considering the developing process of positive attachment leader

NASA Astrophysics Data System (ADS)

Yang, Ning; Zhang, Qilin; Hou, Wenhao; Wen, Ying

2017-03-01

In this paper, we have presented the upward leader propagation model, considering the transition of stream leader process by the finite element method and analyzing the inception and subsequent physical processes of upward leader and the attractive radius for large wind turbines. For validating our model, the comparison of simulated results with the optically high-speed video observation shows that the model can predict an accepted result of upward leader from a 163 m tall tower, the simulated upward leader velocity and length before final jump are 2.3 × 105 m/s and 187.67 m presented by Warner (2010), which are very similar to the observed results of 2.8 × 105 m/s and 184 m, respectively. At the same time, we find that the assumed constant speed ratio of downward/upward leader is improper and cannot accurately predict the attractive radius by lightning strike. Also, the simulated results are compared with the widely used EGM (electro geometric model), and it is found that the EGM has an obvious underestimation of attractive radius more than 50%.

Improved failure prediction in forming simulations through pre-strain mapping

NASA Astrophysics Data System (ADS)

Upadhya, Siddharth; Staupendahl, Daniel; Heuse, Martin; Tekkaya, A. Erman

2018-05-01

The sensitivity of sheared edges of advanced high strength steel (AHSS) sheets to cracking during subsequent forming operations and the difficulty to predict this failure with any degree of accuracy using conventionally used FLC based failure criteria is a major problem plaguing the manufacturing industry. A possible method that allows for an accurate prediction of edge cracks is the simulation of the shearing operation and carryover of this model into a subsequent forming simulation. But even with an efficient combination of a solid element shearing operation and a shell element forming simulation, the need for a fine mesh, and the resulting high computation time makes this approach not viable from an industry point of view. The crack sensitivity of sheared edges is due to work hardening in the shear-affected zone (SAZ). A method to predict plastic strains induced by the shearing process is to measure the hardness after shearing and calculate the ultimate tensile strength as well as the flow stress. In combination with the flow curve, the relevant strain data can be obtained. To eliminate the time-intensive shearing simulation necessary to obtain the strain data in the SAZ, a new pre-strain mapping approach is proposed. The pre-strains to be mapped are, hereby, determined from hardness values obtained in the proximity of the sheared edge. To investigate the performance of this approach the ISO/TS 16630 hole expansion test was simulated with shell elements for different materials, whereby the pre-strains were mapped onto the edge of the hole. The hole expansion ratios obtained from such pre-strain mapped simulations are in close agreement with the experimental results. Furthermore, the simulations can be carried out with no increase in computation time, making this an interesting and viable solution for predicting edge failure due to shearing.

Novel patch modelling method for efficient simulation and prediction uncertainty analysis of multi-scale groundwater flow and transport processes

NASA Astrophysics Data System (ADS)

Sreekanth, J.; Moore, Catherine

2018-04-01

The application of global sensitivity and uncertainty analysis techniques to groundwater models of deep sedimentary basins are typically challenged by large computational burdens combined with associated numerical stability issues. The highly parameterized approaches required for exploring the predictive uncertainty associated with the heterogeneous hydraulic characteristics of multiple aquifers and aquitards in these sedimentary basins exacerbate these issues. A novel Patch Modelling Methodology is proposed for improving the computational feasibility of stochastic modelling analysis of large-scale and complex groundwater models. The method incorporates a nested groundwater modelling framework that enables efficient simulation of groundwater flow and transport across multiple spatial and temporal scales. The method also allows different processes to be simulated within different model scales. Existing nested model methodologies are extended by employing 'joining predictions' for extrapolating prediction-salient information from one model scale to the next. This establishes a feedback mechanism supporting the transfer of information from child models to parent models as well as parent models to child models in a computationally efficient manner. This feedback mechanism is simple and flexible and ensures that while the salient small scale features influencing larger scale prediction are transferred back to the larger scale, this does not require the live coupling of models. This method allows the modelling of multiple groundwater flow and transport processes using separate groundwater models that are built for the appropriate spatial and temporal scales, within a stochastic framework, while also removing the computational burden associated with live model coupling. The utility of the method is demonstrated by application to an actual large scale aquifer injection scheme in Australia.

Typical action perception and interpretation without motor simulation.

PubMed

Vannuscorps, Gilles; Caramazza, Alfonso

2016-01-05

Every day, we interact with people synchronously, immediately understand what they are doing, and easily infer their mental state and the likely outcome of their actions from their kinematics. According to various motor simulation theories of perception, such efficient perceptual processing of others' actions cannot be achieved by visual analysis of the movements alone but requires a process of motor simulation--an unconscious, covert imitation of the observed movements. According to this hypothesis, individuals incapable of simulating observed movements in their motor system should have difficulty perceiving and interpreting observed actions. Contrary to this prediction, we found across eight sensitive experiments that individuals born with absent or severely shortened upper limbs (upper limb dysplasia), despite some variability, could perceive, anticipate, predict, comprehend, and memorize upper limb actions, which they cannot simulate, as efficiently as typically developed participants. We also found that, like the typically developed participants, the dysplasic participants systematically perceived the position of moving upper limbs slightly ahead of their real position but only when the anticipated position was not biomechanically awkward. Such anticipatory bias and its modulation by implicit knowledge of the body biomechanical constraints were previously considered as indexes of the crucial role of motor simulation in action perception. Our findings undermine this assumption and the theories that place the locus of action perception and comprehension in the motor system and invite a shift in the focus of future research to the question of how the visuo-perceptual system represents and processes observed body movements and actions.

Development of a CSP plant energy yield calculation tool applying predictive models to analyze plant performance sensitivities

NASA Astrophysics Data System (ADS)

Haack, Lukas; Peniche, Ricardo; Sommer, Lutz; Kather, Alfons

2017-06-01

At early project stages, the main CSP plant design parameters such as turbine capacity, solar field size, and thermal storage capacity are varied during the techno-economic optimization to determine most suitable plant configurations. In general, a typical meteorological year with at least hourly time resolution is used to analyze each plant configuration. Different software tools are available to simulate the annual energy yield. Software tools offering a thermodynamic modeling approach of the power block and the CSP thermal cycle, such as EBSILONProfessional®, allow a flexible definition of plant topologies. In EBSILON, the thermodynamic equilibrium for each time step is calculated iteratively (quasi steady state), which requires approximately 45 minutes to process one year with hourly time resolution. For better presentation of gradients, 10 min time resolution is recommended, which increases processing time by a factor of 5. Therefore, analyzing a large number of plant sensitivities, as required during the techno-economic optimization procedure, the detailed thermodynamic simulation approach becomes impracticable. Suntrace has developed an in-house CSP-Simulation tool (CSPsim), based on EBSILON and applying predictive models, to approximate the CSP plant performance for central receiver and parabolic trough technology. CSPsim significantly increases the speed of energy yield calculations by factor ≥ 35 and has automated the simulation run of all predefined design configurations in sequential order during the optimization procedure. To develop the predictive models, multiple linear regression techniques and Design of Experiment methods are applied. The annual energy yield and derived LCOE calculated by the predictive model deviates less than ±1.5 % from the thermodynamic simulation in EBSILON and effectively identifies the optimal range of main design parameters for further, more specific analysis.

Predicting the Macroscopic Fracture Energy of Epoxy Resins from Atomistic Molecular Simulations

DOE PAGES

Meng, Zhaoxu; Bessa, Miguel A.; Xia, Wenjie; ...

2016-12-06

Predicting the macroscopic fracture energy of highly crosslinked glassy polymers from atomistic simulations is challenging due to the size of the process zone being large in these systems. Here, we present a scale-bridging approach that links atomistic molecular dynamics simulations to macroscopic fracture properties on the basis of a continuum fracture mechanics model for two different epoxy materials. Our approach reveals that the fracture energy of epoxy resins strongly depends on the functionality of epoxy resin and the component ratio between the curing agent (amine) and epoxide. The most intriguing part of our study is that we demonstrate that themore » fracture energy exhibits a maximum value within the range of conversion degrees considered (from 65% to 95%), which can be attributed to the combined effects of structural rigidity and post-yield deformability. Our study provides physical insight into the molecular mechanisms that govern the fracture characteristics of epoxy resins and demonstrates the success of utilizing atomistic molecular simulations towards predicting macroscopic material properties.« less

Predicting the Macroscopic Fracture Energy of Epoxy Resins from Atomistic Molecular Simulations

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

Meng, Zhaoxu; Bessa, Miguel A.; Xia, Wenjie

Predicting the macroscopic fracture energy of highly crosslinked glassy polymers from atomistic simulations is challenging due to the size of the process zone being large in these systems. Here, we present a scale-bridging approach that links atomistic molecular dynamics simulations to macroscopic fracture properties on the basis of a continuum fracture mechanics model for two different epoxy materials. Our approach reveals that the fracture energy of epoxy resins strongly depends on the functionality of epoxy resin and the component ratio between the curing agent (amine) and epoxide. The most intriguing part of our study is that we demonstrate that themore » fracture energy exhibits a maximum value within the range of conversion degrees considered (from 65% to 95%), which can be attributed to the combined effects of structural rigidity and post-yield deformability. Our study provides physical insight into the molecular mechanisms that govern the fracture characteristics of epoxy resins and demonstrates the success of utilizing atomistic molecular simulations towards predicting macroscopic material properties.« less

Temperature distribution analysis of tissue water vaporization during microwave ablation: experiments and simulations.

PubMed

Ai, Haiming; Wu, Shuicai; Gao, Hongjian; Zhao, Lei; Yang, Chunlan; Zeng, Yi

2012-01-01

The temperature distribution in the region near a microwave antenna is a critical factor that affects the entire temperature field during microwave ablation of tissue. It is challenging to predict this distribution precisely, because the temperature in the near-antenna region varies greatly. The effects of water vaporisation and subsequent tissue carbonisation in an ex vivo porcine liver were therefore studied experimentally and in simulations. The enthalpy and high-temperature specific absorption rate (SAR) of liver tissues were calculated and incorporated into the simulation process. The accuracy of predictions for near-field temperatures in our simulations has reached the level where the average maximum error is less than 5°C. In addition, a modified thermal model that accounts for water vaporisation and the change in the SAR distribution pattern is proposed and validated with experiment. The results from this study may be useful in the clinical practice of microwave ablation and can be applied to predict the temperature field in surgical planning.

DXRaySMCS: a user-friendly interface developed for prediction of diagnostic radiology X-ray spectra produced by Monte Carlo (MCNP-4C) simulation.

PubMed

Bahreyni Toossi, M T; Moradi, H; Zare, H

2008-01-01

In this work, the general purpose Monte Carlo N-particle radiation transport computer code (MCNP-4C) was used for the simulation of X-ray spectra in diagnostic radiology. The electron's path in the target was followed until its energy was reduced to 10 keV. A user-friendly interface named 'diagnostic X-ray spectra by Monte Carlo simulation (DXRaySMCS)' was developed to facilitate the application of MCNP-4C code for diagnostic radiology spectrum prediction. The program provides a user-friendly interface for: (i) modifying the MCNP input file, (ii) launching the MCNP program to simulate electron and photon transport and (iii) processing the MCNP output file to yield a summary of the results (relative photon number per energy bin). In this article, the development and characteristics of DXRaySMCS are outlined. As part of the validation process, output spectra for 46 diagnostic radiology system settings produced by DXRaySMCS were compared with the corresponding IPEM78. Generally, there is a good agreement between the two sets of spectra. No statistically significant differences have been observed between IPEM78 reported spectra and the simulated spectra generated in this study.

Cutting process simulation of flat drill

NASA Astrophysics Data System (ADS)

Tamura, Shoichi; Matsumura, Takashi

2018-05-01

Flat drills at a point angle of 180 deg. have recently been developed for drilling of automobile parts with the inclination of the workpiece surfaces. The paper studies the cutting processes of the flat drills in the analytical simulation. A predictive force model is applied to simulation of the cutting force with the chip flow direction. The chip flow model is piled up with orthogonal cuttings in the plane containing the cutting velocities and the chip flow velocities, in which the chip flow direction is determined to minimize the cutting energy. Then, the cutting force is predicted in the determined in the chip flow model. The typical cutting force of the flat drill is discussed with comparing to that of the standard drill. The typical differences are confirmed in the cutting force change during the tool engagement and disengagement. The cutting force, then, is simulated in drilling for an inclined workpiece with a flat drill. The horizontal components in the cutting forces are simulated with changing the inclination angle of the plate. The horizontal force component in the flat drilling is stable to be controlled in terms of the machining accuracy and the tool breakage.

Trialability, observability and risk reduction accelerating individual innovation adoption decisions.

PubMed

Hayes, Kathryn J; Eljiz, Kathy; Dadich, Ann; Fitzgerald, Janna-Anneke; Sloan, Terry

2015-01-01

The purpose of this paper is to provide a retrospective analysis of computer simulation's role in accelerating individual innovation adoption decisions. The process innovation examined is Lean Systems Thinking, and the organizational context is the imaging department of an Australian public hospital. Intrinsic case study methods including observation, interviews with radiology and emergency personnel about scheduling procedures, mapping patient appointment processes and document analysis were used over three years and then complemented with retrospective interviews with key hospital staff. The multiple data sources and methods were combined in a pragmatic and reflexive manner to explore an extreme case that provides potential to act as an instructive template for effective change. Computer simulation of process change ideas offered by staff to improve patient-flow accelerated the adoption of the process changes, largely because animated computer simulation permitted experimentation (trialability), provided observable predictions of change results (observability) and minimized perceived risk. The difficulty of making accurate comparisons between time periods in a health care setting is acknowledged. This work has implications for policy, practice and theory, particularly for inducing the rapid diffusion of process innovations to address challenges facing health service organizations and national health systems. Originality/value - The research demonstrates the value of animated computer simulation in presenting the need for change, identifying options, and predicting change outcomes and is the first work to indicate the importance of trialability, observability and risk reduction in individual adoption decisions in health services.

Change in the color of heat-treated, vacuum-packed broccoli stems and florets during storage: effects of process conditions and modeling by an artificial neural network.

PubMed

Pero, Milad; Askari, Gholamreza; Skåra, Torstein; Skipnes, Dagbjørn; Kiani, Hossein

2018-02-08

Vacuum-packed broccoli stems and florets were subjected to heat treatment (60-99 °C) for various time intervals. The activity of peroxidase was measured after processing. Thermally processed samples were then stored at 4 °C for 35 days, and the color of the samples was measured every 7 days. Effects of parameters (heating temperature and duration, storage time) on the color of broccoli were modeled and simulated by an artificial neural network (ANN). Simulations confirmed that stems were predicted to be more prone to changes than florets. More color loss was observed with longer processing or storage combinations. The simulations also confirmed that higher temperatures during heat processing could retard color changes during storage. For stems treated at 80 °C for short durations, color loss was more predominant than both 65 and 99 °C, probably due to the incomplete inactivation of enzymes besides more tissue damage, with increased enzyme access to the substrate. The greenness of both stems and florets during storage can be better preserved at higher temperatures (99 °C) and short times. The simulation results revealed that the ANN method could be used as an effective tool for predicting and analyzing the color values of heat-treated broccoli. © 2018 Society of Chemical Industry. © 2018 Society of Chemical Industry.

Computer-aided analysis and design of the shape rolling process for producing turbine engine airfoils

NASA Technical Reports Server (NTRS)

Lahoti, G. D.; Akgerman, N.; Altan, T.

1978-01-01

Mild steel (AISI 1018) was selected as model cold-rolling material and Ti-6Al-4V and INCONEL 718 were selected as typical hot-rolling and cold-rolling alloys, respectively. The flow stress and workability of these alloys were characterized and friction factor at the roll/workpiece interface was determined at their respective working conditions by conducting ring tests. Computer-aided mathematical models for predicting metal flow and stresses, and for simulating the shape-rolling process were developed. These models utilize the upper-bound and the slab methods of analysis, and are capable of predicting the lateral spread, roll-separating force, roll torque and local stresses, strains and strain rates. This computer-aided design (CAD) system is also capable of simulating the actual rolling process and thereby designing roll-pass schedule in rolling of an airfoil or similar shape. The predictions from the CAD system were verified with respect to cold rolling of mild steel plates. The system is being applied to cold and hot isothermal rolling of an airfoil shape, and will be verified with respect to laboratory experiments under controlled conditions.

A numerical simulation of the NFAC (National Full-scale Aerodynamics Complex) open-return wind tunnel inlet flow

NASA Technical Reports Server (NTRS)

Kaul, U. K.; Ross, J. C.; Jacocks, J. L.

1985-01-01

The flow into an open return wind tunnel inlet was simulated using Euler equations. An explicit predictor-corrector method was employed to solve the system. The calculation is time-accurate and was performed to achieve a steady-state solution. The predictions are in reasonable agreement with the experimental data. Wall pressures are accurately predicted except in a region of recirculating flow. Flow-field surveys agree qualitatively with laser velocimeter measurements. The method can be used in the design process for open return wind tunnels.

? filtering for stochastic systems driven by Poisson processes

NASA Astrophysics Data System (ADS)

Song, Bo; Wu, Zheng-Guang; Park, Ju H.; Shi, Guodong; Zhang, Ya

2015-01-01

This paper investigates the ? filtering problem for stochastic systems driven by Poisson processes. By utilising the martingale theory such as the predictable projection operator and the dual predictable projection operator, this paper transforms the expectation of stochastic integral with respect to the Poisson process into the expectation of Lebesgue integral. Then, based on this, this paper designs an ? filter such that the filtering error system is mean-square asymptotically stable and satisfies a prescribed ? performance level. Finally, a simulation example is given to illustrate the effectiveness of the proposed filtering scheme.

Applying a multi-replication framework to support dynamic situation assessment and predictive capabilities

NASA Astrophysics Data System (ADS)

Lammers, Craig; McGraw, Robert M.; Steinman, Jeffrey S.

2005-05-01

Technological advances and emerging threats reduce the time between target detection and action to an order of a few minutes. To effectively assist with the decision-making process, C4I decision support tools must quickly and dynamically predict and assess alternative Courses Of Action (COAs) to assist Commanders in anticipating potential outcomes. These capabilities can be provided through the faster-than-real-time predictive simulation of plans that are continuously re-calibrating with the real-time picture. This capability allows decision-makers to assess the effects of re-tasking opportunities, providing the decision-maker with tremendous freedom to make time-critical, mid-course decisions. This paper presents an overview and demonstrates the use of a software infrastructure that supports DSAP capabilities. These DSAP capabilities are demonstrated through the use of a Multi-Replication Framework that supports (1) predictivie simulations using JSAF (Joint Semi-Automated Forces); (2) real-time simulation, also using JSAF, as a state estimation mechanism; and, (3) real-time C4I data updates through TBMCS (Theater Battle Management Core Systems). This infrastructure allows multiple replications of a simulation to be executed simultaneously over a grid faster-than-real-time, calibrated with live data feeds. A cost evaluator mechanism analyzes potential outcomes and prunes simulations that diverge from the real-time picture. In particular, this paper primarily serves to walk a user through the process for using the Multi-Replication Framework providing an enhanced decision aid.

Simulation and Prediction of Warm Season Drought in North America

NASA Technical Reports Server (NTRS)

Wang, Hailan; Chang, Yehui; Schubert, Siegfried D.; Koster, Randal D.

2018-01-01

This presentation presents our recent work on model simulation and prediction of warm season drought in North America. The emphasis will be on the contribution from the leading modes of subseasonal atmospheric circulation variability, which are often present in the form of stationary Rossby waves. Here we take advantage of the results from observations, reanalyses, and simulations and reforecasts performed using the NASA Goddard Earth Observing System (GEOS-5) atmospheric and coupled General Circulation Model (GCM). Our results show that stationary Rossby waves play a key role in Northern Hemisphere (NH) atmospheric circulation and surface meteorology variability on subseasonal timescales. In particular, such waves have been crucial to the development of recent short-term warm season heat waves and droughts over North America (e.g. the 1988, 1998, and 2012 summer droughts) and northern Eurasia (e.g., the 2003 summer heat wave over Europe and the 2010 summer drought and heat wave over Russia). Through an investigation of the physical processes by which these waves lead to the development of warm season drought in North America, it is further found that these waves can serve as a potential source of drought predictability. In order to properly represent their effect and exploit this source of predictability, a model needs to correctly simulate the Northern Hemisphere (NH) mean jet streams and be able to predict the sources of these waves. Given the NASA GEOS-5 AGCM deficiency in simulating the NH jet streams and tropical convection during boreal summer, an approach has been developed to artificially remove much of model mean biases, which leads to considerable improvement in model simulation and prediction of stationary Rossby waves and drought development in North America. Our study points to the need to identify key model biases that limit model simulation and prediction of regional climate extremes, and diagnose the origin of these biases so as to inform modeling group for model improvement.

Process-based modeling of temperature and water profiles in the seedling recruitment zone: Part II. Seedling emergence timing

USDA-ARS?s Scientific Manuscript database

Predictions of seedling emergence timing for spring wheat are facilitated by process-based modeling of the microsite environment in the shallow seedling recruitment zone. Hourly temperature and water profiles within the recruitment zone for 60 days after planting were simulated from the process-base...

A REVIEW AND COMPARISON OF MODELS FOR PREDICTING DYNAMIC CHEMICAL BIOCONCENTRATION IN FISH

EPA Science Inventory

Over the past 20 years, a variety of models have been developed to simulate the bioconcentration of hydrophobic organic chemicals by fish. These models differ not only in the processes they address but also in the way a given process is described. Processes described by these m...

Computer-aided analysis and design of the shape rolling process for producing turbine engine airfoils

NASA Technical Reports Server (NTRS)

Lahoti, G. D.; Akgerman, N.; Altan, T.

1978-01-01

Mild steel (AISI 1018) was selected as model cold rolling material and Ti-6A1-4V and Inconel 718 were selected as typical hot rolling and cold rolling alloys, respectively. The flow stress and workability of these alloys were characterized and friction factor at the roll/workpiece interface was determined at their respective working conditions by conducting ring tests. Computer-aided mathematical models for predicting metal flow and stresses, and for simulating the shape rolling process were developed. These models utilized the upper bound and the slab methods of analysis, and were capable of predicting the lateral spread, roll separating force, roll torque, and local stresses, strains and strain rates. This computer-aided design system was also capable of simulating the actual rolling process, and thereby designing the roll pass schedule in rolling of an airfoil or a similar shape.

Seasonal-to-Interannual Precipitation Variability and Predictability in a Coupled Land-Atmosphere System

NASA Technical Reports Server (NTRS)

Koster, Randal D.; Suarez, M. J.; Heiser, M.

1998-01-01

In an earlier GCM study, we showed that interactive land surface processes generally contribute more to continental precipitation variance than do variable sea surface temperatures (SSTs). A new study extends this result through an analysis of 16-member ensembles of multi-decade GCM simulations. We can now show that in many regions, although land processes determine the amplitude of the interannual precipitation anomalies, variable SSTs nevertheless control their timing. The GCM data can be processed into indices that describe geographical variations in (1) the potential for seasonal-to-interannual prediction, and (2) the extent to which the predictability relies on the proper representation of land-atmosphere feedback.

Climate Modeling and Causal Identification for Sea Ice Predictability

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

Hunke, Elizabeth Clare; Urrego Blanco, Jorge Rolando; Urban, Nathan Mark

This project aims to better understand causes of ongoing changes in the Arctic climate system, particularly as decreasing sea ice trends have been observed in recent decades and are expected to continue in the future. As part of the Sea Ice Prediction Network, a multi-agency effort to improve sea ice prediction products on seasonal-to-interannual time scales, our team is studying sensitivity of sea ice to a collection of physical process and feedback mechanism in the coupled climate system. During 2017 we completed a set of climate model simulations using the fully coupled ACME-HiLAT model. The simulations consisted of experiments inmore » which cloud, sea ice, and air-ocean turbulent exchange parameters previously identified as important for driving output uncertainty in climate models were perturbed to account for parameter uncertainty in simulated climate variables. We conducted a sensitivity study to these parameters, which built upon a previous study we made for standalone simulations (Urrego-Blanco et al., 2016, 2017). Using the results from the ensemble of coupled simulations, we are examining robust relationships between climate variables that emerge across the experiments. We are also using causal discovery techniques to identify interaction pathways among climate variables which can help identify physical mechanisms and provide guidance in predictability studies. This work further builds on and leverages the large ensemble of standalone sea ice simulations produced in our previous w14_seaice project.« less

Using a Gaussian Process Emulator for Data-driven Surrogate Modelling of a Complex Urban Drainage Simulator

NASA Astrophysics Data System (ADS)

Bellos, V.; Mahmoodian, M.; Leopold, U.; Torres-Matallana, J. A.; Schutz, G.; Clemens, F.

2017-12-01

Surrogate models help to decrease the run-time of computationally expensive, detailed models. Recent studies show that Gaussian Process Emulators (GPE) are promising techniques in the field of urban drainage modelling. However, this study focusses on developing a GPE-based surrogate model for later application in Real Time Control (RTC) using input and output time series of a complex simulator. The case study is an urban drainage catchment in Luxembourg. A detailed simulator, implemented in InfoWorks ICM, is used to generate 120 input-output ensembles, from which, 100 are used for training the emulator and 20 for validation of the results. An ensemble of historical rainfall events with 2 hours duration and 10 minutes time steps are considered as the input data. Two example outputs, are selected as wastewater volume and total COD concentration in a storage tank in the network. The results of the emulator are tested with unseen random rainfall events from the ensemble dataset. The emulator is approximately 1000 times faster than the original simulator for this small case study. Whereas the overall patterns of the simulator are matched by the emulator, in some cases the emulator deviates from the simulator. To quantify the accuracy of the emulator in comparison with the original simulator, Nash-Sutcliffe efficiency (NSE) between the emulator and simulator is calculated for unseen rainfall scenarios. The range of NSE for the case of tank volume is from 0.88 to 0.99 with a mean value of 0.95, whereas for COD is from 0.71 to 0.99 with a mean value of 0.92. The emulator is able to predict the tank volume with higher accuracy as the relationship between rainfall intensity and tank volume is linear. For COD, which has a non-linear behaviour, the predictions are less accurate and more uncertain, in particular when rainfall intensity increases. This predictions were improved by including a larger amount of training data for the higher rainfall intensities. It was observed that, the accuracy of the emulator predictions depends on the ensemble training dataset design and the amount of data fed. Finally, more investigation is required to test the possibility of applying this type of fast emulators for model-based RTC applications in which limited number of inputs and outputs are considered in a short prediction horizon.

Design and testing of a process-based groundwater vulnerability assessment (P-GWAVA) system for predicting concentrations of agrichemicals in groundwater across the United States

USGS Publications Warehouse

Barbash, Jack E; Voss, Frank D.

2016-03-29

Efforts to assess the likelihood of groundwater contamination from surface-derived compounds have spanned more than three decades. Relatively few of these assessments, however, have involved the use of process-based simulations of contaminant transport and fate in the subsurface, or compared the predictions from such models with measured data—especially over regional to national scales. To address this need, a process-based groundwater vulnerability assessment (P-GWAVA) system was constructed to use transport-and-fate simulations to predict the concentration of any surface-derived compound at a specified depth in the vadose zone anywhere in the conterminous United States. The system was then used to simulate the concentrations of selected agrichemicals in the vadose zone beneath agricultural areas in multiple locations across the conterminous United States. The simulated concentrations were compared with measured concentrations of the compounds detected in shallow groundwater (that is, groundwater drawn from within a depth of 6.3 ± 0.5 meters [mean ± 95 percent confidence interval] below the water table) in more than 1,400 locations across the United States. The results from these comparisons were used to select the simulation approaches that led to the closest agreement between the simulated and the measured concentrations.The P-GWAVA system uses computer simulations that account for a broader range of the hydrologic, physical, biological and chemical phenomena known to control the transport and fate of solutes in the subsurface than has been accounted for by any other vulnerability assessment over regional to national scales. Such phenomena include preferential transport and the influences of temperature, soil properties, and depth on the partitioning, transport, and transformation of pesticides in the subsurface. Published methods and detailed soil property data are used to estimate a wide range of model input parameters for each site, including surface albedo, surface crust permeability, soil water content, Brooks-Corey parameters, saturated hydraulic conductivity, macroporosity and sizes of microbial populations, as well as solute partition coefficients, reaction rates, and meso-micropore diffusion rates. To ensure geographic consistency among the predictions, the only site-specific input data that are used are those that are available for all of the 48 conterminous states.

Prediction of laser cutting heat affected zone by extreme learning machine

NASA Astrophysics Data System (ADS)

Anicic, Obrad; Jović, Srđan; Skrijelj, Hivzo; Nedić, Bogdan

2017-01-01

Heat affected zone (HAZ) of the laser cutting process may be developed based on combination of different factors. In this investigation the HAZ forecasting, based on the different laser cutting parameters, was analyzed. The main goal was to predict the HAZ according to three inputs. The purpose of this research was to develop and apply the Extreme Learning Machine (ELM) to predict the HAZ. The ELM results were compared with genetic programming (GP) and artificial neural network (ANN). The reliability of the computational models were accessed based on simulation results and by using several statistical indicators. Based upon simulation results, it was demonstrated that ELM can be utilized effectively in applications of HAZ forecasting.

A discrete event simulation tool to support and predict hospital and clinic staffing.

PubMed

DeRienzo, Christopher M; Shaw, Ryan J; Meanor, Phillip; Lada, Emily; Ferranti, Jeffrey; Tanaka, David

2017-06-01

We demonstrate how to develop a simulation tool to help healthcare managers and administrators predict and plan for staffing needs in a hospital neonatal intensive care unit using administrative data. We developed a discrete event simulation model of nursing staff needed in a neonatal intensive care unit and then validated the model against historical data. The process flow was translated into a discrete event simulation model. Results demonstrated that the model can be used to give a respectable estimate of annual admissions, transfers, and deaths based upon two different staffing levels. The discrete event simulation tool model can provide healthcare managers and administrators with (1) a valid method of modeling patient mix, patient acuity, staffing needs, and costs in the present state and (2) a forecast of how changes in a unit's staffing, referral patterns, or patient mix would affect a unit in a future state.

Software reliability report

NASA Technical Reports Server (NTRS)

Wilson, Larry

1991-01-01

There are many software reliability models which try to predict future performance of software based on data generated by the debugging process. Unfortunately, the models appear to be unable to account for the random nature of the data. If the same code is debugged multiple times and one of the models is used to make predictions, intolerable variance is observed in the resulting reliability predictions. It is believed that data replication can remove this variance in lab type situations and that it is less than scientific to talk about validating a software reliability model without considering replication. It is also believed that data replication may prove to be cost effective in the real world, thus the research centered on verification of the need for replication and on methodologies for generating replicated data in a cost effective manner. The context of the debugging graph was pursued by simulation and experimentation. Simulation was done for the Basic model and the Log-Poisson model. Reasonable values of the parameters were assigned and used to generate simulated data which is then processed by the models in order to determine limitations on their accuracy. These experiments exploit the existing software and program specimens which are in AIR-LAB to measure the performance of reliability models.

A framework for modeling scenario-based barrier island storm impacts

USGS Publications Warehouse

Mickey, Rangley; Long, Joseph W.; Dalyander, P. Soupy; Plant, Nathaniel G.; Thompson, David M.

2018-01-01

Methods for investigating the vulnerability of existing or proposed coastal features to storm impacts often rely on simplified parametric models or one-dimensional process-based modeling studies that focus on changes to a profile across a dune or barrier island. These simple studies tend to neglect the impacts to curvilinear or alongshore varying island planforms, influence of non-uniform nearshore hydrodynamics and sediment transport, irregular morphology of the offshore bathymetry, and impacts from low magnitude wave events (e.g. cold fronts). Presented here is a framework for simulating regionally specific, low and high magnitude scenario-based storm impacts to assess the alongshore variable vulnerabilities of a coastal feature. Storm scenarios based on historic hydrodynamic conditions were derived and simulated using the process-based morphologic evolution model XBeach. Model results show that the scenarios predicted similar patterns of erosion and overwash when compared to observed qualitative morphologic changes from recent storm events that were not included in the dataset used to build the scenarios. The framework model simulations were capable of predicting specific areas of vulnerability in the existing feature and the results illustrate how this storm vulnerability simulation framework could be used as a tool to help inform the decision-making process for scientists, engineers, and stakeholders involved in coastal zone management or restoration projects.

On modelling the interaction between two rotating bodies with statistically distributed features: an application to dressing of grinding wheels

NASA Astrophysics Data System (ADS)

Spampinato, A.; Axinte, D. A.

2017-12-01

The mechanisms of interaction between bodies with statistically arranged features present characteristics common to different abrasive processes, such as dressing of abrasive tools. In contrast with the current empirical approach used to estimate the results of operations based on attritive interactions, the method we present in this paper allows us to predict the output forces and the topography of a simulated grinding wheel for a set of specific operational parameters (speed ratio and radial feed-rate), providing a thorough understanding of the complex mechanisms regulating these processes. In modelling the dressing mechanisms, the abrasive characteristics of both bodies (grain size, geometry, inter-space and protrusion) are first simulated; thus, their interaction is simulated in terms of grain collisions. Exploiting a specifically designed contact/impact evaluation algorithm, the model simulates the collisional effects of the dresser abrasives on the grinding wheel topography (grain fracture/break-out). The method has been tested for the case of a diamond rotary dresser, predicting output forces within less than 10% error and obtaining experimentally validated grinding wheel topographies. The study provides a fundamental understanding of the dressing operation, enabling the improvement of its performance in an industrial scenario, while being of general interest in modelling collision-based processes involving statistically distributed elements.

Using APEX to Model Anticipated Human Error: Analysis of a GPS Navigational Aid

NASA Technical Reports Server (NTRS)

VanSelst, Mark; Freed, Michael; Shefto, Michael (Technical Monitor)

1997-01-01

The interface development process can be dramatically improved by predicting design facilitated human error at an early stage in the design process. The approach we advocate is to SIMULATE the behavior of a human agent carrying out tasks with a well-specified user interface, ANALYZE the simulation for instances of human error, and then REFINE the interface or protocol to minimize predicted error. This approach, incorporated into the APEX modeling architecture, differs from past approaches to human simulation in Its emphasis on error rather than e.g. learning rate or speed of response. The APEX model consists of two major components: (1) a powerful action selection component capable of simulating behavior in complex, multiple-task environments; and (2) a resource architecture which constrains cognitive, perceptual, and motor capabilities to within empirically demonstrated limits. The model mimics human errors arising from interactions between limited human resources and elements of the computer interface whose design falls to anticipate those limits. We analyze the design of a hand-held Global Positioning System (GPS) device used for radical and navigational decisions in small yacht recalls. The analysis demonstrates how human system modeling can be an effective design aid, helping to accelerate the process of refining a product (or procedure).

Improving stamping simulation accuracy by accounting for realistic friction and lubrication conditions: Application to the door-outer of the Mercedes-Benz C-class Coupé

NASA Astrophysics Data System (ADS)

Hol, J.; Wiebenga, J. H.; Stock, J.; Wied, J.; Wiegand, K.; Carleer, B.

2016-08-01

In the stamping of automotive parts, friction and lubrication play a key role in achieving high quality products. In the development process of new automotive parts, it is therefore crucial to accurately account for these effects in sheet metal forming simulations. Only then, one can obtain reliable and realistic simulation results that correspond to the actual try-out and mass production conditions. In this work, the TriboForm software is used to accurately account for tribology-, friction-, and lubrication conditions in stamping simulations. The enhanced stamping simulations are applied and validated for the door-outer of the Mercedes- Benz C-Class Coupe. The project results demonstrate the improved prediction accuracy of stamping simulations with respect to both part quality and actual stamping process conditions.

Crystal engineering of ibuprofen compounds: From molecule to crystal structure to morphology prediction by computational simulation and experimental study

NASA Astrophysics Data System (ADS)

Zhang, Min; Liang, Zuozhong; Wu, Fei; Chen, Jian-Feng; Xue, Chunyu; Zhao, Hong

2017-06-01

We selected the crystal structures of ibuprofen with seven common space groups (Cc, P21/c, P212121, P21, Pbca, Pna21, and Pbcn), which was generated from ibuprofen molecule by molecular simulation. The predicted crystal structures of ibuprofen with space group P21/c has the lowest total energy and the largest density, which is nearly indistinguishable with experimental result. In addition, the XRD patterns for predicted crystal structure are highly consistent with recrystallization from solvent of ibuprofen. That indicates that the simulation can accurately predict the crystal structure of ibuprofen from the molecule. Furthermore, based on this crystal structure, we predicted the crystal habit in vacuum using the attachment energy (AE) method and considered solvent effects in a systematic way using the modified attachment energy (MAE) model. The simulation can accurately construct a complete process from molecule to crystal structure to morphology prediction. Experimentally, we observed crystal morphologies in four different polarity solvents compounds (ethanol, acetonitrile, ethyl acetate, and toluene). We found that the aspect ratio decreases of crystal habits in this ibuprofen system were found to vary with increasing solvent relative polarity. Besides, the modified crystal morphologies are in good agreement with the observed experimental morphologies. Finally, this work may guide computer-aided design of the desirable crystal morphology.

Representational Momentum for the Human Body: Awkwardness Matters, Experience Does Not

ERIC Educational Resources Information Center

Wilson, Margaret; Lancaster, Jessy; Emmorey, Karen

2010-01-01

Perception of the human body appears to involve predictive simulations that project forward to track unfolding body-motion events. Here we use representational momentum (RM) to investigate whether implicit knowledge of a learned arbitrary system of body movement such as sign language influences this prediction process, and how this compares to…

A systematic review of validated sinus surgery simulators.

PubMed

Stew, B; Kao, S S-T; Dharmawardana, N; Ooi, E H

2018-06-01

Simulation provides a safe and effective opportunity to develop surgical skills. A variety of endoscopic sinus surgery (ESS) simulators has been described in the literature. Validation of these simulators allows for effective utilisation in training. To conduct a systematic review of the published literature to analyse the evidence for validated ESS simulation. Pubmed, Embase, Cochrane and Cinahl were searched from inception of the databases to 11 January 2017. Twelve thousand five hundred and sixteen articles were retrieved of which 10 112 were screened following the removal of duplicates. Thirty-eight full-text articles were reviewed after meeting search criteria. Evidence of face, content, construct, discriminant and predictive validity was extracted. Twenty articles were included in the analysis describing 12 ESS simulators. Eleven of these simulators had undergone validation: 3 virtual reality, 7 physical bench models and 1 cadaveric simulator. Seven of the simulators were shown to have face validity, 7 had construct validity and 1 had predictive validity. None of the simulators demonstrated discriminate validity. This systematic review demonstrates that a number of ESS simulators have been comprehensively validated. Many of the validation processes, however, lack standardisation in outcome reporting, thus limiting a meta-analysis comparison between simulators. © 2017 John Wiley & Sons Ltd.

Simulating the flow of entangled polymers.

PubMed

Masubuchi, Yuichi

2014-01-01

To optimize automation for polymer processing, attempts have been made to simulate the flow of entangled polymers. In industry, fluid dynamics simulations with phenomenological constitutive equations have been practically established. However, to account for molecular characteristics, a method to obtain the constitutive relationship from the molecular structure is required. Molecular dynamics simulations with atomic description are not practical for this purpose; accordingly, coarse-grained models with reduced degrees of freedom have been developed. Although the modeling of entanglement is still a challenge, mesoscopic models with a priori settings to reproduce entangled polymer dynamics, such as tube models, have achieved remarkable success. To use the mesoscopic models as staging posts between atomistic and fluid dynamics simulations, studies have been undertaken to establish links from the coarse-grained model to the atomistic and macroscopic simulations. Consequently, integrated simulations from materials chemistry to predict the macroscopic flow in polymer processing are forthcoming.

A finite element simulation of biological conversion processes in landfills.

PubMed

Robeck, M; Ricken, T; Widmann, R

2011-04-01

Landfills are the most common way of waste disposal worldwide. Biological processes convert the organic material into an environmentally harmful landfill gas, which has an impact on the greenhouse effect. After the depositing of waste has been stopped, current conversion processes continue and emissions last for several decades and even up to 100years and longer. A good prediction of these processes is of high importance for landfill operators as well as for authorities, but suitable models for a realistic description of landfill processes are rather poor. In order to take the strong coupled conversion processes into account, a constitutive three-dimensional model based on the multiphase Theory of Porous Media (TPM) has been developed at the University of Duisburg-Essen. The theoretical formulations are implemented in the finite element code FEAP. With the presented calculation concept we are able to simulate the coupled processes that occur in an actual landfill. The model's theoretical background and the results of the simulations as well as the meantime successfully performed simulation of a real landfill body will be shown in the following. Copyright © 2010 Elsevier Ltd. All rights reserved.

Finite Element Simulation of Temperature and Strain Distribution during Friction Stir Welding of AA2024 Aluminum Alloy

NASA Astrophysics Data System (ADS)

Jain, Rahul; Pal, Surjya Kanta; Singh, Shiv Brat

2017-02-01

Friction Stir Welding (FSW) is a solid state joining process and is handy for welding aluminum alloys. Finite Element Method (FEM) is an important tool to predict state variables of the process but numerical simulation of FSW is highly complex due to non-linear contact interactions between tool and work piece and interdependency of displacement and temperature. In the present work, a three dimensional coupled thermo-mechanical method based on Lagrangian implicit method is proposed to study the thermal history, strain distribution and thermo-mechanical process in butt welding of Aluminum alloy 2024 using DEFORM-3D software. Workpiece is defined as rigid-visco plastic material and sticking condition between tool and work piece is defined. Adaptive re-meshing is used to tackle high mesh distortion. Effect of tool rotational and welding speed on plastic strain is studied and insight is given on asymmetric nature of FSW process. Temperature distribution on the workpiece and tool is predicted and maximum temperature is found in workpiece top surface.

Boosting flood warning schemes with fast emulator of detailed hydrodynamic models

NASA Astrophysics Data System (ADS)

Bellos, V.; Carbajal, J. P.; Leitao, J. P.

2017-12-01

Floods are among the most destructive catastrophic events and their frequency has incremented over the last decades. To reduce flood impact and risks, flood warning schemes are installed in flood prone areas. Frequently, these schemes are based on numerical models which quickly provide predictions of water levels and other relevant observables. However, the high complexity of flood wave propagation in the real world and the need of accurate predictions in urban environments or in floodplains hinders the use of detailed simulators. This sets the difficulty, we need fast predictions that meet the accuracy requirements. Most physics based detailed simulators although accurate, will not fulfill the speed demand. Even if High Performance Computing techniques are used (the magnitude of required simulation time is minutes/hours). As a consequence, most flood warning schemes are based in coarse ad-hoc approximations that cannot take advantage a detailed hydrodynamic simulation. In this work, we present a methodology for developing a flood warning scheme using an Gaussian Processes based emulator of a detailed hydrodynamic model. The methodology consists of two main stages: 1) offline stage to build the emulator; 2) online stage using the emulator to predict and generate warnings. The offline stage consists of the following steps: a) definition of the critical sites of the area under study, and the specification of the observables to predict at those sites, e.g. water depth, flow velocity, etc.; b) generation of a detailed simulation dataset to train the emulator; c) calibration of the required parameters (if measurements are available). The online stage is carried on using the emulator to predict the relevant observables quickly, and the detailed simulator is used in parallel to verify key predictions of the emulator. The speed gain given by the emulator allows also to quantify uncertainty in predictions using ensemble methods. The above methodology is applied in real world scenario.

Integrated Predictive Tools for Customizing Microstructure and Material Properties of Additively Manufactured Aerospace Components

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

Radhakrishnan, Balasubramaniam; Fattebert, Jean-Luc; Gorti, Sarma B.

Additive Manufacturing (AM) refers to a process by which digital three-dimensional (3-D) design data is converted to build up a component by depositing material layer-by-layer. United Technologies Corporation (UTC) is currently involved in fabrication and certification of several AM aerospace structural components made from aerospace materials. This is accomplished by using optimized process parameters determined through numerous design-of-experiments (DOE)-based studies. Certification of these components is broadly recognized as a significant challenge, with long lead times, very expensive new product development cycles and very high energy consumption. Because of these challenges, United Technologies Research Center (UTRC), together with UTC business unitsmore » have been developing and validating an advanced physics-based process model. The specific goal is to develop a physics-based framework of an AM process and reliably predict fatigue properties of built-up structures as based on detailed solidification microstructures. Microstructures are predicted using process control parameters including energy source power, scan velocity, deposition pattern, and powder properties. The multi-scale multi-physics model requires solution and coupling of governing physics that will allow prediction of the thermal field and enable solution at the microstructural scale. The state-of-the-art approach to solve these problems requires a huge computational framework and this kind of resource is only available within academia and national laboratories. The project utilized the parallel phase-fields codes at Oak Ridge National Laboratory (ORNL) and Lawrence Livermore National Laboratory (LLNL), along with the high-performance computing (HPC) capabilities existing at the two labs to demonstrate the simulation of multiple dendrite growth in threedimensions (3-D). The LLNL code AMPE was used to implement the UTRC phase field model that was previously developed for a model binary alloy, and the simulation results were compared against the UTRC simulation results, followed by extension of the UTRC model to simulate multiple dendrite growth in 3-D. The ORNL MEUMAPPS code was used to simulate dendritic growth in a model ternary alloy with the same equilibrium solidification range as the Ni-base alloy 718 using realistic model parameters, including thermodynamic integration with a Calphad based model for the ternary alloy. Implementation of the UTRC model in AMPE met with several numerical and parametric issues that were resolved and good comparison between the simulation results obtained by the two codes was demonstrated for two dimensional (2-D) dendrites. 3-D dendrite growth was then demonstrated with the AMPE code using nondimensional parameters obtained in 2-D simulations. Multiple dendrite growth in 2-D and 3-D were demonstrated using ORNL’s MEUMAPPS code using simple thermal boundary conditions. MEUMAPPS was then modified to incorporate the complex, time-dependent thermal boundary conditions obtained by UTRC’s thermal modeling of single track AM experiments to drive the phase field simulations. The results were in good agreement with UTRC’s experimental measurements.« less

A mathematical model and computational framework for three-dimensional chondrocyte cell growth in a porous tissue scaffold placed inside a bi-directional flow perfusion bioreactor.

PubMed

Shakhawath Hossain, Md; Bergstrom, D J; Chen, X B

2015-12-01

The in vitro chondrocyte cell culture for cartilage tissue regeneration in a perfusion bioreactor is a complex process. Mathematical modeling and computational simulation can provide important insights into the culture process, which would be helpful for selecting culture conditions to improve the quality of the developed tissue constructs. However, simulation of the cell culture process is a challenging task due to the complicated interaction between the cells and local fluid flow and nutrient transport inside the complex porous scaffolds. In this study, a mathematical model and computational framework has been developed to simulate the three-dimensional (3D) cell growth in a porous scaffold placed inside a bi-directional flow perfusion bioreactor. The model was developed by taking into account the two-way coupling between the cell growth and local flow field and associated glucose concentration, and then used to perform a resolved-scale simulation based on the lattice Boltzmann method (LBM). The simulation predicts the local shear stress, glucose concentration, and 3D cell growth inside the porous scaffold for a period of 30 days of cell culture. The predicted cell growth rate was in good overall agreement with the experimental results available in the literature. This study demonstrates that the bi-directional flow perfusion culture system can enhance the homogeneity of the cell growth inside the scaffold. The model and computational framework developed is capable of providing significant insight into the culture process, thus providing a powerful tool for the design and optimization of the cell culture process. © 2015 Wiley Periodicals, Inc.

Planning intensive care unit design using computer simulation modeling: optimizing integration of clinical, operational, and architectural requirements.

PubMed

OʼHara, Susan

2014-01-01

Nurses have increasingly been regarded as critical members of the planning team as architects recognize their knowledge and value. But the nurses' role as knowledge experts can be expanded to leading efforts to integrate the clinical, operational, and architectural expertise through simulation modeling. Simulation modeling allows for the optimal merge of multifactorial data to understand the current state of the intensive care unit and predict future states. Nurses can champion the simulation modeling process and reap the benefits of a cost-effective way to test new designs, processes, staffing models, and future programming trends prior to implementation. Simulation modeling is an evidence-based planning approach, a standard, for integrating the sciences with real client data, to offer solutions for improving patient care.

Simulation and Prediction of Groundwater Pollution from Planned Feed Additive Project in Nanning City Based on GMS Model

NASA Astrophysics Data System (ADS)

Liang, Yimin; Lan, Junkang; Wen, Zhixiong

2018-01-01

In order to predict the pollution of underground aquifers and rivers by the proposed project, Specialized hydrogeological investigation was carried out. After hydrogeological surveying and mapping, drilling, and groundwater level monitoring, the scope of the hydrogeological unit and the regional hydrogeological condition were found out. The permeability coefficients of the aquifers were also obtained by borehole water injection tests. In order to predict the impact on groundwater environment by the project, a GMS software was used in numerical simulation. The simulation results show that when unexpected sewage leakage accident happened, the pollutants will be gradually diluted by groundwater, and the diluted contaminants will slowly spread to southeast with groundwater flow, eventually they are discharged into Gantang River. However, the process of the pollutants discharging into the river is very long, the long-term dilution of the river water will keep Gantang River from being polluted.

A new method for the prediction of chatter stability lobes based on dynamic cutting force simulation model and support vector machine

NASA Astrophysics Data System (ADS)

Peng, Chong; Wang, Lun; Liao, T. Warren

2015-10-01

Currently, chatter has become the critical factor in hindering machining quality and productivity in machining processes. To avoid cutting chatter, a new method based on dynamic cutting force simulation model and support vector machine (SVM) is presented for the prediction of chatter stability lobes. The cutting force is selected as the monitoring signal, and the wavelet energy entropy theory is used to extract the feature vectors. A support vector machine is constructed using the MATLAB LIBSVM toolbox for pattern classification based on the feature vectors derived from the experimental cutting data. Then combining with the dynamic cutting force simulation model, the stability lobes diagram (SLD) can be estimated. Finally, the predicted results are compared with existing methods such as zero-order analytical (ZOA) and semi-discretization (SD) method as well as actual cutting experimental results to confirm the validity of this new method.

Prediction of ultrasonic pulse velocity for enhanced peat bricks using adaptive neuro-fuzzy methodology.

PubMed

Motamedi, Shervin; Roy, Chandrabhushan; Shamshirband, Shahaboddin; Hashim, Roslan; Petković, Dalibor; Song, Ki-Il

2015-08-01

Ultrasonic pulse velocity is affected by defects in material structure. This study applied soft computing techniques to predict the ultrasonic pulse velocity for various peats and cement content mixtures for several curing periods. First, this investigation constructed a process to simulate the ultrasonic pulse velocity with adaptive neuro-fuzzy inference system. Then, an ANFIS network with neurons was developed. The input and output layers consisted of four and one neurons, respectively. The four inputs were cement, peat, sand content (%) and curing period (days). The simulation results showed efficient performance of the proposed system. The ANFIS and experimental results were compared through the coefficient of determination and root-mean-square error. In conclusion, use of ANFIS network enhances prediction and generation of strength. The simulation results confirmed the effectiveness of the suggested strategies. Copyright © 2015 Elsevier B.V. All rights reserved.

Divorced Eutectic Solidification of Mg-Al Alloys

NASA Astrophysics Data System (ADS)

Monas, Alexander; Shchyglo, Oleg; Kim, Se-Jong; Yim, Chang Dong; Höche, Daniel; Steinbach, Ingo

2015-08-01

We present simulations of the nucleation and equiaxed dendritic growth of the primary hexagonal close-packed -Mg phase followed by the nucleation of the -phase in interdendritic regions. A zoomed-in region of a melt channel under eutectic conditions is investigated and compared with experiments. The presented simulations allow prediction of the final properties of an alloy based on process parameters. The obtained results give insight into the solidification processes governing the microstructure formation of Mg-Al alloys, allowing their targeted design for different applications.

Intentional strategies that make co-actors more predictable: the case of signaling.

PubMed

Pezzulo, Giovanni; Dindo, Haris

2013-08-01

Pickering & Garrod (P&G) explain dialogue dynamics in terms of forward modeling and prediction-by-simulation mechanisms. Their theory dissolves a strict segregation between production and comprehension processes, and it links dialogue to action-based theories of joint action. We propose that the theory can also incorporate intentional strategies that increase communicative success: for example, signaling strategies that help remaining predictable and forming common ground.

The salt marsh vegetation spread dynamics simulation and prediction based on conditions optimized CA

NASA Astrophysics Data System (ADS)

Guan, Yujuan; Zhang, Liquan

2006-10-01

The biodiversity conservation and management of the salt marsh vegetation relies on processing their spatial information. Nowadays, more attentions are focused on their classification surveying and describing qualitatively dynamics based on RS images interpreted, rather than on simulating and predicting their dynamics quantitatively, which is of greater importance for managing and planning the salt marsh vegetation. In this paper, our notion is to make a dynamic model on large-scale and to provide a virtual laboratory in which researchers can run it according requirements. Firstly, the characteristic of the cellular automata was analyzed and a conclusion indicated that it was necessary for a CA model to be extended geographically under varying conditions of space-time circumstance in order to make results matched the facts accurately. Based on the conventional cellular automata model, the author introduced several new conditions to optimize it for simulating the vegetation objectively, such as elevation, growth speed, invading ability, variation and inheriting and so on. Hence the CA cells and remote sensing image pixels, cell neighbors and pixel neighbors, cell rules and nature of the plants were unified respectively. Taking JiuDuanSha as the test site, where holds mainly Phragmites australis (P.australis) community, Scirpus mariqueter (S.mariqueter) community and Spartina alterniflora (S.alterniflora) community. The paper explored the process of making simulation and predictions about these salt marsh vegetable changing with the conditions optimized CA (COCA) model, and examined the links among data, statistical models, and ecological predictions. This study exploited the potential of applying Conditioned Optimized CA model technique to solve this problem.

Efficiency and Accuracy in Thermal Simulation of Powder Bed Fusion of Bulk Metallic Glass

NASA Astrophysics Data System (ADS)

Lindwall, J.; Malmelöv, A.; Lundbäck, A.; Lindgren, L.-E.

2018-05-01

Additive manufacturing by powder bed fusion processes can be utilized to create bulk metallic glass as the process yields considerably high cooling rates. However, there is a risk that reheated material set in layers may become devitrified, i.e., crystallize. Therefore, it is advantageous to simulate the process to fully comprehend it and design it to avoid the aforementioned risk. However, a detailed simulation is computationally demanding. It is necessary to increase the computational speed while maintaining accuracy of the computed temperature field in critical regions. The current study evaluates a few approaches based on temporal reduction to achieve this. It is found that the evaluated approaches save a lot of time and accurately predict the temperature history.

Chevron: Refinery Identifies $4.4 Million in Annual Savings by Using Process Simulation Models to Perform Energy-Efficiency Assessment

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

Not Available

2004-05-01

In an energy-efficiency study at its refinery near Salt Lake City, Utah, Chevron focused on light hydrocarbons processing. The company found it could recover hydrocarbons from its fuel gas system and sell them. By using process simulation models of special distillation columns and associated reboilers and condensers, Chevron could predict the performance of potential equipment configuration changes and process modifications. More than 25,000 MMBtu in natural gas could be saved annually if a debutanizer upgrade project and a new saturated gas plant project were completed. Together, these projects would save $4.4 million annually.

Numerical modeling of materials processes with fluid-fluid interfaces

NASA Astrophysics Data System (ADS)

Yanke, Jeffrey Michael

A numerical model has been developed to study material processes that depend on the interaction between fluids with a large discontinuity in thermophysical properties. A base model capable of solving equations of mass, momentum, energy conservation, and solidification has been altered to enable tracking of the interface between two immiscible fluids and correctly predict the interface deformation using a volume of fluid (VOF) method. Two materials processes investigated using this technique are Electroslag Remelting (ESR) and plasma spray deposition. ESR is a secondary melting technique that passes an AC current through an electrically resistive slag to provide the heat necessary to melt the alloy. The simulation tracks the interface between the slag and metal. The model was validated against industrial scale ESR ingots and was able to predict trends in melt rate, sump depth, macrosegregation, and liquid sump depth. In order to better understand the underlying physics of the process, several constant current ESR runs simulated the effects of freezing slag in the model. Including the solidifying slag in the imulations was found to have an effect on the melt rate and sump shape but there is too much uncertainty in ESR slag property data at this time for quantitative predictions. The second process investigated in this work is the deposition of ceramic coatings via plasma spray deposition. In plasma spray deposition, powderized coating material is injected into a plasma that melts and carries the powder towards the substrate were it impacts, flattening out and freezing. The impacting droplets pile up to form a porous coating. The model is used to simulate this rain of liquid ceramic particles impacting the substrate and forming a coating. Trends in local solidification time and porosity are calculated for various particle sizes and velocities. The predictions of decreasing porosity with increasing particle velocity matches previous experimental results. Also, a preliminary study was conducted to investigate the effects of substrate surface defects and droplet impact angle on the propensity to form columnar porosity.

Numerical simulation of a low-lying barrier island's morphological response to Hurricane Katrina

USGS Publications Warehouse

Lindemer, C.A.; Plant, N.G.; Puleo, J.A.; Thompson, D.M.; Wamsley, T.V.

2010-01-01

Tropical cyclones that enter or form in the Gulf of Mexico generate storm surge and large waves that impact low-lying coastlines along the Gulf Coast. The Chandeleur Islands, located 161. km east of New Orleans, Louisiana, have endured numerous hurricanes that have passed nearby. Hurricane Katrina (landfall near Waveland MS, 29 Aug 2005) caused dramatic changes to the island elevation and shape. In this paper the predictability of hurricane-induced barrier island erosion and accretion is evaluated using a coupled hydrodynamic and morphodynamic model known as XBeach. Pre- and post-storm island topography was surveyed with an airborne lidar system. Numerical simulations utilized realistic surge and wave conditions determined from larger-scale hydrodynamic models. Simulations included model sensitivity tests with varying grid size and temporal resolutions. Model-predicted bathymetry/topography and post-storm survey data both showed similar patterns of island erosion, such as increased dissection by channels. However, the model under predicted the magnitude of erosion. Potential causes for under prediction include (1) errors in the initial conditions (the initial bathymetry/topography was measured three years prior to Katrina), (2) errors in the forcing conditions (a result of our omission of storms prior to Katrina and/or errors in Katrina storm conditions), and/or (3) physical processes that were omitted from the model (e.g., inclusion of sediment variations and bio-physical processes). ?? 2010.

Computer simulation of stochastic processes through model-sampling (Monte Carlo) techniques.

PubMed

Sheppard, C W.

1969-03-01

A simple Monte Carlo simulation program is outlined which can be used for the investigation of random-walk problems, for example in diffusion, or the movement of tracers in the blood circulation. The results given by the simulation are compared with those predicted by well-established theory, and it is shown how the model can be expanded to deal with drift, and with reflexion from or adsorption at a boundary.

Monitoring of Batch Industrial Crystallization with Growth, Nucleation, and Agglomeration. Part 1: Modeling with Method of Characteristics.

PubMed

Porru, Marcella; Özkan, Leyla

2017-05-24

This paper develops a new simulation model for crystal size distribution dynamics in industrial batch crystallization. The work is motivated by the necessity of accurate prediction models for online monitoring purposes. The proposed numerical scheme is able to handle growth, nucleation, and agglomeration kinetics by means of the population balance equation and the method of characteristics. The former offers a detailed description of the solid phase evolution, while the latter provides an accurate and efficient numerical solution. In particular, the accuracy of the prediction of the agglomeration kinetics, which cannot be ignored in industrial crystallization, has been assessed by comparing it with solutions in the literature. The efficiency of the solution has been tested on a simulation of a seeded flash cooling batch process. Since the proposed numerical scheme can accurately simulate the system behavior more than hundred times faster than the batch duration, it is suitable for online applications such as process monitoring tools based on state estimators.

Prediction of Burst Pressure in Multistage Tube Hydroforming of Aerospace Alloys.

PubMed

Saboori, M; Gholipour, J; Champliaud, H; Wanjara, P; Gakwaya, A; Savoie, J

2016-08-01

Bursting, an irreversible failure in tube hydroforming (THF), results mainly from the local plastic instabilities that occur when the biaxial stresses imparted during the process exceed the forming limit strains of the material. To predict the burst pressure, Oyan's and Brozzo's decoupled ductile fracture criteria (DFC) were implemented as user material models in a dynamic nonlinear commercial 3D finite-element (FE) software, ls-dyna. THF of a round to V-shape was selected as a generic representative of an aerospace component for the FE simulations and experimental trials. To validate the simulation results, THF experiments up to bursting were carried out using Inconel 718 (IN 718) tubes with a thickness of 0.9 mm to measure the internal pressures during the process. When comparing the experimental and simulation results, the burst pressure predicated based on Oyane's decoupled damage criterion was found to agree better with the measured data for IN 718 than Brozzo's fracture criterion.

ProtPOS: a python package for the prediction of protein preferred orientation on a surface.

PubMed

Ngai, Jimmy C F; Mak, Pui-In; Siu, Shirley W I

2016-08-15

Atomistic molecular dynamics simulation is a promising technique to investigate the energetics and dynamics in the protein-surface adsorption process which is of high relevance to modern biotechnological applications. To increase the chance of success in simulating the adsorption process, favorable orientations of the protein at the surface must be determined. Here, we present ProtPOS which is a lightweight and easy-to-use python package that can predict low-energy protein orientations on a surface of interest. It combines a fast conformational sampling algorithm with the energy calculation of GROMACS. The advantage of ProtPOS is it allows users to select any force fields suitable for the system at hand and provide structural output readily available for further simulation studies. ProtPOS is freely available for academic and non-profit uses at http://cbbio.cis.umac.mo/software/protpos Supplementary data are available at Bioinformatics online. shirleysiu@umac.mo. © The Author 2016. Published by Oxford University Press.

ProtPOS: a python package for the prediction of protein preferred orientation on a surface

PubMed Central

Ngai, Jimmy C. F.; Mak, Pui-In; Siu, Shirley W. I.

2016-01-01

Summary: Atomistic molecular dynamics simulation is a promising technique to investigate the energetics and dynamics in the protein–surface adsorption process which is of high relevance to modern biotechnological applications. To increase the chance of success in simulating the adsorption process, favorable orientations of the protein at the surface must be determined. Here, we present ProtPOS which is a lightweight and easy-to-use python package that can predict low-energy protein orientations on a surface of interest. It combines a fast conformational sampling algorithm with the energy calculation of GROMACS. The advantage of ProtPOS is it allows users to select any force fields suitable for the system at hand and provide structural output readily available for further simulation studies. Availability and Implementation: ProtPOS is freely available for academic and non-profit uses at http://cbbio.cis.umac.mo/software/protpos Supplementary information: Supplementary data are available at Bioinformatics online. Contact: shirleysiu@umac.mo PMID:27153619

Monitoring of Batch Industrial Crystallization with Growth, Nucleation, and Agglomeration. Part 1: Modeling with Method of Characteristics

PubMed Central

2017-01-01

This paper develops a new simulation model for crystal size distribution dynamics in industrial batch crystallization. The work is motivated by the necessity of accurate prediction models for online monitoring purposes. The proposed numerical scheme is able to handle growth, nucleation, and agglomeration kinetics by means of the population balance equation and the method of characteristics. The former offers a detailed description of the solid phase evolution, while the latter provides an accurate and efficient numerical solution. In particular, the accuracy of the prediction of the agglomeration kinetics, which cannot be ignored in industrial crystallization, has been assessed by comparing it with solutions in the literature. The efficiency of the solution has been tested on a simulation of a seeded flash cooling batch process. Since the proposed numerical scheme can accurately simulate the system behavior more than hundred times faster than the batch duration, it is suitable for online applications such as process monitoring tools based on state estimators. PMID:28603342

Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2 O emissions.

PubMed

Ehrhardt, Fiona; Soussana, Jean-François; Bellocchi, Gianni; Grace, Peter; McAuliffe, Russel; Recous, Sylvie; Sándor, Renáta; Smith, Pete; Snow, Val; de Antoni Migliorati, Massimiliano; Basso, Bruno; Bhatia, Arti; Brilli, Lorenzo; Doltra, Jordi; Dorich, Christopher D; Doro, Luca; Fitton, Nuala; Giacomini, Sandro J; Grant, Brian; Harrison, Matthew T; Jones, Stephanie K; Kirschbaum, Miko U F; Klumpp, Katja; Laville, Patricia; Léonard, Joël; Liebig, Mark; Lieffering, Mark; Martin, Raphaël; Massad, Raia S; Meier, Elizabeth; Merbold, Lutz; Moore, Andrew D; Myrgiotis, Vasileios; Newton, Paul; Pattey, Elizabeth; Rolinski, Susanne; Sharp, Joanna; Smith, Ward N; Wu, Lianhai; Zhang, Qing

2018-02-01

Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi-species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi-model ensembles to predict productivity and nitrous oxide (N 2 O) emissions for wheat, maize, rice and temperate grasslands. Using a multi-stage modelling protocol, from blind simulations (stage 1) to partial (stages 2-4) and full calibration (stage 5), 24 process-based biogeochemical models were assessed individually or as an ensemble against long-term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N 2 O emissions. Results showed that across sites and crop/grassland types, 23%-40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1 SD of observed N 2 O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N 2 O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2-4) markedly reduced prediction errors of the full model ensemble E-median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44% to 27%) and to a lesser and more variable extent for N 2 O emissions. Yield-scaled N 2 O emissions (N 2 O emissions divided by crop yields) were ranked accurately by three-model ensembles across crop species and field sites. The potential of using process-based model ensembles to predict jointly productivity and N 2 O emissions at field scale is discussed. © 2017 John Wiley & Sons Ltd.

Multi-scale predictions of coniferous forest mortality in the northern hemisphere

NASA Astrophysics Data System (ADS)

McDowell, N. G.

2015-12-01

Global temperature rise and extremes accompanying drought threaten forests and their associated climatic feedbacks. Our incomplete understanding of the fundamental physiological thresholds of vegetation mortality during drought limits our ability to accurately simulate future vegetation distributions and associated climate feedbacks. Here we integrate experimental evidence with models to show potential widespread loss of needleleaf evergreen trees (NET; ~ conifers) within the Southwest USA by 2100; with rising temperature being the primary cause of mortality. Experimentally, dominant Southwest USA NET species died when they fell below predawn water potential (Ypd) thresholds (April-August mean) beyond which photosynthesis, stomatal and hydraulic conductance, and carbohydrate availability approached zero. Empirical and mechanistic models accurately predicted NET Ypd, and 91% of predictions (10/11) exceeded mortality thresholds within the 21st century due to temperature rise. Completely independent global models predicted >50% loss of northern hemisphere NET by 2100, consistent with the findings for Southwest USA. The global models disagreed with the ecosystem process models in regards to future mortality in Southwest USA, however, highlighting the potential underestimates of future NET mortality as simulated by the global models and signifying the importance of improving regional predictions. Taken together, these results from the validated regional predictions and the global simulations predict global-scale conifer loss in coming decades under projected global warming.

Simulation and Analysis of One-time Forming Process of Automobile Steering Ball Head

NASA Astrophysics Data System (ADS)

Shi, Peicheng; Zhang, Xujun; Xu, Zengwei; Zhang, Rongyun

2018-03-01

Aiming at the problems such as large machining allowance, low production efficiency and material waste during die forging of ball pin, the cold extrusion process of ball head was studied and the analog simulation of the forming process was carried out by using the finite element analysis software DEFORM-3D. Through the analysis of the equivalent stress strain, velocity vector field and load-displacement curve, the flow regularity of the metal during the cold extrusion process of ball pin was clarified, and possible defects during the molding were predicted. The results showed that this process could solve the forming problem of ball pin and provide theoretical basis for actual production of enterprises.

Fully kinetic simulations of dense plasma focus Z-pinch devices.

PubMed

Schmidt, A; Tang, V; Welch, D

2012-11-16

Dense plasma focus Z-pinch devices are sources of copious high energy electrons and ions, x rays, and neutrons. The mechanisms through which these physically simple devices generate such high-energy beams in a relatively short distance are not fully understood. We now have, for the first time, demonstrated a capability to model these plasmas fully kinetically, allowing us to simulate the pinch process at the particle scale. We present here the results of the initial kinetic simulations, which reproduce experimental neutron yields (~10(7)) and high-energy (MeV) beams for the first time. We compare our fluid, hybrid (kinetic ions and fluid electrons), and fully kinetic simulations. Fluid simulations predict no neutrons and do not allow for nonthermal ions, while hybrid simulations underpredict neutron yield by ~100x and exhibit an ion tail that does not exceed 200 keV. Only fully kinetic simulations predict MeV-energy ions and experimental neutron yields. A frequency analysis in a fully kinetic simulation shows plasma fluctuations near the lower hybrid frequency, possibly implicating lower hybrid drift instability as a contributor to anomalous resistivity in the plasma.

3-PG simulations of young ponderosa pine plantations under varied management intensity: why do they grow so differently?

Treesearch

Liang Wei; Marshall John; Jianwei Zhang; Hang Zhou; Robert Powers

2014-01-01

Models can be powerful tools for estimating forest productivity and guiding forest management, but their credibility and complexity are often an issue for forest managers. We parameterized a process-based forest growth model, 3-PG (Physiological Principles Predicting Growth), to simulate growth of ponderosa pine (Pinus ponderosa) plantations in...

African Easterly Waves in 30-day High-Resolution Global Simulations: A Case Study During the 2006 NAMMA Period

NASA Technical Reports Server (NTRS)

Shen, Bo-Wen; Tao, Wei-Kuo; Wu, Man-Li C.

2010-01-01

In this study, extended -range (30 -day) high-resolution simulations with the NASA global mesoscale model are conducted to simulate the initiation and propagation of six consecutive African easterly waves (AEWs) from late August to September 2006 and their association with hurricane formation. It is shown that the statistical characteristics of individual AEWs are realistically simulated with larger errors in the 5th and 6th AEWs. Remarkable simulations of a mean African easterly jet (AEJ) are also obtained. Nine additional 30 -day experiments suggest that although land surface processes might contribute to the predictability of the AEJ and AEWs, the initiation and detailed evolution of AEWs still depend on the accurate representation of dynamic and land surface initial conditions and their time -varying nonlinear interactions. Of interest is the potential to extend the lead time for predicting hurricane formation (e.g., a lead time of up to 22 days) as the 4th AEW is realistically simulated.

Numerical simulations in the development of propellant management devices

NASA Astrophysics Data System (ADS)

Gaulke, Diana; Winkelmann, Yvonne; Dreyer, Michael

Propellant management devices (PMDs) are used for positioning the propellant at the propel-lant port. It is important to provide propellant without gas bubbles. Gas bubbles can inflict cavitation and may lead to system failures in the worst case. Therefore, the reliable operation of such devices must be guaranteed. Testing these complex systems is a very intricate process. Furthermore, in most cases only tests with downscaled geometries are possible. Numerical sim-ulations are used here as an aid to optimize the tests and to predict certain results. Based on these simulations, parameters can be determined in advance and parts of the equipment can be adjusted in order to minimize the number of experiments. In return, the simulations are validated regarding the test results. Furthermore, if the accuracy of the numerical prediction is verified, then numerical simulations can be used for validating the scaling of the experiments. This presentation demonstrates some selected numerical simulations for the development of PMDs at ZARM.

Finite Element Modelling and Analysis of Conventional Pultrusion Processes

NASA Astrophysics Data System (ADS)

Akishin, P.; Barkanov, E.; Bondarchuk, A.

2015-11-01

Pultrusion is one of many composite manufacturing techniques and one of the most efficient methods for producing fiber reinforced polymer composite parts with a constant cross-section. Numerical simulation is helpful for understanding the manufacturing process and developing scientific means for the pultrusion tooling design. Numerical technique based on the finite element method has been developed for the simulation of pultrusion processes. It uses the general purpose finite element software ANSYS Mechanical. It is shown that the developed technique predicts the temperature and cure profiles, which are in good agreement with those published in the open literature.

Experiment and simulation of the fabrication process of lithium-ion battery cathodes for determining microstructure and mechanical properties

NASA Astrophysics Data System (ADS)

Forouzan, Mehdi M.; Chao, Chien-Wei; Bustamante, Danilo; Mazzeo, Brian A.; Wheeler, Dean R.

2016-04-01

The fabrication process of Li-ion battery electrodes plays a prominent role in the microstructure and corresponding cell performance. Here, a mesoscale particle dynamics simulation is developed to relate the manufacturing process of a cathode containing Toda NCM-523 active material to physical and structural properties of the dried film. Particle interactions are simulated with shifted-force Lennard-Jones and granular Hertzian functions. LAMMPS, a freely available particle simulator, is used to generate particle trajectories and resulting predicted properties. To make simulations of the full film thickness feasible, the carbon binder domain (CBD) is approximated with μm-scale particles, each representing about 1000 carbon black particles and associated binder. Metrics for model parameterization and validation are measured experimentally and include the following: slurry viscosity, elasticity of the dried film, shrinkage ratio during drying, volume fraction of phases, slurry and dried film densities, and microstructure cross sections. Simulation results are in substantial agreement with experiment, showing that the simulations reasonably reproduce the relevant physics of particle arrangement during fabrication.

Single-Event Effects in High-Frequency Linear Amplifiers: Experiment and Analysis

NASA Astrophysics Data System (ADS)

Zeinolabedinzadeh, Saeed; Ying, Hanbin; Fleetwood, Zachary E.; Roche, Nicolas J.-H.; Khachatrian, Ani; McMorrow, Dale; Buchner, Stephen P.; Warner, Jeffrey H.; Paki-Amouzou, Pauline; Cressler, John D.

2017-01-01

The single-event transient (SET) response of two different silicon-germanium (SiGe) X-band (8-12 GHz) low noise amplifier (LNA) topologies is fully investigated in this paper. The two LNAs were designed and implemented in 130nm SiGe HBT BiCMOS process technology. Two-photon absorption (TPA) laser pulses were utilized to induce transients within various devices in these LNAs. Impulse response theory is identified as a useful tool for predicting the settling behavior of the LNAs subjected to heavy ion strikes. Comprehensive device and circuit level modeling and simulations were performed to accurately simulate the behavior of the circuits under ion strikes. The simulations agree well with TPA measurements. The simulation, modeling and analysis presented in this paper can be applied for any other circuit topologies for SET modeling and prediction.

Computational Materials Science and Chemistry: Accelerating Discovery and Innovation through Simulation-Based Engineering and Science

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

Crabtree, George; Glotzer, Sharon; McCurdy, Bill

This report is based on a SC Workshop on Computational Materials Science and Chemistry for Innovation on July 26-27, 2010, to assess the potential of state-of-the-art computer simulations to accelerate understanding and discovery in materials science and chemistry, with a focus on potential impacts in energy technologies and innovation. The urgent demand for new energy technologies has greatly exceeded the capabilities of today's materials and chemical processes. To convert sunlight to fuel, efficiently store energy, or enable a new generation of energy production and utilization technologies requires the development of new materials and processes of unprecedented functionality and performance. Newmore » materials and processes are critical pacing elements for progress in advanced energy systems and virtually all industrial technologies. Over the past two decades, the United States has developed and deployed the world's most powerful collection of tools for the synthesis, processing, characterization, and simulation and modeling of materials and chemical systems at the nanoscale, dimensions of a few atoms to a few hundred atoms across. These tools, which include world-leading x-ray and neutron sources, nanoscale science facilities, and high-performance computers, provide an unprecedented view of the atomic-scale structure and dynamics of materials and the molecular-scale basis of chemical processes. For the first time in history, we are able to synthesize, characterize, and model materials and chemical behavior at the length scale where this behavior is controlled. This ability is transformational for the discovery process and, as a result, confers a significant competitive advantage. Perhaps the most spectacular increase in capability has been demonstrated in high performance computing. Over the past decade, computational power has increased by a factor of a million due to advances in hardware and software. This rate of improvement, which shows no sign of abating, has enabled the development of computer simulations and models of unprecedented fidelity. We are at the threshold of a new era where the integrated synthesis, characterization, and modeling of complex materials and chemical processes will transform our ability to understand and design new materials and chemistries with predictive power. In turn, this predictive capability will transform technological innovation by accelerating the development and deployment of new materials and processes in products and manufacturing. Harnessing the potential of computational science and engineering for the discovery and development of materials and chemical processes is essential to maintaining leadership in these foundational fields that underpin energy technologies and industrial competitiveness. Capitalizing on the opportunities presented by simulation-based engineering and science in materials and chemistry will require an integration of experimental capabilities with theoretical and computational modeling; the development of a robust and sustainable infrastructure to support the development and deployment of advanced computational models; and the assembly of a community of scientists and engineers to implement this integration and infrastructure. This community must extend to industry, where incorporating predictive materials science and chemistry into design tools can accelerate the product development cycle and drive economic competitiveness. The confluence of new theories, new materials synthesis capabilities, and new computer platforms has created an unprecedented opportunity to implement a "materials-by-design" paradigm with wide-ranging benefits in technological innovation and scientific discovery. The Workshop on Computational Materials Science and Chemistry for Innovation was convened in Bethesda, Maryland, on July 26-27, 2010. Sponsored by the Department of Energy (DOE) Offices of Advanced Scientific Computing Research and Basic Energy Sciences, the workshop brought together 160 experts in materials science, chemistry, and computational science representing more than 65 universities, laboratories, and industries, and four agencies. The workshop examined seven foundational challenge areas in materials science and chemistry: materials for extreme conditions, self-assembly, light harvesting, chemical reactions, designer fluids, thin films and interfaces, and electronic structure. Each of these challenge areas is critical to the development of advanced energy systems, and each can be accelerated by the integrated application of predictive capability with theory and experiment. The workshop concluded that emerging capabilities in predictive modeling and simulation have the potential to revolutionize the development of new materials and chemical processes. Coupled with world-leading materials characterization and nanoscale science facilities, this predictive capability provides the foundation for an innovation ecosystem that can accelerate the discovery, development, and deployment of new technologies, including advanced energy systems. Delivering on the promise of this innovation ecosystem requires the following: Integration of synthesis, processing, characterization, theory, and simulation and modeling. Many of the newly established Energy Frontier Research Centers and Energy Hubs are exploiting this integration. Achieving/strengthening predictive capability in foundational challenge areas. Predictive capability in the seven foundational challenge areas described in this report is critical to the development of advanced energy technologies. Developing validated computational approaches that span vast differences in time and length scales. This fundamental computational challenge crosscuts all of the foundational challenge areas. Similarly challenging is coupling of analytical data from multiple instruments and techniques that are required to link these length and time scales. Experimental validation and quantification of uncertainty in simulation and modeling. Uncertainty quantification becomes increasingly challenging as simulations become more complex. Robust and sustainable computational infrastructure, including software and applications. For modeling and simulation, software equals infrastructure. To validate the computational tools, software is critical infrastructure that effectively translates huge arrays of experimental data into useful scientific understanding. An integrated approach for managing this infrastructure is essential. Efficient transfer and incorporation of simulation-based engineering and science in industry. Strategies for bridging the gap between research and industrial applications and for widespread industry adoption of integrated computational materials engineering are needed.« less

Adaptive constructive processes and the future of memory.

PubMed

Schacter, Daniel L

2012-11-01

Memory serves critical functions in everyday life but is also prone to error. This article examines adaptive constructive processes, which play a functional role in memory and cognition but can also produce distortions, errors, and illusions. The article describes several types of memory errors that are produced by adaptive constructive processes and focuses in particular on the process of imagining or simulating events that might occur in one's personal future. Simulating future events relies on many of the same cognitive and neural processes as remembering past events, which may help to explain why imagination and memory can be easily confused. The article considers both pitfalls and adaptive aspects of future event simulation in the context of research on planning, prediction, problem solving, mind-wandering, prospective and retrospective memory, coping and positivity bias, and the interconnected set of brain regions known as the default network. PsycINFO Database Record (c) 2012 APA, all rights reserved.

Prediction of SOFC Performance with or without Experiments: A Study on Minimum Requirements for Experimental Data

DOE PAGES

Yang, Tao; Sezer, Hayri; Celik, Ismail B.; ...

2015-06-02

In the present paper, a physics-based procedure combining experiments and multi-physics numerical simulations is developed for overall analysis of SOFCs operational diagnostics and performance predictions. In this procedure, essential information for the fuel cell is extracted first by utilizing empirical polarization analysis in conjunction with experiments and refined by multi-physics numerical simulations via simultaneous analysis and calibration of polarization curve and impedance behavior. The performance at different utilization cases and operating currents is also predicted to confirm the accuracy of the proposed model. It is demonstrated that, with the present electrochemical model, three air/fuel flow conditions are needed to producemore » a set of complete data for better understanding of the processes occurring within SOFCs. After calibration against button cell experiments, the methodology can be used to assess performance of planar cell without further calibration. The proposed methodology would accelerate the calibration process and improve the efficiency of design and diagnostics.« less

Effect of geometric and process variables on the performance of inclined plate settlers in treating aquacultural waste.

PubMed

Sarkar, Sudipto; Kamilya, Dibyendu; Mal, B C

2007-03-01

Inclined plate settlers are used in treating wastewater due to their low space requirement and high removal rates. The prediction of sedimentation efficiency of these settlers is essential for their performance evaluation. In the present study, the technique of dimensional analysis was applied to predict the sedimentation efficiency of these inclined plate settlers. The effect of various geometric parameters namely, distance between plates (w(p)), plate angle (alpha), length of plate (l(p)), plate roughness (epsilon(p)), number of plates (n(p)) and particle diameter (d(s)) on the dynamic conditions, influencing the sedimentation process was studied. From the study it was established that neither the Reynolds criterion nor the Froude criterion was singularly valid to simulate the sedimentation efficiency (E) for different values of w(p) and flow velocity (v(f)). Considering the prevalent scale effect, simulation equations were developed to predict E at different dynamic conditions. The optimum dynamic condition producing the maximum E is also discussed.

Prediction Of Tensile And Shear Strength Of Friction Surfaced Tool Steel Deposit By Using Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Manzoor Hussain, M.; Pitchi Raju, V.; Kandasamy, J.; Govardhan, D.

2018-04-01

Friction surface treatment is well-established solid technology and is used for deposition, abrasion and corrosion protection coatings on rigid materials. This novel process has wide range of industrial applications, particularly in the field of reclamation and repair of damaged and worn engineering components. In this paper, we present the prediction of tensile and shear strength of friction surface treated tool steel using ANN for simulated results of friction surface treatment. This experiment was carried out to obtain tool steel coatings of low carbon steel parts by changing contribution process parameters essentially friction pressure, rotational speed and welding speed. The simulation is performed by a 33-factor design that takes into account the maximum and least limits of the experimental work performed with the 23-factor design. Neural network structures, such as the Feed Forward Neural Network (FFNN), were used to predict tensile and shear strength of tool steel sediments caused by friction.

Predicting Motor Vehicle Collisions in a Driving Simulator in Young Adults Using the Useful Field of View Assessment.

PubMed

McManus, Benjamin; Cox, Molly K; Vance, David E; Stavrinos, Despina

2015-01-01

Being involved in motor vehicle collisions is the leading cause of death in 1- to 34-year-olds, and risk is particularly high in young adults. The Useful Field of View (UFOV) task, a cognitive measure of processing speed, divided attention, and selective attention, has been shown to be predictive of motor vehicle collisions in older adults, but its use as a predictor of driving performance in a young adult population has not been investigated. The present study examined whether UFOV was a predictive measure of motor vehicle collisions in a driving simulator in a young adult population. The 3-subtest version of UFOV (lower scores measured in milliseconds indicate better performance) was administered to 60 college students. Participants also completed an 11-mile simulated drive to provide driving performance metrics. Findings suggested that subtests 1 and 2 suffered from a ceiling effect. UFOV subtest 3 significantly predicted collisions in the simulated drive. Each 30 ms slower on the subtest was associated with nearly a 10% increase in the risk of a simulated collision. Post hoc analyses revealed a small partially mediating effect of subtest 3 on the relationship between driving experience and collisions. The selective attention component of UFOV subtest 3 may be a predictive measure of crash involvement in a young adult population. Improvements in selective attention may be the underlying mechanism in how driving experience improves driving performance.

Dynamic modeling of Tampa Bay urban development using parallel computing

USGS Publications Warehouse

Xian, G.; Crane, M.; Steinwand, D.

2005-01-01

Urban land use and land cover has changed significantly in the environs of Tampa Bay, Florida, over the past 50 years. Extensive urbanization has created substantial change to the region's landscape and ecosystems. This paper uses a dynamic urban-growth model, SLEUTH, which applies six geospatial data themes (slope, land use, exclusion, urban extent, transportation, hillside), to study the process of urbanization and associated land use and land cover change in the Tampa Bay area. To reduce processing time and complete the modeling process within an acceptable period, the model is recoded and ported to a Beowulf cluster. The parallel-processing computer system accomplishes the massive amount of computation the modeling simulation requires. SLEUTH calibration process for the Tampa Bay urban growth simulation spends only 10 h CPU time. The model predicts future land use/cover change trends for Tampa Bay from 1992 to 2025. Urban extent is predicted to double in the Tampa Bay watershed between 1992 and 2025. Results show an upward trend of urbanization at the expense of a decline of 58% and 80% in agriculture and forested lands, respectively.

Modeling of the flow stress for AISI H13 Tool Steel during Hard Machining Processes

NASA Astrophysics Data System (ADS)

Umbrello, Domenico; Rizzuti, Stefania; Outeiro, José C.; Shivpuri, Rajiv

2007-04-01

In general, the flow stress models used in computer simulation of machining processes are a function of effective strain, effective strain rate and temperature developed during the cutting process. However, these models do not adequately describe the material behavior in hard machining, where a range of material hardness between 45 and 60 HRC are used. Thus, depending on the specific material hardness different material models must be used in modeling the cutting process. This paper describes the development of a hardness-based flow stress and fracture models for the AISI H13 tool steel, which can be applied for range of material hardness mentioned above. These models were implemented in a non-isothermal viscoplastic numerical model to simulate the machining process for AISI H13 with various hardness values and applying different cutting regime parameters. Predicted results are validated by comparing them with experimental results found in the literature. They are found to predict reasonably well the cutting forces as well as the change in chip morphology from continuous to segmented chip as the material hardness change.

Discriminative Random Field Models for Subsurface Contamination Uncertainty Quantification

NASA Astrophysics Data System (ADS)

Arshadi, M.; Abriola, L. M.; Miller, E. L.; De Paolis Kaluza, C.

2017-12-01

Application of flow and transport simulators for prediction of the release, entrapment, and persistence of dense non-aqueous phase liquids (DNAPLs) and associated contaminant plumes is a computationally intensive process that requires specification of a large number of material properties and hydrologic/chemical parameters. Given its computational burden, this direct simulation approach is particularly ill-suited for quantifying both the expected performance and uncertainty associated with candidate remediation strategies under real field conditions. Prediction uncertainties primarily arise from limited information about contaminant mass distributions, as well as the spatial distribution of subsurface hydrologic properties. Application of direct simulation to quantify uncertainty would, thus, typically require simulating multiphase flow and transport for a large number of permeability and release scenarios to collect statistics associated with remedial effectiveness, a computationally prohibitive process. The primary objective of this work is to develop and demonstrate a methodology that employs measured field data to produce equi-probable stochastic representations of a subsurface source zone that capture the spatial distribution and uncertainty associated with key features that control remediation performance (i.e., permeability and contamination mass). Here we employ probabilistic models known as discriminative random fields (DRFs) to synthesize stochastic realizations of initial mass distributions consistent with known, and typically limited, site characterization data. Using a limited number of full scale simulations as training data, a statistical model is developed for predicting the distribution of contaminant mass (e.g., DNAPL saturation and aqueous concentration) across a heterogeneous domain. Monte-Carlo sampling methods are then employed, in conjunction with the trained statistical model, to generate realizations conditioned on measured borehole data. Performance of the statistical model is illustrated through comparisons of generated realizations with the `true' numerical simulations. Finally, we demonstrate how these realizations can be used to determine statistically optimal locations for further interrogation of the subsurface.

Process simulation and dynamic control for marine oily wastewater treatment using UV irradiation.

PubMed

Jing, Liang; Chen, Bing; Zhang, Baiyu; Li, Pu

2015-09-15

UV irradiation and advanced oxidation processes have been recently regarded as promising solutions in removing polycyclic aromatic hydrocarbons (PAHs) from marine oily wastewater. However, such treatment methods are generally not sufficiently understood in terms of reaction mechanisms, process simulation and process control. These deficiencies can drastically hinder their application in shipping and offshore petroleum industries which produce bilge/ballast water and produced water as the main streams of marine oily wastewater. In this study, the factorial design of experiment was carried out to investigate the degradation mechanism of a typical PAH, namely naphthalene, under UV irradiation in seawater. Based on the experimental results, a three-layer feed-forward artificial neural network simulation model was developed to simulate the treatment process and to forecast the removal performance. A simulation-based dynamic mixed integer nonlinear programming (SDMINP) approach was then proposed to intelligently control the treatment process by integrating the developed simulation model, genetic algorithm and multi-stage programming. The applicability and effectiveness of the developed approach were further tested though a case study. The experimental results showed that the influences of fluence rate and temperature on the removal of naphthalene were greater than those of salinity and initial concentration. The developed simulation model could well predict the UV-induced removal process under varying conditions. The case study suggested that the SDMINP approach, with the aid of the multi-stage control strategy, was able to significantly reduce treatment cost when comparing to the traditional single-stage process optimization. The developed approach and its concept/framework have high potential of applicability in other environmental fields where a treatment process is involved and experimentation and modeling are used for process simulation and control. Copyright © 2015 Elsevier Ltd. All rights reserved.

Modeling the influence of coupled mass transfer processes on mass flux downgradient of heterogeneous DNAPL source zones

NASA Astrophysics Data System (ADS)

Yang, Lurong; Wang, Xinyu; Mendoza-Sanchez, Itza; Abriola, Linda M.

2018-04-01

Sequestered mass in low permeability zones has been increasingly recognized as an important source of organic chemical contamination that acts to sustain downgradient plume concentrations above regulated levels. However, few modeling studies have investigated the influence of this sequestered mass and associated (coupled) mass transfer processes on plume persistence in complex dense nonaqueous phase liquid (DNAPL) source zones. This paper employs a multiphase flow and transport simulator (a modified version of the modular transport simulator MT3DMS) to explore the two- and three-dimensional evolution of source zone mass distribution and near-source plume persistence for two ensembles of highly heterogeneous DNAPL source zone realizations. Simulations reveal the strong influence of subsurface heterogeneity on the complexity of DNAPL and sequestered (immobile/sorbed) mass distribution. Small zones of entrapped DNAPL are shown to serve as a persistent source of low concentration plumes, difficult to distinguish from other (sorbed and immobile dissolved) sequestered mass sources. Results suggest that the presence of DNAPL tends to control plume longevity in the near-source area; for the examined scenarios, a substantial fraction (43.3-99.2%) of plume life was sustained by DNAPL dissolution processes. The presence of sorptive media and the extent of sorption non-ideality are shown to greatly affect predictions of near-source plume persistence following DNAPL depletion, with plume persistence varying one to two orders of magnitude with the selected sorption model. Results demonstrate the importance of sorption-controlled back diffusion from low permeability zones and reveal the importance of selecting the appropriate sorption model for accurate prediction of plume longevity. Large discrepancies for both DNAPL depletion time and plume longevity were observed between 2-D and 3-D model simulations. Differences between 2- and 3-D predictions increased in the presence of sorption, especially for the case of non-ideal sorption, demonstrating the limitations of employing 2-D predictions for field-scale modeling.

A Thermo-Poromechanics Finite Element Model for Predicting Arterial Tissue Fusion

NASA Astrophysics Data System (ADS)

Fankell, Douglas P.

This work provides modeling efforts and supplemental experimental work performed towards the ultimate goal of modeling heat transfer, mass transfer, and deformation occurring in biological tissue, in particular during arterial fusion and cutting. Developing accurate models of these processes accomplishes two goals. First, accurate models would enable engineers to design devices to be safer and less expensive. Second, the mechanisms behind tissue fusion and cutting are widely unknown; models with the ability to accurately predict physical phenomena occurring in the tissue will allow for insight into the underlying mechanisms of the processes. This work presents three aims and the efforts in achieving them, leading to an accurate model of tissue fusion and more broadly the thermo-poromechanics (TPM) occurring within biological tissue. Chapters 1 and 2 provide the motivation for developing accurate TPM models of biological tissue and an overview of previous modeling efforts. In Chapter 3, a coupled thermo-structural finite element (FE) model with the ability to predict arterial cutting is offered. From the work presented in Chapter 3, it became obvious a more detailed model was needed. Chapter 4 meets this need by presenting small strain TPM theory and its implementation in an FE code. The model is then used to simulate thermal tissue fusion. These simulations show the model's promise in predicting the water content and temperature of arterial wall tissue during the fusion process, but it is limited by its small deformation assumptions. Chapters 5-7 attempt to address this limitation by developing and implementing a large deformation TPM FE model. Chapters 5, 6, and 7 present a thermodynamically consistent, large deformation TPM FE model and its ability to simulate tissue fusion. Ultimately, this work provides several methods of simulating arterial tissue fusion and the thermo-poromechanics of biological tissue. It is the first work, to the author's knowledge, to simulate the fully coupled TPM of biological tissue and the first to present a fully coupled large deformation TPM FE model. In doing so, a stepping stone for more advanced modeling of biological tissue has been laid.

The Fractional Step Method Applied to Simulations of Natural Convective Flows

NASA Technical Reports Server (NTRS)

Westra, Douglas G.; Heinrich, Juan C.; Saxon, Jeff (Technical Monitor)

2002-01-01

This paper describes research done to apply the Fractional Step Method to finite-element simulations of natural convective flows in pure liquids, permeable media, and in a directionally solidified metal alloy casting. The Fractional Step Method has been applied commonly to high Reynold's number flow simulations, but is less common for low Reynold's number flows, such as natural convection in liquids and in permeable media. The Fractional Step Method offers increased speed and reduced memory requirements by allowing non-coupled solution of the pressure and the velocity components. The Fractional Step Method has particular benefits for predicting flows in a directionally solidified alloy, since other methods presently employed are not very efficient. Previously, the most suitable method for predicting flows in a directionally solidified binary alloy was the penalty method. The penalty method requires direct matrix solvers, due to the penalty term. The Fractional Step Method allows iterative solution of the finite element stiffness matrices, thereby allowing more efficient solution of the matrices. The Fractional Step Method also lends itself to parallel processing, since the velocity component stiffness matrices can be built and solved independently of each other. The finite-element simulations of a directionally solidified casting are used to predict macrosegregation in directionally solidified castings. In particular, the finite-element simulations predict the existence of 'channels' within the processing mushy zone and subsequently 'freckles' within the fully processed solid, which are known to result from macrosegregation, or what is often referred to as thermo-solutal convection. These freckles cause material property non-uniformities in directionally solidified castings; therefore many of these castings are scrapped. The phenomenon of natural convection in an alloy under-going directional solidification, or thermo-solutal convection, will be explained. The development of the momentum and continuity equations for natural convection in a fluid, a permeable medium, and in a binary alloy undergoing directional solidification will be presented. Finally, results for natural convection in a pure liquid, natural convection in a medium with a constant permeability, and for directional solidification will be presented.

Enhancements to the Water Erosion Prediction Project (WEPP) for modeling large snow-dominated mountainous forest watersheds

Treesearch

Anurag Srivastava; Joan Q. Wu; William J. Elliot; Erin S. Brooks

2015-01-01

The Water Erosion Prediction Project (WEPP) model, originally developed for hillslope and small watershed applications, simulates complex interactive processes influencing erosion. Recent incorporations to the model have improved the subsurface hydrology components for forest applications. Incorporation of channel routing has made the WEPP model well suited for large...

NAME Modeling and Climate Process Team

NASA Astrophysics Data System (ADS)

Schemm, J. E.; Williams, L. N.; Gutzler, D. S.

2007-05-01

NAME Climate Process and Modeling Team (CPT) has been established to address the need of linking climate process research to model development and testing activities for warm season climate prediction. The project builds on two existing NAME-related modeling efforts. One major component of this project is the organization and implementation of a second phase of NAMAP, based on the 2004 season. NAMAP2 will re-examine the metrics proposed by NAMAP, extend the NAMAP analysis to transient variability, exploit the extensive observational database provided by NAME 2004 to analyze simulation targets of special interest, and expand participation. Vertical column analysis will bring local NAME observations and model outputs together in a context where key physical processes in the models can be evaluated and improved. The second component builds on the current NAME-related modeling effort focused on the diurnal cycle of precipitation in several global models, including those implemented at NCEP, NASA and GFDL. Our activities will focus on the ability of the operational NCEP Global Forecast System (GFS) to simulate the diurnal and seasonal evolution of warm season precipitation during the NAME 2004 EOP, and on changes to the treatment of deep convection in the complicated terrain of the NAMS domain that are necessary to improve the simulations, and ultimately predictions of warm season precipitation These activities will be strongly tied to NAMAP2 to ensure technology transfer from research to operations. Results based on experiments conducted with the NCEP CFS GCM will be reported at the conference with emphasis on the impact of horizontal resolution in predicting warm season precipitation over North America.

Sensitivity Analysis of the Sheet Metal Stamping Processes Based on Inverse Finite Element Modeling and Monte Carlo Simulation

NASA Astrophysics Data System (ADS)

Yu, Maolin; Du, R.

2005-08-01

Sheet metal stamping is one of the most commonly used manufacturing processes, and hence, much research has been carried for economic gain. Searching through the literatures, however, it is found that there are still a lots of problems unsolved. For example, it is well known that for a same press, same workpiece material, and same set of die, the product quality may vary owing to a number of factors, such as the inhomogeneous of the workpice material, the loading error, the lubrication, and etc. Presently, few seem able to predict the quality variation, not to mention what contribute to the quality variation. As a result, trial-and-error is still needed in the shop floor, causing additional cost and time delay. This paper introduces a new approach to predict the product quality variation and identify the sensitive design / process parameters. The new approach is based on a combination of inverse Finite Element Modeling (FEM) and Monte Carlo Simulation (more specifically, the Latin Hypercube Sampling (LHS) approach). With an acceptable accuracy, the inverse FEM (also called one-step FEM) requires much less computation load than that of the usual incremental FEM and hence, can be used to predict the quality variations under various conditions. LHS is a statistical method, through which the sensitivity analysis can be carried out. The result of the sensitivity analysis has clear physical meaning and can be used to optimize the die design and / or the process design. Two simulation examples are presented including drawing a rectangular box and drawing a two-step rectangular box.

Modeling regional air quality and climate: improving organic aerosol and aerosol activation processes in WRF/Chem version 3.7.1

NASA Astrophysics Data System (ADS)

Yahya, Khairunnisa; Glotfelty, Timothy; Wang, Kai; Zhang, Yang; Nenes, Athanasios

2017-06-01

Air quality and climate influence each other through the uncertain processes of aerosol formation and cloud droplet activation. In this study, both processes are improved in the Weather, Research and Forecasting model with Chemistry (WRF/Chem) version 3.7.1. The existing Volatility Basis Set (VBS) treatments for organic aerosol (OA) formation in WRF/Chem are improved by considering the following: the secondary OA (SOA) formation from semi-volatile primary organic aerosol (POA), a semi-empirical formulation for the enthalpy of vaporization of SOA, and functionalization and fragmentation reactions for multiple generations of products from the oxidation of VOCs. Over the continental US, 2-month-long simulations (May to June 2010) are conducted and results are evaluated against surface and aircraft observations during the Nexus of Air Quality and Climate Change (CalNex) campaign. Among all the configurations considered, the best performance is found for the simulation with the 2005 Carbon Bond mechanism (CB05) and the VBS SOA module with semivolatile POA treatment, 25 % fragmentation, and the emissions of semi-volatile and intermediate volatile organic compounds being 3 times the original POA emissions. Among the three gas-phase mechanisms (CB05, CB6, and SAPRC07) used, CB05 gives the best performance for surface ozone and PM2. 5 concentrations. Differences in SOA predictions are larger for the simulations with different VBS treatments (e.g., nonvolatile POA versus semivolatile POA) compared to the simulations with different gas-phase mechanisms. Compared to the simulation with CB05 and the default SOA module, the simulations with the VBS treatment improve cloud droplet number concentration (CDNC) predictions (normalized mean biases from -40.8 % to a range of -34.6 to -27.7 %), with large differences between CB05-CB6 and SAPRC07 due to large differences in their OH and HO2 predictions. An advanced aerosol activation parameterization based on the Fountoukis and Nenes (2005) series reduces the large negative CDNC bias associated with the default Abdul Razzak and Ghan (2000) parameterization from -35.4 % to a range of -0.8 to 7.1 %. However, it increases the errors due to overpredictions of CDNC, mainly over the northeastern US. This work indicates a need to improve other aerosol-cloud-radiation processes in the model, such as the spatial distribution of aerosol optical depth and cloud condensation nuclei, in order to further improve CDNC predictions.

Typical action perception and interpretation without motor simulation

PubMed Central

Vannuscorps, Gilles; Caramazza, Alfonso

2016-01-01

Every day, we interact with people synchronously, immediately understand what they are doing, and easily infer their mental state and the likely outcome of their actions from their kinematics. According to various motor simulation theories of perception, such efficient perceptual processing of others’ actions cannot be achieved by visual analysis of the movements alone but requires a process of motor simulation—an unconscious, covert imitation of the observed movements. According to this hypothesis, individuals incapable of simulating observed movements in their motor system should have difficulty perceiving and interpreting observed actions. Contrary to this prediction, we found across eight sensitive experiments that individuals born with absent or severely shortened upper limbs (upper limb dysplasia), despite some variability, could perceive, anticipate, predict, comprehend, and memorize upper limb actions, which they cannot simulate, as efficiently as typically developed participants. We also found that, like the typically developed participants, the dysplasic participants systematically perceived the position of moving upper limbs slightly ahead of their real position but only when the anticipated position was not biomechanically awkward. Such anticipatory bias and its modulation by implicit knowledge of the body biomechanical constraints were previously considered as indexes of the crucial role of motor simulation in action perception. Our findings undermine this assumption and the theories that place the locus of action perception and comprehension in the motor system and invite a shift in the focus of future research to the question of how the visuo-perceptual system represents and processes observed body movements and actions. PMID:26699468

Key Process Uncertainties in Soil Carbon Dynamics: Comparing Multiple Model Structures and Observational Meta-analysis

NASA Astrophysics Data System (ADS)

Sulman, B. N.; Moore, J.; Averill, C.; Abramoff, R. Z.; Bradford, M.; Classen, A. T.; Hartman, M. D.; Kivlin, S. N.; Luo, Y.; Mayes, M. A.; Morrison, E. W.; Riley, W. J.; Salazar, A.; Schimel, J.; Sridhar, B.; Tang, J.; Wang, G.; Wieder, W. R.

2016-12-01

Soil carbon (C) dynamics are crucial to understanding and predicting C cycle responses to global change and soil C modeling is a key tool for understanding these dynamics. While first order model structures have historically dominated this area, a recent proliferation of alternative model structures representing different assumptions about microbial activity and mineral protection is providing new opportunities to explore process uncertainties related to soil C dynamics. We conducted idealized simulations of soil C responses to warming and litter addition using models from five research groups that incorporated different sets of assumptions about processes governing soil C decomposition and stabilization. We conducted a meta-analysis of published warming and C addition experiments for comparison with simulations. Assumptions related to mineral protection and microbial dynamics drove strong differences among models. In response to C additions, some models predicted long-term C accumulation while others predicted transient increases that were counteracted by accelerating decomposition. In experimental manipulations, doubling litter addition did not change soil C stocks in studies spanning as long as two decades. This result agreed with simulations from models with strong microbial growth responses and limited mineral sorption capacity. In observations, warming initially drove soil C loss via increased CO2 production, but in some studies soil C rebounded and increased over decadal time scales. In contrast, all models predicted sustained C losses under warming. The disagreement with experimental results could be explained by physiological or community-level acclimation, or by warming-related changes in plant growth. In addition to the role of microbial activity, assumptions related to mineral sorption and protected C played a key role in driving long-term model responses. In general, simulations were similar in their initial responses to perturbations but diverged over decadal time scales. This suggests that more long-term soil experiments may be necessary to resolve important process uncertainties related to soil C storage. We also suggest future experiments examine how microbial activity responds to warming under a range of soil clay contents and in concert with changes in litter inputs.

Laser Doppler velocimeter system simulation for sensing aircraft wake vortices. Part 2: Processing and analysis of LDV data (for runs 1023 and 2023)

NASA Technical Reports Server (NTRS)

Meng, J. C. S.; Thomson, J. A. L.

1975-01-01

A data analysis program constructed to assess LDV system performance, to validate the simulation model, and to test various vortex location algorithms is presented. Real or simulated Doppler spectra versus range and elevation is used and the spatial distributions of various spectral moments or other spectral characteristics are calculated and displayed. Each of the real or simulated scans can be processed by one of three different procedures: simple frequency or wavenumber filtering, matched filtering, and deconvolution filtering. The final output is displayed as contour plots in an x-y coordinate system, as well as in the form of vortex tracks deduced from the maxima of the processed data. A detailed analysis of run number 1023 and run number 2023 is presented to demonstrate the data analysis procedure. Vortex tracks and system range resolutions are compared with theoretical predictions.

Finite Element Analysis of Single Wheat Mechanical Response to Wind and Rain Loads

NASA Astrophysics Data System (ADS)

Liang, Li; Guo, Yuming

One variety of wheat in the breeding process was chosen to determine the wheat morphological traits and biomechanical properties. ANSYS was used to build the mechanical model of wheat to wind load and the dynamic response of wheat to wind load was simulated. The maximum Von Mises stress is obtained by the powerful calculation function of ANSYS. And the changing stress and displacement of each node and finite element in the process of simulation can be output through displacement nephogram and stress nephogram. The load support capability can be evaluated and to predict the wheat lodging. It is concluded that computer simulation technology has unique advantages such as convenient and efficient in simulating mechanical response of wheat stalk under wind and rain load. Especially it is possible to apply various load types on model and the deformation process can be observed simultaneously.

Fiber pushout test: A three-dimensional finite element computational simulation

NASA Technical Reports Server (NTRS)

Mital, Subodh K.; Chamis, Christos C.

1990-01-01

A fiber pushthrough process was computationally simulated using three-dimensional finite element method. The interface material is replaced by an anisotropic material with greatly reduced shear modulus in order to simulate the fiber pushthrough process using a linear analysis. Such a procedure is easily implemented and is computationally very effective. It can be used to predict fiber pushthrough load for a composite system at any temperature. The average interface shear strength obtained from pushthrough load can easily be separated into its two components: one that comes from frictional stresses and the other that comes from chemical adhesion between fiber and the matrix and mechanical interlocking that develops due to shrinkage of the composite because of phase change during the processing. Step-by-step procedures are described to perform the computational simulation, to establish bounds on interfacial bond strength and to interpret interfacial bond quality.

A process-based model for cattle manure compost windrows: Model performance and application

USDA-ARS?s Scientific Manuscript database

A model was developed and incorporated in the Integrated Farm System Model (IFSM, v.4.3) that simulates important processes occurring during windrow composting of manure. The model, documented in an accompanying paper, predicts changes in windrow properties and conditions and the resulting emissions...

Computer Models of Personality: Implications for Measurement

ERIC Educational Resources Information Center

Cranton, P. A.

1976-01-01

Current research on computer models of personality is reviewed and categorized under five headings: (1) models of belief systems; (2) models of interpersonal behavior; (3) models of decision-making processes; (4) prediction models; and (5) theory-based simulations of specific processes. The use of computer models in personality measurement is…

Proposal of Modification Strategy of NC Program in the Virtual Manufacturing Environment

NASA Astrophysics Data System (ADS)

Narita, Hirohisa; Chen, Lian-Yi; Fujimoto, Hideo; Shirase, Keiichi; Arai, Eiji

Virtual manufacturing will be a key technology in process planning, because there are no evaluation tools for cutting conditions. Therefore, virtual machining simulator (VMSim), which can predict end milling processes, has been developed. The modification strategy of NC program using VMSim is proposed in this paper.

Different Parameters Support Generalization and Discrimination Learning in "Drosophila" at the Flight Simulator

ERIC Educational Resources Information Center

Brembs, Bjorn; de Ibarra, Natalie Hempel

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…

Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations.

PubMed

Cournia, Zoe; Allen, Bryce; Sherman, Woody

2017-12-26

Accurate in silico prediction of protein-ligand binding affinities has been a primary objective of structure-based drug design for decades due to the putative value it would bring to the drug discovery process. However, computational methods have historically failed to deliver value in real-world drug discovery applications due to a variety of scientific, technical, and practical challenges. Recently, a family of approaches commonly referred to as relative binding free energy (RBFE) calculations, which rely on physics-based molecular simulations and statistical mechanics, have shown promise in reliably generating accurate predictions in the context of drug discovery projects. This advance arises from accumulating developments in the underlying scientific methods (decades of research on force fields and sampling algorithms) coupled with vast increases in computational resources (graphics processing units and cloud infrastructures). Mounting evidence from retrospective validation studies, blind challenge predictions, and prospective applications suggests that RBFE simulations can now predict the affinity differences for congeneric ligands with sufficient accuracy and throughput to deliver considerable value in hit-to-lead and lead optimization efforts. Here, we present an overview of current RBFE implementations, highlighting recent advances and remaining challenges, along with examples that emphasize practical considerations for obtaining reliable RBFE results. We focus specifically on relative binding free energies because the calculations are less computationally intensive than absolute binding free energy (ABFE) calculations and map directly onto the hit-to-lead and lead optimization processes, where the prediction of relative binding energies between a reference molecule and new ideas (virtual molecules) can be used to prioritize molecules for synthesis. We describe the critical aspects of running RBFE calculations, from both theoretical and applied perspectives, using a combination of retrospective literature examples and prospective studies from drug discovery projects. This work is intended to provide a contemporary overview of the scientific, technical, and practical issues associated with running relative binding free energy simulations, with a focus on real-world drug discovery applications. We offer guidelines for improving the accuracy of RBFE simulations, especially for challenging cases, and emphasize unresolved issues that could be improved by further research in the field.

Intraindividual variability in basic reaction time predicts middle-aged and older pilots' flight simulator performance.

PubMed

Kennedy, Quinn; Taylor, Joy; Heraldez, Daniel; Noda, Art; Lazzeroni, Laura C; Yesavage, Jerome

2013-07-01

Intraindividual variability (IIV) is negatively associated with cognitive test performance and is positively associated with age and some neurological disorders. We aimed to extend these findings to a real-world task, flight simulator performance. We hypothesized that IIV predicts poorer initial flight performance and increased rate of decline in performance among middle-aged and older pilots. Two-hundred and thirty-six pilots (40-69 years) completed annual assessments comprising a cognitive battery and two 75-min simulated flights in a flight simulator. Basic and complex IIV composite variables were created from measures of basic reaction time and shifting and divided attention tasks. Flight simulator performance was characterized by an overall summary score and scores on communication, emergencies, approach, and traffic avoidance components. Although basic IIV did not predict rate of decline in flight performance, it had a negative association with initial performance for most flight measures. After taking into account processing speed, basic IIV explained an additional 8%-12% of the negative age effect on initial flight performance. IIV plays an important role in real-world tasks and is another aspect of cognition that underlies age-related differences in cognitive performance.

Intraindividual Variability in Basic Reaction Time Predicts Middle-Aged and Older Pilots’ Flight Simulator Performance

PubMed Central

2013-01-01

Objectives. Intraindividual variability (IIV) is negatively associated with cognitive test performance and is positively associated with age and some neurological disorders. We aimed to extend these findings to a real-world task, flight simulator performance. We hypothesized that IIV predicts poorer initial flight performance and increased rate of decline in performance among middle-aged and older pilots. Method. Two-hundred and thirty-six pilots (40–69 years) completed annual assessments comprising a cognitive battery and two 75-min simulated flights in a flight simulator. Basic and complex IIV composite variables were created from measures of basic reaction time and shifting and divided attention tasks. Flight simulator performance was characterized by an overall summary score and scores on communication, emergencies, approach, and traffic avoidance components. Results. Although basic IIV did not predict rate of decline in flight performance, it had a negative association with initial performance for most flight measures. After taking into account processing speed, basic IIV explained an additional 8%–12% of the negative age effect on initial flight performance. Discussion. IIV plays an important role in real-world tasks and is another aspect of cognition that underlies age-related differences in cognitive performance. PMID:23052365

An Exercise Health Simulation Method Based on Integrated Human Thermophysiological Model

PubMed Central

Chen, Xiaohui; Yu, Liang; Yang, Kaixing

2017-01-01

Research of healthy exercise has garnered a keen research for the past few years. It is known that participation in a regular exercise program can help improve various aspects of cardiovascular function and reduce the risk of suffering from illness. But some exercise accidents like dehydration, exertional heatstroke, and even sudden death need to be brought to attention. If these exercise accidents can be analyzed and predicted before they happened, it will be beneficial to alleviate or avoid disease or mortality. To achieve this objective, an exercise health simulation approach is proposed, in which an integrated human thermophysiological model consisting of human thermal regulation model and a nonlinear heart rate regulation model is reported. The human thermoregulatory mechanism as well as the heart rate response mechanism during exercise can be simulated. On the basis of the simulated physiological indicators, a fuzzy finite state machine is constructed to obtain the possible health transition sequence and predict the exercise health status. The experiment results show that our integrated exercise thermophysiological model can numerically simulate the thermal and physiological processes of the human body during exercise and the predicted exercise health transition sequence from finite state machine can be used in healthcare. PMID:28702074

Prediction of Aerodynamic Coefficient using Genetic Algorithm Optimized Neural Network for Sparse Data

NASA Technical Reports Server (NTRS)

Rajkumar, T.; Bardina, Jorge; Clancy, Daniel (Technical Monitor)

2002-01-01

Wind tunnels use scale models to characterize aerodynamic coefficients, Wind tunnel testing can be slow and costly due to high personnel overhead and intensive power utilization. Although manual curve fitting can be done, it is highly efficient to use a neural network to define the complex relationship between variables. Numerical simulation of complex vehicles on the wide range of conditions required for flight simulation requires static and dynamic data. Static data at low Mach numbers and angles of attack may be obtained with simpler Euler codes. Static data of stalled vehicles where zones of flow separation are usually present at higher angles of attack require Navier-Stokes simulations which are costly due to the large processing time required to attain convergence. Preliminary dynamic data may be obtained with simpler methods based on correlations and vortex methods; however, accurate prediction of the dynamic coefficients requires complex and costly numerical simulations. A reliable and fast method of predicting complex aerodynamic coefficients for flight simulation I'S presented using a neural network. The training data for the neural network are derived from numerical simulations and wind-tunnel experiments. The aerodynamic coefficients are modeled as functions of the flow characteristics and the control surfaces of the vehicle. The basic coefficients of lift, drag and pitching moment are expressed as functions of angles of attack and Mach number. The modeled and training aerodynamic coefficients show good agreement. This method shows excellent potential for rapid development of aerodynamic models for flight simulation. Genetic Algorithms (GA) are used to optimize a previously built Artificial Neural Network (ANN) that reliably predicts aerodynamic coefficients. Results indicate that the GA provided an efficient method of optimizing the ANN model to predict aerodynamic coefficients. The reliability of the ANN using the GA includes prediction of aerodynamic coefficients to an accuracy of 110% . In our problem, we would like to get an optimized neural network architecture and minimum data set. This has been accomplished within 500 training cycles of a neural network. After removing training pairs (outliers), the GA has produced much better results. The neural network constructed is a feed forward neural network with a back propagation learning mechanism. The main goal has been to free the network design process from constraints of human biases, and to discover better forms of neural network architectures. The automation of the network architecture search by genetic algorithms seems to have been the best way to achieve this goal.

Evaluation of Boreal Summer Monsoon Intraseasonal Variability in the GASS-YOTC Multi-Model Physical Processes Experiment

NASA Astrophysics Data System (ADS)

Mani, N. J.; Waliser, D. E.; Jiang, X.

2014-12-01

While the boreal summer monsoon intraseasonal variability (BSISV) exerts profound influence on the south Asian monsoon, the capability of present day dynamical models in simulating and predicting the BSISV is still limited. The global model evaluation project on vertical structure and diabatic processes of the Madden Julian Oscillations (MJO) is a joint venture, coordinated by the Working Group on Numerical Experimentation (WGNE) MJO Task Force and GEWEX Atmospheric System Study (GASS) program, for assessing the model deficiencies in simulating the ISV and for improving our understanding of the underlying processes. In this study the simulation of the northward propagating BSISV is investigated in 26 climate models with special focus on the vertical diabatic heating structure and clouds. Following parallel lines of inquiry as the MJO Task Force has done with the eastward propagating MJO, we utilize previously proposed and newly developed model performance metrics and process diagnostics and apply them to the global climate model simulations of BSISV.

Reverse logistics system planning for recycling computers hardware: A case study

NASA Astrophysics Data System (ADS)

Januri, Siti Sarah; Zulkipli, Faridah; Zahari, Siti Meriam; Shamsuri, Siti Hajar

2014-09-01

This paper describes modeling and simulation of reverse logistics networks for collection of used computers in one of the company in Selangor. The study focuses on design of reverse logistics network for used computers recycling operation. Simulation modeling, presented in this work allows the user to analyze the future performance of the network and to understand the complex relationship between the parties involved. The findings from the simulation suggest that the model calculates processing time and resource utilization in a predictable manner. In this study, the simulation model was developed by using Arena simulation package.

Impact of polymer film thickness and cavity size on polymer flow during embossing : towards process design rules for nanoimprint lithography.

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

Schunk, Peter Randall; King, William P.; Sun, Amy Cha-Tien

2006-08-01

This paper presents continuum simulations of polymer flow during nanoimprint lithography (NIL). The simulations capture the underlying physics of polymer flow from the nanometer to millimeter length scale and examine geometry and thermophysical process quantities affecting cavity filling. Variations in embossing tool geometry and polymer film thickness during viscous flow distinguish different flow driving mechanisms. Three parameters can predict polymer deformation mode: cavity width to polymer thickness ratio, polymer supply ratio, and Capillary number. The ratio of cavity width to initial polymer film thickness determines vertically or laterally dominant deformation. The ratio of indenter width to residual film thickness measuresmore » polymer supply beneath the indenter which determines Stokes or squeeze flow. The local geometry ratios can predict a fill time based on laminar flow between plates, Stokes flow, or squeeze flow. Characteristic NIL capillary number based on geometry-dependent fill time distinguishes between capillary or viscous driven flows. The three parameters predict filling modes observed in published studies of NIL deformation over nanometer to millimeter length scales. The work seeks to establish process design rules for NIL and to provide tools for the rational design of NIL master templates, resist polymers, and process parameters.« less

The Impeller Exit Flow Coefficient As a Performance Map Variable for Predicting Centrifugal Compressor Off-Design Operation Applied to a Supercritical CO 2 Working Fluid

DOE PAGES

Liese, Eric; Zitney, Stephen E.

2017-06-26

A multi-stage centrifugal compressor model is presented with emphasis on analyzing use of an exit flow coefficient vs. an inlet flow coefficient performance parameter to predict off-design conditions in the critical region of a supercritical carbon dioxide (CO 2) power cycle. A description of the performance parameters is given along with their implementation in a design model (number of stages, basic sizing, etc.) and a dynamic model (for use in transient studies). A design case is shown for two compressors, a bypass compressor and a main compressor, as defined in a process simulation of a 10 megawatt (MW) supercritical COmore » 2 recompression Brayton cycle. Simulation results are presented for a simple open cycle and closed cycle process with changes to the inlet temperature of the main compressor which operates near the CO 2 critical point. Results showed some difference in results using the exit vs. inlet flow coefficient correction, however, it was not significant for the range of conditions examined. Here, this paper also serves as a reference for future works, including a full process simulation of the 10 MW recompression Brayton cycle.« less

A Physics-Based Engineering Approach to Predict the Cross Section for Advanced SRAMs

NASA Astrophysics Data System (ADS)

Li, Lei; Zhou, Wanting; Liu, Huihua

2012-12-01

This paper presents a physics-based engineering approach to estimate the heavy ion induced upset cross section for 6T SRAM cells from layout and technology parameters. The new approach calculates the effects of radiation with junction photocurrent, which is derived based on device physics. The new and simple approach handles the problem by using simple SPICE simulations. At first, the approach uses a standard SPICE program on a typical PC to predict the SPICE-simulated curve of the collected charge vs. its affected distance from the drain-body junction with the derived junction photocurrent. And then, the SPICE-simulated curve is used to calculate the heavy ion induced upset cross section with a simple model, which considers that the SEU cross section of a SRAM cell is more related to a “radius of influence” around a heavy ion strike than to the physical size of a diffusion node in the layout for advanced SRAMs in nano-scale process technologies. The calculated upset cross section based on this method is in good agreement with the test results for 6T SRAM cells processed using 90 nm process technology.

The Impeller Exit Flow Coefficient As a Performance Map Variable for Predicting Centrifugal Compressor Off-Design Operation Applied to a Supercritical CO 2 Working Fluid

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

Liese, Eric; Zitney, Stephen E.

A multi-stage centrifugal compressor model is presented with emphasis on analyzing use of an exit flow coefficient vs. an inlet flow coefficient performance parameter to predict off-design conditions in the critical region of a supercritical carbon dioxide (CO 2) power cycle. A description of the performance parameters is given along with their implementation in a design model (number of stages, basic sizing, etc.) and a dynamic model (for use in transient studies). A design case is shown for two compressors, a bypass compressor and a main compressor, as defined in a process simulation of a 10 megawatt (MW) supercritical COmore » 2 recompression Brayton cycle. Simulation results are presented for a simple open cycle and closed cycle process with changes to the inlet temperature of the main compressor which operates near the CO 2 critical point. Results showed some difference in results using the exit vs. inlet flow coefficient correction, however, it was not significant for the range of conditions examined. Here, this paper also serves as a reference for future works, including a full process simulation of the 10 MW recompression Brayton cycle.« less

Simulation of void formation in interconnect lines

NASA Astrophysics Data System (ADS)

Sheikholeslami, Alireza; Heitzinger, Clemens; Puchner, Helmut; Badrieh, Fuad; Selberherr, Siegfried

2003-04-01

The predictive simulation of the formation of voids in interconnect lines is important for improving capacitance and timing in current memory cells. The cells considered are used in wireless applications such as cell phones, pagers, radios, handheld games, and GPS systems. In backend processes for memory cells, ILD (interlayer dielectric) materials and processes result in void formation during gap fill. This approach lowers the overall k-value of a given metal layer and is economically advantageous. The effect of the voids on the overall capacitive load is tremendous. In order to simulate the shape and positions of the voids and thus the overall capacitance, the topography simulator ELSA (Enhanced Level Set Applications) has been developed which consists of three modules, a level set module, a radiosity module, and a surface reaction module. The deposition process considered is deposition of silicon nitride. Test structures of interconnect lines of memory cells were fabricated and several SEM images thereof were used to validate the corresponding simulations.

Computer simulation: A modern day crystal ball?

NASA Technical Reports Server (NTRS)

Sham, Michael; Siprelle, Andrew

1994-01-01

It has long been the desire of managers to be able to look into the future and predict the outcome of decisions. With the advent of computer simulation and the tremendous capability provided by personal computers, that desire can now be realized. This paper presents an overview of computer simulation and modeling, and discusses the capabilities of Extend. Extend is an iconic-driven Macintosh-based software tool that brings the power of simulation to the average computer user. An example of an Extend based model is presented in the form of the Space Transportation System (STS) Processing Model. The STS Processing Model produces eight shuttle launches per year, yet it takes only about ten minutes to run. In addition, statistical data such as facility utilization, wait times, and processing bottlenecks are produced. The addition or deletion of resources, such as orbiters or facilities, can be easily modeled and their impact analyzed. Through the use of computer simulation, it is possible to look into the future to see the impact of today's decisions.

Using artificial intelligence to control fluid flow computations

NASA Technical Reports Server (NTRS)

Gelsey, Andrew

1992-01-01

Computational simulation is an essential tool for the prediction of fluid flow. Many powerful simulation programs exist today. However, using these programs to reliably analyze fluid flow and other physical situations requires considerable human effort and expertise to set up a simulation, determine whether the output makes sense, and repeatedly run the simulation with different inputs until a satisfactory result is achieved. Automating this process is not only of considerable practical importance but will also significantly advance basic artificial intelligence (AI) research in reasoning about the physical world.

Simulation of the Francis-99 Hydro Turbine During Steady and Transient Operation

NASA Astrophysics Data System (ADS)

Dewan, Yuvraj; Custer, Chad; Ivashchenko, Artem

2017-01-01

Numerical simulation of the Francis-99 hydroturbine with correlation to experimental measurements are presented. Steady operation of the hydroturbine is analyzed at three operating conditions: the best efficiency point (BEP), high load (HL), and part load (PL). It is shown that global quantities such as net head, discharge and efficiency are well predicted. Additionally, time-averaged velocity predictions compare well with PIV measurements obtained in the draft tube immediately downstream of the runner. Differences in vortex rope structure between operating points are discussed. Unsteady operation of the hydroturbine from BEP to HL and from BEP to PL are modeled. It is shown that simulation methods used to model the steady operation produce predictions that correlate well with experiment for transient operation. Time-domain unsteady simulation is used for both steady and unsteady operation. The full-fidelity geometry including all components is meshed using an unstructured polyhedral mesh with body-fitted prism layers. Guide vane rotation for transient operation is imposed using fully-conservative, computationally efficient mesh morphing. The commercial solver STAR-CCM+ is used for all portions of the analysis including meshing, solving and post-processing.

Coarse-Graining Polymer Field Theory for Fast and Accurate Simulations of Directed Self-Assembly

NASA Astrophysics Data System (ADS)

Liu, Jimmy; Delaney, Kris; Fredrickson, Glenn

To design effective manufacturing processes using polymer directed self-assembly (DSA), the semiconductor industry benefits greatly from having a complete picture of stable and defective polymer configurations. Field-theoretic simulations are an effective way to study these configurations and predict defect populations. Self-consistent field theory (SCFT) is a particularly successful theory for studies of DSA. Although other models exist that are faster to simulate, these models are phenomenological or derived through asymptotic approximations, often leading to a loss of accuracy relative to SCFT. In this study, we employ our recently-developed method to produce an accurate coarse-grained field theory for diblock copolymers. The method uses a force- and stress-matching strategy to map output from SCFT simulations into parameters for an optimized phase field model. This optimized phase field model is just as fast as existing phenomenological phase field models, but makes more accurate predictions of polymer self-assembly, both in bulk and in confined systems. We study the performance of this model under various conditions, including its predictions of domain spacing, morphology and defect formation energies. Samsung Electronics.

Application of all relevant feature selection for failure analysis of parameter-induced simulation crashes in climate models

NASA Astrophysics Data System (ADS)

Paja, W.; Wrzesień, M.; Niemiec, R.; Rudnicki, W. R.

2015-07-01

The climate models are extremely complex pieces of software. They reflect best knowledge on physical components of the climate, nevertheless, they contain several parameters, which are too weakly constrained by observations, and can potentially lead to a crash of simulation. Recently a study by Lucas et al. (2013) has shown that machine learning methods can be used for predicting which combinations of parameters can lead to crash of simulation, and hence which processes described by these parameters need refined analyses. In the current study we reanalyse the dataset used in this research using different methodology. We confirm the main conclusion of the original study concerning suitability of machine learning for prediction of crashes. We show, that only three of the eight parameters indicated in the original study as relevant for prediction of the crash are indeed strongly relevant, three other are relevant but redundant, and two are not relevant at all. We also show that the variance due to split of data between training and validation sets has large influence both on accuracy of predictions and relative importance of variables, hence only cross-validated approach can deliver robust prediction of performance and relevance of variables.

Comparison of LOPES measurements with CoREAS and REAS 3.11 simulations

NASA Astrophysics Data System (ADS)

Ludwig, M.; Apel, W. D.; Arteaga-Velázquez, J. C.; Bähren, L.; Bekk, K.; Bertaina, M.; Biermann, P. L.; Blümer, J.; Bozdog, H.; Brancus, I. M.; Chiavassa, A.; Daumiller, K.; de Souza, V.; Di Pierro, F.; Doll, P.; Engel, R.; Falcke, H.; Fuchs, B.; Fuhrmann, D.; Gemmeke, H.; Grupen, C.; Haug, M.; Haungs, A.; Heck, D.; Hörandel, J. R.; Horneffer, A.; Huber, D.; Huege, T.; Isar, P. G.; Kampert, K.-H.; Kang, D.; Krömer, O.; Kuijpers, J.; Link, K.; Łuczak, P.; Mathes, H. J.; Melissas, M.; Morello, C.; Oehlschläger, J.; Palmieri, N.; Pierog, T.; Rautenberg, J.; Rebel, H.; Roth, M.; Rühle, C.; Saftoiu, A.; Schieler, H.; Schmidt, A.; Schröder, F. G.; Sima, O.; Toma, G.; Trinchero, G. C.; Weindl, A.; Wochele, J.; Zabierowski, J.; Zensus, J. A.

2013-05-01

In the previous years, LOPES emerged as a very successful experiment measuring the radio emission from air showers in the MHz frequency range. In parallel, the theoretical description of radio emission was developed further and REAS became a widely used simulation Monte Carlo code. REAS 3 as well as CoREAS are based on the endpoint formalism, i.e. they calculate the emission of the air-shower without assuming specific emission mechanisms. While REAS 3 is based on histograms derived from CORSIKA simulations, CoREAS is directly implemented into CORSIKA without loss of information due to histogramming of the particle distributions. In contrast to the earlier versions of REAS, the newest version REAS 3.11 and CoREAS take into account a realistic atmospheric refractive index. To improve the understanding of the emission processes and judge the quality of the simulations, we compare their predictions with high-quality events measured by LOPES. We present results concerning the lateral distribution measured with 30 east-west aligned LOPES antennas. Only the simulation codes including the refractive index (REAS 3.11 and CoREAS) are able to reproduce the slope of measured lateral distributions, but REAS 3.0 predicts too steep lateral distributions, and does not predict rising lateral distributions as seen in a few LOPES events. Moreover, REAS 3.11 predicts an absolute amplitude compatible with the LOPES measurements.

Influence of the power law index on the fiber breakage during injection molding by numerical simulations

NASA Astrophysics Data System (ADS)

Desplentere, Frederik; Six, Wim; Bonte, Hilde; Debrabandere, Eric

2013-04-01

In predictive engineering for polymer processes, the proper prediction of material microstructure from known processing conditions and constituent material properties is a critical step forward properly predicting bulk properties in the finished composite. Operating within the context of long-fiber thermoplastics (LFT, length > 15mm) this investigation concentrates on the influence of the power law index on the final fiber length distribution within the injection molded part. To realize this, the Autodesk Simulation Moldflow Insight Scandium 2013 software has been used. In this software, a fiber breakage algorithm is available from this release on. Using virtual material data with realistic viscosity levels allows to separate the influence of the power law index on the fiber breakage from the other material and process parameters. Applying standard settings for the fiber breakage parameters results in an obvious influence on the fiber length distribution through the thickness of the part and also as function of position in the part. Finally, the influence of the shear rate constant within the fiber breakage model has been investigated illustrating the possibility to fit the virtual fiber length distribution to the possible experimentally available data.

Microstructure Modeling of 3rd Generation Disk Alloy

NASA Technical Reports Server (NTRS)

Jou, Herng-Jeng

2008-01-01

The objective of this initiative, funded by NASA's Aviation Safety Program, is to model, validate, and predict, with high fidelity, the microstructural evolution of third-generation high-refractory Ni-based disc superalloys during heat treating and service conditions. This initiative is a natural extension of the DARPA-AIM (Accelerated Insertion of Materials) initiative with GE/Pratt-Whitney and with other process simulation tools. Strong collaboration with the NASA Glenn Research Center (GRC) is a key component of this initiative and the focus of this program is on industrially relevant disk alloys and heat treatment processes identified by GRC. Employing QuesTek s Computational Materials Dynamics technology and PrecipiCalc precipitation simulator, physics-based models are being used to achieve high predictive accuracy and precision. Combining these models with experimental data and probabilistic analysis, "virtual alloy design" can be performed. The predicted microstructures can be optimized to promote desirable features and concurrently eliminate nondesirable phases that can limit the reliability and durability of the alloys. The well-calibrated and well-integrated software tools that are being applied under the proposed program will help gas turbine disk alloy manufacturers, processing facilities, and NASA, to efficiently and effectively improve the performance of current and future disk materials.

Automatic Speech Recognition Predicts Speech Intelligibility and Comprehension for Listeners with Simulated Age-Related Hearing Loss

ERIC Educational Resources Information Center

Fontan, Lionel; Ferrané, Isabelle; Farinas, Jérôme; Pinquier, Julien; Tardieu, Julien; Magnen, Cynthia; Gaillard, Pascal; Aumont, Xavier; Füllgrabe, Christian

2017-01-01

Purpose: The purpose of this article is to assess speech processing for listeners with simulated age-related hearing loss (ARHL) and to investigate whether the observed performance can be replicated using an automatic speech recognition (ASR) system. The long-term goal of this research is to develop a system that will assist…

RFA Guardian: Comprehensive Simulation of Radiofrequency Ablation Treatment of Liver Tumors.

PubMed

Voglreiter, Philip; Mariappan, Panchatcharam; Pollari, Mika; Flanagan, Ronan; Blanco Sequeiros, Roberto; Portugaller, Rupert Horst; Fütterer, Jurgen; Schmalstieg, Dieter; Kolesnik, Marina; Moche, Michael

2018-01-15

The RFA Guardian is a comprehensive application for high-performance patient-specific simulation of radiofrequency ablation of liver tumors. We address a wide range of usage scenarios. These include pre-interventional planning, sampling of the parameter space for uncertainty estimation, treatment evaluation and, in the worst case, failure analysis. The RFA Guardian is the first of its kind that exhibits sufficient performance for simulating treatment outcomes during the intervention. We achieve this by combining a large number of high-performance image processing, biomechanical simulation and visualization techniques into a generalized technical workflow. Further, we wrap the feature set into a single, integrated application, which exploits all available resources of standard consumer hardware, including massively parallel computing on graphics processing units. This allows us to predict or reproduce treatment outcomes on a single personal computer with high computational performance and high accuracy. The resulting low demand for infrastructure enables easy and cost-efficient integration into the clinical routine. We present a number of evaluation cases from the clinical practice where users performed the whole technical workflow from patient-specific modeling to final validation and highlight the opportunities arising from our fast, accurate prediction techniques.

Cellular Particle Dynamics simulation of biomechanical relaxation processes of multi-cellular systems

NASA Astrophysics Data System (ADS)

McCune, Matthew; Kosztin, Ioan

2013-03-01

Cellular Particle Dynamics (CPD) is a theoretical-computational-experimental framework for describing and predicting the time evolution of biomechanical relaxation processes of multi-cellular systems, such as fusion, sorting and compression. In CPD, cells are modeled as an ensemble of cellular particles (CPs) that interact via short range contact interactions, characterized by an attractive (adhesive interaction) and a repulsive (excluded volume interaction) component. The time evolution of the spatial conformation of the multicellular system is determined by following the trajectories of all CPs through numerical integration of their equations of motion. Here we present CPD simulation results for the fusion of both spherical and cylindrical multi-cellular aggregates. First, we calibrate the relevant CPD model parameters for a given cell type by comparing the CPD simulation results for the fusion of two spherical aggregates to the corresponding experimental results. Next, CPD simulations are used to predict the time evolution of the fusion of cylindrical aggregates. The latter is relevant for the formation of tubular multi-cellular structures (i.e., primitive blood vessels) created by the novel bioprinting technology. Work supported by NSF [PHY-0957914]. Computer time provided by the University of Missouri Bioinformatics Consortium.

Study of Natural Fiber Breakage during Composite Processing

NASA Astrophysics Data System (ADS)

Quijano-Solis, Carlos Jafet

Biofiber-thermoplastic composites have gained considerable importance in the last century. To provide mechanical reinforcement to the polymer, fibers must be larger than a critical aspect ratio (length-to-width ratio). However, biofibers undergo breakage in length or width during processing, affecting their final aspect ratio in the composites. In this study, influence on biofiber breakage by factors related to processing conditions, fiber morphology and the flow type was investigated through: a) experiments using an internal mixer, a twin-screw extruder (TSE) or a capillary rheometer; and b) a Monte Carlo computer simulation. Composites of thermomechanical fibers of aspen or wheat straw mixed with polypropylene were studied. Internal mixer experiments analyzed wheat straw and two batches of aspen fibers, named AL and AS. AL fibers had longer average length. Processing variables included the temperature, rotors speed and fiber concentration. TSE experiments studied AL and AS fiber composites under various screws speeds, temperatures and feeding rates of the polymer and fibers. Capillary rheometers experiments determined AL fiber breakage in shear and elongational flows for composites processed at different concentrations, temperatures, and strain rates. Finally, the internal mixer experimental results where compared to Monte Carlo simulation predictions. The simulation focused on fiber length breakage due to fiber-polymer interactions. Internal mixer results showed that final fiber average length depended almost solely on processing conditions while final fiber average width depended on both processing conditions and initial fiber morphology. In the TSE, processing conditions as well as initial fiber length influenced final average length. TSE results showed that the fiber concentration regime seems to influence the effect of processing variables on fiber breakage. Capillary rheometer experiments demonstrated that biofiber breakage happens in both elongational and shear flows. In some cases, percentage of biofiber breakage in elongational flow is higher. In general, simulation predictions of final average lengths were in good agreement with experiments, indicating the importance of fiber-polymer interactions on fiber breakage. The largest discrepancies were obtained at higher fiber concentration composites; these differences might be resolved, in future simulations, by including the effect of fiber-fiber interactions.

Comparison of predictive estimates of high-latitude electrodynamics with observations of global-scale Birkeland currents

NASA Astrophysics Data System (ADS)

Anderson, Brian J.; Korth, Haje; Welling, Daniel T.; Merkin, Viacheslav G.; Wiltberger, Michael J.; Raeder, Joachim; Barnes, Robin J.; Waters, Colin L.; Pulkkinen, Antti A.; Rastaetter, Lutz

2017-02-01

Two of the geomagnetic storms for the Space Weather Prediction Center Geospace Environment Modeling challenge occurred after data were first acquired by the Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE). We compare Birkeland currents from AMPERE with predictions from four models for the 4-5 April 2010 and 5-6 August 2011 storms. The four models are the Weimer (2005b) field-aligned current statistical model, the Lyon-Fedder-Mobarry magnetohydrodynamic (MHD) simulation, the Open Global Geospace Circulation Model MHD simulation, and the Space Weather Modeling Framework MHD simulation. The MHD simulations were run as described in Pulkkinen et al. (2013) and the results obtained from the Community Coordinated Modeling Center. The total radial Birkeland current, ITotal, and the distribution of radial current density, Jr, for all models are compared with AMPERE results. While the total currents are well correlated, the quantitative agreement varies considerably. The Jr distributions reveal discrepancies between the models and observations related to the latitude distribution, morphologies, and lack of nightside current systems in the models. The results motivate enhancing the simulations first by increasing the simulation resolution and then by examining the relative merits of implementing more sophisticated ionospheric conductance models, including ionospheric outflows or other omitted physical processes. Some aspects of the system, including substorm timing and location, may remain challenging to simulate, implying a continuing need for real-time specification.

Process Optimization of Dual-Laser Beam Welding of Advanced Al-Li Alloys Through Hot Cracking Susceptibility Modeling

NASA Astrophysics Data System (ADS)

Tian, Yingtao; Robson, Joseph D.; Riekehr, Stefan; Kashaev, Nikolai; Wang, Li; Lowe, Tristan; Karanika, Alexandra

2016-07-01

Laser welding of advanced Al-Li alloys has been developed to meet the increasing demand for light-weight and high-strength aerospace structures. However, welding of high-strength Al-Li alloys can be problematic due to the tendency for hot cracking. Finding suitable welding parameters and filler material for this combination currently requires extensive and costly trial and error experimentation. The present work describes a novel coupled model to predict hot crack susceptibility (HCS) in Al-Li welds. Such a model can be used to shortcut the weld development process. The coupled model combines finite element process simulation with a two-level HCS model. The finite element process model predicts thermal field data for the subsequent HCS hot cracking prediction. The model can be used to predict the influences of filler wire composition and welding parameters on HCS. The modeling results have been validated by comparing predictions with results from fully instrumented laser welds performed under a range of process parameters and analyzed using high-resolution X-ray tomography to identify weld defects. It is shown that the model is capable of accurately predicting the thermal field around the weld and the trend of HCS as a function of process parameters.

Tornado detection data reduction and analysis

NASA Technical Reports Server (NTRS)

Davisson, L. D.

1977-01-01

Data processing and analysis was provided in support of tornado detection by analysis of radio frequency interference in various frequency bands. Sea state determination data from short pulse radar measurements were also processed and analyzed. A backscatter simulation was implemented to predict radar performance as a function of wind velocity. Computer programs were developed for the various data processing and analysis goals of the effort.

Gaussian process emulators for quantifying uncertainty in CO2 spreading predictions in heterogeneous media

NASA Astrophysics Data System (ADS)

Tian, Liang; Wilkinson, Richard; Yang, Zhibing; Power, Henry; Fagerlund, Fritjof; Niemi, Auli

2017-08-01

We explore the use of Gaussian process emulators (GPE) in the numerical simulation of CO2 injection into a deep heterogeneous aquifer. The model domain is a two-dimensional, log-normally distributed stochastic permeability field. We first estimate the cumulative distribution functions (CDFs) of the CO2 breakthrough time and the total CO2 mass using a computationally expensive Monte Carlo (MC) simulation. We then show that we can accurately reproduce these CDF estimates with a GPE, using only a small fraction of the computational cost required by traditional MC simulation. In order to build a GPE that can predict the simulator output from a permeability field consisting of 1000s of values, we use a truncated Karhunen-Loève (K-L) expansion of the permeability field, which enables the application of the Bayesian functional regression approach. We perform a cross-validation exercise to give an insight of the optimization of the experiment design for selected scenarios: we find that it is sufficient to use 100s values for the size of training set and that it is adequate to use as few as 15 K-L components. Our work demonstrates that GPE with truncated K-L expansion can be effectively applied to uncertainty analysis associated with modelling of multiphase flow and transport processes in heterogeneous media.

Alloy Shrinkage Factors for the Investment Casting of 17-4PH Stainless Steel Parts

NASA Astrophysics Data System (ADS)

Sabau, Adrian S.; Porter, Wallace D.

2008-04-01

In this study, alloy shrinkage factors were obtained for the investment casting of 17-4PH stainless steel parts. For the investment casting process, unfilled wax and fused silica with a zircon prime coat were used for patterns and shell molds, respectively. The dimensions of the die tooling, wax pattern, and casting were measured using a coordinate measurement machine (CMM). For all the properties, the experimental data available in the literature did not cover the entire temperature range necessary for process simulation. A comparison between the predicted material property data and measured property data is made. It was found that most material properties were accurately predicted over most of the temperature range of the process. Several assumptions were made, in order to obtain a complete set of mechanical property data at high temperatures. Thermal expansion measurements for the 17-4PH alloy were conducted during heating and cooling. As a function of temperature, the thermal expansion for both the alloy and shell mold materials showed a different evolution on heating and cooling. Thus, one generic simulation was performed with thermal expansion obtained on heating, and another one was performed with thermal expansion obtained on cooling. The alloy dimensions were obtained from the numerical simulation results of the solidification, heat transfer, and deformation phenomena. As compared with experimental results, the numerical simulation results for the shrinkage factors were slightly overpredicted.

Comprehensive model of a hermetic reciprocating compressor

NASA Astrophysics Data System (ADS)

Yang, B.; Ziviani, D.; Groll, E. A.

2017-08-01

A comprehensive simulation model is presented to predict the performance of a hermetic reciprocating compressor and to reveal the underlying mechanisms when the compressor is running. The presented model is composed of sub-models simulating the in-cylinder compression process, piston ring/journal bearing frictional power loss, single phase induction motor and the overall compressor energy balance among different compressor components. The valve model, leakage through piston ring model and in-cylinder heat transfer model are also incorporated into the in-cylinder compression process model. A numerical algorithm solving the model is introduced. The predicted results of the compressor mass flow rate and input power consumption are compared to the published compressor map values. Future work will focus on detailed experimental validation of the model and parametric studies investigating the effects of structural parameters, including the stroke-to-bore ratio, on the compressor performance.

Physical Processes and Applications of the Monte Carlo Radiative Energy Deposition (MRED) Code

NASA Astrophysics Data System (ADS)

Reed, Robert A.; Weller, Robert A.; Mendenhall, Marcus H.; Fleetwood, Daniel M.; Warren, Kevin M.; Sierawski, Brian D.; King, Michael P.; Schrimpf, Ronald D.; Auden, Elizabeth C.

2015-08-01

MRED is a Python-language scriptable computer application that simulates radiation transport. It is the computational engine for the on-line tool CRÈME-MC. MRED is based on c++ code from Geant4 with additional Fortran components to simulate electron transport and nuclear reactions with high precision. We provide a detailed description of the structure of MRED and the implementation of the simulation of physical processes used to simulate radiation effects in electronic devices and circuits. Extensive discussion and references are provided that illustrate the validation of models used to implement specific simulations of relevant physical processes. Several applications of MRED are summarized that demonstrate its ability to predict and describe basic physical phenomena associated with irradiation of electronic circuits and devices. These include effects from single particle radiation (including both direct ionization and indirect ionization effects), dose enhancement effects, and displacement damage effects. MRED simulations have also helped to identify new single event upset mechanisms not previously observed by experiment, but since confirmed, including upsets due to muons and energetic electrons.

Excellent approach to modeling urban expansion by fuzzy cellular automata: agent base model

NASA Astrophysics Data System (ADS)

Khajavigodellou, Yousef; Alesheikh, Ali A.; Mohammed, Abdulrazak A. S.; Chapi, Kamran

2014-09-01

Recently, the interaction between humans and their environment is the one of important challenges in the world. Landuse/ cover change (LUCC) is a complex process that includes actors and factors at different social and spatial levels. The complexity and dynamics of urban systems make the applicable practice of urban modeling very difficult. With the increased computational power and the greater availability of spatial data, micro-simulation such as the agent based and cellular automata simulation methods, has been developed by geographers, planners, and scholars, and it has shown great potential for representing and simulating the complexity of the dynamic processes involved in urban growth and land use change. This paper presents Fuzzy Cellular Automata in Geospatial Information System and remote Sensing to simulated and predicted urban expansion pattern. These FCA-based dynamic spatial urban models provide an improved ability to forecast and assess future urban growth and to create planning scenarios, allowing us to explore the potential impacts of simulations that correspond to urban planning and management policies. A fuzzy inference guided cellular automata approach. Semantic or linguistic knowledge on Land use change is expressed as fuzzy rules, based on which fuzzy inference is applied to determine the urban development potential for each pixel. The model integrates an ABM (agent-based model) and FCA (Fuzzy Cellular Automata) to investigate a complex decision-making process and future urban dynamic processes. Based on this model rapid development and green land protection under the influences of the behaviors and decision modes of regional authority agents, real estate developer agents, resident agents and non- resident agents and their interactions have been applied to predict the future development patterns of the Erbil metropolitan region.

Parameter Sensitivity and Laboratory Benchmarking of a Biogeochemical Process Model for Enhanced Anaerobic Dechlorination

NASA Astrophysics Data System (ADS)

Kouznetsova, I.; Gerhard, J. I.; Mao, X.; Barry, D. A.; Robinson, C.; Brovelli, A.; Harkness, M.; Fisher, A.; Mack, E. E.; Payne, J. A.; Dworatzek, S.; Roberts, J.

2008-12-01

A detailed model to simulate trichloroethene (TCE) dechlorination in anaerobic groundwater systems has been developed and implemented through PHAST, a robust and flexible geochemical modeling platform. The approach is comprehensive but retains flexibility such that models of varying complexity can be used to simulate TCE biodegradation in the vicinity of nonaqueous phase liquid (NAPL) source zones. The complete model considers a full suite of biological (e.g., dechlorination, fermentation, sulfate and iron reduction, electron donor competition, toxic inhibition, pH inhibition), physical (e.g., flow and mass transfer) and geochemical processes (e.g., pH modulation, gas formation, mineral interactions). Example simulations with the model demonstrated that the feedback between biological, physical, and geochemical processes is critical. Successful simulation of a thirty-two-month column experiment with site soil, complex groundwater chemistry, and exhibiting both anaerobic dechlorination and endogenous respiration, provided confidence in the modeling approach. A comprehensive suite of batch simulations was then conducted to estimate the sensitivity of predicted TCE degradation to the 36 model input parameters. A local sensitivity analysis was first employed to rank the importance of parameters, revealing that 5 parameters consistently dominated model predictions across a range of performance metrics. A global sensitivity analysis was then performed to evaluate the influence of a variety of full parameter data sets available in the literature. The modeling study was performed as part of the SABRE (Source Area BioREmediation) project, a public/private consortium whose charter is to determine if enhanced anaerobic bioremediation can result in effective and quantifiable treatment of chlorinated solvent DNAPL source areas. The modelling conducted has provided valuable insight into the complex interactions between processes in the evolving biogeochemical systems, particularly at the laboratory scale.

Numerical Simulation on Hydrodynamics and Combustion in a Circulating Fluidized Bed under O2/CO2 and Air Atmospheres

NASA Astrophysics Data System (ADS)

Zhou, W.; Zhao, C. S.; Duan, L. B.; Qu, C. R.; Lu, J. Y.; Chen, X. P.

Oxy-fuel circulating fluidized bed (CFB) combustion technology is in the stage of initial development for carbon capture and storage (CCS). Numerical simulation is helpful to better understanding the combustion process and will be significant for CFB scale-up. In this paper, a computational fluid dynamics (CFD) model was employed to simulate the hydrodynamics of gas-solid flow in a CFB riser based on the Eulerian-Granular multiphase model. The cold model predicted the main features of the complex gas-solid flow, including the cluster formation of the solid phase along the walls, the flow structure of up-flow in the core and downward flow in the annular region. Furthermore, coal devolatilization, char combustion and heat transfer were considered by coupling semi-empirical sub-models with CFD model to establish a comprehensive model. The gas compositions and temperature profiles were predicted and the outflow gas fractions are validated with the experimental data in air combustion. With the experimentally validated model being applied, the concentration and temperature distributions in O2/CO2 combustion were predicted. The model is useful for the further development of a comprehensive model including more sub-models, such as pollutant emissions, and better understanding the combustion process in furnace.

Initial Cognitive Performance Predicts Longitudinal Aviator Performance

PubMed Central

Jo, Booil; Adamson, Maheen M.; Kennedy, Quinn; Noda, Art; Hernandez, Beatriz; Zeitzer, Jamie M.; Friedman, Leah F.; Fairchild, Kaci; Scanlon, Blake K.; Murphy, Greer M.; Taylor, Joy L.

2011-01-01

Objectives. The goal of the study was to improve prediction of longitudinal flight simulator performance by studying cognitive factors that may moderate the influence of chronological age. Method. We examined age-related change in aviation performance in aircraft pilots in relation to baseline cognitive ability measures and aviation expertise. Participants were aircraft pilots (N = 276) aged 40–77.9. Flight simulator performance and cognition were tested yearly; there were an average of 4.3 (± 2.7; range 1–13) data points per participant. Each participant was classified into one of the three levels of aviation expertise based on Federal Aviation Administration pilot proficiency ratings: least, moderate, or high expertise. Results. Addition of measures of cognitive processing speed and executive function to a model of age-related change in aviation performance significantly improved the model. Processing speed and executive function performance interacted such that the slowest rate of decline in flight simulator performance was found in aviators with the highest scores on tests of these abilities. Expertise was beneficial to pilots across the age range studied; however, expertise did not show evidence of reducing the effect of age. Discussion. These data suggest that longitudinal performance on an important real-world activity can be predicted by initial assessment of relevant cognitive abilities. PMID:21586627

Springback Simulation and Compensation for High Strength Parts Using JSTAMP

NASA Astrophysics Data System (ADS)

Shindo, Terumasa; Sugitomo, Nobuhiko; Ma, Ninshu

2011-08-01

The stamping parts made from high strength steel have a large springback which is difficult to control. With the development of simulation technology, the springback can be accurately predicted using advanced kinematic material models and CAE systems. In this paper, a stamping process for a pillar part made from several classes of high strength steel was simulated using a Yoshida-Uemori kinematic material model and the springback was well predicted. To obtain the desired part shape, CAD surfaces of the stamping tools were compensated by a CAE system JSTAMP. After applying the compensation 2 or 3 times, the dimension accuracy of the simulation for the part shape achieved was about 0.5 mm. The compensated CAD surfaces of the stamping tools were directly exported from JSTAMP to CAM for machining. The effectiveness of the compensation was verified by an experiment using the compensated tools.

Multi-Scale Modeling of Liquid Phase Sintering Affected by Gravity: Preliminary Analysis

NASA Technical Reports Server (NTRS)

Olevsky, Eugene; German, Randall M.

2012-01-01

A multi-scale simulation concept taking into account impact of gravity on liquid phase sintering is described. The gravity influence can be included at both the micro- and macro-scales. At the micro-scale, the diffusion mass-transport is directionally modified in the framework of kinetic Monte-Carlo simulations to include the impact of gravity. The micro-scale simulations can provide the values of the constitutive parameters for macroscopic sintering simulations. At the macro-scale, we are attempting to embed a continuum model of sintering into a finite-element framework that includes the gravity forces and substrate friction. If successful, the finite elements analysis will enable predictions relevant to space-based processing, including size and shape and property predictions. Model experiments are underway to support the models via extraction of viscosity moduli versus composition, particle size, heating rate, temperature and time.

Predicting the constitutive behavior of semi-solids via a direct finite element simulation: application to AA5182

NASA Astrophysics Data System (ADS)

Phillion, A. B.; Cockcroft, S. L.; Lee, P. D.

2009-07-01

The methodology of direct finite element (FE) simulation was used to predict the semi-solid constitutive behavior of an industrially important aluminum-magnesium alloy, AA5182. Model microstructures were generated that detail key features of the as-cast semi-solid: equiaxed-globular grains of random size and shape, interconnected liquid films, and pores at the triple-junctions. Based on the results of over fifty different simulations, a model-based constitutive relationship which includes the effects of the key microstructure features—fraction solid, grain size and fraction porosity—was derived using regression analysis. This novel constitutive equation was then validated via comparison with both the FE simulations and experimental stress/strain data. Such an equation can now be used to incorporate the effects of microstructure on the bulk semi-solid flow stress within a macro- scale process model.

Development and Use of Engineering Standards for Computational Fluid Dynamics for Complex Aerospace Systems

NASA Technical Reports Server (NTRS)

Lee, Hyung B.; Ghia, Urmila; Bayyuk, Sami; Oberkampf, William L.; Roy, Christopher J.; Benek, John A.; Rumsey, Christopher L.; Powers, Joseph M.; Bush, Robert H.; Mani, Mortaza

2016-01-01

Computational fluid dynamics (CFD) and other advanced modeling and simulation (M&S) methods are increasingly relied on for predictive performance, reliability and safety of engineering systems. Analysts, designers, decision makers, and project managers, who must depend on simulation, need practical techniques and methods for assessing simulation credibility. The AIAA Guide for Verification and Validation of Computational Fluid Dynamics Simulations (AIAA G-077-1998 (2002)), originally published in 1998, was the first engineering standards document available to the engineering community for verification and validation (V&V) of simulations. Much progress has been made in these areas since 1998. The AIAA Committee on Standards for CFD is currently updating this Guide to incorporate in it the important developments that have taken place in V&V concepts, methods, and practices, particularly with regard to the broader context of predictive capability and uncertainty quantification (UQ) methods and approaches. This paper will provide an overview of the changes and extensions currently underway to update the AIAA Guide. Specifically, a framework for predictive capability will be described for incorporating a wide range of error and uncertainty sources identified during the modeling, verification, and validation processes, with the goal of estimating the total prediction uncertainty of the simulation. The Guide's goal is to provide a foundation for understanding and addressing major issues and concepts in predictive CFD. However, this Guide will not recommend specific approaches in these areas as the field is rapidly evolving. It is hoped that the guidelines provided in this paper, and explained in more detail in the Guide, will aid in the research, development, and use of CFD in engineering decision-making.

Modeling the Gas Nitriding Process of Low Alloy Steels

NASA Astrophysics Data System (ADS)

Yang, M.; Zimmerman, C.; Donahue, D.; Sisson, R. D.

2013-07-01

The effort to simulate the nitriding process has been ongoing for the last 20 years. Most of the work has been done to simulate the nitriding process of pure iron. In the present work a series of experiments have been done to understand the effects of the nitriding process parameters such as the nitriding potential, temperature, and time as well as surface condition on the gas nitriding process for the steels. The compound layer growth model has been developed to simulate the nitriding process of AISI 4140 steel. In this paper the fundamentals of the model are presented and discussed including the kinetics of compound layer growth and the determination of the nitrogen diffusivity in the diffusion zone. The excellent agreements have been achieved for both as-washed and pre-oxided nitrided AISI 4140 between the experimental data and simulation results. The nitrogen diffusivity in the diffusion zone is determined to be constant and only depends on the nitriding temperature, which is ~5 × 10-9 cm2/s at 548 °C. It proves the concept of utilizing the compound layer growth model in other steels. The nitriding process of various steels can thus be modeled and predicted in the future.

Standing wave design and experimental validation of a tandem simulated moving bed process for insulin purification.

PubMed

Xie, Yi; Mun, Sungyong; Kim, Jinhyun; Wang, Nien-Hwa Linda

2002-01-01

A tandem simulated moving bed (SMB) process for insulin purification has been proposed and validated experimentally. The mixture to be separated consists of insulin, high molecular weight proteins, and zinc chloride. A systematic approach based on the standing wave design, rate model simulations, and experiments was used to develop this multicomponent separation process. The standing wave design was applied to specify the SMB operating conditions of a lab-scale unit with 10 columns. The design was validated with rate model simulations prior to experiments. The experimental results show 99.9% purity and 99% yield, which closely agree with the model predictions and the standing wave design targets. The agreement proves that the standing wave design can ensure high purity and high yield for the tandem SMB process. Compared to a conventional batch SEC process, the tandem SMB has 10% higher yield, 400% higher throughput, and 72% lower eluant consumption. In contrast, a design that ignores the effects of mass transfer and nonideal flow cannot meet the purity requirement and gives less than 96% yield.

Predicting Protein-protein Association Rates using Coarse-grained Simulation and Machine Learning

NASA Astrophysics Data System (ADS)

Xie, Zhong-Ru; Chen, Jiawen; Wu, Yinghao

2017-04-01

Protein-protein interactions dominate all major biological processes in living cells. We have developed a new Monte Carlo-based simulation algorithm to study the kinetic process of protein association. We tested our method on a previously used large benchmark set of 49 protein complexes. The predicted rate was overestimated in the benchmark test compared to the experimental results for a group of protein complexes. We hypothesized that this resulted from molecular flexibility at the interface regions of the interacting proteins. After applying a machine learning algorithm with input variables that accounted for both the conformational flexibility and the energetic factor of binding, we successfully identified most of the protein complexes with overestimated association rates and improved our final prediction by using a cross-validation test. This method was then applied to a new independent test set and resulted in a similar prediction accuracy to that obtained using the training set. It has been thought that diffusion-limited protein association is dominated by long-range interactions. Our results provide strong evidence that the conformational flexibility also plays an important role in regulating protein association. Our studies provide new insights into the mechanism of protein association and offer a computationally efficient tool for predicting its rate.

Predicting Protein–protein Association Rates using Coarse-grained Simulation and Machine Learning

PubMed Central

Xie, Zhong-Ru; Chen, Jiawen; Wu, Yinghao

2017-01-01

Protein–protein interactions dominate all major biological processes in living cells. We have developed a new Monte Carlo-based simulation algorithm to study the kinetic process of protein association. We tested our method on a previously used large benchmark set of 49 protein complexes. The predicted rate was overestimated in the benchmark test compared to the experimental results for a group of protein complexes. We hypothesized that this resulted from molecular flexibility at the interface regions of the interacting proteins. After applying a machine learning algorithm with input variables that accounted for both the conformational flexibility and the energetic factor of binding, we successfully identified most of the protein complexes with overestimated association rates and improved our final prediction by using a cross-validation test. This method was then applied to a new independent test set and resulted in a similar prediction accuracy to that obtained using the training set. It has been thought that diffusion-limited protein association is dominated by long-range interactions. Our results provide strong evidence that the conformational flexibility also plays an important role in regulating protein association. Our studies provide new insights into the mechanism of protein association and offer a computationally efficient tool for predicting its rate. PMID:28418043

Predicting Protein-protein Association Rates using Coarse-grained Simulation and Machine Learning.

PubMed

Xie, Zhong-Ru; Chen, Jiawen; Wu, Yinghao

2017-04-18

Protein-protein interactions dominate all major biological processes in living cells. We have developed a new Monte Carlo-based simulation algorithm to study the kinetic process of protein association. We tested our method on a previously used large benchmark set of 49 protein complexes. The predicted rate was overestimated in the benchmark test compared to the experimental results for a group of protein complexes. We hypothesized that this resulted from molecular flexibility at the interface regions of the interacting proteins. After applying a machine learning algorithm with input variables that accounted for both the conformational flexibility and the energetic factor of binding, we successfully identified most of the protein complexes with overestimated association rates and improved our final prediction by using a cross-validation test. This method was then applied to a new independent test set and resulted in a similar prediction accuracy to that obtained using the training set. It has been thought that diffusion-limited protein association is dominated by long-range interactions. Our results provide strong evidence that the conformational flexibility also plays an important role in regulating protein association. Our studies provide new insights into the mechanism of protein association and offer a computationally efficient tool for predicting its rate.

Simulation and control of the technological processes of metal forming

NASA Astrophysics Data System (ADS)

Salikhov, Z. G.; Genkin, A. L.

2015-11-01

Theoretical and applied reports in the field of simulation, prediction, and control of the technological processes of metal forming are reviewed. These reports were presented by researchers from Austria, Great Britain, Germany, Italy, Kazakhstan, Canada, the Netherlands, Poland, Russia, the United States, Thailand, Ukraine, Finland, Czech Republic, and Switzerland in international scientific and technical congress on metal forming "OMD-2014. Fundamental Problems. Innovative Materials and Technologies." The advanced innovative trends in MF investigations, which were presented by well-known scientific teams and Russian and foreign companies, are discussed.

Advanced Simulation Technology to Design Etching Process on CMOS Devices

NASA Astrophysics Data System (ADS)

Kuboi, Nobuyuki

2015-09-01

Prediction and control of plasma-induced damage is needed to mass-produce high performance CMOS devices. In particular, side-wall (SW) etching with low damage is a key process for the next generation of MOSFETs and FinFETs. To predict and control the damage, we have developed a SiN etching simulation technique for CHxFy/Ar/O2 plasma processes using a three-dimensional (3D) voxel model. This model includes new concepts for the gas transportation in the pattern, detailed surface reactions on the SiN reactive layer divided into several thin slabs and C-F polymer layer dependent on the H/N ratio, and use of ``smart voxels''. We successfully predicted the etching properties such as the etch rate, polymer layer thickness, and selectivity for Si, SiO2, and SiN films along with process variations and demonstrated the 3D damage distribution time-dependently during SW etching on MOSFETs and FinFETs. We confirmed that a large amount of Si damage was caused in the source/drain region with the passage of time in spite of the existing SiO2 layer of 15 nm in the over etch step and the Si fin having been directly damaged by a large amount of high energy H during the removal step of the parasitic fin spacer leading to Si fin damage to a depth of 14 to 18 nm. By analyzing the results of these simulations and our previous simulations, we found that it is important to carefully control the dose of high energy H, incident energy of H, polymer layer thickness, and over-etch time considering the effects of the pattern structure, chamber-wall condition, and wafer open area ratio. In collaboration with Masanaga Fukasawa and Tetsuya Tatsumi, Sony Corporation. We thank Mr. T. Shigetoshi and Mr. T. Kinoshita of Sony Corporation for their assistance with the experiments.

A Nonlinear Dynamical Systems based Model for Stochastic Simulation of Streamflow

NASA Astrophysics Data System (ADS)

Erkyihun, S. T.; Rajagopalan, B.; Zagona, E. A.

2014-12-01

Traditional time series methods model the evolution of the underlying process as a linear or nonlinear function of the autocorrelation. These methods capture the distributional statistics but are incapable of providing insights into the dynamics of the process, the potential regimes, and predictability. This work develops a nonlinear dynamical model for stochastic simulation of streamflows. In this, first a wavelet spectral analysis is employed on the flow series to isolate dominant orthogonal quasi periodic timeseries components. The periodic bands are added denoting the 'signal' component of the time series and the residual being the 'noise' component. Next, the underlying nonlinear dynamics of this combined band time series is recovered. For this the univariate time series is embedded in a d-dimensional space with an appropriate lag T to recover the state space in which the dynamics unfolds. Predictability is assessed by quantifying the divergence of trajectories in the state space with time, as Lyapunov exponents. The nonlinear dynamics in conjunction with a K-nearest neighbor time resampling is used to simulate the combined band, to which the noise component is added to simulate the timeseries. We demonstrate this method by applying it to the data at Lees Ferry that comprises of both the paleo reconstructed and naturalized historic annual flow spanning 1490-2010. We identify interesting dynamics of the signal in the flow series and epochal behavior of predictability. These will be of immense use for water resources planning and management.

A simulation framework for auditory discrimination experiments: Revealing the importance of across-frequency processing in speech perception.

PubMed

Schädler, Marc René; Warzybok, Anna; Ewert, Stephan D; Kollmeier, Birger

2016-05-01

A framework for simulating auditory discrimination experiments, based on an approach from Schädler, Warzybok, Hochmuth, and Kollmeier [(2015). Int. J. Audiol. 54, 100-107] which was originally designed to predict speech recognition thresholds, is extended to also predict psychoacoustic thresholds. The proposed framework is used to assess the suitability of different auditory-inspired feature sets for a range of auditory discrimination experiments that included psychoacoustic as well as speech recognition experiments in noise. The considered experiments were 2 kHz tone-in-broadband-noise simultaneous masking depending on the tone length, spectral masking with simultaneously presented tone signals and narrow-band noise maskers, and German Matrix sentence test reception threshold in stationary and modulated noise. The employed feature sets included spectro-temporal Gabor filter bank features, Mel-frequency cepstral coefficients, logarithmically scaled Mel-spectrograms, and the internal representation of the Perception Model from Dau, Kollmeier, and Kohlrausch [(1997). J. Acoust. Soc. Am. 102(5), 2892-2905]. The proposed framework was successfully employed to simulate all experiments with a common parameter set and obtain objective thresholds with less assumptions compared to traditional modeling approaches. Depending on the feature set, the simulated reference-free thresholds were found to agree with-and hence to predict-empirical data from the literature. Across-frequency processing was found to be crucial to accurately model the lower speech reception threshold in modulated noise conditions than in stationary noise conditions.

Allocating emissions to 4 km and 1 km horizontal spatial resolutions and its impact on simulated NOx and O3 in Houston, TX

NASA Astrophysics Data System (ADS)

Pan, Shuai; Choi, Yunsoo; Roy, Anirban; Jeon, Wonbae

2017-09-01

A WRF-SMOKE-CMAQ air quality modeling system was used to investigate the impact of horizontal spatial resolution on simulated nitrogen oxides (NOx) and ozone (O3) in the Greater Houston area (a non-attainment area for O3). We employed an approach recommended by the United States Environmental Protection Agency to allocate county-based emissions to model grid cells in 1 km and 4 km horizontal grid resolutions. The CMAQ Integrated Process Rate analyses showed a substantial difference in emissions contributions between 1 and 4 km grids but similar NOx and O3 concentrations over urban and industrial locations. For example, the peak NOx emissions at an industrial and urban site differed by a factor of 20 for the 1 km and 8 for the 4 km grid, but simulated NOx concentrations changed only by a factor of 1.2 in both cases. Hence, due to the interplay of the atmospheric processes, we cannot expect a similar level of reduction of the gas-phase air pollutants as the reduction of emissions. Both simulations reproduced the variability of NASA P-3B aircraft measurements of NOy and O3 in the lower atmosphere (from 90 m to 4.5 km). Both simulations provided similar reasonable predictions at surface, while 1 km case depicted more detailed features of emissions and concentrations in heavily polluted areas, such as highways, airports, and industrial regions, which are useful in understanding the major causes of O3 pollution in such regions, and to quantify transport of O3 to populated communities in urban areas. The Integrated Reaction Rate analyses indicated a distinctive difference of chemistry processes between the model surface layer and upper layers, implying that correcting the meteorological conditions at the surface may not help to enhance the O3 predictions. The model-observation O3 bias in our studies (e.g., large over-prediction during the nighttime or along Gulf of Mexico coastline), were due to uncertainties in meteorology, chemistry or other processes. Horizontal grid resolution is unlikely the major contributor to these biases.

Climate change and the eco-hydrology of fire: Will area burned increase in a warming western USA?

Treesearch

Donald McKenzie; Jeremy S. Littell

2017-01-01

Wildfire area is predicted to increase with global warming. Empirical statistical models and process-based simulations agree almost universally. The key relationship for this unanimity, observed at multiple spatial and temporal scales, is between drought and fire. Predictive models often focus on ecosystems in which this relationship appears to be particularly strong,...

ASME V\\&V challenge problem: Surrogate-based V&V

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

Beghini, Lauren L.; Hough, Patricia D.

2015-12-18

The process of verification and validation can be resource intensive. From the computational model perspective, the resource demand typically arises from long simulation run times on multiple cores coupled with the need to characterize and propagate uncertainties. In addition, predictive computations performed for safety and reliability analyses have similar resource requirements. For this reason, there is a tradeoff between the time required to complete the requisite studies and the fidelity or accuracy of the results that can be obtained. At a high level, our approach is cast within a validation hierarchy that provides a framework in which we perform sensitivitymore » analysis, model calibration, model validation, and prediction. The evidence gathered as part of these activities is mapped into the Predictive Capability Maturity Model to assess credibility of the model used for the reliability predictions. With regard to specific technical aspects of our analysis, we employ surrogate-based methods, primarily based on polynomial chaos expansions and Gaussian processes, for model calibration, sensitivity analysis, and uncertainty quantification in order to reduce the number of simulations that must be done. The goal is to tip the tradeoff balance to improving accuracy without increasing the computational demands.« less

Monsoons: Processes, predictability, and the prospects for prediction

NASA Astrophysics Data System (ADS)

Webster, P. J.; Magaña, V. O.; Palmer, T. N.; Shukla, J.; Thomas, R. A.; Yanai, M.; Yasunari, T.

1998-06-01

The Tropical Ocean-Global Atmosphere (TOGA) program sought to determine the predictability of the coupled ocean-atmosphere system. The World Climate Research Programme's (WCRP) Global Ocean-Atmosphere-Land System (GOALS) program seeks to explore predictability of the global climate system through investigation of the major planetary heat sources and sinks, and interactions between them. The Asian-Australian monsoon system, which undergoes aperiodic and high amplitude variations on intraseasonal, annual, biennial and interannual timescales is a major focus of GOALS. Empirical seasonal forecasts of the monsoon have been made with moderate success for over 100 years. More recent modeling efforts have not been successful. Even simulation of the mean structure of the Asian monsoon has proven elusive and the observed ENSO-monsoon relationships has been difficult to replicate. Divergence in simulation skill occurs between integrations by different models or between members of ensembles of the same model. This degree of spread is surprising given the relative success of empirical forecast techniques. Two possible explanations are presented: difficulty in modeling the monsoon regions and nonlinear error growth due to regional hydrodynamical instabilities. It is argued that the reconciliation of these explanations is imperative for prediction of the monsoon to be improved. To this end, a thorough description of observed monsoon variability and the physical processes that are thought to be important is presented. Prospects of improving prediction and some strategies that may help achieve improvement are discussed.

MP-Pic simulation of CFB riser with EMMS-based drag model

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

Li, F.; Song, F.; Benyahia, S.

2012-01-01

MP-PIC (multi-phase particle in cell) method combined with the EMMS (energy minimization multi- scale) drag force model was implemented with the open source program MFIX to simulate the gas–solid flows in CFB (circulatingfluidizedbed) risers. Calculated solid flux by the EMMS drag agrees well with the experimental value; while the traditional homogeneous drag over-predicts this value. EMMS drag force model can also predict the macro-and meso-scale structures. Quantitative comparison of the results by the EMMS drag force model and the experimental measurements show high accuracy of the model. The effects of the number of particles per parcel and wall conditions onmore » the simulation results have also been investigated in the paper. This work proved that MP-PIC combined with the EMMS drag model can successfully simulate the fluidized flows in CFB risers and it serves as a candidate to realize real-time simulation of industrial processes in the future.« less

Application of RANS Simulations for Contact Time Predictions in Turbulent Reactor Tanks for Water Purification Process

NASA Astrophysics Data System (ADS)

Nickles, Cassandra; Goodman, Matthew; Saez, Jose; Issakhanian, Emin

2016-11-01

California's current drought has renewed public interest in recycled water from Water Reclamation Plants (WRPs). It is critical that the recycled water meets public health standards. This project consists of simulating the transport of an instantaneous conservative tracer through the WRP chlorine contact tanks. Local recycled water regulations stipulate a minimum 90-minute modal contact time during disinfection at peak dry weather design flow. In-situ testing is extremely difficult given flowrate dependence on real world sewage line supply and recycled water demand. Given as-built drawings and operation parameters, the chlorine contact tanks are modeled to simulate extreme situations, which may not meet regulatory standards. The turbulent flow solutions are used as the basis to model the transport of a turbulently diffusing conservative tracer added instantaneously to the inlet of the reactors. This tracer simulates the transport through advection and dispersion of chlorine in the WRPs. Previous work validated the models against experimental data. The current work shows the predictive value of the simulations.

Depiction of interfacial morphology in impact welded Ti/Cu bimetallic systems using smoothed particle hydrodynamics

NASA Astrophysics Data System (ADS)

Nassiri, Ali; Vivek, Anupam; Abke, Tim; Liu, Bert; Lee, Taeseon; Daehn, Glenn

2017-06-01

Numerical simulations of high-velocity impact welding are extremely challenging due to the coupled physics and highly dynamic nature of the process. Thus, conventional mesh-based numerical methodologies are not able to accurately model the process owing to the excessive mesh distortion close to the interface of two welded materials. A simulation platform was developed using smoothed particle hydrodynamics, implemented in a parallel architecture on a supercomputer. Then, the numerical simulations were compared to experimental tests conducted by vaporizing foil actuator welding. The close correspondence of the experiment and modeling in terms of interface characteristics allows the prediction of local temperature and strain distributions, which are not easily measured.

Simulated digestion for testing the stability of edible vaccine based on Cucumber mosaic virus (CMV) chimeric particle display Hepatitis C virus (HCV) peptide.

PubMed

Vitti, Antonella; Nuzzaci, Maria; Condelli, Valentina; Piazzolla, Pasquale

2014-01-01

Edible vaccines must survive digestive process and preserve the specific structure of the antigenic peptide to elicit effective immune response. The stability of a protein to digestive process can be predicted by subjecting it to the in vitro assay with simulated gastric fluid (SGF) and simulated intestinal fluid (SIF). Here, we describe the protocol of producing and using chimeric Cucumber mosaic virus (CMV) displaying Hepatitis C virus (HCV) derived peptide (R9) in double copy as an oral vaccine. Its stability after treatment with SGF and SIF and the preservation of the antigenic properties were verified by SDS-PAGE and immuno western blot techniques.

Identifying Structure-Property Relationships Through DREAM.3D Representative Volume Elements and DAMASK Crystal Plasticity Simulations: An Integrated Computational Materials Engineering Approach

NASA Astrophysics Data System (ADS)

Diehl, Martin; Groeber, Michael; Haase, Christian; Molodov, Dmitri A.; Roters, Franz; Raabe, Dierk

2017-05-01

Predicting, understanding, and controlling the mechanical behavior is the most important task when designing structural materials. Modern alloy systems—in which multiple deformation mechanisms, phases, and defects are introduced to overcome the inverse strength-ductility relationship—give raise to multiple possibilities for modifying the deformation behavior, rendering traditional, exclusively experimentally-based alloy development workflows inappropriate. For fast and efficient alloy design, it is therefore desirable to predict the mechanical performance of candidate alloys by simulation studies to replace time- and resource-consuming mechanical tests. Simulation tools suitable for this task need to correctly predict the mechanical behavior in dependence of alloy composition, microstructure, texture, phase fractions, and processing history. Here, an integrated computational materials engineering approach based on the open source software packages DREAM.3D and DAMASK (Düsseldorf Advanced Materials Simulation Kit) that enables such virtual material development is presented. More specific, our approach consists of the following three steps: (1) acquire statistical quantities that describe a microstructure, (2) build a representative volume element based on these quantities employing DREAM.3D, and (3) evaluate the representative volume using a predictive crystal plasticity material model provided by DAMASK. Exemplarily, these steps are here conducted for a high-manganese steel.

Evaluation of tocopherol recovery through simulation of molecular distillation process.

PubMed

Moraes, E B; Batistella, C B; Alvarez, M E Torres; Filho, Rubens Maciel; Maciel, M R Wolf

2004-01-01

DISMOL simulator was used to determine the best possible operating conditions to guide, in future studies, experimental works. This simulator needs several physical-chemical properties and often it is very difficult to determine them because of the complexity of the involved components. Their determinations must be made through correlations and/or predictions, in order to characterize the system and calculate it. The first try is to have simulation results of a system that later can be validated with experimental data. To implement, in the simulator, the necessary parameters of complex systems is a difficult task. In this work, we aimed to determe these properties in order to evaluate the tocopherol (vitamin E) recovery using a DISMOL simulator. The raw material used was the crude deodorizer distillate of soya oil. With this procedure, it is possible to determine the best operating conditions for experimental works and to evaluate the process in the separation of new systems, analyzing the profiles obtained from these simulations for the falling film molecular distillator.

Study on the CFD simulation of refrigerated container

NASA Astrophysics Data System (ADS)

Arif Budiyanto, Muhammad; Shinoda, Takeshi; Nasruddin

2017-10-01

The objective this study is to performed Computational Fluid Dynamic (CFD) simulation of refrigerated container in the container port. Refrigerated container is a thermal cargo container constructed from an insulation wall to carry kind of perishable goods. CFD simulation was carried out use cross sectional of container walls to predict surface temperatures of refrigerated container and to estimate its cooling load. The simulation model is based on the solution of the partial differential equations governing the fluid flow and heat transfer processes. The physical model of heat-transfer processes considered in this simulation are consist of solar radiation from the sun, heat conduction on the container walls, heat convection on the container surfaces and thermal radiation among the solid surfaces. The validation of simulation model was assessed uses surface temperatures at center points on each container walls obtained from the measurement experimentation in the previous study. The results shows the surface temperatures of simulation model has good agreement with the measurement data on all container walls.

[Effects of sampling plot number on tree species distribution prediction under climate change].

PubMed

Liang, Yu; He, Hong-Shi; Wu, Zhi-Wei; Li, Xiao-Na; Luo, Xu

2013-05-01

Based on the neutral landscapes under different degrees of landscape fragmentation, this paper studied the effects of sampling plot number on the prediction of tree species distribution at landscape scale under climate change. The tree species distribution was predicted by the coupled modeling approach which linked an ecosystem process model with a forest landscape model, and three contingent scenarios and one reference scenario of sampling plot numbers were assumed. The differences between the three scenarios and the reference scenario under different degrees of landscape fragmentation were tested. The results indicated that the effects of sampling plot number on the prediction of tree species distribution depended on the tree species life history attributes. For the generalist species, the prediction of their distribution at landscape scale needed more plots. Except for the extreme specialist, landscape fragmentation degree also affected the effects of sampling plot number on the prediction. With the increase of simulation period, the effects of sampling plot number on the prediction of tree species distribution at landscape scale could be changed. For generalist species, more plots are needed for the long-term simulation.

Analysis of operator splitting errors for near-limit flame simulations

NASA Astrophysics Data System (ADS)

Lu, Zhen; Zhou, Hua; Li, Shan; Ren, Zhuyin; Lu, Tianfeng; Law, Chung K.

2017-04-01

High-fidelity simulations of ignition, extinction and oscillatory combustion processes are of practical interest in a broad range of combustion applications. Splitting schemes, widely employed in reactive flow simulations, could fail for stiff reaction-diffusion systems exhibiting near-limit flame phenomena. The present work first employs a model perfectly stirred reactor (PSR) problem with an Arrhenius reaction term and a linear mixing term to study the effects of splitting errors on the near-limit combustion phenomena. Analysis shows that the errors induced by decoupling of the fractional steps may result in unphysical extinction or ignition. The analysis is then extended to the prediction of ignition, extinction and oscillatory combustion in unsteady PSRs of various fuel/air mixtures with a 9-species detailed mechanism for hydrogen oxidation and an 88-species skeletal mechanism for n-heptane oxidation, together with a Jacobian-based analysis for the time scales. The tested schemes include the Strang splitting, the balanced splitting, and a newly developed semi-implicit midpoint method. Results show that the semi-implicit midpoint method can accurately reproduce the dynamics of the near-limit flame phenomena and it is second-order accurate over a wide range of time step size. For the extinction and ignition processes, both the balanced splitting and midpoint method can yield accurate predictions, whereas the Strang splitting can lead to significant shifts on the ignition/extinction processes or even unphysical results. With an enriched H radical source in the inflow stream, a delay of the ignition process and the deviation on the equilibrium temperature are observed for the Strang splitting. On the contrary, the midpoint method that solves reaction and diffusion together matches the fully implicit accurate solution. The balanced splitting predicts the temperature rise correctly but with an over-predicted peak. For the sustainable and decaying oscillatory combustion from cool flames, both the Strang splitting and the midpoint method can successfully capture the dynamic behavior, whereas the balanced splitting scheme results in significant errors.

Analysis of operator splitting errors for near-limit flame simulations

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

Lu, Zhen; Zhou, Hua; Li, Shan

High-fidelity simulations of ignition, extinction and oscillatory combustion processes are of practical interest in a broad range of combustion applications. Splitting schemes, widely employed in reactive flow simulations, could fail for stiff reaction–diffusion systems exhibiting near-limit flame phenomena. The present work first employs a model perfectly stirred reactor (PSR) problem with an Arrhenius reaction term and a linear mixing term to study the effects of splitting errors on the near-limit combustion phenomena. Analysis shows that the errors induced by decoupling of the fractional steps may result in unphysical extinction or ignition. The analysis is then extended to the prediction ofmore » ignition, extinction and oscillatory combustion in unsteady PSRs of various fuel/air mixtures with a 9-species detailed mechanism for hydrogen oxidation and an 88-species skeletal mechanism for n-heptane oxidation, together with a Jacobian-based analysis for the time scales. The tested schemes include the Strang splitting, the balanced splitting, and a newly developed semi-implicit midpoint method. Results show that the semi-implicit midpoint method can accurately reproduce the dynamics of the near-limit flame phenomena and it is second-order accurate over a wide range of time step size. For the extinction and ignition processes, both the balanced splitting and midpoint method can yield accurate predictions, whereas the Strang splitting can lead to significant shifts on the ignition/extinction processes or even unphysical results. With an enriched H radical source in the inflow stream, a delay of the ignition process and the deviation on the equilibrium temperature are observed for the Strang splitting. On the contrary, the midpoint method that solves reaction and diffusion together matches the fully implicit accurate solution. The balanced splitting predicts the temperature rise correctly but with an over-predicted peak. For the sustainable and decaying oscillatory combustion from cool flames, both the Strang splitting and the midpoint method can successfully capture the dynamic behavior, whereas the balanced splitting scheme results in significant errors.« less

Direct Simulation of Magnetic Resonance Relaxation Rates and Line Shapes from Molecular Trajectories

PubMed Central

Rangel, David P.; Baveye, Philippe C.; Robinson, Bruce H.

2012-01-01

We simulate spin relaxation processes, which may be measured by either continuous wave or pulsed magnetic resonance techniques, using trajectory-based simulation methodologies. The spin–lattice relaxation rates are extracted numerically from the relaxation simulations. The rates obtained from the numerical fitting of the relaxation curves are compared to those obtained by direct simulation from the relaxation Bloch–Wangsness–Abragam– Redfield theory (BWART). We have restricted our study to anisotropic rigid-body rotational processes, and to the chemical shift anisotropy (CSA) and a single spin–spin dipolar (END) coupling mechanisms. Examples using electron paramagnetic resonance (EPR) nitroxide and nuclear magnetic resonance (NMR) deuterium quadrupolar systems are provided. The objective is to compare those rates obtained by numerical simulations with the rates obtained by BWART. There is excellent agreement between the simulated and BWART rates for a Hamiltonian describing a single spin (an electron) interacting with the bath through the chemical shift anisotropy (CSA) mechanism undergoing anisotropic rotational diffusion. In contrast, when the Hamiltonian contains both the chemical shift anisotropy (CSA) and the spin–spin dipolar (END) mechanisms, the decay rate of a single exponential fit of the simulated spin–lattice relaxation rate is up to a factor of 0.2 smaller than that predicted by BWART. When the relaxation curves are fit to a double exponential, the slow and fast rates extracted from the decay curves bound the BWART prediction. An extended BWART theory, in the literature, includes the need for multiple relaxation rates and indicates that the multiexponential decay is due to the combined effects of direct and cross-relaxation mechanisms. PMID:22540276

Sludge batch 9 simulant runs using the nitric-glycolic acid flowsheet

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

Lambert, D. P.; Williams, M. S.; Brandenburg, C. H.

Testing was completed to develop a Sludge Batch 9 (SB9) nitric-glycolic acid chemical process flowsheet for the Defense Waste Processing Facility’s (DWPF) Chemical Process Cell (CPC). CPC simulations were completed using SB9 sludge simulant, Strip Effluent Feed Tank (SEFT) simulant and Precipitate Reactor Feed Tank (PRFT) simulant. Ten sludge-only Sludge Receipt and Adjustment Tank (SRAT) cycles and four SRAT/Slurry Mix Evaporator (SME) cycles, and one actual SB9 sludge (SRAT/SME cycle) were completed. As has been demonstrated in over 100 simulations, the replacement of formic acid with glycolic acid virtually eliminates the CPC’s largest flammability hazards, hydrogen and ammonia. Recommended processingmore » conditions are summarized in section 3.5.1. Testing demonstrated that the interim chemistry and Reduction/Oxidation (REDOX) equations are sufficient to predict the composition of DWPF SRAT product and SME product. Additional reports will finalize the chemistry and REDOX equations. Additional testing developed an antifoam strategy to minimize the hexamethyldisiloxane (HMDSO) peak at boiling, while controlling foam based on testing with simulant and actual waste. Implementation of the nitric-glycolic acid flowsheet in DWPF is recommended. This flowsheet not only eliminates the hydrogen and ammonia hazards but will lead to shorter processing times, higher elemental mercury recovery, and more concentrated SRAT and SME products. The steady pH profile is expected to provide flexibility in processing the high volume of strip effluent expected once the Salt Waste Processing Facility starts up.« less

Using Advanced Analysis Approaches to Complete Long-Term Evaluations of Natural Attenuation Processes on the Remediation of Dissolved Chlorinated Solvent Contamination

DTIC Science & Technology

2008-10-01

and UTCHEM (Clement et al., 1998). While all four of these software packages use conservation of mass as the basic principle for tracking NAPL...simulate dissolution of a single NAPL component. UTCHEM can be used to simulate dissolution of a multiple NAPL components using either linear or first...parameters. No UTCHEM a/ 3D model, general purpose NAPL simulator. Yes Virulo a/ Probabilistic model for predicting leaching of viruses in unsaturated

Prediction of agriculture derived groundwater nitrate distribution in North China Plain with GIS-based BPNN

NASA Astrophysics Data System (ADS)

Wang, M. X.; Liu, G. D.; Wu, W. L.; Bao, Y. H.; Liu, W. N.

2006-07-01

In recent years, nitrate contamination of groundwater has become a growing concern for people in rural areas in North China Plain (NCP) where groundwater is used as drinking water. The objective of this study was to simulate agriculture derived groundwater nitrate pollution patterns with artificial neural network (ANN), which has been proved to be an effective tool for prediction in many branches of hydrology when data are not sufficient to understand the physical process of the systems but relative accurate predictions is needed. In our study, a back propagation neural network (BPNN) was developed to simulate spatial distribution of NO3-N concentrations in groundwater with land use information and site-specific hydrogeological properties in Huantai County, a typical agriculture dominated region of NCP. Geographic information system (GIS) tools were used in preparing and processing input-output vectors data for the BPNN. The circular buffer zones centered on the sampling wells were designated so as to consider the nitrate contamination of groundwater due to neighboring field. The result showed that the GIS-based BPNN simulated groundwater NO3-N concentration efficiently and captured the general trend of groundwater nitrate pollution patterns. The optimal result was obtained with a learning rate of 0.02, a 4-7-1 architecture and a buffer zone radius of 400 m. Nitrogen budget combined with GIS-based BPNN can serve as a cost-effective tool for prediction and management of groundwater nitrate pollution in an agriculture dominated regions in North China Plain.

Sensorimotor synchronization with tempo-changing auditory sequences: Modeling temporal adaptation and anticipation.

PubMed

van der Steen, M C Marieke; Jacoby, Nori; Fairhurst, Merle T; Keller, Peter E

2015-11-11

The current study investigated the human ability to synchronize movements with event sequences containing continuous tempo changes. This capacity is evident, for example, in ensemble musicians who maintain precise interpersonal coordination while modulating the performance tempo for expressive purposes. Here we tested an ADaptation and Anticipation Model (ADAM) that was developed to account for such behavior by combining error correction processes (adaptation) with a predictive temporal extrapolation process (anticipation). While previous computational models of synchronization incorporate error correction, they do not account for prediction during tempo-changing behavior. The fit between behavioral data and computer simulations based on four versions of ADAM was assessed. These versions included a model with adaptation only, one in which adaptation and anticipation act in combination (error correction is applied on the basis of predicted tempo changes), and two models in which adaptation and anticipation were linked in a joint module that corrects for predicted discrepancies between the outcomes of adaptive and anticipatory processes. The behavioral experiment required participants to tap their finger in time with three auditory pacing sequences containing tempo changes that differed in the rate of change and the number of turning points. Behavioral results indicated that sensorimotor synchronization accuracy and precision, while generally high, decreased with increases in the rate of tempo change and number of turning points. Simulations and model-based parameter estimates showed that adaptation mechanisms alone could not fully explain the observed precision of sensorimotor synchronization. Including anticipation in the model increased the precision of simulated sensorimotor synchronization and improved the fit of model to behavioral data, especially when adaptation and anticipation mechanisms were linked via a joint module based on the notion of joint internal models. Overall results suggest that adaptation and anticipation mechanisms both play an important role during sensorimotor synchronization with tempo-changing sequences. This article is part of a Special Issue entitled SI: Prediction and Attention. Copyright © 2015 Elsevier B.V. All rights reserved.

A mechanistic Individual-based Model of microbial communities.

PubMed

Jayathilake, Pahala Gedara; Gupta, Prashant; Li, Bowen; Madsen, Curtis; Oyebamiji, Oluwole; González-Cabaleiro, Rebeca; Rushton, Steve; Bridgens, Ben; Swailes, David; Allen, Ben; McGough, A Stephen; Zuliani, Paolo; Ofiteru, Irina Dana; Wilkinson, Darren; Chen, Jinju; Curtis, Tom

2017-01-01

Accurate predictive modelling of the growth of microbial communities requires the credible representation of the interactions of biological, chemical and mechanical processes. However, although biological and chemical processes are represented in a number of Individual-based Models (IbMs) the interaction of growth and mechanics is limited. Conversely, there are mechanically sophisticated IbMs with only elementary biology and chemistry. This study focuses on addressing these limitations by developing a flexible IbM that can robustly combine the biological, chemical and physical processes that dictate the emergent properties of a wide range of bacterial communities. This IbM is developed by creating a microbiological adaptation of the open source Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS). This innovation should provide the basis for "bottom up" prediction of the emergent behaviour of entire microbial systems. In the model presented here, bacterial growth, division, decay, mechanical contact among bacterial cells, and adhesion between the bacteria and extracellular polymeric substances are incorporated. In addition, fluid-bacteria interaction is implemented to simulate biofilm deformation and erosion. The model predicts that the surface morphology of biofilms becomes smoother with increased nutrient concentration, which agrees well with previous literature. In addition, the results show that increased shear rate results in smoother and more compact biofilms. The model can also predict shear rate dependent biofilm deformation, erosion, streamer formation and breakup.

A mechanistic Individual-based Model of microbial communities

PubMed Central

Gupta, Prashant; Li, Bowen; Madsen, Curtis; Oyebamiji, Oluwole; González-Cabaleiro, Rebeca; Rushton, Steve; Bridgens, Ben; Swailes, David; Allen, Ben; McGough, A. Stephen; Zuliani, Paolo; Ofiteru, Irina Dana; Wilkinson, Darren; Chen, Jinju; Curtis, Tom

2017-01-01

Accurate predictive modelling of the growth of microbial communities requires the credible representation of the interactions of biological, chemical and mechanical processes. However, although biological and chemical processes are represented in a number of Individual-based Models (IbMs) the interaction of growth and mechanics is limited. Conversely, there are mechanically sophisticated IbMs with only elementary biology and chemistry. This study focuses on addressing these limitations by developing a flexible IbM that can robustly combine the biological, chemical and physical processes that dictate the emergent properties of a wide range of bacterial communities. This IbM is developed by creating a microbiological adaptation of the open source Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS). This innovation should provide the basis for “bottom up” prediction of the emergent behaviour of entire microbial systems. In the model presented here, bacterial growth, division, decay, mechanical contact among bacterial cells, and adhesion between the bacteria and extracellular polymeric substances are incorporated. In addition, fluid-bacteria interaction is implemented to simulate biofilm deformation and erosion. The model predicts that the surface morphology of biofilms becomes smoother with increased nutrient concentration, which agrees well with previous literature. In addition, the results show that increased shear rate results in smoother and more compact biofilms. The model can also predict shear rate dependent biofilm deformation, erosion, streamer formation and breakup. PMID:28771505

Global sensitivity analysis of DRAINMOD-FOREST, an integrated forest ecosystem model

Treesearch

Shiying Tian; Mohamed A. Youssef; Devendra M. Amatya; Eric D. Vance

2014-01-01

Global sensitivity analysis is a useful tool to understand process-based ecosystem models by identifying key parameters and processes controlling model predictions. This study reported a comprehensive global sensitivity analysis for DRAINMOD-FOREST, an integrated model for simulating water, carbon (C), and nitrogen (N) cycles and plant growth in lowland forests. The...

MODELING PROCESSES CONTROLLING MERCURY FATE IN WATERSHEDS RECEIVING ATMOSPHERIC DEPOSITION - COMPARISON OF FIELD SCALE GLEAMS AND WATERSHED SCALE WCS-GBMM

EPA Science Inventory

Long-term simulations of mercury fate in watersheds are needed to support regulations such as TMDLs and to predict the effectiveness of regulatory proposals, such as the Clean Air Mercury Rule (CAMR). Scientific uncertainties in mercury fate process descriptions combined with in...

Early Shear Failure Prediction in Incremental Sheet Forming Process Using FEM and ANN

NASA Astrophysics Data System (ADS)

Moayedfar, Majid; Hanaei, Hengameh; Majdi Rani, Ahmad; Musa, Mohd Azam Bin; Sadegh Momeni, Mohammad

2018-03-01

The application of incremental sheet forming process as a rapid forming technique is rising in variety of industries such as aerospace, automotive and biomechanical purposes. However, the sheet failure is a big challenge in this process which leads wasting lots of materials. Hence, this study tried to propose a method to predict the early sheet failure in this process using mathematical solution. For the feasibility of the study, design of experiment with the respond surface method is employed to extract a set of experiments data for the simulation. The significant forming parameters were recognized and their integration was used for prediction system. Then, the results were inserted to the artificial neural network as input parameters to predict a vast range of applicable parameters avoiding sheet failure in ISF. The value of accuracy R2 ∼0.93 was obtained and the maximum sheet stretch in the depth of 25mm were recorded. The figures generate from the trend of interaction between effective parameters were provided for future studies.

Design of a data-driven predictive controller for start-up process of AMT vehicles.

PubMed

Lu, Xiaohui; Chen, Hong; Wang, Ping; Gao, Bingzhao

2011-12-01

In this paper, a data-driven predictive controller is designed for the start-up process of vehicles with automated manual transmissions (AMTs). It is obtained directly from the input-output data of a driveline simulation model constructed by the commercial software AMESim. In order to obtain offset-free control for the reference input, the predictor equation is gained with incremental inputs and outputs. Because of the physical characteristics, the input and output constraints are considered explicitly in the problem formulation. The contradictory requirements of less friction losses and less driveline shock are included in the objective function. The designed controller is tested under nominal conditions and changed conditions. The simulation results show that, during the start-up process, the AMT clutch with the proposed controller works very well, and the process meets the control objectives: fast clutch lockup time, small friction losses, and the preservation of driver comfort, i.e., smooth acceleration of the vehicle. At the same time, the closed-loop system has the ability to reject uncertainties, such as the vehicle mass and road grade.

The development of a Kalman filter clock predictor

NASA Technical Reports Server (NTRS)

Davis, John A.; Greenhall, Charles A.; Boudjemaa, Redoane

2005-01-01

A Kalman filter based clock predictor is developed, and its performance evaluated using both simulated and real data. The clock predictor is shown to possess a neat to optimal Prediction Error Variance (PEV) when the underlying noise consists of one of the power law noise processes commonly encountered in time and frequency measurements. The predictor's performance is the presence of multiple noise processes is also examined. The relationship between the PEV obtained in the presence of multiple noise processes and those obtained for the individual component noise processes is examined. Comparisons are made with a simple linear clock predictor. The clock predictor is used to predict future values of the time offset between pairs of NPL's active hydrogen masers.

A neural network strategy for end-point optimization of batch processes.

PubMed

Krothapally, M; Palanki, S

1999-01-01

The traditional way of operating batch processes has been to utilize an open-loop "golden recipe". However, there can be substantial batch to batch variation in process conditions and this open-loop strategy can lead to non-optimal operation. In this paper, a new approach is presented for end-point optimization of batch processes by utilizing neural networks. This strategy involves the training of two neural networks; one to predict switching times and the other to predict the input profile in the singular region. This approach alleviates the computational problems associated with the classical Pontryagin's approach and the nonlinear programming approach. The efficacy of this scheme is illustrated via simulation of a fed-batch fermentation.

Progress of Aircraft System Noise Assessment with Uncertainty Quantification for the Environmentally Responsible Aviation Project

NASA Technical Reports Server (NTRS)

Thomas, Russell H.; Burley, Casey L.; Guo, Yueping

2016-01-01

Aircraft system noise predictions have been performed for NASA modeled hybrid wing body aircraft advanced concepts with 2025 entry-into-service technology assumptions. The system noise predictions developed over a period from 2009 to 2016 as a result of improved modeling of the aircraft concepts, design changes, technology development, flight path modeling, and the use of extensive integrated system level experimental data. In addition, the system noise prediction models and process have been improved in many ways. An additional process is developed here for quantifying the uncertainty with a 95% confidence level. This uncertainty applies only to the aircraft system noise prediction process. For three points in time during this period, the vehicle designs, technologies, and noise prediction process are documented. For each of the three predictions, and with the information available at each of those points in time, the uncertainty is quantified using the direct Monte Carlo method with 10,000 simulations. For the prediction of cumulative noise of an advanced aircraft at the conceptual level of design, the total uncertainty band has been reduced from 12.2 to 9.6 EPNL dB. A value of 3.6 EPNL dB is proposed as the lower limit of uncertainty possible for the cumulative system noise prediction of an advanced aircraft concept.

A model for prediction of STOVL ejector dynamics

NASA Technical Reports Server (NTRS)

Drummond, Colin K.

1989-01-01

A semi-empirical control-volume approach to ejector modeling for transient performance prediction is presented. This new approach is motivated by the need for a predictive real-time ejector sub-system simulation for Short Take-Off Verticle Landing (STOVL) integrated flight and propulsion controls design applications. Emphasis is placed on discussion of the approximate characterization of the mixing process central to thrust augmenting ejector operation. The proposed ejector model suggests transient flow predictions are possible with a model based on steady-flow data. A practical test case is presented to illustrate model calibration.

Turbulent Flow Effects on the Biological Performance of Hydro-Turbines

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

Richmond, Marshall C.; Romero Gomez, Pedro DJ

2014-08-25

The hydro-turbine industry uses Computational Fluid Dynamics (CFD) tools to predict the flow conditions as part of the design process for new and rehabilitated turbine units. Typically the hydraulic design process uses steady-state simulations based on Reynolds-Averaged Navier-Stokes (RANS) formulations for turbulence modeling because these methods are computationally efficient and work well to predict averaged hydraulic performance, e.g. power output, efficiency, etc. However, in view of the increasing emphasis on environmental concerns, such as fish passage, the consideration of the biological performance of hydro-turbines is also required in addition to hydraulic performance. This leads to the need to assess whethermore » more realistic simulations of the turbine hydraulic environment -those that resolve unsteady turbulent eddies not captured in steady-state RANS computations- are needed to better predict the occurrence and extent of extreme flow conditions that could be important in the evaluation of fish injury and mortality risks. In the present work, we conduct unsteady, eddy-resolving CFD simulations on a Kaplan hydro-turbine at a normal operational discharge. The goal is to quantify the impact of turbulence conditions on both the hydraulic and biological performance of the unit. In order to achieve a high resolution of the incoming turbulent flow, Detached Eddy Simulation (DES) turbulence model is used. These transient simulations are compared to RANS simulations to evaluate whether extreme hydraulic conditions are better captured with advanced eddy-resolving turbulence modeling techniques. The transient simulations of key quantities such as pressure and hydraulic shear flow that arise near the various components (e.g. wicket gates, stay vanes, runner blades) are then further analyzed to evaluate their impact on the statistics for the lowest absolute pressure (nadir pressures) and for the frequency of collisions that are known to cause mortal injury in fish passing through hydro-turbines.« less

Reduced order models for assessing CO 2 impacts in shallow unconfined aquifers

DOE PAGES

Keating, Elizabeth H.; Harp, Dylan H.; Dai, Zhenxue; ...

2016-01-28

Risk assessment studies of potential CO 2 sequestration projects consider many factors, including the possibility of brine and/or CO 2 leakage from the storage reservoir. Detailed multiphase reactive transport simulations have been developed to predict the impact of such leaks on shallow groundwater quality; however, these simulations are computationally expensive and thus difficult to directly embed in a probabilistic risk assessment analysis. Here we present a process for developing computationally fast reduced-order models which emulate key features of the more detailed reactive transport simulations. A large ensemble of simulations that take into account uncertainty in aquifer characteristics and CO 2/brinemore » leakage scenarios were performed. Twelve simulation outputs of interest were used to develop response surfaces (RSs) using a MARS (multivariate adaptive regression splines) algorithm (Milborrow, 2015). A key part of this study is to compare different measures of ROM accuracy. We then show that for some computed outputs, MARS performs very well in matching the simulation data. The capability of the RS to predict simulation outputs for parameter combinations not used in RS development was tested using cross-validation. Again, for some outputs, these results were quite good. For other outputs, however, the method performs relatively poorly. Performance was best for predicting the volume of depressed-pH-plumes, and was relatively poor for predicting organic and trace metal plume volumes. We believe several factors, including the non-linearity of the problem, complexity of the geochemistry, and granularity in the simulation results, contribute to this varied performance. The reduced order models were developed principally to be used in probabilistic performance analysis where a large range of scenarios are considered and ensemble performance is calculated. We demonstrate that they effectively predict the ensemble behavior. But, the performance of the RSs is much less accurate when used to predict time-varying outputs from a single simulation. If an analysis requires only a small number of scenarios to be investigated, computationally expensive physics-based simulations would likely provide more reliable results. Finally, if the aggregate behavior of a large number of realizations is the focus, as will be the case in probabilistic quantitative risk assessment, the methodology presented here is relatively robust.« less

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

Zamecnik, J. R.; Edwards, T. B.

The conversions of nitrite to nitrate, the destruction of glycolate, and the conversion of glycolate to formate and oxalate were modeled for the Nitric-Glycolic flowsheet using data from Chemical Process Cell (CPC) simulant runs conducted by SRNL from 2011 to 2015. The goal of this work was to develop empirical correlations for these variables versus measureable variables from the chemical process so that these quantities could be predicted a-priori from the sludge composition and measurable processing variables. The need for these predictions arises from the need to predict the REDuction/OXidation (REDOX) state of the glass from the Defense Waste Processingmore » Facility (DWPF) melter. This report summarizes the initial work on these correlations based on the aforementioned data. Further refinement of the models as additional data is collected is recommended.« less

Numerical simulation of the rapid intensification of Hurricane Katrina (2005): Sensitivity to boundary layer parameterization schemes

NASA Astrophysics Data System (ADS)

Liu, Jianjun; Zhang, Feimin; Pu, Zhaoxia

2017-04-01

Accurate forecasting of the intensity changes of hurricanes is an important yet challenging problem in numerical weather prediction. The rapid intensification of Hurricane Katrina (2005) before its landfall in the southern US is studied with the Advanced Research version of the WRF (Weather Research and Forecasting) model. The sensitivity of numerical simulations to two popular planetary boundary layer (PBL) schemes, the Mellor-Yamada-Janjic (MYJ) and the Yonsei University (YSU) schemes, is investigated. It is found that, compared with the YSU simulation, the simulation with the MYJ scheme produces better track and intensity evolution, better vortex structure, and more accurate landfall time and location. Large discrepancies (e.g., over 10 hPa in simulated minimum sea level pressure) are found between the two simulations during the rapid intensification period. Further diagnosis indicates that stronger surface fluxes and vertical mixing in the PBL from the simulation with the MYJ scheme lead to enhanced air-sea interaction, which helps generate more realistic simulations of the rapid intensification process. Overall, the results from this study suggest that improved representation of surface fluxes and vertical mixing in the PBL is essential for accurate prediction of hurricane intensity changes.

SQERTSS: Dynamic rank based throttling of transition probabilities in kinetic Monte Carlo simulations

DOE PAGES

Danielson, Thomas; Sutton, Jonathan E.; Hin, Céline; ...

2017-06-09

Lattice based Kinetic Monte Carlo (KMC) simulations offer a powerful simulation technique for investigating large reaction networks while retaining spatial configuration information, unlike ordinary differential equations. However, large chemical reaction networks can contain reaction processes with rates spanning multiple orders of magnitude. This can lead to the problem of “KMC stiffness” (similar to stiffness in differential equations), where the computational expense has the potential to be overwhelmed by very short time-steps during KMC simulations, with the simulation spending an inordinate amount of KMC steps / cpu-time simulating fast frivolous processes (FFPs) without progressing the system (reaction network). In order tomore » achieve simulation times that are experimentally relevant or desired for predictions, a dynamic throttling algorithm involving separation of the processes into speed-ranks based on event frequencies has been designed and implemented with the intent of decreasing the probability of FFP events, and increasing the probability of slow process events -- allowing rate limiting events to become more likely to be observed in KMC simulations. This Staggered Quasi-Equilibrium Rank-based Throttling for Steady-state (SQERTSS) algorithm designed for use in achieving and simulating steady-state conditions in KMC simulations. Lastly, as shown in this work, the SQERTSS algorithm also works for transient conditions: the correct configuration space and final state will still be achieved if the required assumptions are not violated, with the caveat that the sizes of the time-steps may be distorted during the transient period.« less

SQERTSS: Dynamic rank based throttling of transition probabilities in kinetic Monte Carlo simulations

NASA Astrophysics Data System (ADS)

Danielson, Thomas; Sutton, Jonathan E.; Hin, Céline; Savara, Aditya

2017-10-01

Lattice based Kinetic Monte Carlo (KMC) simulations offer a powerful simulation technique for investigating large reaction networks while retaining spatial configuration information, unlike ordinary differential equations. However, large chemical reaction networks can contain reaction processes with rates spanning multiple orders of magnitude. This can lead to the problem of "KMC stiffness" (similar to stiffness in differential equations), where the computational expense has the potential to be overwhelmed by very short time-steps during KMC simulations, with the simulation spending an inordinate amount of KMC steps/CPU time simulating fast frivolous processes (FFPs) without progressing the system (reaction network). In order to achieve simulation times that are experimentally relevant or desired for predictions, a dynamic throttling algorithm involving separation of the processes into speed-ranks based on event frequencies has been designed and implemented with the intent of decreasing the probability of FFP events, and increasing the probability of slow process events-allowing rate limiting events to become more likely to be observed in KMC simulations. This Staggered Quasi-Equilibrium Rank-based Throttling for Steady-state (SQERTSS) algorithm is designed for use in achieving and simulating steady-state conditions in KMC simulations. As shown in this work, the SQERTSS algorithm also works for transient conditions: the correct configuration space and final state will still be achieved if the required assumptions are not violated, with the caveat that the sizes of the time-steps may be distorted during the transient period.

NLO QCD predictions for Z+γ + jets production with Sherpa

NASA Astrophysics Data System (ADS)

Krause, Johannes; Siegert, Frank

2018-02-01

We present precise predictions for prompt photon production in association with a Z boson and jets. They are obtained within the Sherpa framework as a consistently merged inclusive sample. Leptonic decays of the Z boson are fully included in the calculation with all off-shell effects. Virtual matrix elements are provided by OpenLoops and parton-shower effects are simulated with a dipole parton shower. Thanks to the NLO QCD corrections included not only for inclusive Zγ production but also for the Zγ + 1-jet process we find significantly reduced systematic uncertainties and very good agreement with experimental measurements at √{s}=8 TeV. Predictions at √{s}=13 TeV are displayed including a study of theoretical uncertainties. In view of an application of these simulations within LHC experiments, we discuss in detail the necessary combination with a simulation of the Z + jets final state. In addition to a corresponding prescription we introduce recommended cross checks to avoid common pitfalls during the overlap removal between the two samples.

Phase field model of the nanoscale evolution during the explosive crystallization phenomenon

NASA Astrophysics Data System (ADS)

Lombardo, S. F.; Boninelli, S.; Cristiano, F.; Deretzis, I.; Grimaldi, M. G.; Huet, K.; Napolitani, E.; La Magna, A.

2018-03-01

Explosive crystallization is a well known phenomenon occurring due to the thermodynamic instability of strongly under-cooled liquids, which is particularly relevant in pulsed laser annealing processes of amorphous semiconductor materials due to the globally exothermic amorphous-to-liquid-to-crystal transition pathway. In spite of the assessed understanding of this phenomenon, quantitative predictions of the material kinetics promoted by explosive crystallization are hardly achieved due to the lack of a consistent model able to simulate the concurrent kinetics of the amorphous-liquid and liquid-crystal interfaces. Here, we propose a multi-well phase-field model specifically suited for the simulation of explosive crystallization induced by pulsed laser irradiation in the nanosecond time scale. The numerical implementation of the model is robust despite the discontinuous jumps of the interface speed induced by the phenomenon. The predictive potential of the simulations is demonstrated by means of comparisons of the modelling predictions with experimental data in terms of in situ reflectivity measurements and ex-situ micro-structural and chemical characterization.

Simulating forest productivity along a neotropical elevational transect: temperature variation and carbon use efficiency

NASA Astrophysics Data System (ADS)

Marthews, T.; Malhi, Y.; Girardin, C.; Silva-Espejo, J.; Aragão, L.; Metcalfe, D.; Rapp, J.; Mercado, L.; Fisher, R.; Galbraith, D.; Fisher, J.; Salinas-Revilla, N.; Friend, A.; Restrepo-Coupe, N.; Williams, R.

2012-04-01

A better understanding of the mechanisms controlling the magnitude and sign of carbon components in tropical forest ecosystems is important for reliable estimation of this important regional component of the global carbon cycle. We used the JULES vegetation model to simulate all components of the carbon balance at six sites along an Andes-Amazon transect across Peru and Brazil and compared the results to published field measurements. In the upper montane zone the model predicted a vegetation dieback, indicating a need for better parameterisation of cloud forest vegetation. In the lower montane and lowland zones simulated ecosystem productivity and respiration were predicted with reasonable accuracy, although not always within the error bounds of the observations. Model-predicted carbon use efficiency in this transect surprisingly did not increase with elevation, but remained close to the 'temperate' value 0.5. This may be explained by elevational changes in the balance between growth and maintenance respiration within the forest canopy, as controlled by both temperature- and pressure-mediated processes.

LOX/Hydrogen Coaxial Injector Atomization Test Program

NASA Technical Reports Server (NTRS)

Zaller, M.

1990-01-01

Quantitative information about the atomization of injector sprays is needed to improve the accuracy of computational models that predict the performance and stability margin of liquid propellant rocket engines. To obtain this data, a facility for the study of spray atomization is being established at NASA-Lewis to determine the drop size and velocity distributions occurring in vaporizing liquid sprays at supercritical pressures. Hardware configuration and test conditions are selected to make the cold flow simulant testing correspond as closely as possible to conditions in liquid oxygen (LOX)/gaseous H2 rocket engines. Drop size correlations from the literature, developed for liquid/gas coaxial injector geometries, are used to make drop size predictions for LOX/H2 coaxial injectors. The mean drop size predictions for a single element coaxial injector range from 0.1 to 2000 microns, emphasizing the need for additional studies of the atomization process in LOX/H2 engines. Selection of cold flow simulants, measured techniques, and hardware for LOX/H2 atomization simulations are discussed.

A method to identify and analyze biological programs through automated reasoning

PubMed Central

Yordanov, Boyan; Dunn, Sara-Jane; Kugler, Hillel; Smith, Austin; Martello, Graziano; Emmott, Stephen

2016-01-01

Predictive biology is elusive because rigorous, data-constrained, mechanistic models of complex biological systems are difficult to derive and validate. Current approaches tend to construct and examine static interaction network models, which are descriptively rich, but often lack explanatory and predictive power, or dynamic models that can be simulated to reproduce known behavior. However, in such approaches implicit assumptions are introduced as typically only one mechanism is considered, and exhaustively investigating all scenarios is impractical using simulation. To address these limitations, we present a methodology based on automated formal reasoning, which permits the synthesis and analysis of the complete set of logical models consistent with experimental observations. We test hypotheses against all candidate models, and remove the need for simulation by characterizing and simultaneously analyzing all mechanistic explanations of observed behavior. Our methodology transforms knowledge of complex biological processes from sets of possible interactions and experimental observations to precise, predictive biological programs governing cell function. PMID:27668090

Impact of vegetation dynamics on hydrological processes in a semi-arid basin by using a land surface-hydrology coupled model

NASA Astrophysics Data System (ADS)

Jiao, Yang; Lei, Huimin; Yang, Dawen; Huang, Maoyi; Liu, Dengfeng; Yuan, Xing

2017-08-01

Land surface models (LSMs) are widely used to understand the interactions between hydrological processes and vegetation dynamics, which is important for the attribution and prediction of regional hydrological variations. However, most LSMs have large uncertainties in their representations of eco-hydrological processes due to deficiencies in hydrological parameterizations. In this study, the Community Land Model version 4 (CLM4) LSM was modified with an advanced runoff generation and flow routing scheme, resulting in a new land surface-hydrology coupled model, CLM-GBHM. Both models were implemented in the Wudinghe River Basin (WRB), which is a semi-arid basin located in the middle reaches of the Yellow River, China. Compared with CLM, CLM-GBHM increased the Nash Sutcliffe efficiency for daily river discharge simulation (1965-1969) from -0.03 to 0.23 and reduced the relative bias in water table depth simulations (2010-2012) from 32.4% to 13.4%. The CLM-GBHM simulations with static, remotely sensed and model-predicted vegetation conditions showed that the vegetation in the WRB began to recover in the 2000s due to the Grain for Green Program but had not reached the same level of vegetation cover as regions in natural eco-hydrological equilibrium. Compared with a simulation using remotely sensed vegetation cover, the simulation with a dynamic vegetation model that considers only climate-induced change showed a 10.3% increase in evapotranspiration, a 47.8% decrease in runoff, and a 62.7% and 71.3% deceleration in changing trend of the outlet river discharge before and after the year 2000, respectively. This result suggests that both natural and anthropogenic factors should be incorporated in dynamic vegetation models to better simulate the eco-hydrological cycle.

Analysis of l-glutamic acid fermentation by using a dynamic metabolic simulation model of Escherichia coli

PubMed Central

2013-01-01

Background Understanding the process of amino acid fermentation as a comprehensive system is a challenging task. Previously, we developed a literature-based dynamic simulation model, which included transcriptional regulation, transcription, translation, and enzymatic reactions related to glycolysis, the pentose phosphate pathway, the tricarboxylic acid (TCA) cycle, and the anaplerotic pathway of Escherichia coli. During simulation, cell growth was defined such as to reproduce the experimental cell growth profile of fed-batch cultivation in jar fermenters. However, to confirm the biological appropriateness of our model, sensitivity analysis and experimental validation were required. Results We constructed an l-glutamic acid fermentation simulation model by removing sucAB, a gene encoding α-ketoglutarate dehydrogenase. We then performed systematic sensitivity analysis for l-glutamic acid production; the results of this process corresponded with previous experimental data regarding l-glutamic acid fermentation. Furthermore, it allowed us to predicted the possibility that accumulation of 3-phosphoglycerate in the cell would regulate the carbon flux into the TCA cycle and lead to an increase in the yield of l-glutamic acid via fermentation. We validated this hypothesis through a fermentation experiment involving a model l-glutamic acid-production strain, E. coli MG1655 ΔsucA in which the phosphoglycerate kinase gene had been amplified to cause accumulation of 3-phosphoglycerate. The observed increase in l-glutamic acid production verified the biologically meaningful predictive power of our dynamic metabolic simulation model. Conclusions In this study, dynamic simulation using a literature-based model was shown to be useful for elucidating the precise mechanisms involved in fermentation processes inside the cell. Further exhaustive sensitivity analysis will facilitate identification of novel factors involved in the metabolic regulation of amino acid fermentation. PMID:24053676

Analysis of L-glutamic acid fermentation by using a dynamic metabolic simulation model of Escherichia coli.

PubMed

Nishio, Yousuke; Ogishima, Soichi; Ichikawa, Masao; Yamada, Yohei; Usuda, Yoshihiro; Masuda, Tadashi; Tanaka, Hiroshi

2013-09-22

Understanding the process of amino acid fermentation as a comprehensive system is a challenging task. Previously, we developed a literature-based dynamic simulation model, which included transcriptional regulation, transcription, translation, and enzymatic reactions related to glycolysis, the pentose phosphate pathway, the tricarboxylic acid (TCA) cycle, and the anaplerotic pathway of Escherichia coli. During simulation, cell growth was defined such as to reproduce the experimental cell growth profile of fed-batch cultivation in jar fermenters. However, to confirm the biological appropriateness of our model, sensitivity analysis and experimental validation were required. We constructed an L-glutamic acid fermentation simulation model by removing sucAB, a gene encoding α-ketoglutarate dehydrogenase. We then performed systematic sensitivity analysis for L-glutamic acid production; the results of this process corresponded with previous experimental data regarding L-glutamic acid fermentation. Furthermore, it allowed us to predicted the possibility that accumulation of 3-phosphoglycerate in the cell would regulate the carbon flux into the TCA cycle and lead to an increase in the yield of L-glutamic acid via fermentation. We validated this hypothesis through a fermentation experiment involving a model L-glutamic acid-production strain, E. coli MG1655 ΔsucA in which the phosphoglycerate kinase gene had been amplified to cause accumulation of 3-phosphoglycerate. The observed increase in L-glutamic acid production verified the biologically meaningful predictive power of our dynamic metabolic simulation model. In this study, dynamic simulation using a literature-based model was shown to be useful for elucidating the precise mechanisms involved in fermentation processes inside the cell. Further exhaustive sensitivity analysis will facilitate identification of novel factors involved in the metabolic regulation of amino acid fermentation.

Impact of vegetation dynamics on hydrological processes in a semi-arid basin by using a land surface-hydrology coupled model

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

Jiao, Yang; Lei, Huimin; Yang, Dawen

Land surface models (LSMs) are widely used to understand the interactions between hydrological processes and vegetation dynamics, which is important for the attribution and prediction of regional hydrological variations. However, most LSMs have large uncertainties in their representations of ecohydrological processes due to deficiencies in hydrological parameterizations. In this study, the Community Land Model version 4 (CLM4) LSM was modified with an advanced runoff generation and flow routing scheme, resulting in a new land surface-hydrology coupled model, CLM-GBHM. Both models were implemented in the Wudinghe River Basin (WRB), which is a semi-arid basin located in the middle reaches of themore » Yellow River, China. Compared with CLM, CLM-GBHM increased the Nash Sutcliffe efficiency for daily river discharge simulation (1965–1969) from 0.03 to 0.23 and reduced the relative bias in water table depth simulations (2010–2012) from 32.4% to 13.4%. The CLM-GBHM simulations with static, remotely sensed and model-predicted vegetation conditions showed that the vegetation in the WRB began to recover in the 2000s due to the Grain for Green Program but had not reached the same level of vegetation cover as regions in natural eco-hydrological equilibrium. Compared with a simulation using remotely sensed vegetation cover, the simulation with a dynamic vegetation model that considers only climate-induced change showed a 10.3% increase in evapotranspiration, a 47.8% decrease in runoff, and a 62.7% and 71.3% deceleration in changing trend of the outlet river discharge before and after the year 2000, respectively. This result suggests that both natural and anthropogenic factors should be incorporated in dynamic vegetation models to better simulate the eco-hydrological cycle.« less

Molecular Simulations of Adsorption and Diffusion in Silicalite.

NASA Astrophysics Data System (ADS)

Snurr, Randall Quentin

The adsorption and diffusion of hydrocarbons in the zeolite silicalite have been studied using molecular simulations. The simulations use an atomistic description of zeolite/sorbate interactions and are based on principles of statistical mechanics. Emphasis was placed on developing new simulation techniques to allow complex systems relevant to industrial applications in catalysis and separations processes to be studied. Adsorption isotherms and heats of sorption for methane in silicalite were calculated from grand canonical Monte Carlo (GCMC) simulations and also from molecular dynamics (MD) simulations accompanied by Widom test particle insertions. Good agreement with experimental data from the literature was found. The adsorption thermodynamics of aromatic species in silicalite at low loading was predicted by direct evaluation of the configurational integrals. Good agreement with experiment was obtained for the Henry's constants and the heats of adsorption. Molecules were predicted to be localized in the channel intersections at low loading. At higher loading, conventional GCMC simulations were found to be infeasible. Several variations of the GCMC technique were developed incorporating biased insertion moves. These new techniques are much more efficient than conventional GCMC and allow for the prediction of adsorption isotherms of tightly-fitting aromatic molecules in silicalite. Our simulations when combined with experimental evidence of a phase change in the zeolite structure at intermediate loading provide an explanation of the characteristic steps seen in the experimental isotherms. A hierarchical atomistic/lattice model for studying these systems was also developed. The hierarchical model is more than an order of magnitude more efficient computationally than direct atomistic simulation. Diffusion of benzene in silicalite was studied using transition-state theory (TST). Such an approach overcomes the time-scale limitations of using MD simulations for studying sorbate dynamics. Predicted diffusion coefficients were found to be too low compared to experiment. This was attributed to the assumption of a rigid zeolite structure in the calculations and the use of a harmonic approximation for calculating the TST rate constants. Details of sorbate motion were also investigated.

Simulation of the Press Hardening Process and Prediction of the Final Mechanical Material Properties

NASA Astrophysics Data System (ADS)

Hochholdinger, Bernd; Hora, Pavel; Grass, Hannes; Lipp, Arnulf

2011-08-01

Press hardening is a well-established production process in the automotive industry today. The actual trend of this process technology points towards the manufacturing of parts with tailored properties. Since the knowledge of the mechanical properties of a structural part after forming and quenching is essential for the evaluation of for example the crash performance, an accurate as possible virtual assessment of the production process is more than ever necessary. In order to achieve this, the definition of reliable input parameters and boundary conditions for the thermo-mechanically coupled simulation of the process steps is required. One of the most important input parameters, especially regarding the final properties of the quenched material, is the contact heat transfer coefficient (IHTC). The CHTC depends on the effective pressure or the gap distance between part and tool. The CHTC at different contact pressures and gap distances is determined through inverse parameter identification. Furthermore a simulation strategy for the subsequent steps of the press hardening process as well as adequate modeling approaches for part and tools are discussed. For the prediction of the yield curves of the material after press hardening a phenomenological model is presented. This model requires the knowledge of the microstructure within the part. By post processing the nodal temperature history with a CCT diagram the quantitative distribution of the phase fractions martensite, bainite, ferrite and pearlite after press hardening is determined. The model itself is based on a Hockett-Sherby approach with the Hockett-Sherby parameters being defined in function of the phase fractions and a characteristic cooling rate.

Spread prediction model of continuous steel tube based on BP neural network

NASA Astrophysics Data System (ADS)

Zhai, Jian-wei; Yu, Hui; Zou, Hai-bei; Wang, San-zhong; Liu, Li-gang

2017-07-01

According to the geometric pass of roll and technological parameters of three-roller continuous mandrel rolling mill in a factory, a finite element model is established to simulate the continuous rolling process of seamless steel tube, and the reliability of finite element model is verified by comparing with the simulation results and actual results of rolling force, wall thickness and outer diameter of the tube. The effect of roller reduction, roller rotation speed and blooming temperature on the spread rule is studied. Based on BP(Back Propagation) neural network technology, a spread prediction model of continuous rolling tube is established for training wall thickness coefficient and spread coefficient of the continuous rolling tube, and the rapid and accurate prediction of continuous rolling tube size is realized.

Predicting transmittance spectra of electrophotographic color prints

NASA Astrophysics Data System (ADS)

Mourad, Safer; Emmel, Patrick; Hersch, Roger D.

2000-12-01

For dry toner electrophotographic color printers, we present a numerical simulation model describing the color printer responses based on a physical characterization of the different electrophotographic process steps. The proposed model introduces a Cross Transfer Efficiency designed to predict the color transmittance spectra of multi-color prints by taking into account the transfer influence of each deposited color toner layer upon the other layers. The simulation model leads to a better understanding of the factors that have an impact on printing quality. In order to avoid the additional optical non-linearities produced by light reflection on paper, we have limited the present investigation to transparency prints. The proposed model succeeded to predict the transmittance spectra of printed wedges combining two color toner layers with a mean deviation less than CIE-LAB (Delta) E equals 2.5.

Scaling Analysis of Alloy Solidification and Fluid Flow in a Rectangular Cavity

NASA Astrophysics Data System (ADS)

Plotkowski, A.; Fezi, K.; Krane, M. J. M.

A scaling analysis was performed to predict trends in alloy solidification in a side-cooled rectangular cavity. The governing equations for energy and momentum were scaled in order to determine the dependence of various aspects of solidification on the process parameters for a uniform initial temperature and an isothermal boundary condition. This work improved on previous analyses by adding considerations for the cooling bulk fluid flow. The analysis predicted the time required to extinguish the superheat, the maximum local solidification time, and the total solidification time. The results were compared to a numerical simulation for a Al-4.5 wt.% Cu alloy with various initial and boundary conditions. Good agreement was found between the simulation results and the trends predicted by the scaling analysis.

Towards wall functions for the prediction of solute segregation in plane front directional solidification

NASA Astrophysics Data System (ADS)

Chatelain, M.; Rhouzlane, S.; Botton, V.; Albaric, M.; Henry, D.; Millet, S.; Pelletier, D.; Garandet, J. P.

2017-10-01

The present paper focuses on solute segregation occurring in directional solidification processes with sharp solid/liquid interface, like silicon crystal growth. A major difficulty for the simulation of such processes is their inherently multi-scale nature: the impurity segregation problem is controlled at the solute boundary layer scale (micrometers) while the thermal problem is ruled at the crucible scale (meters). The thickness of the solute boundary layer is controlled by the convection regime and requires a specific refinement of the mesh of numerical models. In order to improve numerical simulations, wall functions describing solute boundary layers for convecto-diffusive regimes are derived from a scaling analysis. The aim of these wall functions is to obtain segregation profiles from purely thermo-hydrodynamic simulations, which do not require solute boundary layer refinement at the solid/liquid interface. Regarding industrial applications, various stirring techniques can be used to enhance segregation, leading to fully turbulent flows in the melt. In this context, the scaling analysis is further improved by taking into account the turbulent solute transport. The solute boundary layers predicted by the analytical model are compared to those obtained by transient segregation simulations in a canonical 2D lid driven cavity configuration for validation purposes. Convective regimes ranging from laminar to fully turbulent are considered. Growth rate and molecular diffusivity influences are also investigated. Then, a procedure to predict concentration fields in the solid phase from a hydrodynamic simulation of the solidification process is proposed. This procedure is based on the analytical wall functions and on solute mass conservation. It only uses wall shear-stress profiles at the solidification front as input data. The 2D analytical concentration fields are directly compared to the results of the complete simulation of segregation in the lid driven cavity configuration. Finally, an additional output from the analytical model is also presented. We put in light the correlation between different species convecto-diffusive behaviour; we use it to propose an estimation method for the segregation parameters of various chemical species knowing segregation parameters of one specific species.

Analytical modeling and sensor monitoring for optimal processing of advanced textile structural composites by resin transfer molding

NASA Technical Reports Server (NTRS)

Loos, Alfred C.; Macrae, John D.; Hammond, Vincent H.; Kranbuehl, David E.; Hart, Sean M.; Hasko, Gregory H.; Markus, Alan M.

1993-01-01

A two-dimensional model of the resin transfer molding (RTM) process was developed which can be used to simulate the infiltration of resin into an anisotropic fibrous preform. Frequency dependent electromagnetic sensing (FDEMS) has been developed for in situ monitoring of the RTM process. Flow visualization tests were performed to obtain data which can be used to verify the sensor measurements and the model predictions. Results of the tests showed that FDEMS can accurately detect the position of the resin flow-front during mold filling, and that the model predicted flow-front patterns agreed well with the measured flow-front patterns.

Research on orbit prediction for solar-based calibration proper satellite

NASA Astrophysics Data System (ADS)

Chen, Xuan; Qi, Wenwen; Xu, Peng

2018-03-01

Utilizing the mathematical model of the orbit mechanics, the orbit prediction is to forecast the space target's orbit information of a certain time based on the orbit of the initial moment. The proper satellite radiometric calibration and calibration orbit prediction process are introduced briefly. On the basis of the research of the calibration space position design method and the radiative transfer model, an orbit prediction method for proper satellite radiometric calibration is proposed to select the appropriate calibration arc for the remote sensor and to predict the orbit information of the proper satellite and the remote sensor. By analyzing the orbit constraint of the proper satellite calibration, the GF-1solar synchronous orbit is chose as the proper satellite orbit in order to simulate the calibration visible durance for different satellites to be calibrated. The results of simulation and analysis provide the basis for the improvement of the radiometric calibration accuracy of the satellite remote sensor, which lays the foundation for the high precision and high frequency radiometric calibration.

Understanding differences between DELFT3D and empirical predictions of alongshore sediment transport gradients

USGS Publications Warehouse

List, Jeffrey; Benedet, Lindino; Hanes, Daniel M.; Ruggiero, Peter

2009-01-01

Predictions of alongshore transport gradients are critical for forecasting shoreline change. At the previous ICCE conference, it was demonstrated that alongshore transport gradients predicted by the empirical CERC equation can differ substantially from predictions made by the hydrodynamics-based model Delft3D in the case of a simulated borrow pit on the shoreface. Here we use the Delft3D momentum balance to examine the reason for this difference. Alongshore advective flow accelerations in our Delft3D simulation are mainly driven by pressure gradients resulting from alongshore variations in wave height and setup, and Delft3D transport gradients are controlled by these flow accelerations. The CERC equation does not take this process into account, and for this reason a second empirical transport term is sometimes added when alongshore gradients in wave height are thought to be significant. However, our test case indicates that this second term does not properly predict alongshore transport gradients.

Field scale test of multi-dimensional flow and morphodynamic simulations used for restoration design analysis

USGS Publications Warehouse

McDonald, Richard R.; Nelson, Jonathan M.; Fosness, Ryan L.; Nelson, Peter O.; Constantinescu, George; Garcia, Marcelo H.; Hanes, Dan

2016-01-01

Two- and three-dimensional morphodynamic simulations are becoming common in studies of channel form and process. The performance of these simulations are often validated against measurements from laboratory studies. Collecting channel change information in natural settings for model validation is difficult because it can be expensive and under most channel forming flows the resulting channel change is generally small. Several channel restoration projects designed in part to armor large meanders with several large spurs constructed of wooden piles on the Kootenai River, ID, have resulted in rapid bed elevation change following construction. Monitoring of these restoration projects includes post- restoration (as-built) Digital Elevation Models (DEMs) as well as additional channel surveys following high channel forming flows post-construction. The resulting sequence of measured bathymetry provides excellent validation data for morphodynamic simulations at the reach scale of a real river. In this paper we test the performance a quasi-three-dimensional morphodynamic simulation against the measured elevation change. The resulting simulations predict the pattern of channel change reasonably well but many of the details such as the maximum scour are under predicted.

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

Lai, Canhai; Xu, Zhijie; Li, Tingwen

In virtual design and scale up of pilot-scale carbon capture systems, the coupled reactive multiphase flow problem must be solved to predict the adsorber’s performance and capture efficiency under various operation conditions. This paper focuses on the detailed computational fluid dynamics (CFD) modeling of a pilot-scale fluidized bed adsorber equipped with vertical cooling tubes. Multiphase Flow with Interphase eXchanges (MFiX), an open-source multiphase flow CFD solver, is used for the simulations with custom code to simulate the chemical reactions and filtered models to capture the effect of the unresolved details in the coarser mesh for simulations with reasonable simulations andmore » manageable computational effort. Previously developed two filtered models for horizontal cylinder drag, heat transfer, and reaction kinetics have been modified to derive the 2D filtered models representing vertical cylinders in the coarse-grid CFD simulations. The effects of the heat exchanger configurations (i.e., horizontal or vertical) on the adsorber’s hydrodynamics and CO2 capture performance are then examined. The simulation result subsequently is compared and contrasted with another predicted by a one-dimensional three-region process model.« less

Observational Signatures of Magnetic Reconnection

NASA Technical Reports Server (NTRS)

Savage, Sabrina

2014-01-01

Magnetic reconnection is often referred to as the primary source of energy release during solar flares. Directly observing reconnection occurring in the solar atmosphere, however, is not trivial considering that the scale size of the diffusion region is magnitudes smaller than the observational capabilities of current instrumentation, and coronal magnetic field measurements are not currently sufficient to capture the process. Therefore, predicting and studying observationally feasible signatures of the precursors and consequences of reconnection is necessary for guiding and verifying the simulations that dominate our understanding. I will present a set of such observations, particularly in connection with long-duration solar events, and compare them with recent simulations and theoretical predictions.

Dynamical mechanical characteristic simulation and analysis of the low voltage switch under vibration and shock conditions

NASA Astrophysics Data System (ADS)

Miao, Xiaodan; Han, Feng

2017-04-01

The low voltage switch has widely application especially in the hostile environment such as large vibration and shock conditions. In order to ensure the validity of the switch in the hostile environment, it is necessary to predict its mechanical characteristic. In traditional method, the complex and expensive testing system is build up to verify its validity. This paper presented a method based on finite element analysis to predict the dynamic mechanical characteristic of the switch by using ANSYS software. This simulation could provide the basis for the design and optimization of the switch to shorten the design process to improve the product efficiency.

Modeling of Triangular Lattice Space Structures with Curved Battens

NASA Technical Reports Server (NTRS)

Chen, Tzikang; Wang, John T.

2005-01-01

Techniques for simulating an assembly process of lattice structures with curved battens were developed. The shape of the curved battens, the tension in the diagonals, and the compression in the battens were predicted for the assembled model. To be able to perform the assembly simulation, a cable-pulley element was implemented, and geometrically nonlinear finite element analyses were performed. Three types of finite element models were created from assembled lattice structures for studying the effects of design and modeling variations on the load carrying capability. Discrepancies in the predictions from these models were discussed. The effects of diagonal constraint failure were also studied.

DOA Finding with Support Vector Regression Based Forward-Backward Linear Prediction.

PubMed

Pan, Jingjing; Wang, Yide; Le Bastard, Cédric; Wang, Tianzhen

2017-05-27

Direction-of-arrival (DOA) estimation has drawn considerable attention in array signal processing, particularly with coherent signals and a limited number of snapshots. Forward-backward linear prediction (FBLP) is able to directly deal with coherent signals. Support vector regression (SVR) is robust with small samples. This paper proposes the combination of the advantages of FBLP and SVR in the estimation of DOAs of coherent incoming signals with low snapshots. The performance of the proposed method is validated with numerical simulations in coherent scenarios, in terms of different angle separations, numbers of snapshots, and signal-to-noise ratios (SNRs). Simulation results show the effectiveness of the proposed method.

R-process Enrichment in Cosmological Zoom Simulation of a Milkyway Type Halo by Neutron Star Mergers; The Origin of the MP-R and CEMP-R Stars

NASA Astrophysics Data System (ADS)

Safarzadeh, Mohammadtaher; Scannapieco, Evan

2018-06-01

The history of r-process enrichment in our galaxy is modeled through a novel set of zoom cosmo- logical simulations on a MilkyWay type galaxy. r-process sources are assumed to be neutron star mergers with a distribution of natal kicks and merge time distribution. We model turbulent mixing to estimate the pristine gas fraction in each simulation cell which we use to determine the Pop III star formation with assigned Carbon rich ejecta when going off as SNe. We follow the formation of Carbon-Enhanced Metal-Poor (CEMP) stars and the statistics of different r-process enhanced class of stars. The simulation underpredict the frequency of CEMP/MP stars by a factor of 2-4. Likewise the MP-rI/MP and MP-rII/MP and CEMP-r/CEMP cumulative ratios are all under predicted by 1-2 orders of magnitude. Our results show that NS binaries by themselves fall too short to explain the observed frequency of r-process enhanced stars and other sources of r-process enrichment at high redshifts are needed to fill the gap.

Process simulations for manufacturing of thick composites

NASA Astrophysics Data System (ADS)

Kempner, Evan A.

The availability of manufacturing simulations for composites can significantly reduce the costs associated with process development. Simulations provide a tool for evaluating the effect of processing conditions on the quality of parts produced without requiring numerous experiments. This is especially significant in parts that have troublesome features such as large thickness. The development of simulations for thick walled composites has been approached by examining the mechanics of resin flow and fiber deformation during processing, applying these evaluations to develop simulations, and evaluating the simulation with experimental results. A unified analysis is developed to describe the three-dimensional resin flow and fiber preform deformation during processing regardless of the manufacturing process used. It is shown how the generic governing evaluations in the unified analysis can be applied to autoclave molding, compression molding, pultrusion, filament winding, and resin transfer molding. A comparison is provided with earlier models derived individually for these processes. The evaluations described for autoclave curing were used to produce a one-dimensional cure simulation for autoclave curing of thick composites. The simulation consists of an analysis for heat transfer and resin flow in the composite as well as bleeder plies used to absorb resin removed from the part. Experiments were performed in a hot press to approximate curing in an autoclave. Graphite/epoxy laminates of 3 cm and 5 cm thickness were cured while monitoring temperatures at several points inside the laminate and thickness. The simulation predicted temperatures fairly closely, but difficulties were encountered in correlation of thickness results. This simulation was also used to study the effects of prepreg aging on processing of thick composites. An investigation was also performed on filament winding with prepreg tow. Cylinders were wound of approximately 12 mm thickness with pressure gages at the mandrel-composite interface. Cylinders were hoop wound with tensions ranging from 13-34 N. An analytical model was developed to calculate change in stress due to relaxation during winding. Although compressive circumferential stresses occurred throughout each of the cylinders, the magnitude was fairly low.

Numerical Simulation of Rheological, Chemical and Hydromechanical Processes of Thrombolysis

NASA Astrophysics Data System (ADS)

Khramchenkov, E.; Khramchenkov, M.

2015-04-01

Mathematical model of clot lysis in blood vessels is developed on the basis of equations of convection-diffusion. Fibrin of the clot is considered stationary solid phase, and plasminogen, plasmin and plasminogen-activators - as dissolved fluid phases. As a result of numerical solution of the model predictions of lysis process are gained. Important influence of clot swelling on the process of lysis is revealed.

Molecular simulation of the thermophysical properties and phase behaviour of impure CO2 relevant to CCS.

PubMed

Cresswell, Alexander J; Wheatley, Richard J; Wilkinson, Richard D; Graham, Richard S

2016-10-20

Impurities from the CCS chain can greatly influence the physical properties of CO 2 . This has important design, safety and cost implications for the compression, transport and storage of CO 2 . There is an urgent need to understand and predict the properties of impure CO 2 to assist with CCS implementation. However, CCS presents demanding modelling requirements. A suitable model must both accurately and robustly predict CO 2 phase behaviour over a wide range of temperatures and pressures, and maintain that predictive power for CO 2 mixtures with numerous, mutually interacting chemical species. A promising technique to address this task is molecular simulation. It offers a molecular approach, with foundations in firmly established physical principles, along with the potential to predict the wide range of physical properties required for CCS. The quality of predictions from molecular simulation depends on accurate force-fields to describe the interactions between CO 2 and other molecules. Unfortunately, there is currently no universally applicable method to obtain force-fields suitable for molecular simulation. In this paper we present two methods of obtaining force-fields: the first being semi-empirical and the second using ab initio quantum-chemical calculations. In the first approach we optimise the impurity force-field against measurements of the phase and pressure-volume behaviour of CO 2 binary mixtures with N 2 , O 2 , Ar and H 2 . A gradient-free optimiser allows us to use the simulation itself as the underlying model. This leads to accurate and robust predictions under conditions relevant to CCS. In the second approach we use quantum-chemical calculations to produce ab initio evaluations of the interactions between CO 2 and relevant impurities, taking N 2 as an exemplar. We use a modest number of these calculations to train a machine-learning algorithm, known as a Gaussian process, to describe these data. The resulting model is then able to accurately predict a much broader set of ab initio force-field calculations at comparatively low numerical cost. Although our method is not yet ready to be implemented in a molecular simulation, we outline the necessary steps here. Such simulations have the potential to deliver first-principles simulation of the thermodynamic properties of impure CO 2 , without fitting to experimental data.

Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel

DOE PAGES

Tapia, Gustavo; Khairallah, Saad A.; Matthews, Manyalibo J.; ...

2017-09-22

Here, Laser Powder-Bed Fusion (L-PBF) metal-based additive manufacturing (AM) is complex and not fully understood. Successful processing for one material, might not necessarily apply to a different material. This paper describes a workflow process that aims at creating a material data sheet standard that describes regimes where the process can be expected to be robust. The procedure consists of building a Gaussian process-based surrogate model of the L-PBF process that predicts melt pool depth in single-track experiments given a laser power, scan speed, and laser beam size combination. The predictions are then mapped onto a power versus scan speed diagrammore » delimiting the conduction from the keyhole melting controlled regimes. This statistical framework is shown to be robust even for cases where experimental training data might be suboptimal in quality, if appropriate physics-based filters are applied. Additionally, it is demonstrated that a high-fidelity simulation model of L-PBF can equally be successfully used for building a surrogate model, which is beneficial since simulations are getting more efficient and are more practical to study the response of different materials, than to re-tool an AM machine for new material powder.« less

Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel

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

Tapia, Gustavo; Khairallah, Saad A.; Matthews, Manyalibo J.

Here, Laser Powder-Bed Fusion (L-PBF) metal-based additive manufacturing (AM) is complex and not fully understood. Successful processing for one material, might not necessarily apply to a different material. This paper describes a workflow process that aims at creating a material data sheet standard that describes regimes where the process can be expected to be robust. The procedure consists of building a Gaussian process-based surrogate model of the L-PBF process that predicts melt pool depth in single-track experiments given a laser power, scan speed, and laser beam size combination. The predictions are then mapped onto a power versus scan speed diagrammore » delimiting the conduction from the keyhole melting controlled regimes. This statistical framework is shown to be robust even for cases where experimental training data might be suboptimal in quality, if appropriate physics-based filters are applied. Additionally, it is demonstrated that a high-fidelity simulation model of L-PBF can equally be successfully used for building a surrogate model, which is beneficial since simulations are getting more efficient and are more practical to study the response of different materials, than to re-tool an AM machine for new material powder.« less

Optimization of processing parameters of UAV integral structural components based on yield response

NASA Astrophysics Data System (ADS)

Chen, Yunsheng

2018-05-01

In order to improve the overall strength of unmanned aerial vehicle (UAV), it is necessary to optimize the processing parameters of UAV structural components, which is affected by initial residual stress in the process of UAV structural components processing. Because machining errors are easy to occur, an optimization model for machining parameters of UAV integral structural components based on yield response is proposed. The finite element method is used to simulate the machining parameters of UAV integral structural components. The prediction model of workpiece surface machining error is established, and the influence of the path of walking knife on residual stress of UAV integral structure is studied, according to the stress of UAV integral component. The yield response of the time-varying stiffness is analyzed, and the yield response and the stress evolution mechanism of the UAV integral structure are analyzed. The simulation results show that this method is used to optimize the machining parameters of UAV integral structural components and improve the precision of UAV milling processing. The machining error is reduced, and the deformation prediction and error compensation of UAV integral structural parts are realized, thus improving the quality of machining.

Prediction of Continuous Cooling Transformation Diagrams for Dual-Phase Steels from the Intercritical Region

NASA Astrophysics Data System (ADS)

Colla, V.; Desanctis, M.; Dimatteo, A.; Lovicu, G.; Valentini, R.

2011-09-01

The purpose of the present work is the implementation and validation of a model able to predict the microstructure changes and the mechanical properties in the modern high-strength dual-phase steels after the continuous annealing process line (CAPL) and galvanizing (Galv) process. Experimental continuous cooling transformation (CCT) diagrams for 13 differently alloying dual-phase steels were measured by dilatometry from the intercritical range and were used to tune the parameters of the microstructural prediction module of the model. Mechanical properties and microstructural features were measured for more than 400 dual-phase steels simulating the CAPL and Galv industrial process, and the results were used to construct the mechanical model that predicts mechanical properties from microstructural features, chemistry, and process parameters. The model was validated and proved its efficiency in reproducing the transformation kinetic and mechanical properties of dual-phase steels produced by typical industrial process. Although it is limited to the dual-phase grades and chemical compositions explored, this model will constitute a useful tool for the steel industry.

Optimization of porthole die geometrical variables by Taguchi method

NASA Astrophysics Data System (ADS)

Gagliardi, F.; Ciancio, C.; Ambrogio, G.; Filice, L.

2017-10-01

Porthole die extrusion is commonly used to manufacture hollow profiles made of lightweight alloys for numerous industrial applications. The reliability of extruded parts is affected strongly by the quality of the longitudinal and transversal seam welds. According to that, the die geometry must be designed correctly and the process parameters must be selected properly to achieve the desired product quality. In this study, numerical 3D simulations have been created and run to investigate the role of various geometrical variables on punch load and maximum pressure inside the welding chamber. These are important outputs to take into account affecting, respectively, the necessary capacity of the extrusion press and the quality of the welding lines. The Taguchi technique has been used to reduce the number of the required numerical simulations necessary for considering the influence of twelve different geometric variables. Moreover, the Analysis of variance (ANOVA) has been implemented to individually analyze the effect of each input parameter on the two responses. Then, the methodology has been utilized to determine the optimal process configuration individually optimizing the two investigated process outputs. Finally, the responses of the optimized parameters have been verified through finite element simulations approximating the predicted value closely. This study shows the feasibility of the Taguchi technique for predicting performance, optimization and therefore for improving the design of a porthole extrusion process.

Adaptive vibration control of structures under earthquakes

NASA Astrophysics Data System (ADS)

Lew, Jiann-Shiun; Juang, Jer-Nan; Loh, Chin-Hsiung

2017-04-01

techniques, for structural vibration suppression under earthquakes. Various control strategies have been developed to protect structures from natural hazards and improve the comfort of occupants in buildings. However, there has been little development of adaptive building control with the integration of real-time system identification and control design. Generalized predictive control, which combines the process of real-time system identification and the process of predictive control design, has received widespread acceptance and has been successfully applied to various test-beds. This paper presents a formulation of the predictive control scheme for adaptive vibration control of structures under earthquakes. Comprehensive simulations are performed to demonstrate and validate the proposed adaptive control technique for earthquake-induced vibration of a building.

Calibration and simulation of two large wastewater treatment plants operated for nutrient removal.

PubMed

Ferrer, J; Morenilla, J J; Bouzas, A; García-Usach, F

2004-01-01

Control and optimisation of plant processes has become a priority for WWTP managers. The calibration and verification of a mathematical model provides an important tool for the investigation of advanced control strategies that may assist in the design or optimization of WWTPs. This paper describes the calibration of the ASM2d model for two full scale biological nitrogen and phosphorus removal plants in order to characterize the biological process and to upgrade the plants' performance. Results from simulation showed a good correspondence with experimental data demonstrating that the model and the calibrated parameters were able to predict the behaviour of both WWTPs. Once the calibration and simulation process was finished, a study for each WWTP was done with the aim of improving its performance. Modifications focused on reactor configuration and operation strategies were proposed.

Composition and Manufacturing Effects on Electrical Conductivity of Li/FeS 2 Thermal Battery Cathodes

DOE PAGES

Reinholz, Emilee L.; Roberts, Scott A.; Apblett, Christopher A.; ...

2016-06-11

The electrical conductivity is key to the performance of thermal battery cathodes. In this work we present the effects of manufacturing and processing conditions on the electrical conductivity of Li/FeS2 thermal battery cathodes. Finite element simulations were used to compute the conductivity of three-dimensional microcomputed tomography cathode microstructures and compare results to experimental impedance spectroscopy measurements. A regression analysis reveals a predictive relationship between composition, processing conditions, and electrical conductivity; a trend which is largely erased after thermally-induced deformation. Moreover, the trend applies to both experimental and simulation results, although is not as apparent in simulations. This research is amore » step toward a more fundamental understanding of the effects of processing and composition on thermal battery component microstructure, properties, and performance.« less

Thermodynamic model effects on the design and optimization of natural gas plants

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

Diaz, S.; Zabaloy, M.; Brignole, E.A.

1999-07-01

The design and optimization of natural gas plants is carried out on the basis of process simulators. The physical property package is generally based on cubic equations of state. By rigorous thermodynamics phase equilibrium conditions, thermodynamic functions, equilibrium phase separations, work and heat are computed. The aim of this work is to analyze the NGL turboexpansion process and identify possible process computations that are more sensitive to model predictions accuracy. Three equations of state, PR, SRK and Peneloux modification, are used to study the effect of property predictions on process calculations and plant optimization. It is shown that turboexpander plantsmore » have moderate sensitivity with respect to phase equilibrium computations, but higher accuracy is required for the prediction of enthalpy and turboexpansion work. The effect of modeling CO{sub 2} solubility is also critical in mixtures with high CO{sub 2} content in the feed.« less

How Hot was Africa during the Mid-Holocene? Reexamining Africa's Thermal History via integrated Climate and Proxy System Modeling

NASA Astrophysics Data System (ADS)

Dee, S.; Russell, J. M.; Morrill, C.

2017-12-01

Climate models predict Africa will warm by up to 5°C in the coming century. Reconstructions of African temperature since the Last Glacial Maximum (LGM) have made fundamental contributions to our understanding of past, present, and future climate and can help constrain predictions from general circulation models (GCMs). However, many of these reconstructions are based on proxies of lake temperature, so the confounding influences of lacustrine processes may complicate our interpretations of past changes in tropical climate. These proxy-specific uncertainties require robust methodology for data-model comparison. We develop a new proxy system model (PSM) for paleolimnology to facilitate data-model comparison and to fully characterize uncertainties in climate reconstructions. Output from GCMs are used to force the PSM to simulate lake temperature, hydrology, and associated proxy uncertainties. We compare reconstructed East African lake and air temperatures in individual records and in a stack of 9 lake records to those predicted by our PSM forced with Paleoclimate Model Intercomparison Project (PMIP3) simulations, focusing on the mid-Holocene (6 kyr BP). We additionally employ single-forcing transient climate simulations from TraCE (10 kyr to 4 kyr B.P. and historical), as well as 200-yr time slice simulations from CESM1.0 to run the lake PSM. We test the sensitivity of African climate change during the mid-Holocene to orbital, greenhouse gas, and ice-sheet forcing in single-forcing simulations, and investigate dynamical hypotheses for these changes. Reconstructions of tropical African temperature indicate 1-2ºC warming during the mid-Holocene relative to the present, similar to changes predicted in the coming decades. However, most climate models underestimate the warming observed in these paleoclimate data (Fig. 1, 6kyr B.P.). We investigate this discrepancy using the new lake PSM and climate model simulations, with attention to the (potentially non-stationary) relationship between lake surface temperature and air temperature. The data-model comparison helps partition the impacts of lake-specific processes such as energy balance, mixing, sedimentation and bioturbation. We provide new insight into the patterns, amplitudes, sensitivity, and mechanisms of African temperature change.

Multispectral simulation environment for modeling low-light-level sensor systems

NASA Astrophysics Data System (ADS)

Ientilucci, Emmett J.; Brown, Scott D.; Schott, John R.; Raqueno, Rolando V.

1998-11-01

Image intensifying cameras have been found to be extremely useful in low-light-level (LLL) scenarios including military night vision and civilian rescue operations. These sensors utilize the available visible region photons and an amplification process to produce high contrast imagery. It has been demonstrated that processing techniques can further enhance the quality of this imagery. For example, fusion with matching thermal IR imagery can improve image content when very little visible region contrast is available. To aid in the improvement of current algorithms and the development of new ones, a high fidelity simulation environment capable of producing radiometrically correct multi-band imagery for low- light-level conditions is desired. This paper describes a modeling environment attempting to meet these criteria by addressing the task as two individual components: (1) prediction of a low-light-level radiance field from an arbitrary scene, and (2) simulation of the output from a low- light-level sensor for a given radiance field. The radiance prediction engine utilized in this environment is the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model which is a first principles based multi-spectral synthetic image generation model capable of producing an arbitrary number of bands in the 0.28 to 20 micrometer region. The DIRSIG model is utilized to produce high spatial and spectral resolution radiance field images. These images are then processed by a user configurable multi-stage low-light-level sensor model that applies the appropriate noise and modulation transfer function (MTF) at each stage in the image processing chain. This includes the ability to reproduce common intensifying sensor artifacts such as saturation and 'blooming.' Additionally, co-registered imagery in other spectral bands may be simultaneously generated for testing fusion and exploitation algorithms. This paper discusses specific aspects of the DIRSIG radiance prediction for low- light-level conditions including the incorporation of natural and man-made sources which emphasizes the importance of accurate BRDF. A description of the implementation of each stage in the image processing and capture chain for the LLL model is also presented. Finally, simulated images are presented and qualitatively compared to lab acquired imagery from a commercial system.

Robust model predictive control for satellite formation keeping with eccentricity/inclination vector separation

NASA Astrophysics Data System (ADS)

Lim, Yeerang; Jung, Youeyun; Bang, Hyochoong

2018-05-01

This study presents model predictive formation control based on an eccentricity/inclination vector separation strategy. Alternative collision avoidance can be accomplished by using eccentricity/inclination vectors and adding a simple goal function term for optimization process. Real-time control is also achievable with model predictive controller based on convex formulation. Constraint-tightening approach is address as well improve robustness of the controller, and simulation results are presented to verify performance enhancement for the proposed approach.

The role of simulation in the design of a neural network chip

NASA Technical Reports Server (NTRS)

Desai, Utpal; Roppel, Thaddeus A.; Padgett, Mary L.

1993-01-01

An iterative, simulation-based design procedure for a neural network chip is introduced. For this design procedure, the goal is to produce a chip layout for a neural network in which the weights are determined by transistor gate width-to-length ratios. In a given iteration, the current layout is simulated using the circuit simulator SPICE, and layout adjustments are made based on conventional gradient-decent methods. After the iteration converges, the chip is fabricated. Monte Carlo analysis is used to predict the effect of statistical fabrication process variations on the overall performance of the neural network chip.

Monte Carlo Simulation of the Rapid Crystallization of Bismuth-Doped Silicon

NASA Technical Reports Server (NTRS)

Jackson, Kenneth A.; Gilmer, George H.; Temkin, Dmitri E.

1995-01-01

In this Letter we report Ising model simulations of the growth of alloys which predict quite different behavior near and far from equilibrium. Our simulations reproduce the phenomenon which has been termed 'solute trapping,' where concentrations of solute, which are far in excess of the equilibrium concentrations, are observed in the crystal after rapid crystallization. This phenomenon plays an important role in many processes which involve first order phase changes which take place under conditions far from equilibrium. The underlying physical basis for it has not been understood, but these Monte Carlo simulations provide a powerful means for investigating it.

An Integrated Computational Materials Engineering Method for Woven Carbon Fiber Composites Preforming Process

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

Zhang, Weizhao; Ren, Huaqing; Wang, Zequn

2016-10-19

An integrated computational materials engineering method is proposed in this paper for analyzing the design and preforming process of woven carbon fiber composites. The goal is to reduce the cost and time needed for the mass production of structural composites. It integrates the simulation methods from the micro-scale to the macro-scale to capture the behavior of the composite material in the preforming process. In this way, the time consuming and high cost physical experiments and prototypes in the development of the manufacturing process can be circumvented. This method contains three parts: the micro-scale representative volume element (RVE) simulation to characterizemore » the material; the metamodeling algorithm to generate the constitutive equations; and the macro-scale preforming simulation to predict the behavior of the composite material during forming. The results show the potential of this approach as a guidance to the design of composite materials and its manufacturing process.« less

Speech Perception With Combined Electric-Acoustic Stimulation: A Simulation and Model Comparison.

PubMed

Rader, Tobias; Adel, Youssef; Fastl, Hugo; Baumann, Uwe

2015-01-01

The aim of this study is to simulate speech perception with combined electric-acoustic stimulation (EAS), verify the advantage of combined stimulation in normal-hearing (NH) subjects, and then compare it with cochlear implant (CI) and EAS user results from the authors' previous study. Furthermore, an automatic speech recognition (ASR) system was built to examine the impact of low-frequency information and is proposed as an applied model to study different hypotheses of the combined-stimulation advantage. Signal-detection-theory (SDT) models were applied to assess predictions of subject performance without the need to assume any synergistic effects. Speech perception was tested using a closed-set matrix test (Oldenburg sentence test), and its speech material was processed to simulate CI and EAS hearing. A total of 43 NH subjects and a customized ASR system were tested. CI hearing was simulated by an aurally adequate signal spectrum analysis and representation, the part-tone-time-pattern, which was vocoded at 12 center frequencies according to the MED-EL DUET speech processor. Residual acoustic hearing was simulated by low-pass (LP)-filtered speech with cutoff frequencies 200 and 500 Hz for NH subjects and in the range from 100 to 500 Hz for the ASR system. Speech reception thresholds were determined in amplitude-modulated noise and in pseudocontinuous noise. Previously proposed SDT models were lastly applied to predict NH subject performance with EAS simulations. NH subjects tested with EAS simulations demonstrated the combined-stimulation advantage. Increasing the LP cutoff frequency from 200 to 500 Hz significantly improved speech reception thresholds in both noise conditions. In continuous noise, CI and EAS users showed generally better performance than NH subjects tested with simulations. In modulated noise, performance was comparable except for the EAS at cutoff frequency 500 Hz where NH subject performance was superior. The ASR system showed similar behavior to NH subjects despite a positive signal-to-noise ratio shift for both noise conditions, while demonstrating the synergistic effect for cutoff frequencies ≥300 Hz. One SDT model largely predicted the combined-stimulation results in continuous noise, while falling short of predicting performance observed in modulated noise. The presented simulation was able to demonstrate the combined-stimulation advantage for NH subjects as observed in EAS users. Only NH subjects tested with EAS simulations were able to take advantage of the gap listening effect, while CI and EAS user performance was consistently degraded in modulated noise compared with performance in continuous noise. The application of ASR systems seems feasible to assess the impact of different signal processing strategies on speech perception with CI and EAS simulations. In continuous noise, SDT models were largely able to predict the performance gain without assuming any synergistic effects, but model amendments are required to explain the gap listening effect in modulated noise.

GASP: Gapped Ancestral Sequence Prediction for proteins

PubMed Central

Edwards, Richard J; Shields, Denis C

2004-01-01

Background The prediction of ancestral protein sequences from multiple sequence alignments is useful for many bioinformatics analyses. Predicting ancestral sequences is not a simple procedure and relies on accurate alignments and phylogenies. Several algorithms exist based on Maximum Parsimony or Maximum Likelihood methods but many current implementations are unable to process residues with gaps, which may represent insertion/deletion (indel) events or sequence fragments. Results Here we present a new algorithm, GASP (Gapped Ancestral Sequence Prediction), for predicting ancestral sequences from phylogenetic trees and the corresponding multiple sequence alignments. Alignments may be of any size and contain gaps. GASP first assigns the positions of gaps in the phylogeny before using a likelihood-based approach centred on amino acid substitution matrices to assign ancestral amino acids. Important outgroup information is used by first working down from the tips of the tree to the root, using descendant data only to assign probabilities, and then working back up from the root to the tips using descendant and outgroup data to make predictions. GASP was tested on a number of simulated datasets based on real phylogenies. Prediction accuracy for ungapped data was similar to three alternative algorithms tested, with GASP performing better in some cases and worse in others. Adding simple insertions and deletions to the simulated data did not have a detrimental effect on GASP accuracy. Conclusions GASP (Gapped Ancestral Sequence Prediction) will predict ancestral sequences from multiple protein alignments of any size. Although not as accurate in all cases as some of the more sophisticated maximum likelihood approaches, it can process a wide range of input phylogenies and will predict ancestral sequences for gapped and ungapped residues alike. PMID:15350199

A Cellular Automaton / Finite Element model for predicting grain texture development in galvanized coatings

NASA Astrophysics Data System (ADS)

Guillemot, G.; Avettand-Fènoël, M.-N.; Iosta, A.; Foct, J.

2011-01-01

Hot-dipping galvanizing process is a widely used and efficient way to protect steel from corrosion. We propose to master the microstructure of zinc grains by investigating the relevant process parameters. In order to improve the texture of this coating, we model grain nucleation and growth processes and simulate the zinc solid phase development. A coupling scheme model has been applied with this aim. This model improves a previous two-dimensional model of the solidification process. It couples a cellular automaton (CA) approach and a finite element (FE) method. CA grid and FE mesh are superimposed on the same domain. The grain development is simulated at the micro-scale based on the CA grid. A nucleation law is defined using a Gaussian probability and a random set of nucleating cells. A crystallographic orientation is defined for each one with a choice of Euler's angle (Ψ,θ,φ). A small growing shape is then associated to each cell in the mushy domain and a dendrite tip kinetics is defined using the model of Kurz [2]. The six directions of basal plane and the two perpendicular directions develop in each mushy cell. During each time step, cell temperature and solid fraction are then determined at micro-scale using the enthalpy conservation relation and variations are reassigned at macro-scale. This coupling scheme model enables to simulate the three-dimensional growing kinetics of the zinc grain in a two-dimensional approach. Grain structure evolutions for various cooling times have been simulated. Final grain structure has been compared to EBSD measurements. We show that the preferentially growth of dendrite arms in the basal plane of zinc grains is correctly predicted. The described coupling scheme model could be applied for simulated other product or manufacturing processes. It constitutes an approach gathering both micro and macro scale models.

The Soil Model Development and Intercomparison Panel (SoilMIP) of the International Soil Modeling Consortium (ISMC)

NASA Astrophysics Data System (ADS)

Vanderborght, Jan; Priesack, Eckart

2017-04-01

The Soil Model Development and Intercomparison Panel (SoilMIP) is an initiative of the International Soil Modeling Consortium. Its mission is to foster the further development of soil models that can predict soil functions and their changes (i) due to soil use and land management and (ii) due to external impacts of climate change and pollution. Since soil functions and soil threats are diverse but linked with each other, the overall aim is to develop holistic models that represent the key functions of the soil system and the links between them. These models should be scaled up and integrated in terrestrial system models that describe the feedbacks between processes in the soil and the other terrestrial compartments. We propose and illustrate a few steps that could be taken to achieve these goals. A first step is the development of scenarios that compare simulations by models that predict the same or different soil services. Scenarios can be considered at three different levels of comparisons: scenarios that compare the numerics (accuracy but also speed) of models, scenarios that compare the effect of differences in process descriptions, and scenarios that compare simulations with experimental data. A second step involves the derivation of metrics or summary statistics that effectively compare model simulations and disentangle parameterization from model concept differences. These metrics can be used to evaluate how more complex model simulations can be represented by simpler models using an appropriate parameterization. A third step relates to the parameterization of models. Application of simulation models implies that appropriate model parameters have to be defined for a range of environmental conditions and locations. Spatial modelling approaches are used to derive parameter distributions. Considering that soils and their properties emerge from the interaction between physical, chemical and biological processes, the combination of spatial models with process models would lead to consistent parameter distributions correlations and could potentially represent self-organizing processes in soils and landscapes.

Predicting geomorphic evolution through integration of numerical-model scenarios and topographic/bathymetric-survey updates

NASA Astrophysics Data System (ADS)

Plant, N. G.; Long, J.; Dalyander, S.; Thompson, D.; Miselis, J. L.

2013-12-01

Natural resource and hazard management of barrier islands requires an understanding of geomorphic changes associated with long-term processes and storms. Uncertainty exists in understanding how long-term processes interact with the geomorphic changes caused by storms and the resulting perturbations of the long-term evolution trajectories. We use high-resolution data sets to initialize and correct high-fidelity numerical simulations of oceanographic forcing and resulting barrier island evolution. We simulate two years of observed storms to determine the individual and cumulative impacts of these events. Results are separated into cross-shore and alongshore components of sediment transport and compared with observed topographic and bathymetric changes during these time periods. The discrete island change induced by these storms is integrated with previous knowledge of long-term net alongshore sediment transport to project island evolution. The approach has been developed and tested using data collected at the Chandeleur Island chain off the coast of Louisiana (USA). The simulation time period included impacts from tropical and winter storms, as well as a human-induced perturbation associated with construction of a sand berm along the island shoreline. The predictions and observations indicated that storm and long-term processes both contribute to the migration, lowering, and disintegration of the artificial berm and natural island. Further analysis will determine the relative importance of cross-shore and alongshore sediment transport processes and the dominant time scales that drive each of these processes and subsequent island morphologic response.

The Effects of Blade Count on Boundary Layer Development in a Low-Pressure Turbine

NASA Technical Reports Server (NTRS)

Dorney, Daniel J.; Flitan, Horia C.; Ashpis, David E.; Solomon, William J.

2000-01-01

Experimental data from jet-engine tests have indicated that turbine efficiencies at takeoff can be as much as two points higher than those at cruise conditions. Recent studies have shown that Reynolds number effects contribute to the lower efficiencies at cruise conditions. In the current study numerical simulations have been performed to study the boundary layer development in a two-stage low-pressure turbine, and to evaluate the models available for low Reynolds number flows in turbomachinery. In a previous study using the same geometry the predicted time-averaged boundary layer quantities showed excellent agreement with the experimental data, but the predicted unsteady results showed only fair agreement with the experimental data. It was surmised that the blade count approximation used in the numerical simulations generated more unsteadiness than was observed in the experiments. In this study a more accurate blade approximation has been used to model the turbine, and the method of post-processing the boundary layer information has been modified to more closely resemble the process used in the experiments. The predicted results show improved agreement with the unsteady experimental data.

Studying the Processes Contributed to the Hairpin Turn of Hurricane Joaquin with WRF numerical simulations and TCI-2015 observations

NASA Astrophysics Data System (ADS)

Pu, Z.; Yu, Y.

2016-12-01

The prediction of Hurricane Joaquin's hairpin clockwise during 1 and 2 October 2015 presents a forecasting challenge during real-time numerical weather prediction, as tracks of several major numerical weather prediction models differ from each other. To investigate the large-scale environment and hurricane inner-core structures related to the hairpin turn of Joaquin, a series of high-resolution mesoscale numerical simulations of Hurricane Joaquin had been performed with an advanced research version of the Weather Research and Forecasting (WRF) model. The outcomes were compared with the observations obtained from the US Office of Naval Research's Tropical Cyclone Intensity (TCI) Experiment during 2015 hurricane season. Specifically, five groups of sensitivity experiments with different cumulus, boundary layer, and microphysical schemes as well as different initial and boundary conditions and initial times in WRF simulations had been performed. It is found that the choice of the cumulus parameterization scheme plays a significant role in reproducing reasonable track forecast during Joaquin's hairpin turn. The mid-level environmental steering flows can be the reason that leads to different tracks in the simulations with different cumulus schemes. In addition, differences in the distribution and amounts of the latent heating over the inner-core region are associated with discrepancies in the simulated intensity among different experiments. Detailed simulation results, comparison with TCI-2015 observations, and comprehensive diagnoses will be presented.

Large eddy simulation for predicting turbulent heat transfer in gas turbines

PubMed Central

Tafti, Danesh K.; He, Long; Nagendra, K.

2014-01-01

Blade cooling technology will play a critical role in the next generation of propulsion and power generation gas turbines. Accurate prediction of blade metal temperature can avoid the use of excessive compressed bypass air and allow higher turbine inlet temperature, increasing fuel efficiency and decreasing emissions. Large eddy simulation (LES) has been established to predict heat transfer coefficients with good accuracy under various non-canonical flows, but is still limited to relatively simple geometries and low Reynolds numbers. It is envisioned that the projected increase in computational power combined with a drop in price-to-performance ratio will make system-level simulations using LES in complex blade geometries at engine conditions accessible to the design process in the coming one to two decades. In making this possible, two key challenges are addressed in this paper: working with complex intricate blade geometries and simulating high-Reynolds-number (Re) flows. It is proposed to use the immersed boundary method (IBM) combined with LES wall functions. A ribbed duct at Re=20 000 is simulated using the IBM, and a two-pass ribbed duct is simulated at Re=100 000 with and without rotation (rotation number Ro=0.2) using LES with wall functions. The results validate that the IBM is a viable alternative to body-conforming grids and that LES with wall functions reproduces experimental results at a much lower computational cost. PMID:25024418

Numerical simulation of the casting process of titanium removable partial denture frameworks.

PubMed

Wu, Menghuai; Wagner, Ingo; Sahm, Peter R; Augthun, Michael

2002-03-01

The objective of this work was to study the filling incompleteness and porosity defects in titanium removal partial denture frameworks by means of numerical simulation. Two frameworks, one for lower jaw and one for upper jaw, were chosen according to dentists' recommendation to be simulated. Geometry of the frameworks were laser-digitized and converted into a simulation software (MAGMASOFT). Both mold filling and solidification of the castings with different sprue designs (e.g. tree, ball, and runner-bar) were numerically calculated. The shrinkage porosity was quantitatively predicted by a feeding criterion, the potential filling defect and gas pore sensitivity were estimated based on the filling and solidification results. A satisfactory sprue design with process parameters was finally recommended for real casting trials (four replica for each frameworks). All the frameworks were successfully cast. Through X-ray radiographic inspections it was found that all the castings were acceptably sound except for only one case in which gas bubbles were detected in the grasp region of the frame. It is concluded that numerical simulation aids to achieve understanding of the casting process and defect formation in titanium frameworks, hence to minimize the risk of producing defect casting by improving the sprue design and process parameters.

Modeling the VARTM Composite Manufacturing Process

NASA Technical Reports Server (NTRS)

Song, Xiao-Lan; Loos, Alfred C.; Grimsley, Brian W.; Cano, Roberto J.; Hubert, Pascal

2004-01-01

A comprehensive simulation model of the Vacuum Assisted Resin Transfer Modeling (VARTM) composite manufacturing process has been developed. For isothermal resin infiltration, the model incorporates submodels which describe cure of the resin and changes in resin viscosity due to cure, resin flow through the reinforcement preform and distribution medium and compaction of the preform during the infiltration. The accuracy of the model was validated by measuring the flow patterns during resin infiltration of flat preforms. The modeling software was used to evaluate the effects of the distribution medium on resin infiltration of a flat preform. Different distribution medium configurations were examined using the model and the results were compared with data collected during resin infiltration of a carbon fabric preform. The results of the simulations show that the approach used to model the distribution medium can significantly effect the predicted resin infiltration times. Resin infiltration into the preform can be accurately predicted only when the distribution medium is modeled correctly.

Effects of a cochlear implant simulation on immediate memory in normal-hearing adults

PubMed Central

Burkholder, Rose A.; Pisoni, David B.; Svirsky, Mario A.

2012-01-01

This study assessed the effects of stimulus misidentification and memory processing errors on immediate memory span in 25 normal-hearing adults exposed to degraded auditory input simulating signals provided by a cochlear implant. The identification accuracy of degraded digits in isolation was measured before digit span testing. Forward and backward digit spans were shorter when digits were degraded than when they were normal. Participants’ normal digit spans and their accuracy in identifying isolated digits were used to predict digit spans in the degraded speech condition. The observed digit spans in degraded conditions did not differ significantly from predicted digit spans. This suggests that the decrease in memory span is related primarily to misidentification of digits rather than memory processing errors related to cognitive load. These findings provide complementary information to earlier research on auditory memory span of listeners exposed to degraded speech either experimentally or as a consequence of a hearing-impairment. PMID:16317807

In Vitro Model Simulating Gastro-Intestinal Digestion in the Pediatric Population (Neonates and Young Infants).

PubMed

Kamstrup, Danna; Berthelsen, Ragna; Sassene, Philip Jonas; Selen, Arzu; Müllertz, Anette

2017-02-01

The focus on drug delivery for the pediatric population has been steadily increasing in the last decades. In terms of developing in vitro models simulating characteristics of the targeted pediatric population, with the purpose of predicting drug product performance after oral administration, it is important to simulate the gastro-intestinal conditions and processes the drug will encounter upon oral administration. When a drug is administered in the fed state, which is commonly the case for neonates, as they are typically fed every 3 h, the digestion of the milk will affect the composition of the fluid available for drug dissolution/solubilization. Therefore, in order to predict the solubilized amount of drug available for absorption, an in vitro model simulating digestion in the gastro-intestinal tract should be utilized. In order to simulate the digestion process and the drug solubilization taking place in vivo, the following aspects should be considered; physiologically relevant media, media volume, use of physiological enzymes in proper amounts, as well as correct pH and addition of relevant co-factors, e.g., bile salts and co-enzymes. Furthermore, physiological transit times and appropriate mixing should be considered and mimicked as close as possible. This paper presents a literature review on physiological factors relevant for digestion and drug solubilization in neonates. Based on the available literature data, a novel in vitro digestion model simulating digestion and drug solubilization in the neonate and young infant pediatric population (2 months old and younger) was designed.

Debris flow runup on vertical barriers and adverse slopes

USGS Publications Warehouse

Iverson, Richard M.; George, David L.; Logan, Matthew

2016-01-01

Runup of debris flows against obstacles in their paths is a complex process that involves profound flow deceleration and redirection. We investigate the dynamics and predictability of runup by comparing results from large-scale laboratory experiments, four simple analytical models, and a depth-integrated numerical model (D-Claw). The experiments and numerical simulations reveal the important influence of unsteady, multidimensional flow on runup, and the analytical models highlight key aspects of the underlying physics. Runup against a vertical barrier normal to the flow path is dominated by rapid development of a shock, or jump in flow height, associated with abrupt deceleration of the flow front. By contrast, runup on sloping obstacles is initially dominated by a smooth flux of mass and momentum from the flow body to the flow front, which precedes shock development and commonly increases the runup height. D-Claw simulations that account for the emergence of shocks show that predicted runup heights vary systematically with the adverse slope angle and also with the Froude number and degree of liquefaction (or effective basal friction) of incoming flows. They additionally clarify the strengths and limitations of simplified analytical models. Numerical simulations based on a priori knowledge of the evolving dynamics of incoming flows yield quite accurate runup predictions. Less predictive accuracy is attained in ab initio simulations that compute runup based solely on knowledge of static debris properties in a distant debris flow source area. Nevertheless, the paucity of inputs required in ab initio simulations enhances their prospective value in runup forecasting.

A Mixed Phase Tale: New Ways of using in-situ cloud observations to reduce climate model biases in Southern Ocean

NASA Astrophysics Data System (ADS)

Gettelman, A.; Stith, J. L.

2014-12-01

Southern ocean clouds are a critical part of the earth's energy budget, and significant biases in the climatology of these clouds exist in models used to predict climate change. We compare in situ measurements of cloud microphysical properties of ice and liquid over the S. Ocean with constrained output from the atmospheric component of an Earth System Model. Observations taken during the HIAPER (the NSF/NCAR G-V aircraft) Pole-to-Pole Observations (HIPPO) multi-year field campaign are compared with simulations from the atmospheric component of the Community Earth System Model (CESM). Remarkably, CESM is able to accurately simulate the locations of cloud formation, and even cloud microphysical properties are comparable between the model and observations. Significantly, the simulations do not predict sufficient supercooled liquid. Altering the model cloud and aerosol processes to better reproduce the observations of supercooled liquid acts to reduce long-standing biases in S. Ocean clouds in CESM, which are typical of other models. Furthermore, sensitivity tests show where better observational constraints on aerosols and cloud microphysics can reduce uncertainty and biases in global models. These results are intended to show how we can connect large scale simulations with field observations in the S. Ocean to better understand Southern Ocean cloud processes and reduce biases in global climate simulations.

Uncertainty quantification and validation of 3D lattice scaffolds for computer-aided biomedical applications.

PubMed

Gorguluarslan, Recep M; Choi, Seung-Kyum; Saldana, Christopher J

2017-07-01

A methodology is proposed for uncertainty quantification and validation to accurately predict the mechanical response of lattice structures used in the design of scaffolds. Effective structural properties of the scaffolds are characterized using a developed multi-level stochastic upscaling process that propagates the quantified uncertainties at strut level to the lattice structure level. To obtain realistic simulation models for the stochastic upscaling process and minimize the experimental cost, high-resolution finite element models of individual struts were reconstructed from the micro-CT scan images of lattice structures which are fabricated by selective laser melting. The upscaling method facilitates the process of determining homogenized strut properties to reduce the computational cost of the detailed simulation model for the scaffold. Bayesian Information Criterion is utilized to quantify the uncertainties with parametric distributions based on the statistical data obtained from the reconstructed strut models. A systematic validation approach that can minimize the experimental cost is also developed to assess the predictive capability of the stochastic upscaling method used at the strut level and lattice structure level. In comparison with physical compression test results, the proposed methodology of linking the uncertainty quantification with the multi-level stochastic upscaling method enabled an accurate prediction of the elastic behavior of the lattice structure with minimal experimental cost by accounting for the uncertainties induced by the additive manufacturing process. Copyright © 2017 Elsevier Ltd. All rights reserved.

Benchmarking novel approaches for modelling species range dynamics

PubMed Central

Zurell, Damaris; Thuiller, Wilfried; Pagel, Jörn; Cabral, Juliano S; Münkemüller, Tamara; Gravel, Dominique; Dullinger, Stefan; Normand, Signe; Schiffers, Katja H.; Moore, Kara A.; Zimmermann, Niklaus E.

2016-01-01

Increasing biodiversity loss due to climate change is one of the most vital challenges of the 21st century. To anticipate and mitigate biodiversity loss, models are needed that reliably project species’ range dynamics and extinction risks. Recently, several new approaches to model range dynamics have been developed to supplement correlative species distribution models (SDMs), but applications clearly lag behind model development. Indeed, no comparative analysis has been performed to evaluate their performance. Here, we build on process-based, simulated data for benchmarking five range (dynamic) models of varying complexity including classical SDMs, SDMs coupled with simple dispersal or more complex population dynamic models (SDM hybrids), and a hierarchical Bayesian process-based dynamic range model (DRM). We specifically test the effects of demographic and community processes on model predictive performance. Under current climate, DRMs performed best, although only marginally. Under climate change, predictive performance varied considerably, with no clear winners. Yet, all range dynamic models improved predictions under climate change substantially compared to purely correlative SDMs, and the population dynamic models also predicted reasonable extinction risks for most scenarios. When benchmarking data were simulated with more complex demographic and community processes, simple SDM hybrids including only dispersal often proved most reliable. Finally, we found that structural decisions during model building can have great impact on model accuracy, but prior system knowledge on important processes can reduce these uncertainties considerably. Our results reassure the clear merit in using dynamic approaches for modelling species’ response to climate change but also emphasise several needs for further model and data improvement. We propose and discuss perspectives for improving range projections through combination of multiple models and for making these approaches operational for large numbers of species. PMID:26872305

Benchmarking novel approaches for modelling species range dynamics.

PubMed

Zurell, Damaris; Thuiller, Wilfried; Pagel, Jörn; Cabral, Juliano S; Münkemüller, Tamara; Gravel, Dominique; Dullinger, Stefan; Normand, Signe; Schiffers, Katja H; Moore, Kara A; Zimmermann, Niklaus E

2016-08-01

Increasing biodiversity loss due to climate change is one of the most vital challenges of the 21st century. To anticipate and mitigate biodiversity loss, models are needed that reliably project species' range dynamics and extinction risks. Recently, several new approaches to model range dynamics have been developed to supplement correlative species distribution models (SDMs), but applications clearly lag behind model development. Indeed, no comparative analysis has been performed to evaluate their performance. Here, we build on process-based, simulated data for benchmarking five range (dynamic) models of varying complexity including classical SDMs, SDMs coupled with simple dispersal or more complex population dynamic models (SDM hybrids), and a hierarchical Bayesian process-based dynamic range model (DRM). We specifically test the effects of demographic and community processes on model predictive performance. Under current climate, DRMs performed best, although only marginally. Under climate change, predictive performance varied considerably, with no clear winners. Yet, all range dynamic models improved predictions under climate change substantially compared to purely correlative SDMs, and the population dynamic models also predicted reasonable extinction risks for most scenarios. When benchmarking data were simulated with more complex demographic and community processes, simple SDM hybrids including only dispersal often proved most reliable. Finally, we found that structural decisions during model building can have great impact on model accuracy, but prior system knowledge on important processes can reduce these uncertainties considerably. Our results reassure the clear merit in using dynamic approaches for modelling species' response to climate change but also emphasize several needs for further model and data improvement. We propose and discuss perspectives for improving range projections through combination of multiple models and for making these approaches operational for large numbers of species. © 2016 John Wiley & Sons Ltd.

Verification of a three-dimensional resin transfer molding process simulation model

NASA Technical Reports Server (NTRS)

Fingerson, John C.; Loos, Alfred C.; Dexter, H. Benson

1995-01-01

Experimental evidence was obtained to complete the verification of the parameters needed for input to a three-dimensional finite element model simulating the resin flow and cure through an orthotropic fabric preform. The material characterizations completed include resin kinetics and viscosity models, as well as preform permeability and compaction models. The steady-state and advancing front permeability measurement methods are compared. The results indicate that both methods yield similar permeabilities for a plain weave, bi-axial fiberglass fabric. Also, a method to determine principal directions and permeabilities is discussed and results are shown for a multi-axial warp knit preform. The flow of resin through a blade-stiffened preform was modeled and experiments were completed to verify the results. The predicted inlet pressure was approximately 65% of the measured value. A parametric study was performed to explain differences in measured and predicted flow front advancement and inlet pressures. Furthermore, PR-500 epoxy resin/IM7 8HS carbon fabric flat panels were fabricated by the Resin Transfer Molding process. Tests were completed utilizing both perimeter injection and center-port injection as resin inlet boundary conditions. The mold was instrumented with FDEMS sensors, pressure transducers, and thermocouples to monitor the process conditions. Results include a comparison of predicted and measured inlet pressures and flow front position. For the perimeter injection case, the measured inlet pressure and flow front results compared well to the predicted results. The results of the center-port injection case showed that the predicted inlet pressure was approximately 50% of the measured inlet pressure. Also, measured flow front position data did not agree well with the predicted results. Possible reasons for error include fiber deformation at the resin inlet and a lag in FDEMS sensor wet-out due to low mold pressures.

Experiments and FEM simulations of fracture behaviors for ADC12 aluminum alloy under impact load

NASA Astrophysics Data System (ADS)

Hu, Yumei; Xiao, Yue; Jin, Xiaoqing; Zheng, Haoran; Zhou, Yinge; Shao, Jinhua

2016-11-01

Using the combination of experiment and simulation, the fracture behavior of the brittle metal named ADC12 aluminum alloy was studied. Five typical experiments were carried out on this material, with responding data collected on different stress states and dynamic strain rates. Fractographs revealed that the morphologies of fractured specimen under several rates showed different results, indicating that the fracture was predominantly a brittle one in nature. Simulations of the fracture processes of those specimens were conducted by Finite Element Method, whilst consistency was observed between simulations and experiments. In simulation, the Johnson- Cook model was chosen to describe the damage development and to predict the failure using parameters determined from those experimental data. Subsequently, an ADC12 engine mount bracket crashing simulation was conducted and the results indicated good agreement with the experiments. The accordance showed that our research can provide an accurate description for the deforming and fracture processes of the studied alloy.

Molecular simulation studies on chemical reactivity of methylcyclopentadiene.

PubMed

Wang, Qingsheng; Zhang, Yingchun; Rogers, William J; Mannan, M Sam

2009-06-15

Molecular simulations are important to predict thermodynamic values for reactive chemicals especially when sufficient experimental data are not available. Methylcyclopentadiene (MCP) is an example of a highly reactive and hazardous compound in the chemical process industry. In this work, chemical reactivity of 2-methylcyclopentadiene, including isomerization, dimerization, and oxidation reactions, is investigated in detail by theoretical computational chemistry methods and empirical thermodynamic-energy correlation. On the basis of molecular simulations, an average value of -15.2 kcal/mol for overall heat of dimerization and -45.6 kcal/mol for overall heat of oxidation were obtained in gaseous phase at 298 K and 1 atm. These molecular simulation studies can provide guidance for the design of safer chemical processes, safer handling of MCP, and also provide useful information for an investigation of the T2 Laboratories explosion on December 19, 2007, in Florida.

Use of an uncertainty analysis for genome-scale models as a prediction tool for microbial growth processes in subsurface environments.

PubMed

Klier, Christine

2012-03-06

The integration of genome-scale, constraint-based models of microbial cell function into simulations of contaminant transport and fate in complex groundwater systems is a promising approach to help characterize the metabolic activities of microorganisms in natural environments. In constraint-based modeling, the specific uptake flux rates of external metabolites are usually determined by Michaelis-Menten kinetic theory. However, extensive data sets based on experimentally measured values are not always available. In this study, a genome-scale model of Pseudomonas putida was used to study the key issue of uncertainty arising from the parametrization of the influx of two growth-limiting substrates: oxygen and toluene. The results showed that simulated growth rates are highly sensitive to substrate affinity constants and that uncertainties in specific substrate uptake rates have a significant influence on the variability of simulated microbial growth. Michaelis-Menten kinetic theory does not, therefore, seem to be appropriate for descriptions of substrate uptake processes in the genome-scale model of P. putida. Microbial growth rates of P. putida in subsurface environments can only be accurately predicted if the processes of complex substrate transport and microbial uptake regulation are sufficiently understood in natural environments and if data-driven uptake flux constraints can be applied.

Embedding of multidimensional time-dependent observations.

PubMed

Barnard, J P; Aldrich, C; Gerber, M

2001-10-01

A method is proposed to reconstruct dynamic attractors by embedding of multivariate observations of dynamic nonlinear processes. The Takens embedding theory is combined with independent component analysis to transform the embedding into a vector space of linearly independent vectors (phase variables). The method is successfully tested against prediction of the unembedded state vector in two case studies of simulated chaotic processes.

Embedding of multidimensional time-dependent observations

NASA Astrophysics Data System (ADS)

Barnard, Jakobus P.; Aldrich, Chris; Gerber, Marius

2001-10-01

A method is proposed to reconstruct dynamic attractors by embedding of multivariate observations of dynamic nonlinear processes. The Takens embedding theory is combined with independent component analysis to transform the embedding into a vector space of linearly independent vectors (phase variables). The method is successfully tested against prediction of the unembedded state vector in two case studies of simulated chaotic processes.

The Answering Process for Multiple-Choice Questions in Collaborative Learning: A Mathematical Learning Model Analysis

ERIC Educational Resources Information Center

Nakamura, Yasuyuki; Nishi, Shinnosuke; Muramatsu, Yuta; Yasutake, Koichi; Yamakawa, Osamu; Tagawa, Takahiro

2014-01-01

In this paper, we introduce a mathematical model for collaborative learning and the answering process for multiple-choice questions. The collaborative learning model is inspired by the Ising spin model and the model for answering multiple-choice questions is based on their difficulty level. An intensive simulation study predicts the possibility of…

Using weather prediction data for simulation of mesoscale atmospheric processes

NASA Astrophysics Data System (ADS)

Bart, Andrey A.; Starchenko, Alexander V.

2015-11-01

The paper presents an approach to specify initial and boundary conditions from the output data of global model SLAV for mesoscale modelling of atmospheric processes in areas not covered by meteorological observations. From the data and the model equations for a homogeneous atmospheric boundary layer the meteorological and turbulent characteristics of the atmospheric boundary layer are calculated.

Assessment of Spacecraft Operational Status Using Electro-Optical Predictive Techniques

DTIC Science & Technology

2010-09-01

panel appendages, may require enhanced preflight characterization processes to support monitoring by passive, remote, nonimaging optical sensors...observing and characterizing key spacecraft features. The simulation results are based on electro-optical signatures apparent to nonimaging sensors, along...and communication equipment, may require enhanced preflight characterization processes to support monitoring by passive, remote, nonimaging optical

The effect of bathymetric filtering on nearshore process model results

USGS Publications Warehouse

Plant, N.G.; Edwards, K.L.; Kaihatu, J.M.; Veeramony, J.; Hsu, L.; Holland, K.T.

2009-01-01

Nearshore wave and flow model results are shown to exhibit a strong sensitivity to the resolution of the input bathymetry. In this analysis, bathymetric resolution was varied by applying smoothing filters to high-resolution survey data to produce a number of bathymetric grid surfaces. We demonstrate that the sensitivity of model-predicted wave height and flow to variations in bathymetric resolution had different characteristics. Wave height predictions were most sensitive to resolution of cross-shore variability associated with the structure of nearshore sandbars. Flow predictions were most sensitive to the resolution of intermediate scale alongshore variability associated with the prominent sandbar rhythmicity. Flow sensitivity increased in cases where a sandbar was closer to shore and shallower. Perhaps the most surprising implication of these results is that the interpolation and smoothing of bathymetric data could be optimized differently for the wave and flow models. We show that errors between observed and modeled flow and wave heights are well predicted by comparing model simulation results using progressively filtered bathymetry to results from the highest resolution simulation. The damage done by over smoothing or inadequate sampling can therefore be estimated using model simulations. We conclude that the ability to quantify prediction errors will be useful for supporting future data assimilation efforts that require this information.

Assessment of Near-Field Sonic Boom Simulation Tools

NASA Technical Reports Server (NTRS)

Casper, J. H.; Cliff, S. E.; Thomas, S. D.; Park, M. A.; McMullen, M. S.; Melton, J. E.; Durston, D. A.

2008-01-01

A recent study for the Supersonics Project, within the National Aeronautics and Space Administration, has been conducted to assess current in-house capabilities for the prediction of near-field sonic boom. Such capabilities are required to simulate the highly nonlinear flow near an aircraft, wherein a sonic-boom signature is generated. There are many available computational fluid dynamics codes that could be used to provide the near-field flow for a sonic boom calculation. However, such codes have typically been developed for applications involving aerodynamic configuration, for which an efficiently generated computational mesh is usually not optimum for a sonic boom prediction. Preliminary guidelines are suggested to characterize a state-of-the-art sonic boom prediction methodology. The available simulation tools that are best suited to incorporate into that methodology are identified; preliminary test cases are presented in support of the selection. During this phase of process definition and tool selection, parallel research was conducted in an attempt to establish criteria that link the properties of a computational mesh to the accuracy of a sonic boom prediction. Such properties include sufficient grid density near shocks and within the zone of influence, which are achieved by adaptation and mesh refinement strategies. Prediction accuracy is validated by comparison with wind tunnel data.

Surfactant enhanced recovery of tetrachloroethylene from a porous medium containing low permeability lenses. 2. Numerical simulation.

PubMed

Rathfelder, K M; Abriola, L M; Taylor, T P; Pennell, K D

2001-04-01

A numerical model of surfactant enhanced solubilization was developed and applied to the simulation of nonaqueous phase liquid recovery in two-dimensional heterogeneous laboratory sand tank systems. Model parameters were derived from independent, small-scale, batch and column experiments. These parameters included viscosity, density, solubilization capacity, surfactant sorption, interfacial tension, permeability, capillary retention functions, and interphase mass transfer correlations. Model predictive capability was assessed for the evaluation of the micellar solubilization of tetrachloroethylene (PCE) in the two-dimensional systems. Predicted effluent concentrations and mass recovery agreed reasonably well with measured values. Accurate prediction of enhanced solubilization behavior in the sand tanks was found to require the incorporation of pore-scale, system-dependent, interphase mass transfer limitations, including an explicit representation of specific interfacial contact area. Predicted effluent concentrations and mass recovery were also found to depend strongly upon the initial NAPL entrapment configuration. Numerical results collectively indicate that enhanced solubilization processes in heterogeneous, laboratory sand tank systems can be successfully simulated using independently measured soil parameters and column-measured mass transfer coefficients, provided that permeability and NAPL distributions are accurately known. This implies that the accuracy of model predictions at the field scale will be constrained by our ability to quantify soil heterogeneity and NAPL distribution.

Application of all-relevant feature selection for the failure analysis of parameter-induced simulation crashes in climate models

NASA Astrophysics Data System (ADS)

Paja, Wiesław; Wrzesien, Mariusz; Niemiec, Rafał; Rudnicki, Witold R.

2016-03-01

Climate models are extremely complex pieces of software. They reflect the best knowledge on the physical components of the climate; nevertheless, they contain several parameters, which are too weakly constrained by observations, and can potentially lead to a simulation crashing. Recently a study by Lucas et al. (2013) has shown that machine learning methods can be used for predicting which combinations of parameters can lead to the simulation crashing and hence which processes described by these parameters need refined analyses. In the current study we reanalyse the data set used in this research using different methodology. We confirm the main conclusion of the original study concerning the suitability of machine learning for the prediction of crashes. We show that only three of the eight parameters indicated in the original study as relevant for prediction of the crash are indeed strongly relevant, three others are relevant but redundant and two are not relevant at all. We also show that the variance due to the split of data between training and validation sets has a large influence both on the accuracy of predictions and on the relative importance of variables; hence only a cross-validated approach can deliver a robust prediction of performance and relevance of variables.

Orion Pad Abort 1 Flight Test: Simulation Predictions Versus Flight Data

NASA Technical Reports Server (NTRS)

Stillwater, Ryan Allanque; Merritt, Deborah S.

2011-01-01

The presentation covers the pre-flight simulation predictions of the Orion Pad Abort 1. The pre-flight simulation predictions are compared to the Orion Pad Abort 1 flight test data. Finally the flight test data is compared to the updated simulation predictions, which show a ove rall improvement in the accuracy of the simulation predictions.

Evaluation of processes affecting 1,2-dibromo-3-chloropropane (DBCP) concentrations in ground water in the eastern San Joaquin Valley, California : analysis of chemical data and ground-water flow and transport simulations

USGS Publications Warehouse

Burow, Karen R.; Panshin, Sandra Y.; Dubrovsky, Neil H.; Vanbrocklin, David; Fogg, Graham E.

1999-01-01

A conceptual two-dimensional numerical flow and transport modeling approach was used to test hypotheses addressing dispersion, transformation rate, and in a relative sense, the effects of ground- water pumping and reapplication of irrigation water on DBCP concentrations in the aquifer. The flow and transport simulations, which represent hypothetical steady-state flow conditions in the aquifer, were used to refine the conceptual understanding of the aquifer system rather than to predict future concentrations of DBCP. Results indicate that dispersion reduces peak concentrations, but this process alone does not account for the apparent decrease in DBCP concentrations in ground water in the eastern San Joaquin Valley. Ground-water pumping and reapplication of irrigation water may affect DBCP concentrations to the extent that this process can be simulated indirectly using first-order decay. Transport simulation results indicate that the in situ 'effective' half-life of DBCP caused by processes other than dispersion and transformation to BAA could be on the order of 6 years.

Numerical simulation of multi-layered textile composite reinforcement forming

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

Wang, P.; Hamila, N.; Boisse, P.

2011-05-04

One important perspective in aeronautics is to produce large, thick or/and complex structural composite parts. The forming stage presents an important role during the whole manufacturing process, especially for LCM processes (Liquid Composites Moulding) or CFRTP (Continuous Fibre Reinforcements and Thermoplastic resin). Numerical simulations corresponding to multi-layered composite forming allow the prediction for a successful process to produce the thick parts, and importantly, the positions of the fibres after forming to be known. This paper details a set of simulation examples carried out by using a semi-discrete shell finite element made up of unit woven cells. The internal virtual workmore » is applied on all woven cells of the element taking into account tensions, in-plane shear and bending effects. As one key problem, the contact behaviours of tool/ply and ply/ply are described in the numerical model. The simulation results not only improve our understanding of the multi-layered composite forming process but also point out the importance of the fibre orientation and inter-ply friction during formability.« less

GPU based 3D feature profile simulation of high-aspect ratio contact hole etch process under fluorocarbon plasmas

NASA Astrophysics Data System (ADS)

Chun, Poo-Reum; Lee, Se-Ah; Yook, Yeong-Geun; Choi, Kwang-Sung; Cho, Deog-Geun; Yu, Dong-Hun; Chang, Won-Seok; Kwon, Deuk-Chul; Im, Yeon-Ho

2013-09-01

Although plasma etch profile simulation has been attracted much interest for developing reliable plasma etching, there still exist big gaps between current research status and predictable modeling due to the inherent complexity of plasma process. As an effort to address this issue, we present 3D feature profile simulation coupled with well-defined plasma-surface kinetic model for silicon dioxide etching process under fluorocarbon plasmas. To capture the realistic plasma surface reaction behaviors, a polymer layer based surface kinetic model was proposed to consider the simultaneous polymer deposition and oxide etching. Finally, the realistic plasma surface model was used for calculation of speed function for 3D topology simulation, which consists of multiple level set based moving algorithm, and ballistic transport module. In addition, the time consumable computations in the ballistic transport calculation were improved drastically by GPU based numerical computation, leading to the real time computation. Finally, we demonstrated that the surface kinetic model could be coupled successfully for 3D etch profile simulations in high-aspect ratio contact hole plasma etching.

On the Performance of Alternate Conceptual Ecohydrological Models for Streamflow Prediction

NASA Astrophysics Data System (ADS)

Naseem, Bushra; Ajami, Hoori; Cordery, Ian; Sharma, Ashish

2016-04-01

A merging of a lumped conceptual hydrological model with two conceptual dynamic vegetation models is presented to assess the performance of these models for simultaneous simulations of streamflow and leaf area index (LAI). Two conceptual dynamic vegetation models with differing representation of ecological processes are merged with a lumped conceptual hydrological model (HYMOD) to predict catchment scale streamflow and LAI. The merged RR-LAI-I model computes relative leaf biomass based on transpiration rates while the RR-LAI-II model computes above ground green and dead biomass based on net primary productivity and water use efficiency in response to soil moisture dynamics. To assess the performance of these models, daily discharge and 8-day MODIS LAI product for 27 catchments of 90 - 1600km2 in size located in the Murray - Darling Basin in Australia are used. Our results illustrate that when single-objective optimisation was focussed on maximizing the objective function for streamflow or LAI, the other un-calibrated predicted outcome (LAI if streamflow is the focus) was consistently compromised. Thus, single-objective optimization cannot take into account the essence of all processes in the conceptual ecohydrological models. However, multi-objective optimisation showed great strength for streamflow and LAI predictions. Both response outputs were better simulated by RR-LAI-II than RR-LAI-I due to better representation of physical processes such as net primary productivity (NPP) in RR-LAI-II. Our results highlight that simultaneous calibration of streamflow and LAI using a multi-objective algorithm proves to be an attractive tool for improved streamflow predictions.

Acoustic response of cemented granular sedimentary rocks: molecular dynamics modeling.

PubMed

García, Xavier; Medina, Ernesto

2007-06-01

The effect of cementation processes on the acoustical properties of sands is studied via molecular dynamics simulation methods. We propose numerical methods where the initial uncemented sand is built by simulating the settling process of sediments. Uncemented samples of different porosity are considered by emulating natural mechanical compaction of sediments due to overburden. Cementation is considered through a particle-based model that captures the underlying physics behind the process. In our simulations, we consider samples with different degrees of compaction and cementing materials with distinct elastic properties. The microstructure of cemented sands is taken into account while adding cement at specific locations within the pores, such as grain-to-grain contacts. Results show that the acoustical properties of cemented sands are strongly dependent on the amount of cement, its stiffness relative to the hosting medium, and its location within the pores. Simulation results are in good correspondence with available experimental data and compare favorably with some theoretical predictions for the sound velocity within a range of cement saturation, porosity, and confining pressure.

A Deep Space Orbit Determination Software: Overview and Event Prediction Capability

NASA Astrophysics Data System (ADS)

Kim, Youngkwang; Park, Sang-Young; Lee, Eunji; Kim, Minsik

2017-06-01

This paper presents an overview of deep space orbit determination software (DSODS), as well as validation and verification results on its event prediction capabilities. DSODS was developed in the MATLAB object-oriented programming environment to support the Korea Pathfinder Lunar Orbiter (KPLO) mission. DSODS has three major capabilities: celestial event prediction for spacecraft, orbit determination with deep space network (DSN) tracking data, and DSN tracking data simulation. To achieve its functionality requirements, DSODS consists of four modules: orbit propagation (OP), event prediction (EP), data simulation (DS), and orbit determination (OD) modules. This paper explains the highest-level data flows between modules in event prediction, orbit determination, and tracking data simulation processes. Furthermore, to address the event prediction capability of DSODS, this paper introduces OP and EP modules. The role of the OP module is to handle time and coordinate system conversions, to propagate spacecraft trajectories, and to handle the ephemerides of spacecraft and celestial bodies. Currently, the OP module utilizes the General Mission Analysis Tool (GMAT) as a third-party software component for highfidelity deep space propagation, as well as time and coordinate system conversions. The role of the EP module is to predict celestial events, including eclipses, and ground station visibilities, and this paper presents the functionality requirements of the EP module. The validation and verification results show that, for most cases, event prediction errors were less than 10 millisec when compared with flight proven mission analysis tools such as GMAT and Systems Tool Kit (STK). Thus, we conclude that DSODS is capable of predicting events for the KPLO in real mission applications.

Direct numerical simulations of temporally developing hydrocarbon shear flames at elevated pressure: effects of the equation of state and the unity Lewis number assumption

NASA Astrophysics Data System (ADS)

Korucu, Ayse; Miller, Richard

2016-11-01

Direct numerical simulations (DNS) of temporally developing shear flames are used to investigate both equation of state (EOS) and unity-Lewis (Le) number assumption effects in hydrocarbon flames at elevated pressure. A reduced Kerosene / Air mechanism including a semi-global soot formation/oxidation model is used to study soot formation/oxidation processes in a temporarlly developing hydrocarbon shear flame operating at both atmospheric and elevated pressures for the cubic Peng-Robinson real fluid EOS. Results are compared to simulations using the ideal gas law (IGL). The results show that while the unity-Le number assumption with the IGL EOS under-predicts the flame temperature for all pressures, with the real fluid EOS it under-predicts the flame temperature for 1 and 35 atm and over-predicts the rest. The soot mass fraction, Ys, is only under-predicted for the 1 atm flame for both IGL and real gas fluid EOS models. While Ys is over-predicted for elevated pressures with IGL EOS, for the real gas EOS Ys's predictions are similar to results using a non-unity Le model derived from non-equilibrium thermodynamics and real diffusivities. Adopting the unity Le assumption is shown to cause misprediction of Ys, the flame temperature, and the mass fractions of CO, H and OH.

Development of a Coupled Hydrological/Sediment Yield Model for a Watershed at Regional Level

NASA Technical Reports Server (NTRS)

Rajbhandaril, Narayan; Crosson, William; Tsegaye, Teferi; Coleman, Tommy; Liu, Yaping; Soman, Vishwas

1998-01-01

Development of a hydrologic model for the study of environmental conservation requires a comprehensive understanding of individual-storm affecting hydrologic and sedimentologic processes. The hydrologic models that we are currently coupling are the Simulator for Hydrology and Energy Exchange at the Land Surface (SHEELS) and the Distributed Runoff Model (DRUM). SHEELS runs continuously to estimate surface energy fluxes and sub-surface soil water fluxes, while DRUM operates during and following precipitation events to predict surface runoff and peak flow through channel routing. The lateral re-distribution of surface water determined by DRUM is passed to SHEELS, which then adjusts soil water contents throughout the profile. The model SHEELS is well documented in Smith et al. (1993) and Laymen and Crosson (1995). The model DRUM is well documented in Vieux et al. (1990) and Vieux and Gauer (1994). The coupled hydrologic model, SHEELS/DRUM, does not simulate sedimentologic processes. The simulation of the sedimentologic process is important for environmental conservation planning and management. Therefore, we attempted to develop a conceptual frame work for coupling a sediment yield model with SHEELS/DRUM to estimate individual-storm sediment yield from a watershed at a regional level. The sediment yield model that will be used for this study is the Universal Soil Loss Equation (USLE) with some modifications to enable the model to predict individual-storm sediment yield. The predicted sediment yield does not include wind erosion and erosion caused by irrigation and snow melt. Units used for this study are those given by Foster et al. (1981) for SI units.

A Generalized Polynomial Chaos-Based Approach to Analyze the Impacts of Process Deviations on MEMS Beams.

PubMed

Gao, Lili; Zhou, Zai-Fa; Huang, Qing-An

2017-11-08

A microstructure beam is one of the fundamental elements in MEMS devices like cantilever sensors, RF/optical switches, varactors, resonators, etc. It is still difficult to precisely predict the performance of MEMS beams with the current available simulators due to the inevitable process deviations. Feasible numerical methods are required and can be used to improve the yield and profits of the MEMS devices. In this work, process deviations are considered to be stochastic variables, and a newly-developed numerical method, i.e., generalized polynomial chaos (GPC), is applied for the simulation of the MEMS beam. The doubly-clamped polybeam has been utilized to verify the accuracy of GPC, compared with our Monte Carlo (MC) approaches. Performance predictions have been made on the residual stress by achieving its distributions in GaAs Monolithic Microwave Integrated Circuit (MMIC)-based MEMS beams. The results show that errors are within 1% for the results of GPC approximations compared with the MC simulations. Appropriate choices of the 4-order GPC expansions with orthogonal terms have also succeeded in reducing the MC simulation labor. The mean value of the residual stress, concluded from experimental tests, shares an error about 1.1% with that of the 4-order GPC method. It takes a probability around 54.3% for the 4-order GPC approximation to attain the mean test value of the residual stress. The corresponding yield occupies over 90 percent around the mean within the twofold standard deviations.

A Generalized Polynomial Chaos-Based Approach to Analyze the Impacts of Process Deviations on MEMS Beams

PubMed Central

Gao, Lili

2017-01-01

A microstructure beam is one of the fundamental elements in MEMS devices like cantilever sensors, RF/optical switches, varactors, resonators, etc. It is still difficult to precisely predict the performance of MEMS beams with the current available simulators due to the inevitable process deviations. Feasible numerical methods are required and can be used to improve the yield and profits of the MEMS devices. In this work, process deviations are considered to be stochastic variables, and a newly-developed numerical method, i.e., generalized polynomial chaos (GPC), is applied for the simulation of the MEMS beam. The doubly-clamped polybeam has been utilized to verify the accuracy of GPC, compared with our Monte Carlo (MC) approaches. Performance predictions have been made on the residual stress by achieving its distributions in GaAs Monolithic Microwave Integrated Circuit (MMIC)-based MEMS beams. The results show that errors are within 1% for the results of GPC approximations compared with the MC simulations. Appropriate choices of the 4-order GPC expansions with orthogonal terms have also succeeded in reducing the MC simulation labor. The mean value of the residual stress, concluded from experimental tests, shares an error about 1.1% with that of the 4-order GPC method. It takes a probability around 54.3% for the 4-order GPC approximation to attain the mean test value of the residual stress. The corresponding yield occupies over 90 percent around the mean within the twofold standard deviations. PMID:29117096

Broken Ergodicity in Ideal, Homogeneous, Incompressible Turbulence

NASA Technical Reports Server (NTRS)

Morin, Lee; Shebalin, John; Fu, Terry; Nguyen, Phu; Shum, Victor

2010-01-01

We discuss the statistical mechanics of numerical models of ideal homogeneous, incompressible turbulence and their relevance for dissipative fluids and magnetofluids. These numerical models are based on Fourier series and the relevant statistical theory predicts that Fourier coefficients of fluid velocity and magnetic fields (if present) are zero-mean random variables. However, numerical simulations clearly show that certain coefficients have a non-zero mean value that can be very large compared to the associated standard deviation. We explain this phenomena in terms of broken ergodicity', which is defined to occur when dynamical behavior does not match ensemble predictions on very long time-scales. We review the theoretical basis of broken ergodicity, apply it to 2-D and 3-D fluid and magnetohydrodynamic simulations of homogeneous turbulence, and show new results from simulations using GPU (graphical processing unit) computers.

Particle-Based Geometric and Mechanical Modelling of Woven Technical Textiles and Reinforcements for Composites

NASA Astrophysics Data System (ADS)

Samadi, Reza

Technical textiles are increasingly being engineered and used in challenging applications, in areas such as safety, biomedical devices, architecture and others, where they must meet stringent demands including excellent and predictable load bearing capabilities. They also form the bases for one of the most widespread group of composite materials, fibre reinforced polymer-matrix composites (PMCs), which comprise materials made of stiff and strong fibres generally available in textile form and selected for their structural potential, combined with a polymer matrix that gives parts their shape. Manufacturing processes for PMCs and technical textiles, as well as parts and advanced textile structures must be engineered, ideally through simulation, and therefore diverse properties of the textiles, textile reinforcements and PMC materials must be available for predictive simulation. Knowing the detailed geometry of technical textiles is essential to predicting accurately the processing and performance properties of textiles and PMC parts. In turn, the geometry taken by a textile or a reinforcement textile is linked in an intricate manner to its constitutive behaviour. This thesis proposes, investigates and validates a general numerical tool for the integrated and comprehensive analysis of textile geometry and constitutive behaviour as required toward engineering applications featuring technical textiles and textile reinforcements. The tool shall be general with regards to the textiles modelled and the loading cases applied. Specifically, the work aims at fulfilling the following objectives: 1) developing and implementing dedicated simulation software for modelling textiles subjected to various load cases; 2) providing, through simulation, geometric descriptions for different textiles subjected to different load cases namely compaction, relaxation and shear; 3) predicting the constitutive behaviour of the textiles undergoing said load cases; 4) identifying parameters affecting the textile geometry and constitutive behaviour under evolving loading; 5) validating simulation results with experimental trials; and 6) demonstrating the applicability of the simulation procedure to textile reinforcements featuring large numbers of small fibres as used in PMCs. As a starting point, the effects of reinforcement configuration on the in-plane permeability of textile reinforcements, through-thickness thermal conductivity of PMCs and in-plane stiffness of unidirectional and bidirectional PMCs were quantified systematically and correlated with specific geometric parameters. Variability was quantified for each property at a constant fibre volume fraction. It was observed that variability differed strongly between properties; as such, the simulated behaviour can be related to variability levels seen in experimental measurements. The effects of the geometry of textile reinforcements on the aforementioned processing and performance properties of the textiles and PMCs made from these textiles was demonstrated and validated, but only for simple cases as thorough and credible geometric models were not available at the onset of this work. Outcomes of this work were published in a peer-reviewed journal [101]. Through this thesis it was demonstrated that predicting changes in textile geometry prior and during loading is feasible using the proposed particle-based modelling method. The particle-based modelling method relies on discrete mechanics and offers an alternative to more traditional methods based on continuum mechanics. Specifically it alleviates issues caused by large strains and management of intricate, evolving contact present in finite element simulations. The particle-based modelling method enables credible, intricate modelling of the geometry of textiles at the mesoscopic scale as well as faithful mechanical modelling under load. Changes to textile geometry and configuration due to the normal compaction pressure, stress relaxation, in-plane shear and other types of loads were successfully predicted.

Online tools for nucleosynthesis studies

NASA Astrophysics Data System (ADS)

Göbel, K.; Glorius, J.; Koloczek, A.; Pignatari, M.; Plag, R.; Reifarth, R.; Ritter, C.; Schmidt, S.; Sonnabend, K.; Thomas, B.; Travaglio, C.

2018-01-01

The nucleosynthesis of the elements between iron and uranium involves many different astrophysical scenarios covering wide ranges of temperatures and densities. Thousands of nuclei and ten thousands of reaction rates have to be included in the corresponding simulations. We investigate the impact of single rates on the predicted abundance distributions with post-processing nucleosynthesis simulations. We present online tools, which allow the investigation of sensitivities and integrated mass fluxes in different astrophysical scenarios.

Development of facial aging simulation system combined with three-dimensional shape prediction from facial photographs

NASA Astrophysics Data System (ADS)

Nagata, Takeshi; Matsuzaki, Kazutoshi; Taniguchi, Kei; Ogawa, Yoshinori; Imaizumi, Kazuhiko

2017-03-01

3D Facial aging changes in more than 10 years of identical persons are being measured at National Research Institute of Police Science. We performed machine learning using such measured data as teacher data and have developed the system which convert input 2D face image into 3D face model and simulate aging. Here, we report about processing and accuracy of our system.

A CFD analysis of blade row interactions within a high-speed axial compressor

NASA Astrophysics Data System (ADS)

Richman, Michael Scott

Aircraft engine design provides many technical and financial hurdles. In an effort to streamline the design process, save money, and improve reliability and performance, many manufacturers are relying on computational fluid dynamic simulations. An overarching goal of the design process for military aircraft engines is to reduce size and weight while maintaining (or improving) reliability. Designers often turn to the compression system to accomplish this goal. As pressure ratios increase and the number of compression stages decrease, many problems arise, for example stability and high cycle fatigue (HCF) become significant as individual stage loading is increased. CFD simulations have recently been employed to assist in the understanding of the aeroelastic problems. For accurate multistage blade row HCF prediction, it is imperative that advanced three-dimensional blade row unsteady aerodynamic interaction codes be validated with appropriate benchmark data. This research addresses this required validation process for TURBO, an advanced three-dimensional multi-blade row turbomachinery CFD code. The solution/prediction accuracy is characterized, identifying key flow field parameters driving the inlet guide vane (IGV) and stator response to the rotor generated forcing functions. The result is a quantified evaluation of the ability of TURBO to predict not only the fundamental flow field characteristics but the three dimensional blade loading.

Coronal heating by the resonant absorption of Alfven waves - Importance of the global mode and scaling laws

NASA Technical Reports Server (NTRS)

Steinolfson, Richard S.; Davila, Joseph M.

1993-01-01

Numerical simulations of the MHD equations for a fully compressible, low-beta, resistive plasma are used to study the resonance absorption process for the heating of coronal active region loops. Comparisons with more approximate analytic models show that the major predictions of the analytic theories are, to a large extent, confirmed by the numerical computations. The simulations demonstrate that the dissipation occurs primarily in a thin resonance layer. Some of the analytically predicted features verified by the simulations are (a) the position of the resonance layer within the initial inhomogeneity; (b) the importance of the global mode for a large range of loop densities; (c) the dependence of the resonance layer thickness and the steady-state heating rate on the dissipation coefficient; and (d) the time required for the resonance layer to form. In contrast with some previous analytic and simulation results, the time for the loop to reach a steady state is found to be the phase-mixing time rather than a dissipation time. This disagreement is shown to result from neglect of the existence of the global mode in some of the earlier analyses. The resonant absorption process is also shown to behave similar to a classical driven harmonic oscillator.

Automatic Speech Recognition Predicts Speech Intelligibility and Comprehension for Listeners With Simulated Age-Related Hearing Loss.

PubMed

Fontan, Lionel; Ferrané, Isabelle; Farinas, Jérôme; Pinquier, Julien; Tardieu, Julien; Magnen, Cynthia; Gaillard, Pascal; Aumont, Xavier; Füllgrabe, Christian

2017-09-18

The purpose of this article is to assess speech processing for listeners with simulated age-related hearing loss (ARHL) and to investigate whether the observed performance can be replicated using an automatic speech recognition (ASR) system. The long-term goal of this research is to develop a system that will assist audiologists/hearing-aid dispensers in the fine-tuning of hearing aids. Sixty young participants with normal hearing listened to speech materials mimicking the perceptual consequences of ARHL at different levels of severity. Two intelligibility tests (repetition of words and sentences) and 1 comprehension test (responding to oral commands by moving virtual objects) were administered. Several language models were developed and used by the ASR system in order to fit human performances. Strong significant positive correlations were observed between human and ASR scores, with coefficients up to .99. However, the spectral smearing used to simulate losses in frequency selectivity caused larger declines in ASR performance than in human performance. Both intelligibility and comprehension scores for listeners with simulated ARHL are highly correlated with the performances of an ASR-based system. In the future, it needs to be determined if the ASR system is similarly successful in predicting speech processing in noise and by older people with ARHL.

A Drone Remote Sensing for Virtual Reality Simulation System for Forest Fires: Semantic Neural Network Approach

NASA Astrophysics Data System (ADS)

Narasimha Rao, Gudikandhula; Jagadeeswara Rao, Peddada; Duvvuru, Rajesh

2016-09-01

Wild fires have significant impact on atmosphere and lives. The demand of predicting exact fire area in forest may help fire management team by using drone as a robot. These are flexible, inexpensive and elevated-motion remote sensing systems that use drones as platforms are important for substantial data gaps and supplementing the capabilities of manned aircraft and satellite remote sensing systems. In addition, powerful computational tools are essential for predicting certain burned area in the duration of a forest fire. The reason of this study is to built up a smart system based on semantic neural networking for the forecast of burned areas. The usage of virtual reality simulator is used to support the instruction process of fire fighters and all users for saving of surrounded wild lives by using a naive method Semantic Neural Network System (SNNS). Semantics are valuable initially to have a enhanced representation of the burned area prediction and better alteration of simulation situation to the users. In meticulous, consequences obtained with geometric semantic neural networking is extensively superior to other methods. This learning suggests that deeper investigation of neural networking in the field of forest fires prediction could be productive.

Simulation of Silicon Photomultiplier Signals

NASA Astrophysics Data System (ADS)

Seifert, Stefan; van Dam, Herman T.; Huizenga, Jan; Vinke, Ruud; Dendooven, Peter; Lohner, Herbert; Schaart, Dennis R.

2009-12-01

In a silicon photomultiplier (SiPM), also referred to as multi-pixel photon counter (MPPC), many Geiger-mode avalanche photodiodes (GM-APDs) are connected in parallel so as to combine the photon counting capabilities of each of these so-called microcells into a proportional light sensor. The discharge of a single microcell is relatively well understood and electronic models exist to simulate this process. In this paper we introduce an extended model that is able to simulate the simultaneous discharge of multiple cells. This model is used to predict the SiPM signal in response to fast light pulses as a function of the number of fired cells, taking into account the influence of the input impedance of the SiPM preamplifier. The model predicts that the electronic signal is not proportional to the number of fired cells if the preamplifier input impedance is not zero. This effect becomes more important for SiPMs with lower parasitic capacitance (which otherwise is a favorable property). The model is validated by comparing its predictions to experimental data obtained with two different SiPMs (Hamamatsu S10362-11-25u and Hamamatsu S10362-33-25c) illuminated with ps laser pulses. The experimental results are in good agreement with the model predictions.

An integrated simulator of structure and anisotropic flow in gas diffusion layers with hydrophobic additives

NASA Astrophysics Data System (ADS)

Burganos, Vasilis N.; Skouras, Eugene D.; Kalarakis, Alexandros N.

2017-10-01

The lattice-Boltzmann (LB) method is used in this work to reproduce the controlled addition of binder and hydrophobicity-promoting agents, like polytetrafluoroethylene (PTFE), into gas diffusion layers (GDLs) and to predict flow permeabilities in the through- and in-plane directions. The present simulator manages to reproduce spreading of binder and hydrophobic additives, sequentially, into the neat fibrous layer using a two-phase flow model. Gas flow simulation is achieved by the same code, sidestepping the need for a post-processing flow code and avoiding the usual input/output and data interface problems that arise in other techniques. Compression effects on flow anisotropy of the impregnated GDL are also studied. The permeability predictions for different compression levels and for different binder or PTFE loadings are found to compare well with experimental data for commercial GDL products and with computational fluid dynamics (CFD) predictions. Alternatively, the PTFE-impregnated structure is reproduced from Scanning Electron Microscopy (SEM) images using an independent, purely geometrical approach. A comparison of the two approaches is made regarding their adequacy to reproduce correctly the main structural features of the GDL and to predict anisotropic flow permeabilities at different volume fractions of binder and hydrophobic additives.

Applying simulation to optimize plastic molded optical parts

NASA Astrophysics Data System (ADS)

Jaworski, Matthew; Bakharev, Alexander; Costa, Franco; Friedl, Chris

2012-10-01

Optical injection molded parts are used in many different industries including electronics, consumer, medical and automotive due to their cost and performance advantages compared to alternative materials such as glass. The injection molding process, however, induces elastic (residual stress) and viscoelastic (flow orientation stress) deformation into the molded article which alters the material's refractive index to be anisotropic in different directions. Being able to predict and correct optical performance issues associated with birefringence early in the design phase is a huge competitive advantage. This paper reviews how to apply simulation analysis of the entire molding process to optimize manufacturability and part performance.

Electromagnetic Modelling of MMIC CPWs for High Frequency Applications

NASA Astrophysics Data System (ADS)

Sinulingga, E. P.; Kyabaggu, P. B. K.; Rezazadeh, A. A.

2018-02-01

Realising the theoretical electrical characteristics of components through modelling can be carried out using computer-aided design (CAD) simulation tools. If the simulation model provides the expected characteristics, the fabrication process of Monolithic Microwave Integrated Circuit (MMIC) can be performed for experimental verification purposes. Therefore improvements can be suggested before mass fabrication takes place. This research concentrates on development of MMIC technology by providing accurate predictions of the characteristics of MMIC components using an improved Electromagnetic (EM) modelling technique. The knowledge acquired from the modelling and characterisation process in this work can be adopted by circuit designers for various high frequency applications.

Hencky's model for elastomer forming process

NASA Astrophysics Data System (ADS)

Oleinikov, A. A.; Oleinikov, A. I.

2016-08-01

In the numerical simulation of elastomer forming process, Henckys isotropic hyperelastic material model can guarantee relatively accurate prediction of strain range in terms of large deformations. It is shown, that this material model prolongate Hooke's law from the area of infinitesimal strains to the area of moderate ones. New representation of the fourth-order elasticity tensor for Hencky's hyperelastic isotropic material is obtained, it possesses both minor symmetries, and the major symmetry. Constitutive relations of considered model is implemented into MSC.Marc code. By calculating and fitting curves, the polyurethane elastomer material constants are selected. Simulation of equipment for elastomer sheet forming are considered.