Stability analysis of multi-group deterministic and stochastic epidemic models with vaccination rate

NASA Astrophysics Data System (ADS)

Wang, Zhi-Gang; Gao, Rui-Mei; Fan, Xiao-Ming; Han, Qi-Xing

2014-09-01

We discuss in this paper a deterministic multi-group MSIR epidemic model with a vaccination rate, the basic reproduction number ℛ0, a key parameter in epidemiology, is a threshold which determines the persistence or extinction of the disease. By using Lyapunov function techniques, we show if ℛ0 is greater than 1 and the deterministic model obeys some conditions, then the disease will prevail, the infective persists and the endemic state is asymptotically stable in a feasible region. If ℛ0 is less than or equal to 1, then the infective disappear so the disease dies out. In addition, stochastic noises around the endemic equilibrium will be added to the deterministic MSIR model in order that the deterministic model is extended to a system of stochastic ordinary differential equations. In the stochastic version, we carry out a detailed analysis on the asymptotic behavior of the stochastic model. In addition, regarding the value of ℛ0, when the stochastic system obeys some conditions and ℛ0 is greater than 1, we deduce the stochastic system is stochastically asymptotically stable. Finally, the deterministic and stochastic model dynamics are illustrated through computer simulations.

Disentangling Mechanisms That Mediate the Balance Between Stochastic and Deterministic Processes in Microbial Succession

DOE PAGES

Dini-Andreote, Francisco; Stegen, James C.; van Elsas, Jan D.; ...

2015-03-17

Despite growing recognition that deterministic and stochastic factors simultaneously influence bacterial communities, little is known about mechanisms shifting their relative importance. To better understand underlying mechanisms, we developed a conceptual model linking ecosystem development during primary succession to shifts in the stochastic/deterministic balance. To evaluate the conceptual model we coupled spatiotemporal data on soil bacterial communities with environmental conditions spanning 105 years of salt marsh development. At the local scale there was a progression from stochasticity to determinism due to Na accumulation with increasing ecosystem age, supporting a main element of the conceptual model. At the regional-scale, soil organic mattermore » (SOM) governed the relative influence of stochasticity and the type of deterministic ecological selection, suggesting scale-dependency in how deterministic ecological selection is imposed. Analysis of a new ecological simulation model supported these conceptual inferences. Looking forward, we propose an extended conceptual model that integrates primary and secondary succession in microbial systems.« less

Identification of gene regulation models from single-cell data

NASA Astrophysics Data System (ADS)

Weber, Lisa; Raymond, William; Munsky, Brian

2018-09-01

In quantitative analyses of biological processes, one may use many different scales of models (e.g. spatial or non-spatial, deterministic or stochastic, time-varying or at steady-state) or many different approaches to match models to experimental data (e.g. model fitting or parameter uncertainty/sloppiness quantification with different experiment designs). These different analyses can lead to surprisingly different results, even when applied to the same data and the same model. We use a simplified gene regulation model to illustrate many of these concerns, especially for ODE analyses of deterministic processes, chemical master equation and finite state projection analyses of heterogeneous processes, and stochastic simulations. For each analysis, we employ MATLAB and PYTHON software to consider a time-dependent input signal (e.g. a kinase nuclear translocation) and several model hypotheses, along with simulated single-cell data. We illustrate different approaches (e.g. deterministic and stochastic) to identify the mechanisms and parameters of the same model from the same simulated data. For each approach, we explore how uncertainty in parameter space varies with respect to the chosen analysis approach or specific experiment design. We conclude with a discussion of how our simulated results relate to the integration of experimental and computational investigations to explore signal-activated gene expression models in yeast (Neuert et al 2013 Science 339 584–7) and human cells (Senecal et al 2014 Cell Rep. 8 75–83)5.

Automatic mesh adaptivity for hybrid Monte Carlo/deterministic neutronics modeling of difficult shielding problems

DOE PAGES

Ibrahim, Ahmad M.; Wilson, Paul P.H.; Sawan, Mohamed E.; ...

2015-06-30

The CADIS and FW-CADIS hybrid Monte Carlo/deterministic techniques dramatically increase the efficiency of neutronics modeling, but their use in the accurate design analysis of very large and geometrically complex nuclear systems has been limited by the large number of processors and memory requirements for their preliminary deterministic calculations and final Monte Carlo calculation. Three mesh adaptivity algorithms were developed to reduce the memory requirements of CADIS and FW-CADIS without sacrificing their efficiency improvement. First, a macromaterial approach enhances the fidelity of the deterministic models without changing the mesh. Second, a deterministic mesh refinement algorithm generates meshes that capture as muchmore » geometric detail as possible without exceeding a specified maximum number of mesh elements. Finally, a weight window coarsening algorithm decouples the weight window mesh and energy bins from the mesh and energy group structure of the deterministic calculations in order to remove the memory constraint of the weight window map from the deterministic mesh resolution. The three algorithms were used to enhance an FW-CADIS calculation of the prompt dose rate throughout the ITER experimental facility. Using these algorithms resulted in a 23.3% increase in the number of mesh tally elements in which the dose rates were calculated in a 10-day Monte Carlo calculation and, additionally, increased the efficiency of the Monte Carlo simulation by a factor of at least 3.4. The three algorithms enabled this difficult calculation to be accurately solved using an FW-CADIS simulation on a regular computer cluster, eliminating the need for a world-class super computer.« less

Deterministic Stress Modeling of Hot Gas Segregation in a Turbine

NASA Technical Reports Server (NTRS)

Busby, Judy; Sondak, Doug; Staubach, Brent; Davis, Roger

1998-01-01

Simulation of unsteady viscous turbomachinery flowfields is presently impractical as a design tool due to the long run times required. Designers rely predominantly on steady-state simulations, but these simulations do not account for some of the important unsteady flow physics. Unsteady flow effects can be modeled as source terms in the steady flow equations. These source terms, referred to as Lumped Deterministic Stresses (LDS), can be used to drive steady flow solution procedures to reproduce the time-average of an unsteady flow solution. The goal of this work is to investigate the feasibility of using inviscid lumped deterministic stresses to model unsteady combustion hot streak migration effects on the turbine blade tip and outer air seal heat loads using a steady computational approach. The LDS model is obtained from an unsteady inviscid calculation. The LDS model is then used with a steady viscous computation to simulate the time-averaged viscous solution. Both two-dimensional and three-dimensional applications are examined. The inviscid LDS model produces good results for the two-dimensional case and requires less than 10% of the CPU time of the unsteady viscous run. For the three-dimensional case, the LDS model does a good job of reproducing the time-averaged viscous temperature migration and separation as well as heat load on the outer air seal at a CPU cost that is 25% of that of an unsteady viscous computation.

Convergence studies of deterministic methods for LWR explicit reflector methodology

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

Canepa, S.; Hursin, M.; Ferroukhi, H.

2013-07-01

The standard approach in modem 3-D core simulators, employed either for steady-state or transient simulations, is to use Albedo coefficients or explicit reflectors at the core axial and radial boundaries. In the latter approach, few-group homogenized nuclear data are a priori produced with lattice transport codes using 2-D reflector models. Recently, the explicit reflector methodology of the deterministic CASMO-4/SIMULATE-3 code system was identified to potentially constitute one of the main sources of errors for core analyses of the Swiss operating LWRs, which are all belonging to GII design. Considering that some of the new GIII designs will rely on verymore » different reflector concepts, a review and assessment of the reflector methodology for various LWR designs appeared as relevant. Therefore, the purpose of this paper is to first recall the concepts of the explicit reflector modelling approach as employed by CASMO/SIMULATE. Then, for selected reflector configurations representative of both GII and GUI designs, a benchmarking of the few-group nuclear data produced with the deterministic lattice code CASMO-4 and its successor CASMO-5, is conducted. On this basis, a convergence study with regards to geometrical requirements when using deterministic methods with 2-D homogenous models is conducted and the effect on the downstream 3-D core analysis accuracy is evaluated for a typical GII deflector design in order to assess the results against available plant measurements. (authors)« less

Stochastic modelling of microstructure formation in solidification processes

NASA Astrophysics Data System (ADS)

Nastac, Laurentiu; Stefanescu, Doru M.

1997-07-01

To relax many of the assumptions used in continuum approaches, a general stochastic model has been developed. The stochastic model can be used not only for an accurate description of the fraction of solid evolution, and therefore accurate cooling curves, but also for simulation of microstructure formation in castings. The advantage of using the stochastic approach is to give a time- and space-dependent description of solidification processes. Time- and space-dependent processes can also be described by partial differential equations. Unlike a differential formulation which, in most cases, has to be transformed into a difference equation and solved numerically, the stochastic approach is essentially a direct numerical algorithm. The stochastic model is comprehensive, since the competition between various phases is considered. Furthermore, grain impingement is directly included through the structure of the model. In the present research, all grain morphologies are simulated with this procedure. The relevance of the stochastic approach is that the simulated microstructures can be directly compared with microstructures obtained from experiments. The computer becomes a `dynamic metallographic microscope'. A comparison between deterministic and stochastic approaches has been performed. An important objective of this research was to answer the following general questions: (1) `Would fully deterministic approaches continue to be useful in solidification modelling?' and (2) `Would stochastic algorithms be capable of entirely replacing purely deterministic models?'

Estimating the epidemic threshold on networks by deterministic connections

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

Li, Kezan, E-mail: lkzzr@sohu.com; Zhu, Guanghu; Fu, Xinchu

2014-12-15

For many epidemic networks some connections between nodes are treated as deterministic, while the remainder are random and have different connection probabilities. By applying spectral analysis to several constructed models, we find that one can estimate the epidemic thresholds of these networks by investigating information from only the deterministic connections. Nonetheless, in these models, generic nonuniform stochastic connections and heterogeneous community structure are also considered. The estimation of epidemic thresholds is achieved via inequalities with upper and lower bounds, which are found to be in very good agreement with numerical simulations. Since these deterministic connections are easier to detect thanmore » those stochastic connections, this work provides a feasible and effective method to estimate the epidemic thresholds in real epidemic networks.« less

Developing Stochastic Models as Inputs for High-Frequency Ground Motion Simulations

NASA Astrophysics Data System (ADS)

Savran, William Harvey

High-frequency ( 10 Hz) deterministic ground motion simulations are challenged by our understanding of the small-scale structure of the earth's crust and the rupture process during an earthquake. We will likely never obtain deterministic models that can accurately describe these processes down to the meter scale length required for broadband wave propagation. Instead, we can attempt to explain the behavior, in a statistical sense, by including stochastic models defined by correlations observed in the natural earth and through physics based simulations of the earthquake rupture process. Toward this goal, we develop stochastic models to address both of the primary considerations for deterministic ground motion simulations: namely, the description of the material properties in the crust, and broadband earthquake source descriptions. Using borehole sonic log data recorded in Los Angeles basin, we estimate the spatial correlation structure of the small-scale fluctuations in P-wave velocities by determining the best-fitting parameters of a von Karman correlation function. We find that Hurst exponents, nu, between 0.0-0.2, vertical correlation lengths, az, of 15-150m, an standard deviation, sigma of about 5% characterize the variability in the borehole data. Usin these parameters, we generated a stochastic model of velocity and density perturbations and combined with leading seismic velocity models to perform a validation exercise for the 2008, Chino Hills, CA using heterogeneous media. We find that models of velocity and density perturbations can have significant effects on the wavefield at frequencies as low as 0.3 Hz, with ensemble median values of various ground motion metrics varying up to +/-50%, at certain stations, compared to those computed solely from the CVM. Finally, we develop a kinematic rupture generator based on dynamic rupture simulations on geometrically complex faults. We analyze 100 dynamic rupture simulations on strike-slip faults ranging from Mw 6.4-7.2. We find that our dynamic simulations follow empirical scaling relationships for inter-plate strike-slip events, and provide source spectra comparable with an o -2 model. Our rupture generator reproduces GMPE medians and intra-event standard deviations spectral accelerations for an ensemble of 10 Hz fully-deterministic ground motion simulations, as compared to NGA West2 GMPE relationships up to 0.2 seconds.

Probabilistic Finite Element Analysis & Design Optimization for Structural Designs

NASA Astrophysics Data System (ADS)

Deivanayagam, Arumugam

This study focuses on implementing probabilistic nature of material properties (Kevlar® 49) to the existing deterministic finite element analysis (FEA) of fabric based engine containment system through Monte Carlo simulations (MCS) and implementation of probabilistic analysis in engineering designs through Reliability Based Design Optimization (RBDO). First, the emphasis is on experimental data analysis focusing on probabilistic distribution models which characterize the randomness associated with the experimental data. The material properties of Kevlar® 49 are modeled using experimental data analysis and implemented along with an existing spiral modeling scheme (SMS) and user defined constitutive model (UMAT) for fabric based engine containment simulations in LS-DYNA. MCS of the model are performed to observe the failure pattern and exit velocities of the models. Then the solutions are compared with NASA experimental tests and deterministic results. MCS with probabilistic material data give a good prospective on results rather than a single deterministic simulation results. The next part of research is to implement the probabilistic material properties in engineering designs. The main aim of structural design is to obtain optimal solutions. In any case, in a deterministic optimization problem even though the structures are cost effective, it becomes highly unreliable if the uncertainty that may be associated with the system (material properties, loading etc.) is not represented or considered in the solution process. Reliable and optimal solution can be obtained by performing reliability optimization along with the deterministic optimization, which is RBDO. In RBDO problem formulation, in addition to structural performance constraints, reliability constraints are also considered. This part of research starts with introduction to reliability analysis such as first order reliability analysis, second order reliability analysis followed by simulation technique that are performed to obtain probability of failure and reliability of structures. Next, decoupled RBDO procedure is proposed with a new reliability analysis formulation with sensitivity analysis, which is performed to remove the highly reliable constraints in the RBDO, thereby reducing the computational time and function evaluations. Followed by implementation of the reliability analysis concepts and RBDO in finite element 2D truss problems and a planar beam problem are presented and discussed.

Simultaneous estimation of deterministic and fractal stochastic components in non-stationary time series

NASA Astrophysics Data System (ADS)

García, Constantino A.; Otero, Abraham; Félix, Paulo; Presedo, Jesús; Márquez, David G.

2018-07-01

In the past few decades, it has been recognized that 1 / f fluctuations are ubiquitous in nature. The most widely used mathematical models to capture the long-term memory properties of 1 / f fluctuations have been stochastic fractal models. However, physical systems do not usually consist of just stochastic fractal dynamics, but they often also show some degree of deterministic behavior. The present paper proposes a model based on fractal stochastic and deterministic components that can provide a valuable basis for the study of complex systems with long-term correlations. The fractal stochastic component is assumed to be a fractional Brownian motion process and the deterministic component is assumed to be a band-limited signal. We also provide a method that, under the assumptions of this model, is able to characterize the fractal stochastic component and to provide an estimate of the deterministic components present in a given time series. The method is based on a Bayesian wavelet shrinkage procedure that exploits the self-similar properties of the fractal processes in the wavelet domain. This method has been validated over simulated signals and over real signals with economical and biological origin. Real examples illustrate how our model may be useful for exploring the deterministic-stochastic duality of complex systems, and uncovering interesting patterns present in time series.

Hybrid stochastic and deterministic simulations of calcium blips.

PubMed

Rüdiger, S; Shuai, J W; Huisinga, W; Nagaiah, C; Warnecke, G; Parker, I; Falcke, M

2007-09-15

Intracellular calcium release is a prime example for the role of stochastic effects in cellular systems. Recent models consist of deterministic reaction-diffusion equations coupled to stochastic transitions of calcium channels. The resulting dynamics is of multiple time and spatial scales, which complicates far-reaching computer simulations. In this article, we introduce a novel hybrid scheme that is especially tailored to accurately trace events with essential stochastic variations, while deterministic concentration variables are efficiently and accurately traced at the same time. We use finite elements to efficiently resolve the extreme spatial gradients of concentration variables close to a channel. We describe the algorithmic approach and we demonstrate its efficiency compared to conventional methods. Our single-channel model matches experimental data and results in intriguing dynamics if calcium is used as charge carrier. Random openings of the channel accumulate in bursts of calcium blips that may be central for the understanding of cellular calcium dynamics.

Understanding agent-based models of financial markets: A bottom-up approach based on order parameters and phase diagrams

NASA Astrophysics Data System (ADS)

Lye, Ribin; Tan, James Peng Lung; Cheong, Siew Ann

2012-11-01

We describe a bottom-up framework, based on the identification of appropriate order parameters and determination of phase diagrams, for understanding progressively refined agent-based models and simulations of financial markets. We illustrate this framework by starting with a deterministic toy model, whereby N independent traders buy and sell M stocks through an order book that acts as a clearing house. The price of a stock increases whenever it is bought and decreases whenever it is sold. Price changes are updated by the order book before the next transaction takes place. In this deterministic model, all traders based their buy decisions on a call utility function, and all their sell decisions on a put utility function. We then make the agent-based model more realistic, by either having a fraction fb of traders buy a random stock on offer, or a fraction fs of traders sell a random stock in their portfolio. Based on our simulations, we find that it is possible to identify useful order parameters from the steady-state price distributions of all three models. Using these order parameters as a guide, we find three phases: (i) the dead market; (ii) the boom market; and (iii) the jammed market in the phase diagram of the deterministic model. Comparing the phase diagrams of the stochastic models against that of the deterministic model, we realize that the primary effect of stochasticity is to eliminate the dead market phase.

Panel summary report

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

Gutjahr, A.L.; Kincaid, C.T.; Mercer, J.W.

1987-04-01

The objective of this report is to summarize the various modeling approaches that were used to simulate solute transport in a variably saturated emission. In particular, the technical strengths and weaknesses of each approach are discussed, and conclusions and recommendations for future studies are made. Five models are considered: (1) one-dimensional analytical and semianalytical solutions of the classical deterministic convection-dispersion equation (van Genuchten, Parker, and Kool, this report ); (2) one-dimensional simulation using a continuous-time Markov process (Knighton and Wagenet, this report); (3) one-dimensional simulation using the time domain method and the frequency domain method (Duffy and Al-Hassan, this report);more » (4) one-dimensional numerical approach that combines a solution of the classical deterministic convection-dispersion equation with a chemical equilibrium speciation model (Cederberg, this report); and (5) three-dimensional numerical solution of the classical deterministic convection-dispersion equation (Huyakorn, Jones, Parker, Wadsworth, and White, this report). As part of the discussion, the input data and modeling results are summarized. The models were used in a data analysis mode, as opposed to a predictive mode. Thus, the following discussion will concentrate on the data analysis aspects of model use. Also, all the approaches were similar in that they were based on a convection-dispersion model of solute transport. Each discussion addresses the modeling approaches in the order listed above.« less

Assessing sequential data assimilation techniques for integrating GRACE data into a hydrological model

NASA Astrophysics Data System (ADS)

Khaki, M.; Hoteit, I.; Kuhn, M.; Awange, J.; Forootan, E.; van Dijk, A. I. J. M.; Schumacher, M.; Pattiaratchi, C.

2017-09-01

The time-variable terrestrial water storage (TWS) products from the Gravity Recovery And Climate Experiment (GRACE) have been increasingly used in recent years to improve the simulation of hydrological models by applying data assimilation techniques. In this study, for the first time, we assess the performance of the most popular data assimilation sequential techniques for integrating GRACE TWS into the World-Wide Water Resources Assessment (W3RA) model. We implement and test stochastic and deterministic ensemble-based Kalman filters (EnKF), as well as Particle filters (PF) using two different resampling approaches of Multinomial Resampling and Systematic Resampling. These choices provide various opportunities for weighting observations and model simulations during the assimilation and also accounting for error distributions. Particularly, the deterministic EnKF is tested to avoid perturbing observations before assimilation (that is the case in an ordinary EnKF). Gaussian-based random updates in the EnKF approaches likely do not fully represent the statistical properties of the model simulations and TWS observations. Therefore, the fully non-Gaussian PF is also applied to estimate more realistic updates. Monthly GRACE TWS are assimilated into W3RA covering the entire Australia. To evaluate the filters performances and analyze their impact on model simulations, their estimates are validated by independent in-situ measurements. Our results indicate that all implemented filters improve the estimation of water storage simulations of W3RA. The best results are obtained using two versions of deterministic EnKF, i.e. the Square Root Analysis (SQRA) scheme and the Ensemble Square Root Filter (EnSRF), respectively, improving the model groundwater estimations errors by 34% and 31% compared to a model run without assimilation. Applying the PF along with Systematic Resampling successfully decreases the model estimation error by 23%.

Flow injection analysis simulations and diffusion coefficient determination by stochastic and deterministic optimization methods.

PubMed

Kucza, Witold

2013-07-25

Stochastic and deterministic simulations of dispersion in cylindrical channels on the Poiseuille flow have been presented. The random walk (stochastic) and the uniform dispersion (deterministic) models have been used for computations of flow injection analysis responses. These methods coupled with the genetic algorithm and the Levenberg-Marquardt optimization methods, respectively, have been applied for determination of diffusion coefficients. The diffusion coefficients of fluorescein sodium, potassium hexacyanoferrate and potassium dichromate have been determined by means of the presented methods and FIA responses that are available in literature. The best-fit results agree with each other and with experimental data thus validating both presented approaches. Copyright © 2013 The Author. Published by Elsevier B.V. All rights reserved.

Modeling disease transmission near eradication: An equation free approach

NASA Astrophysics Data System (ADS)

Williams, Matthew O.; Proctor, Joshua L.; Kutz, J. Nathan

2015-01-01

Although disease transmission in the near eradication regime is inherently stochastic, deterministic quantities such as the probability of eradication are of interest to policy makers and researchers. Rather than running large ensembles of discrete stochastic simulations over long intervals in time to compute these deterministic quantities, we create a data-driven and deterministic "coarse" model for them using the Equation Free (EF) framework. In lieu of deriving an explicit coarse model, the EF framework approximates any needed information, such as coarse time derivatives, by running short computational experiments. However, the choice of the coarse variables (i.e., the state of the coarse system) is critical if the resulting model is to be accurate. In this manuscript, we propose a set of coarse variables that result in an accurate model in the endemic and near eradication regimes, and demonstrate this on a compartmental model representing the spread of Poliomyelitis. When combined with adaptive time-stepping coarse projective integrators, this approach can yield over a factor of two speedup compared to direct simulation, and due to its lower dimensionality, could be beneficial when conducting systems level tasks such as designing eradication or monitoring campaigns.

SIMULATED CLIMATE CHANGE EFFECTS ON DISSOLVED OXYGEN CHARACTERISTICS IN ICE-COVERED LAKES. (R824801)

EPA Science Inventory

A deterministic, one-dimensional model is presented which simulates daily dissolved oxygen (DO) profiles and associated water temperatures, ice covers and snow covers for dimictic and polymictic lakes of the temperate zone. The lake parameters required as model input are surface ...

ShinyGPAS: interactive genomic prediction accuracy simulator based on deterministic formulas.

PubMed

Morota, Gota

2017-12-20

Deterministic formulas for the accuracy of genomic predictions highlight the relationships among prediction accuracy and potential factors influencing prediction accuracy prior to performing computationally intensive cross-validation. Visualizing such deterministic formulas in an interactive manner may lead to a better understanding of how genetic factors control prediction accuracy. The software to simulate deterministic formulas for genomic prediction accuracy was implemented in R and encapsulated as a web-based Shiny application. Shiny genomic prediction accuracy simulator (ShinyGPAS) simulates various deterministic formulas and delivers dynamic scatter plots of prediction accuracy versus genetic factors impacting prediction accuracy, while requiring only mouse navigation in a web browser. ShinyGPAS is available at: https://chikudaisei.shinyapps.io/shinygpas/ . ShinyGPAS is a shiny-based interactive genomic prediction accuracy simulator using deterministic formulas. It can be used for interactively exploring potential factors that influence prediction accuracy in genome-enabled prediction, simulating achievable prediction accuracy prior to genotyping individuals, or supporting in-class teaching. ShinyGPAS is open source software and it is hosted online as a freely available web-based resource with an intuitive graphical user interface.

Agent-Based Deterministic Modeling of the Bone Marrow Homeostasis.

PubMed

Kurhekar, Manish; Deshpande, Umesh

2016-01-01

Modeling of stem cells not only describes but also predicts how a stem cell's environment can control its fate. The first stem cell populations discovered were hematopoietic stem cells (HSCs). In this paper, we present a deterministic model of bone marrow (that hosts HSCs) that is consistent with several of the qualitative biological observations. This model incorporates stem cell death (apoptosis) after a certain number of cell divisions and also demonstrates that a single HSC can potentially populate the entire bone marrow. It also demonstrates that there is a production of sufficient number of differentiated cells (RBCs, WBCs, etc.). We prove that our model of bone marrow is biologically consistent and it overcomes the biological feasibility limitations of previously reported models. The major contribution of our model is the flexibility it allows in choosing model parameters which permits several different simulations to be carried out in silico without affecting the homeostatic properties of the model. We have also performed agent-based simulation of the model of bone marrow system proposed in this paper. We have also included parameter details and the results obtained from the simulation. The program of the agent-based simulation of the proposed model is made available on a publicly accessible website.

A stochastic model for correlated protein motions

NASA Astrophysics Data System (ADS)

Karain, Wael I.; Qaraeen, Nael I.; Ajarmah, Basem

2006-06-01

A one-dimensional Langevin-type stochastic difference equation is used to find the deterministic and Gaussian contributions of time series representing the projections of a Bovine Pancreatic Trypsin Inhibitor (BPTI) protein molecular dynamics simulation along different eigenvector directions determined using principal component analysis. The deterministic part shows a distinct nonlinear behavior only for eigenvectors contributing significantly to the collective protein motion.

A Stochastic Tick-Borne Disease Model: Exploring the Probability of Pathogen Persistence.

PubMed

Maliyoni, Milliward; Chirove, Faraimunashe; Gaff, Holly D; Govinder, Keshlan S

2017-09-01

We formulate and analyse a stochastic epidemic model for the transmission dynamics of a tick-borne disease in a single population using a continuous-time Markov chain approach. The stochastic model is based on an existing deterministic metapopulation tick-borne disease model. We compare the disease dynamics of the deterministic and stochastic models in order to determine the effect of randomness in tick-borne disease dynamics. The probability of disease extinction and that of a major outbreak are computed and approximated using the multitype Galton-Watson branching process and numerical simulations, respectively. Analytical and numerical results show some significant differences in model predictions between the stochastic and deterministic models. In particular, we find that a disease outbreak is more likely if the disease is introduced by infected deer as opposed to infected ticks. These insights demonstrate the importance of host movement in the expansion of tick-borne diseases into new geographic areas.

Modeling stochastic noise in gene regulatory systems

PubMed Central

Meister, Arwen; Du, Chao; Li, Ye Henry; Wong, Wing Hung

2014-01-01

The Master equation is considered the gold standard for modeling the stochastic mechanisms of gene regulation in molecular detail, but it is too complex to solve exactly in most cases, so approximation and simulation methods are essential. However, there is still a lack of consensus about the best way to carry these out. To help clarify the situation, we review Master equation models of gene regulation, theoretical approximations based on an expansion method due to N.G. van Kampen and R. Kubo, and simulation algorithms due to D.T. Gillespie and P. Langevin. Expansion of the Master equation shows that for systems with a single stable steady-state, the stochastic model reduces to a deterministic model in a first-order approximation. Additional theory, also due to van Kampen, describes the asymptotic behavior of multistable systems. To support and illustrate the theory and provide further insight into the complex behavior of multistable systems, we perform a detailed simulation study comparing the various approximation and simulation methods applied to synthetic gene regulatory systems with various qualitative characteristics. The simulation studies show that for large stochastic systems with a single steady-state, deterministic models are quite accurate, since the probability distribution of the solution has a single peak tracking the deterministic trajectory whose variance is inversely proportional to the system size. In multistable stochastic systems, large fluctuations can cause individual trajectories to escape from the domain of attraction of one steady-state and be attracted to another, so the system eventually reaches a multimodal probability distribution in which all stable steady-states are represented proportional to their relative stability. However, since the escape time scales exponentially with system size, this process can take a very long time in large systems. PMID:25632368

Relation Between the Cell Volume and the Cell Cycle Dynamics in Mammalian cell

NASA Astrophysics Data System (ADS)

Magno, A. C. G.; Oliveira, I. L.; Hauck, J. V. S.

2016-08-01

The main goal of this work is to add and analyze an equation that represents the volume in a dynamical model of the mammalian cell cycle proposed by Gérard and Goldbeter (2011) [1]. The cell division occurs when the cyclinB/Cdkl complex is totally degraded (Tyson and Novak, 2011)[2] and it reaches a minimum value. At this point, the cell is divided into two newborn daughter cells and each one will contain the half of the cytoplasmic content of the mother cell. The equations of our base model are only valid if the cell volume, where the reactions occur, is constant. Whether the cell volume is not constant, that is, the rate of change of its volume with respect to time is explicitly taken into account in the mathematical model, then the equations of the original model are no longer valid. Therefore, every equations were modified from the mass conservation principle for considering a volume that changes with time. Through this approach, the cell volume affects all model variables. Two different dynamic simulation methods were accomplished: deterministic and stochastic. In the stochastic simulation, the volume affects every model's parameters which have molar unit, whereas in the deterministic one, it is incorporated into the differential equations. In deterministic simulation, the biochemical species may be in concentration units, while in stochastic simulation such species must be converted to number of molecules which are directly proportional to the cell volume. In an effort to understand the influence of the new equation a stability analysis was performed. This elucidates how the growth factor impacts the stability of the model's limit cycles. In conclusion, a more precise model, in comparison to the base model, was created for the cell cycle as it now takes into consideration the cell volume variation

Hybrid Forecasting of Daily River Discharges Considering Autoregressive Heteroscedasticity

NASA Astrophysics Data System (ADS)

Szolgayová, Elena Peksová; Danačová, Michaela; Komorniková, Magda; Szolgay, Ján

2017-06-01

It is widely acknowledged that in the hydrological and meteorological communities, there is a continuing need to improve the quality of quantitative rainfall and river flow forecasts. A hybrid (combined deterministic-stochastic) modelling approach is proposed here that combines the advantages offered by modelling the system dynamics with a deterministic model and a deterministic forecasting error series with a data-driven model in parallel. Since the processes to be modelled are generally nonlinear and the model error series may exhibit nonstationarity and heteroscedasticity, GARCH-type nonlinear time series models are considered here. The fitting, forecasting and simulation performance of such models have to be explored on a case-by-case basis. The goal of this paper is to test and develop an appropriate methodology for model fitting and forecasting applicable for daily river discharge forecast error data from the GARCH family of time series models. We concentrated on verifying whether the use of a GARCH-type model is suitable for modelling and forecasting a hydrological model error time series on the Hron and Morava Rivers in Slovakia. For this purpose we verified the presence of heteroscedasticity in the simulation error series of the KLN multilinear flow routing model; then we fitted the GARCH-type models to the data and compared their fit with that of an ARMA - type model. We produced one-stepahead forecasts from the fitted models and again provided comparisons of the model's performance.

Finite element modelling of woven composite failure modes at the mesoscopic scale: deterministic versus stochastic approaches

NASA Astrophysics Data System (ADS)

Roirand, Q.; Missoum-Benziane, D.; Thionnet, A.; Laiarinandrasana, L.

2017-09-01

Textile composites are composed of 3D complex architecture. To assess the durability of such engineering structures, the failure mechanisms must be highlighted. Examinations of the degradation have been carried out thanks to tomography. The present work addresses a numerical damage model dedicated to the simulation of the crack initiation and propagation at the scale of the warp yarns. For the 3D woven composites under study, loadings in tension and combined tension and bending were considered. Based on an erosion procedure of broken elements, the failure mechanisms have been modelled on 3D periodic cells by finite element calculations. The breakage of one element was determined using a failure criterion at the mesoscopic scale based on the yarn stress at failure. The results were found to be in good agreement with the experimental data for the two kinds of macroscopic loadings. The deterministic approach assumed a homogeneously distributed stress at failure all over the integration points in the meshes of woven composites. A stochastic approach was applied to a simple representative elementary periodic cell. The distribution of the Weibull stress at failure was assigned to the integration points using a Monte Carlo simulation. It was shown that this stochastic approach allowed more realistic failure simulations avoiding the idealised symmetry due to the deterministic modelling. In particular, the stochastic simulations performed have shown several variations of the stress as well as strain at failure and the failure modes of the yarn.

Initialization of high resolution surface wind simulations using NWS gridded data

Treesearch

J. Forthofer; K. Shannon; Bret Butler

2010-01-01

WindNinja is a standalone computer model designed to provide the user with simulations of surface wind flow. It is deterministic and steady state. It is currently being modified to allow the user to initialize the flow calculation using National Digital Forecast Database. It essentially allows the user to downscale the coarse scale simulations from meso-scale models to...

Neural nets with terminal chaos for simulation of non-deterministic patterns

NASA Technical Reports Server (NTRS)

Zak, Michail

1993-01-01

Models for simulating some aspects of neural intelligence are presented and discussed. Special attention is given to terminal neurodynamics as a particular architecture of terminal dynamics suitable for modeling information flows. Applications of terminal chaos to information fusion as well as to planning and modeling coordination among neurons in biological systems are disussed.

Simulation of anaerobic digestion processes using stochastic algorithm.

PubMed

Palanichamy, Jegathambal; Palani, Sundarambal

2014-01-01

The Anaerobic Digestion (AD) processes involve numerous complex biological and chemical reactions occurring simultaneously. Appropriate and efficient models are to be developed for simulation of anaerobic digestion systems. Although several models have been developed, mostly they suffer from lack of knowledge on constants, complexity and weak generalization. The basis of the deterministic approach for modelling the physico and bio-chemical reactions occurring in the AD system is the law of mass action, which gives the simple relationship between the reaction rates and the species concentrations. The assumptions made in the deterministic models are not hold true for the reactions involving chemical species of low concentration. The stochastic behaviour of the physicochemical processes can be modeled at mesoscopic level by application of the stochastic algorithms. In this paper a stochastic algorithm (Gillespie Tau Leap Method) developed in MATLAB was applied to predict the concentration of glucose, acids and methane formation at different time intervals. By this the performance of the digester system can be controlled. The processes given by ADM1 (Anaerobic Digestion Model 1) were taken for verification of the model. The proposed model was verified by comparing the results of Gillespie's algorithms with the deterministic solution for conversion of glucose into methane through degraders. At higher value of 'τ' (timestep), the computational time required for reaching the steady state is more since the number of chosen reactions is less. When the simulation time step is reduced, the results are similar to ODE solver. It was concluded that the stochastic algorithm is a suitable approach for the simulation of complex anaerobic digestion processes. The accuracy of the results depends on the optimum selection of tau value.

Ordinal optimization and its application to complex deterministic problems

NASA Astrophysics Data System (ADS)

Yang, Mike Shang-Yu

1998-10-01

We present in this thesis a new perspective to approach a general class of optimization problems characterized by large deterministic complexities. Many problems of real-world concerns today lack analyzable structures and almost always involve high level of difficulties and complexities in the evaluation process. Advances in computer technology allow us to build computer models to simulate the evaluation process through numerical means, but the burden of high complexities remains to tax the simulation with an exorbitant computing cost for each evaluation. Such a resource requirement makes local fine-tuning of a known design difficult under most circumstances, let alone global optimization. Kolmogorov equivalence of complexity and randomness in computation theory is introduced to resolve this difficulty by converting the complex deterministic model to a stochastic pseudo-model composed of a simple deterministic component and a white-noise like stochastic term. The resulting randomness is then dealt with by a noise-robust approach called Ordinal Optimization. Ordinal Optimization utilizes Goal Softening and Ordinal Comparison to achieve an efficient and quantifiable selection of designs in the initial search process. The approach is substantiated by a case study in the turbine blade manufacturing process. The problem involves the optimization of the manufacturing process of the integrally bladed rotor in the turbine engines of U.S. Air Force fighter jets. The intertwining interactions among the material, thermomechanical, and geometrical changes makes the current FEM approach prohibitively uneconomical in the optimization process. The generalized OO approach to complex deterministic problems is applied here with great success. Empirical results indicate a saving of nearly 95% in the computing cost.

Hybrid deterministic-stochastic modeling of x-ray beam bowtie filter scatter on a CT system.

PubMed

Liu, Xin; Hsieh, Jiang

2015-01-01

Knowledge of scatter generated by bowtie filter (i.e. x-ray beam compensator) is crucial for providing artifact free images on the CT scanners. Our approach is to use a hybrid deterministic-stochastic simulation to estimate the scatter level generated by a bowtie filter made of a material with low atomic number. First, major components of CT systems, such as source, flat filter, bowtie filter, body phantom, are built into a 3D model. The scattered photon fluence and the primary transmitted photon fluence are simulated by MCNP - a Monte Carlo simulation toolkit. The rejection of scattered photon by the post patient collimator (anti-scatter grid) is simulated with an analytical formula. The biased sinogram is created by superimposing scatter signal generated by the simulation onto the primary x-ray beam signal. Finally, images with artifacts are reconstructed with the biased signal. The effect of anti-scatter grid height on scatter rejection are also discussed and demonstrated.

GAPPARD: a computationally efficient method of approximating gap-scale disturbance in vegetation models

NASA Astrophysics Data System (ADS)

Scherstjanoi, M.; Kaplan, J. O.; Thürig, E.; Lischke, H.

2013-09-01

Models of vegetation dynamics that are designed for application at spatial scales larger than individual forest gaps suffer from several limitations. Typically, either a population average approximation is used that results in unrealistic tree allometry and forest stand structure, or models have a high computational demand because they need to simulate both a series of age-based cohorts and a number of replicate patches to account for stochastic gap-scale disturbances. The detail required by the latter method increases the number of calculations by two to three orders of magnitude compared to the less realistic population average approach. In an effort to increase the efficiency of dynamic vegetation models without sacrificing realism, we developed a new method for simulating stand-replacing disturbances that is both accurate and faster than approaches that use replicate patches. The GAPPARD (approximating GAP model results with a Probabilistic Approach to account for stand Replacing Disturbances) method works by postprocessing the output of deterministic, undisturbed simulations of a cohort-based vegetation model by deriving the distribution of patch ages at any point in time on the basis of a disturbance probability. With this distribution, the expected value of any output variable can be calculated from the output values of the deterministic undisturbed run at the time corresponding to the patch age. To account for temporal changes in model forcing (e.g., as a result of climate change), GAPPARD performs a series of deterministic simulations and interpolates between the results in the postprocessing step. We integrated the GAPPARD method in the vegetation model LPJ-GUESS, and evaluated it in a series of simulations along an altitudinal transect of an inner-Alpine valley. We obtained results very similar to the output of the original LPJ-GUESS model that uses 100 replicate patches, but simulation time was reduced by approximately the factor 10. Our new method is therefore highly suited for rapidly approximating LPJ-GUESS results, and provides the opportunity for future studies over large spatial domains, allows easier parameterization of tree species, faster identification of areas of interesting simulation results, and comparisons with large-scale datasets and results of other forest models.

Enhancing Flood Prediction Reliability Using Bayesian Model Averaging

NASA Astrophysics Data System (ADS)

Liu, Z.; Merwade, V.

2017-12-01

Uncertainty analysis is an indispensable part of modeling the hydrology and hydrodynamics of non-idealized environmental systems. Compared to reliance on prediction from one model simulation, using on ensemble of predictions that consider uncertainty from different sources is more reliable. In this study, Bayesian model averaging (BMA) is applied to Black River watershed in Arkansas and Missouri by combining multi-model simulations to get reliable deterministic water stage and probabilistic inundation extent predictions. The simulation ensemble is generated from 81 LISFLOOD-FP subgrid model configurations that include uncertainty from channel shape, channel width, channel roughness and discharge. Model simulation outputs are trained with observed water stage data during one flood event, and BMA prediction ability is validated for another flood event. Results from this study indicate that BMA does not always outperform all members in the ensemble, but it provides relatively robust deterministic flood stage predictions across the basin. Station based BMA (BMA_S) water stage prediction has better performance than global based BMA (BMA_G) prediction which is superior to the ensemble mean prediction. Additionally, high-frequency flood inundation extent (probability greater than 60%) in BMA_G probabilistic map is more accurate than the probabilistic flood inundation extent based on equal weights.

A Random Variable Approach to Nuclear Targeting and Survivability

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

Undem, Halvor A.

We demonstrate a common mathematical formalism for analyzing problems in nuclear survivability and targeting. This formalism, beginning with a random variable approach, can be used to interpret past efforts in nuclear-effects analysis, including targeting analysis. It can also be used to analyze new problems brought about by the post Cold War Era, such as the potential effects of yield degradation in a permanently untested nuclear stockpile. In particular, we illustrate the formalism through four natural case studies or illustrative problems, linking these to actual past data, modeling, and simulation, and suggesting future uses. In the first problem, we illustrate themore » case of a deterministically modeled weapon used against a deterministically responding target. Classic "Cookie Cutter" damage functions result. In the second problem, we illustrate, with actual target test data, the case of a deterministically modeled weapon used against a statistically responding target. This case matches many of the results of current nuclear targeting modeling and simulation tools, including the result of distance damage functions as complementary cumulative lognormal functions in the range variable. In the third problem, we illustrate the case of a statistically behaving weapon used against a deterministically responding target. In particular, we show the dependence of target damage on weapon yield for an untested nuclear stockpile experiencing yield degradation. Finally, and using actual unclassified weapon test data, we illustrate in the fourth problem the case of a statistically behaving weapon used against a statistically responding target.« less

Deterministic and stochastic models for middle east respiratory syndrome (MERS)

NASA Astrophysics Data System (ADS)

Suryani, Dessy Rizki; Zevika, Mona; Nuraini, Nuning

2018-03-01

World Health Organization (WHO) data stated that since September 2012, there were 1,733 cases of Middle East Respiratory Syndrome (MERS) with 628 death cases that occurred in 27 countries. MERS was first identified in Saudi Arabia in 2012 and the largest cases of MERS outside Saudi Arabia occurred in South Korea in 2015. MERS is a disease that attacks the respiratory system caused by infection of MERS-CoV. MERS-CoV transmission occurs directly through direct contact between infected individual with non-infected individual or indirectly through contaminated object by the free virus. Suspected, MERS can spread quickly because of the free virus in environment. Mathematical modeling is used to illustrate the transmission of MERS disease using deterministic model and stochastic model. Deterministic model is used to investigate the temporal dynamic from the system to analyze the steady state condition. Stochastic model approach using Continuous Time Markov Chain (CTMC) is used to predict the future states by using random variables. From the models that were built, the threshold value for deterministic models and stochastic models obtained in the same form and the probability of disease extinction can be computed by stochastic model. Simulations for both models using several of different parameters are shown, and the probability of disease extinction will be compared with several initial conditions.

Use of Remote Sensing and Dust Modelling to Evaluate Ecosystem Phenology and Pollen Dispersal

NASA Technical Reports Server (NTRS)

Luvall, Jeffrey C.; Sprigg, William A.; Watts, Carol; Shaw, Patrick

2007-01-01

The impact of pollen release and downwind concentrations can be evaluated utilizing remote sensing. Previous NASA studies have addressed airborne dust prediction systems PHAiRS (Public Health Applications in Remote Sensing) which have determined that pollen forecasts and simulations are possible. By adapting the deterministic dust model (as an in-line system with the National Weather Service operational forecast model) used in PHAiRS to simulate downwind dispersal of pollen, initializing the model with pollen source regions from MODIS, assessing the results a rapid prototype concept can be produced. We will present the results of our effort to develop a deterministic model for predicting and simulating pollen emission and downwind concentration to study details or phenology and meteorology and their dependencies, and the promise of a credible real time forecast system to support public health and agricultural science and service. Previous studies have been done with PHAiRS research, the use of NASA data, the dust model and the PHAiRS potential to improve public health and environmental services long into the future.

A deterministic and stochastic model for the system dynamics of tumor-immune responses to chemotherapy

NASA Astrophysics Data System (ADS)

Liu, Xiangdong; Li, Qingze; Pan, Jianxin

2018-06-01

Modern medical studies show that chemotherapy can help most cancer patients, especially for those diagnosed early, to stabilize their disease conditions from months to years, which means the population of tumor cells remained nearly unchanged in quite a long time after fighting against immune system and drugs. In order to better understand the dynamics of tumor-immune responses under chemotherapy, deterministic and stochastic differential equation models are constructed to characterize the dynamical change of tumor cells and immune cells in this paper. The basic dynamical properties, such as boundedness, existence and stability of equilibrium points, are investigated in the deterministic model. Extended stochastic models include stochastic differential equations (SDEs) model and continuous-time Markov chain (CTMC) model, which accounts for the variability in cellular reproduction, growth and death, interspecific competitions, and immune response to chemotherapy. The CTMC model is harnessed to estimate the extinction probability of tumor cells. Numerical simulations are performed, which confirms the obtained theoretical results.

POTENTIAL CLIMATE WARMING EFFECTS ON ICE COVERS OF SMALL LAKES IN THE CONTIGUOUS U.S. (R824801)

EPA Science Inventory

Abstract

To simulate effects of projected climate change on ice covers of small lakes in the northern contiguous U.S., a process-based simulation model is applied. This winter ice/snow cover model is associated with a deterministic, one-dimensional year-round water tem...

Inference of Epidemiological Dynamics Based on Simulated Phylogenies Using Birth-Death and Coalescent Models

PubMed Central

Boskova, Veronika; Bonhoeffer, Sebastian; Stadler, Tanja

2014-01-01

Quantifying epidemiological dynamics is crucial for understanding and forecasting the spread of an epidemic. The coalescent and the birth-death model are used interchangeably to infer epidemiological parameters from the genealogical relationships of the pathogen population under study, which in turn are inferred from the pathogen genetic sequencing data. To compare the performance of these widely applied models, we performed a simulation study. We simulated phylogenetic trees under the constant rate birth-death model and the coalescent model with a deterministic exponentially growing infected population. For each tree, we re-estimated the epidemiological parameters using both a birth-death and a coalescent based method, implemented as an MCMC procedure in BEAST v2.0. In our analyses that estimate the growth rate of an epidemic based on simulated birth-death trees, the point estimates such as the maximum a posteriori/maximum likelihood estimates are not very different. However, the estimates of uncertainty are very different. The birth-death model had a higher coverage than the coalescent model, i.e. contained the true value in the highest posterior density (HPD) interval more often (2–13% vs. 31–75% error). The coverage of the coalescent decreases with decreasing basic reproductive ratio and increasing sampling probability of infecteds. We hypothesize that the biases in the coalescent are due to the assumption of deterministic rather than stochastic population size changes. Both methods performed reasonably well when analyzing trees simulated under the coalescent. The methods can also identify other key epidemiological parameters as long as one of the parameters is fixed to its true value. In summary, when using genetic data to estimate epidemic dynamics, our results suggest that the birth-death method will be less sensitive to population fluctuations of early outbreaks than the coalescent method that assumes a deterministic exponentially growing infected population. PMID:25375100

A deterministic width function model

NASA Astrophysics Data System (ADS)

Puente, C. E.; Sivakumar, B.

Use of a deterministic fractal-multifractal (FM) geometric method to model width functions of natural river networks, as derived distributions of simple multifractal measures via fractal interpolating functions, is reported. It is first demonstrated that the FM procedure may be used to simulate natural width functions, preserving their most relevant features like their overall shape and texture and their observed power-law scaling on their power spectra. It is then shown, via two natural river networks (Racoon and Brushy creeks in the United States), that the FM approach may also be used to closely approximate existing width functions.

Network-level reproduction number and extinction threshold for vector-borne diseases.

PubMed

Xue, Ling; Scoglio, Caterina

2015-06-01

The basic reproduction number of deterministic models is an essential quantity to predict whether an epidemic will spread or not. Thresholds for disease extinction contribute crucial knowledge of disease control, elimination, and mitigation of infectious diseases. Relationships between basic reproduction numbers of two deterministic network-based ordinary differential equation vector-host models, and extinction thresholds of corresponding stochastic continuous-time Markov chain models are derived under some assumptions. Numerical simulation results for malaria and Rift Valley fever transmission on heterogeneous networks are in agreement with analytical results without any assumptions, reinforcing that the relationships may always exist and proposing a mathematical problem for proving existence of the relationships in general. Moreover, numerical simulations show that the basic reproduction number does not monotonically increase or decrease with the extinction threshold. Consistent trends of extinction probability observed through numerical simulations provide novel insights into mitigation strategies to increase the disease extinction probability. Research findings may improve understandings of thresholds for disease persistence in order to control vector-borne diseases.

Modeling the within-host dynamics of cholera: bacterial-viral interaction.

PubMed

Wang, Xueying; Wang, Jin

2017-08-01

Novel deterministic and stochastic models are proposed in this paper for the within-host dynamics of cholera, with a focus on the bacterial-viral interaction. The deterministic model is a system of differential equations describing the interaction among the two types of vibrios and the viruses. The stochastic model is a system of Markov jump processes that is derived based on the dynamics of the deterministic model. The multitype branching process approximation is applied to estimate the extinction probability of bacteria and viruses within a human host during the early stage of the bacterial-viral infection. Accordingly, a closed-form expression is derived for the disease extinction probability, and analytic estimates are validated with numerical simulations. The local and global dynamics of the bacterial-viral interaction are analysed using the deterministic model, and the result indicates that there is a sharp disease threshold characterized by the basic reproduction number [Formula: see text]: if [Formula: see text], vibrios ingested from the environment into human body will not cause cholera infection; if [Formula: see text], vibrios will grow with increased toxicity and persist within the host, leading to human cholera. In contrast, the stochastic model indicates, more realistically, that there is always a positive probability of disease extinction within the human host.

An analytical framework to assist decision makers in the use of forest ecosystem model predictions

USDA-ARS?s Scientific Manuscript database

The predictions of most terrestrial ecosystem models originate from deterministic simulations. Relatively few uncertainty evaluation exercises in model outputs are performed by either model developers or users. This issue has important consequences for decision makers who rely on models to develop n...

The ISI distribution of the stochastic Hodgkin-Huxley neuron.

PubMed

Rowat, Peter F; Greenwood, Priscilla E

2014-01-01

The simulation of ion-channel noise has an important role in computational neuroscience. In recent years several approximate methods of carrying out this simulation have been published, based on stochastic differential equations, and all giving slightly different results. The obvious, and essential, question is: which method is the most accurate and which is most computationally efficient? Here we make a contribution to the answer. We compare interspike interval histograms from simulated data using four different approximate stochastic differential equation (SDE) models of the stochastic Hodgkin-Huxley neuron, as well as the exact Markov chain model simulated by the Gillespie algorithm. One of the recent SDE models is the same as the Kurtz approximation first published in 1978. All the models considered give similar ISI histograms over a wide range of deterministic and stochastic input. Three features of these histograms are an initial peak, followed by one or more bumps, and then an exponential tail. We explore how these features depend on deterministic input and on level of channel noise, and explain the results using the stochastic dynamics of the model. We conclude with a rough ranking of the four SDE models with respect to the similarity of their ISI histograms to the histogram of the exact Markov chain model.

Experimental study and simulation of space charge stimulated discharge

NASA Astrophysics Data System (ADS)

Noskov, M. D.; Malinovski, A. S.; Cooke, C. M.; Wright, K. A.; Schwab, A. J.

2002-11-01

The electrical discharge of volume distributed space charge in poly(methylmethacrylate) (PMMA) has been investigated both experimentally and by computer simulation. The experimental space charge was implanted in dielectric samples by exposure to a monoenergetic electron beam of 3 MeV. Electrical breakdown through the implanted space charge region within the sample was initiated by a local electric field enhancement applied to the sample surface. A stochastic-deterministic dynamic model for electrical discharge was developed and used in a computer simulation of these breakdowns. The model employs stochastic rules to describe the physical growth of the discharge channels, and deterministic laws to describe the electric field, the charge, and energy dynamics within the discharge channels and the dielectric. Simulated spatial-temporal and current characteristics of the expanding discharge structure during physical growth are quantitatively compared with the experimental data to confirm the discharge model. It was found that a single fixed set of physically based dielectric parameter values was adequate to simulate the complete family of experimental space charge discharges in PMMA. It is proposed that such a set of parameters also provides a useful means to quantify the breakdown properties of other dielectrics.

Application of technology developed for flight simulation at NASA. Langley Research Center

NASA Technical Reports Server (NTRS)

Cleveland, Jeff I., II

1991-01-01

In order to meet the stringent time-critical requirements for real-time man-in-the-loop flight simulation, computer processing operations including mathematical model computation and data input/output to the simulators must be deterministic and be completed in as short a time as possible. Personnel at NASA's Langley Research Center are currently developing the use of supercomputers for simulation mathematical model computation for real-time simulation. This, coupled with the use of an open systems software architecture, will advance the state-of-the-art in real-time flight simulation.

Optimal Estimation with Two Process Models and No Measurements

DTIC Science & Technology

2015-08-01

models will be lost if either of the models includes deterministic modeling errors. 12 5. References and Notes 1. Brown RG, Hwang PYC. Introduction to...independent process models when no measurements are present. The observer follows a derivation similar to that of the discrete time Kalman filter. A simulation...discrete time Kalman filter. A simulation example is provided in which a process model based on the dynamics of a ballistic projectile is blended with an

Chaos in plasma simulation and experiment

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

Watts, C.; Newman, D.E.; Sprott, J.C.

1993-09-01

We investigate the possibility that chaos and simple determinism are governing the dynamics of reversed field pinch (RFP) plasmas using data from both numerical simulations and experiment. A large repertoire of nonlinear analysis techniques is used to identify low dimensional chaos. These tools include phase portraits and Poincard sections, correlation dimension, the spectrum of Lyapunov exponents and short term predictability. In addition, nonlinear noise reduction techniques are applied to the experimental data in an attempt to extract any underlying deterministic dynamics. Two model systems are used to simulate the plasma dynamics. These are -the DEBS code, which models global RFPmore » dynamics, and the dissipative trapped electron mode (DTEM) model, which models drift wave turbulence. Data from both simulations show strong indications of low,dimensional chaos and simple determinism. Experimental data were obtained from the Madison Symmetric Torus RFP and consist of a wide array of both global and local diagnostic signals. None of the signals shows any indication of low dimensional chaos or other simple determinism. Moreover, most of the analysis tools indicate the experimental system is very high dimensional with properties similar to noise. Nonlinear noise reduction is unsuccessful at extracting an underlying deterministic system.« less

Effect of nonlinearity in hybrid kinetic Monte Carlo-continuum models.

PubMed

Balter, Ariel; Lin, Guang; Tartakovsky, Alexandre M

2012-01-01

Recently there has been interest in developing efficient ways to model heterogeneous surface reactions with hybrid computational models that couple a kinetic Monte Carlo (KMC) model for a surface to a finite-difference model for bulk diffusion in a continuous domain. We consider two representative problems that validate a hybrid method and show that this method captures the combined effects of nonlinearity and stochasticity. We first validate a simple deposition-dissolution model with a linear rate showing that the KMC-continuum hybrid agrees with both a fully deterministic model and its analytical solution. We then study a deposition-dissolution model including competitive adsorption, which leads to a nonlinear rate, and show that in this case the KMC-continuum hybrid and fully deterministic simulations do not agree. However, we are able to identify the difference as a natural result of the stochasticity coming from the KMC surface process. Because KMC captures inherent fluctuations, we consider it to be more realistic than a purely deterministic model. Therefore, we consider the KMC-continuum hybrid to be more representative of a real system.

Effect of Nonlinearity in Hybrid Kinetic Monte Carlo-Continuum Models

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

Balter, Ariel I.; Lin, Guang; Tartakovsky, Alexandre M.

2012-04-23

Recently there has been interest in developing efficient ways to model heterogeneous surface reactions with hybrid computational models that couple a KMC model for a surface to a finite difference model for bulk diffusion in a continuous domain. We consider two representative problems that validate a hybrid method and also show that this method captures the combined effects of nonlinearity and stochasticity. We first validate a simple deposition/dissolution model with a linear rate showing that the KMC-continuum hybrid agrees with both a fully deterministic model and its analytical solution. We then study a deposition/dissolution model including competitive adsorption, which leadsmore » to a nonlinear rate, and show that, in this case, the KMC-continuum hybrid and fully deterministic simulations do not agree. However, we are able to identify the difference as a natural result of the stochasticity coming from the KMC surface process. Because KMC captures inherent fluctuations, we consider it to be more realistic than a purely deterministic model. Therefore, we consider the KMC-continuum hybrid to be more representative of a real system.« less

Identifying variably saturated water-flow patterns in a steep hillslope under intermittent heavy rainfall

USGS Publications Warehouse

El-Kadi, A. I.; Torikai, J.D.

2001-01-01

The objective of this paper is to identify water-flow patterns in part of an active landslide, through the use of numerical simulations and data obtained during a field study. The approaches adopted include measuring rainfall events and pore-pressure responses in both saturated and unsaturated soils at the site. To account for soil variability, the Richards equation is solved within deterministic and stochastic frameworks. The deterministic simulations considered average water-retention data, adjusted retention data to account for stones or cobbles, retention functions for a heterogeneous pore structure, and continuous retention functions for preferential flow. The stochastic simulations applied the Monte Carlo approach which considers statistical distribution and autocorrelation of the saturated conductivity and its cross correlation with the retention function. Although none of the models is capable of accurately predicting field measurements, appreciable improvement in accuracy was attained using stochastic, preferential flow, and heterogeneous pore-structure models. For the current study, continuum-flow models provide reasonable accuracy for practical purposes, although they are expected to be less accurate than multi-domain preferential flow models.

Stochastic flux analysis of chemical reaction networks

PubMed Central

2013-01-01

Background Chemical reaction networks provide an abstraction scheme for a broad range of models in biology and ecology. The two common means for simulating these networks are the deterministic and the stochastic approaches. The traditional deterministic approach, based on differential equations, enjoys a rich set of analysis techniques, including a treatment of reaction fluxes. However, the discrete stochastic simulations, which provide advantages in some cases, lack a quantitative treatment of network fluxes. Results We describe a method for flux analysis of chemical reaction networks, where flux is given by the flow of species between reactions in stochastic simulations of the network. Extending discrete event simulation algorithms, our method constructs several data structures, and thereby reveals a variety of statistics about resource creation and consumption during the simulation. We use these structures to quantify the causal interdependence and relative importance of the reactions at arbitrary time intervals with respect to the network fluxes. This allows us to construct reduced networks that have the same flux-behavior, and compare these networks, also with respect to their time series. We demonstrate our approach on an extended example based on a published ODE model of the same network, that is, Rho GTP-binding proteins, and on other models from biology and ecology. Conclusions We provide a fully stochastic treatment of flux analysis. As in deterministic analysis, our method delivers the network behavior in terms of species transformations. Moreover, our stochastic analysis can be applied, not only at steady state, but at arbitrary time intervals, and used to identify the flow of specific species between specific reactions. Our cases study of Rho GTP-binding proteins reveals the role played by the cyclic reverse fluxes in tuning the behavior of this network. PMID:24314153

Stochastic flux analysis of chemical reaction networks.

PubMed

Kahramanoğulları, Ozan; Lynch, James F

2013-12-07

Chemical reaction networks provide an abstraction scheme for a broad range of models in biology and ecology. The two common means for simulating these networks are the deterministic and the stochastic approaches. The traditional deterministic approach, based on differential equations, enjoys a rich set of analysis techniques, including a treatment of reaction fluxes. However, the discrete stochastic simulations, which provide advantages in some cases, lack a quantitative treatment of network fluxes. We describe a method for flux analysis of chemical reaction networks, where flux is given by the flow of species between reactions in stochastic simulations of the network. Extending discrete event simulation algorithms, our method constructs several data structures, and thereby reveals a variety of statistics about resource creation and consumption during the simulation. We use these structures to quantify the causal interdependence and relative importance of the reactions at arbitrary time intervals with respect to the network fluxes. This allows us to construct reduced networks that have the same flux-behavior, and compare these networks, also with respect to their time series. We demonstrate our approach on an extended example based on a published ODE model of the same network, that is, Rho GTP-binding proteins, and on other models from biology and ecology. We provide a fully stochastic treatment of flux analysis. As in deterministic analysis, our method delivers the network behavior in terms of species transformations. Moreover, our stochastic analysis can be applied, not only at steady state, but at arbitrary time intervals, and used to identify the flow of specific species between specific reactions. Our cases study of Rho GTP-binding proteins reveals the role played by the cyclic reverse fluxes in tuning the behavior of this network.

GAPPARD: a computationally efficient method of approximating gap-scale disturbance in vegetation models

NASA Astrophysics Data System (ADS)

Scherstjanoi, M.; Kaplan, J. O.; Thürig, E.; Lischke, H.

2013-02-01

Models of vegetation dynamics that are designed for application at spatial scales larger than individual forest gaps suffer from several limitations. Typically, either a population average approximation is used that results in unrealistic tree allometry and forest stand structure, or models have a high computational demand because they need to simulate both a series of age-based cohorts and a number of replicate patches to account for stochastic gap-scale disturbances. The detail required by the latter method increases the number of calculations by two to three orders of magnitude compared to the less realistic population average approach. In an effort to increase the efficiency of dynamic vegetation models without sacrificing realism, and to explore patterns of spatial scaling in forests, we developed a new method for simulating stand-replacing disturbances that is both accurate and 10-50x faster than approaches that use replicate patches. The GAPPARD (approximating GAP model results with a Probabilistic Approach to account for stand Replacing Disturbances) method works by postprocessing the output of deterministic, undisturbed simulations of a cohort-based vegetation model by deriving the distribution of patch ages at any point in time on the basis of a disturbance probability. With this distribution, the expected value of any output variable can be calculated from the output values of the deterministic undisturbed run at the time corresponding to the patch age. To account for temporal changes in model forcing, e.g., as a result of climate change, GAPPARD performs a series of deterministic simulations and interpolates between the results in the postprocessing step. We integrated the GAPPARD method in the forest models LPJ-GUESS and TreeM-LPJ, and evaluated these in a series of simulations along an altitudinal transect of an inner-alpine valley. With GAPPARD applied to LPJ-GUESS results were insignificantly different from the output of the original model LPJ-GUESS using 100 replicate patches, but simulation time was reduced by approximately the factor 10. Our new method is therefore highly suited rapidly approximating LPJ-GUESS results, and provides the opportunity for future studies over large spatial domains, allows easier parameterization of tree species, faster identification of areas of interesting simulation results, and comparisons with large-scale datasets and forest models.

ASSESSMENT OF TWO PHYSICALLY BASED WATERSHED MODELS BASED ON THEIR PERFORMANCES OF SIMULATING SEDIMENT MOVEMENT OVER SMALL WATERSHEDS

EPA Science Inventory

Abstract: Two physically based and deterministic models, CASC2-D and KINEROS are evaluated and compared for their performances on modeling sediment movement on a small agricultural watershed over several events. Each model has different conceptualization of a watershed. CASC...

PARADIGM USING JOINT DETERMINISTIC GRID MODELING AND SUB-GRID VARIABILITY STOCHASTIC DESCRIPTION AS A TEMPLATE FOR MODEL EVALUATION

EPA Science Inventory

The goal of achieving verisimilitude of air quality simulations to observations is problematic. Chemical transport models such as the Community Multi-Scale Air Quality (CMAQ) modeling system produce volume averages of pollutant concentration fields. When grid sizes are such tha...

The Stochastic Multi-strain Dengue Model: Analysis of the Dynamics

NASA Astrophysics Data System (ADS)

Aguiar, Maíra; Stollenwerk, Nico; Kooi, Bob W.

2011-09-01

Dengue dynamics is well known to be particularly complex with large fluctuations of disease incidences. An epidemic multi-strain model motivated by dengue fever epidemiology shows deterministic chaos in wide parameter regions. The addition of seasonal forcing, mimicking the vectorial dynamics, and a low import of infected individuals, which is realistic in the dynamics of infectious diseases epidemics show complex dynamics and qualitatively a good agreement between empirical DHF monitoring data and the obtained model simulation. The addition of noise can explain the fluctuations observed in the empirical data and for large enough population size, the stochastic system can be well described by the deterministic skeleton.

Stochastic Analysis and Probabilistic Downscaling of Soil Moisture

NASA Astrophysics Data System (ADS)

Deshon, J. P.; Niemann, J. D.; Green, T. R.; Jones, A. S.

2017-12-01

Soil moisture is a key variable for rainfall-runoff response estimation, ecological and biogeochemical flux estimation, and biodiversity characterization, each of which is useful for watershed condition assessment. These applications require not only accurate, fine-resolution soil-moisture estimates but also confidence limits on those estimates and soil-moisture patterns that exhibit realistic statistical properties (e.g., variance and spatial correlation structure). The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model downscales coarse-resolution (9-40 km) soil moisture from satellite remote sensing or land-surface models to produce fine-resolution (10-30 m) estimates. The model was designed to produce accurate deterministic soil-moisture estimates at multiple points, but the resulting patterns do not reproduce the variance or spatial correlation of observed soil-moisture patterns. The primary objective of this research is to generalize the EMT+VS model to produce a probability density function (pdf) for soil moisture at each fine-resolution location and time. Each pdf has a mean that is equal to the deterministic soil-moisture estimate, and the pdf can be used to quantify the uncertainty in the soil-moisture estimates and to simulate soil-moisture patterns. Different versions of the generalized model are hypothesized based on how uncertainty enters the model, whether the uncertainty is additive or multiplicative, and which distributions describe the uncertainty. These versions are then tested by application to four catchments with detailed soil-moisture observations (Tarrawarra, Satellite Station, Cache la Poudre, and Nerrigundah). The performance of the generalized models is evaluated by comparing the statistical properties of the simulated soil-moisture patterns to those of the observations and the deterministic EMT+VS model. The versions of the generalized EMT+VS model with normally distributed stochastic components produce soil-moisture patterns with more realistic statistical properties than the deterministic model. Additionally, the results suggest that the variance and spatial correlation of the stochastic soil-moisture variations do not vary consistently with the spatial-average soil moisture.

Non-Deterministic Modelling of Food-Web Dynamics

PubMed Central

Planque, Benjamin; Lindstrøm, Ulf; Subbey, Sam

2014-01-01

A novel approach to model food-web dynamics, based on a combination of chance (randomness) and necessity (system constraints), was presented by Mullon et al. in 2009. Based on simulations for the Benguela ecosystem, they concluded that observed patterns of ecosystem variability may simply result from basic structural constraints within which the ecosystem functions. To date, and despite the importance of these conclusions, this work has received little attention. The objective of the present paper is to replicate this original model and evaluate the conclusions that were derived from its simulations. For this purpose, we revisit the equations and input parameters that form the structure of the original model and implement a comparable simulation model. We restate the model principles and provide a detailed account of the model structure, equations, and parameters. Our model can reproduce several ecosystem dynamic patterns: pseudo-cycles, variation and volatility, diet, stock-recruitment relationships, and correlations between species biomass series. The original conclusions are supported to a large extent by the current replication of the model. Model parameterisation and computational aspects remain difficult and these need to be investigated further. Hopefully, the present contribution will make this approach available to a larger research community and will promote the use of non-deterministic-network-dynamics models as ‘null models of food-webs’ as originally advocated. PMID:25299245

From Weakly Chaotic Dynamics to Deterministic Subdiffusion via Copula Modeling

NASA Astrophysics Data System (ADS)

Nazé, Pierre

2018-03-01

Copula modeling consists in finding a probabilistic distribution, called copula, whereby its coupling with the marginal distributions of a set of random variables produces their joint distribution. The present work aims to use this technique to connect the statistical distributions of weakly chaotic dynamics and deterministic subdiffusion. More precisely, we decompose the jumps distribution of Geisel-Thomae map into a bivariate one and determine the marginal and copula distributions respectively by infinite ergodic theory and statistical inference techniques. We verify therefore that the characteristic tail distribution of subdiffusion is an extreme value copula coupling Mittag-Leffler distributions. We also present a method to calculate the exact copula and joint distributions in the case where weakly chaotic dynamics and deterministic subdiffusion statistical distributions are already known. Numerical simulations and consistency with the dynamical aspects of the map support our results.

Probabilistic Modeling of the Renal Stone Formation Module

NASA Technical Reports Server (NTRS)

Best, Lauren M.; Myers, Jerry G.; Goodenow, Debra A.; McRae, Michael P.; Jackson, Travis C.

2013-01-01

The Integrated Medical Model (IMM) is a probabilistic tool, used in mission planning decision making and medical systems risk assessments. The IMM project maintains a database of over 80 medical conditions that could occur during a spaceflight, documenting an incidence rate and end case scenarios for each. In some cases, where observational data are insufficient to adequately define the inflight medical risk, the IMM utilizes external probabilistic modules to model and estimate the event likelihoods. One such medical event of interest is an unpassed renal stone. Due to a high salt diet and high concentrations of calcium in the blood (due to bone depletion caused by unloading in the microgravity environment) astronauts are at a considerable elevated risk for developing renal calculi (nephrolithiasis) while in space. Lack of observed incidences of nephrolithiasis has led HRP to initiate the development of the Renal Stone Formation Module (RSFM) to create a probabilistic simulator capable of estimating the likelihood of symptomatic renal stone presentation in astronauts on exploration missions. The model consists of two major parts. The first is the probabilistic component, which utilizes probability distributions to assess the range of urine electrolyte parameters and a multivariate regression to transform estimated crystal density and size distributions to the likelihood of the presentation of nephrolithiasis symptoms. The second is a deterministic physical and chemical model of renal stone growth in the kidney developed by Kassemi et al. The probabilistic component of the renal stone model couples the input probability distributions describing the urine chemistry, astronaut physiology, and system parameters with the physical and chemical outputs and inputs to the deterministic stone growth model. These two parts of the model are necessary to capture the uncertainty in the likelihood estimate. The model will be driven by Monte Carlo simulations, continuously randomly sampling the probability distributions of the electrolyte concentrations and system parameters that are inputs into the deterministic model. The total urine chemistry concentrations are used to determine the urine chemistry activity using the Joint Expert Speciation System (JESS), a biochemistry model. Information used from JESS is then fed into the deterministic growth model. Outputs from JESS and the deterministic model are passed back to the probabilistic model where a multivariate regression is used to assess the likelihood of a stone forming and the likelihood of a stone requiring clinical intervention. The parameters used to determine to quantify these risks include: relative supersaturation (RS) of calcium oxalate, citrate/calcium ratio, crystal number density, total urine volume, pH, magnesium excretion, maximum stone width, and ureteral location. Methods and Validation: The RSFM is designed to perform a Monte Carlo simulation to generate probability distributions of clinically significant renal stones, as well as provide an associated uncertainty in the estimate. Initially, early versions will be used to test integration of the components and assess component validation and verification (V&V), with later versions used to address questions regarding design reference mission scenarios. Once integrated with the deterministic component, the credibility assessment of the integrated model will follow NASA STD 7009 requirements.

Subspace algorithms for identifying separable-in-denominator 2D systems with deterministic-stochastic inputs

NASA Astrophysics Data System (ADS)

Ramos, José A.; Mercère, Guillaume

2016-12-01

In this paper, we present an algorithm for identifying two-dimensional (2D) causal, recursive and separable-in-denominator (CRSD) state-space models in the Roesser form with deterministic-stochastic inputs. The algorithm implements the N4SID, PO-MOESP and CCA methods, which are well known in the literature on 1D system identification, but here we do so for the 2D CRSD Roesser model. The algorithm solves the 2D system identification problem by maintaining the constraint structure imposed by the problem (i.e. Toeplitz and Hankel) and computes the horizontal and vertical system orders, system parameter matrices and covariance matrices of a 2D CRSD Roesser model. From a computational point of view, the algorithm has been presented in a unified framework, where the user can select which of the three methods to use. Furthermore, the identification task is divided into three main parts: (1) computing the deterministic horizontal model parameters, (2) computing the deterministic vertical model parameters and (3) computing the stochastic components. Specific attention has been paid to the computation of a stabilised Kalman gain matrix and a positive real solution when required. The efficiency and robustness of the unified algorithm have been demonstrated via a thorough simulation example.

Hydraulic tomography of discrete networks of conduits and fractures in a karstic aquifer by using a deterministic inversion algorithm

NASA Astrophysics Data System (ADS)

Fischer, P.; Jardani, A.; Lecoq, N.

2018-02-01

In this paper, we present a novel inverse modeling method called Discrete Network Deterministic Inversion (DNDI) for mapping the geometry and property of the discrete network of conduits and fractures in the karstified aquifers. The DNDI algorithm is based on a coupled discrete-continuum concept to simulate numerically water flows in a model and a deterministic optimization algorithm to invert a set of observed piezometric data recorded during multiple pumping tests. In this method, the model is partioned in subspaces piloted by a set of parameters (matrix transmissivity, and geometry and equivalent transmissivity of the conduits) that are considered as unknown. In this way, the deterministic optimization process can iteratively correct the geometry of the network and the values of the properties, until it converges to a global network geometry in a solution model able to reproduce the set of data. An uncertainty analysis of this result can be performed from the maps of posterior uncertainties on the network geometry or on the property values. This method has been successfully tested for three different theoretical and simplified study cases with hydraulic responses data generated from hypothetical karstic models with an increasing complexity of the network geometry, and of the matrix heterogeneity.

Oscillatory regulation of Hes1: Discrete stochastic delay modelling and simulation.

PubMed

Barrio, Manuel; Burrage, Kevin; Leier, André; Tian, Tianhai

2006-09-08

Discrete stochastic simulations are a powerful tool for understanding the dynamics of chemical kinetics when there are small-to-moderate numbers of certain molecular species. In this paper we introduce delays into the stochastic simulation algorithm, thus mimicking delays associated with transcription and translation. We then show that this process may well explain more faithfully than continuous deterministic models the observed sustained oscillations in expression levels of hes1 mRNA and Hes1 protein.

What Can We Learn from a Simple Physics-Based Earthquake Simulator?

NASA Astrophysics Data System (ADS)

Artale Harris, Pietro; Marzocchi, Warner; Melini, Daniele

2018-03-01

Physics-based earthquake simulators are becoming a popular tool to investigate on the earthquake occurrence process. So far, the development of earthquake simulators is commonly led by the approach "the more physics, the better". However, this approach may hamper the comprehension of the outcomes of the simulator; in fact, within complex models, it may be difficult to understand which physical parameters are the most relevant to the features of the seismic catalog at which we are interested. For this reason, here, we take an opposite approach and analyze the behavior of a purposely simple earthquake simulator applied to a set of California faults. The idea is that a simple simulator may be more informative than a complex one for some specific scientific objectives, because it is more understandable. Our earthquake simulator has three main components: the first one is a realistic tectonic setting, i.e., a fault data set of California; the second is the application of quantitative laws for earthquake generation on each single fault, and the last is the fault interaction modeling through the Coulomb Failure Function. The analysis of this simple simulator shows that: (1) the short-term clustering can be reproduced by a set of faults with an almost periodic behavior, which interact according to a Coulomb failure function model; (2) a long-term behavior showing supercycles of the seismic activity exists only in a markedly deterministic framework, and quickly disappears introducing a small degree of stochasticity on the recurrence of earthquakes on a fault; (3) faults that are strongly coupled in terms of Coulomb failure function model are synchronized in time only in a marked deterministic framework, and as before, such a synchronization disappears introducing a small degree of stochasticity on the recurrence of earthquakes on a fault. Overall, the results show that even in a simple and perfectly known earthquake occurrence world, introducing a small degree of stochasticity may blur most of the deterministic time features, such as long-term trend and synchronization among nearby coupled faults.

The Validity of Quasi-Steady-State Approximations in Discrete Stochastic Simulations

PubMed Central

Kim, Jae Kyoung; Josić, Krešimir; Bennett, Matthew R.

2014-01-01

In biochemical networks, reactions often occur on disparate timescales and can be characterized as either fast or slow. The quasi-steady-state approximation (QSSA) utilizes timescale separation to project models of biochemical networks onto lower-dimensional slow manifolds. As a result, fast elementary reactions are not modeled explicitly, and their effect is captured by nonelementary reaction-rate functions (e.g., Hill functions). The accuracy of the QSSA applied to deterministic systems depends on how well timescales are separated. Recently, it has been proposed to use the nonelementary rate functions obtained via the deterministic QSSA to define propensity functions in stochastic simulations of biochemical networks. In this approach, termed the stochastic QSSA, fast reactions that are part of nonelementary reactions are not simulated, greatly reducing computation time. However, it is unclear when the stochastic QSSA provides an accurate approximation of the original stochastic simulation. We show that, unlike the deterministic QSSA, the validity of the stochastic QSSA does not follow from timescale separation alone, but also depends on the sensitivity of the nonelementary reaction rate functions to changes in the slow species. The stochastic QSSA becomes more accurate when this sensitivity is small. Different types of QSSAs result in nonelementary functions with different sensitivities, and the total QSSA results in less sensitive functions than the standard or the prefactor QSSA. We prove that, as a result, the stochastic QSSA becomes more accurate when nonelementary reaction functions are obtained using the total QSSA. Our work provides an apparently novel condition for the validity of the QSSA in stochastic simulations of biochemical reaction networks with disparate timescales. PMID:25099817

Chaotic Lagrangian models for turbulent relative dispersion.

PubMed

Lacorata, Guglielmo; Vulpiani, Angelo

2017-04-01

A deterministic multiscale dynamical system is introduced and discussed as a prototype model for relative dispersion in stationary, homogeneous, and isotropic turbulence. Unlike stochastic diffusion models, here trajectory transport and mixing properties are entirely controlled by Lagrangian chaos. The anomalous "sweeping effect," a known drawback common to kinematic simulations, is removed through the use of quasi-Lagrangian coordinates. Lagrangian dispersion statistics of the model are accurately analyzed by computing the finite-scale Lyapunov exponent (FSLE), which is the optimal measure of the scaling properties of dispersion. FSLE scaling exponents provide a severe test to decide whether model simulations are in agreement with theoretical expectations and/or observation. The results of our numerical experiments cover a wide range of "Reynolds numbers" and show that chaotic deterministic flows can be very efficient, and numerically low-cost, models of turbulent trajectories in stationary, homogeneous, and isotropic conditions. The mathematics of the model is relatively simple, and, in a geophysical context, potential applications may regard small-scale parametrization issues in general circulation models, mixed layer, and/or boundary layer turbulence models as well as Lagrangian predictability studies.

Chaotic Lagrangian models for turbulent relative dispersion

NASA Astrophysics Data System (ADS)

Lacorata, Guglielmo; Vulpiani, Angelo

2017-04-01

A deterministic multiscale dynamical system is introduced and discussed as a prototype model for relative dispersion in stationary, homogeneous, and isotropic turbulence. Unlike stochastic diffusion models, here trajectory transport and mixing properties are entirely controlled by Lagrangian chaos. The anomalous "sweeping effect," a known drawback common to kinematic simulations, is removed through the use of quasi-Lagrangian coordinates. Lagrangian dispersion statistics of the model are accurately analyzed by computing the finite-scale Lyapunov exponent (FSLE), which is the optimal measure of the scaling properties of dispersion. FSLE scaling exponents provide a severe test to decide whether model simulations are in agreement with theoretical expectations and/or observation. The results of our numerical experiments cover a wide range of "Reynolds numbers" and show that chaotic deterministic flows can be very efficient, and numerically low-cost, models of turbulent trajectories in stationary, homogeneous, and isotropic conditions. The mathematics of the model is relatively simple, and, in a geophysical context, potential applications may regard small-scale parametrization issues in general circulation models, mixed layer, and/or boundary layer turbulence models as well as Lagrangian predictability studies.

The tug-of-war behavior of a Brownian particle in an asymmetric double optical trap with stochastic fluctuations

NASA Astrophysics Data System (ADS)

Long, Fei; Zhu, Jia-Pei

2018-07-01

A Brownian particle optically trapped in an asymmetric double potential surrounded by a thermal bath was simulated. Under the cooperative action of the resultant deterministic optical force and the stochastic fluctuations of the thermal bath, the confined particle undergoes Kramers transition, and oscillates between the two traps with a probability of trap occupancy that is asymmetrically distributed about the midpoint. The simulation results obtained at different temperatures indicate that the oscillation behavior of the particle can be treated as the result of a tug-of-war game played between the resultant deterministic force and the random force. We also employ a bistable model to explain the observed phenomena.

Bayesian Estimation of the DINA Model with Gibbs Sampling

ERIC Educational Resources Information Center

Culpepper, Steven Andrew

2015-01-01

A Bayesian model formulation of the deterministic inputs, noisy "and" gate (DINA) model is presented. Gibbs sampling is employed to simulate from the joint posterior distribution of item guessing and slipping parameters, subject attribute parameters, and latent class probabilities. The procedure extends concepts in Béguin and Glas,…

Insights into the deterministic skill of air quality ensembles from the analysis of AQMEII data

EPA Science Inventory

Simulations from chemical weather models are subject to uncertainties in the input data (e.g. emission inventory, initial and boundary conditions) as well as those intrinsic to the model (e.g. physical parameterization, chemical mechanism). Multi-model ensembles can improve the f...

Application of Stochastic and Deterministic Approaches to Modeling Interstellar Chemistry

NASA Astrophysics Data System (ADS)

Pei, Yezhe

This work is about simulations of interstellar chemistry using the deterministic rate equation (RE) method and the stochastic moment equation (ME) method. Primordial metal-poor interstellar medium (ISM) is of our interest and the socalled “Population-II” stars could have been formed in this environment during the “Epoch of Reionization” in the baby universe. We build a gas phase model using the RE scheme to describe the ionization-powered interstellar chemistry. We demonstrate that OH replaces CO as the most abundant metal-bearing molecule in such interstellar clouds of the early universe. Grain surface reactions play an important role in the studies of astrochemistry. But the lack of an accurate yet effective simulation method still presents a challenge, especially for large, practical gas-grain system. We develop a hybrid scheme of moment equations and rate equations (HMR) for large gas-grain network to model astrochemical reactions in the interstellar clouds. Specifically, we have used a large chemical gas-grain model, with stochastic moment equations to treat the surface chemistry and deterministic rate equations to treat the gas phase chemistry, to simulate astrochemical systems as of the ISM in the Milky Way, the Large Magellanic Cloud (LMC) and Small Magellanic Cloud (SMC). We compare the results to those of pure rate equations and modified rate equations and present a discussion about how moment equations improve our theoretical modeling and how the abundances of the assorted species are changed by varied metallicity. We also model the observed composition of H2O, CO and CO2 ices toward Young Stellar Objects in the LMC and show that the HMR method gives a better match to the observation than the pure RE method.

A split-step method to include electron–electron collisions via Monte Carlo in multiple rate equation simulations

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

Huthmacher, Klaus; Molberg, Andreas K.; Rethfeld, Bärbel

2016-10-01

A split-step numerical method for calculating ultrafast free-electron dynamics in dielectrics is introduced. The two split steps, independently programmed in C++11 and FORTRAN 2003, are interfaced via the presented open source wrapper. The first step solves a deterministic extended multi-rate equation for the ionization, electron–phonon collisions, and single photon absorption by free-carriers. The second step is stochastic and models electron–electron collisions using Monte-Carlo techniques. This combination of deterministic and stochastic approaches is a unique and efficient method of calculating the nonlinear dynamics of 3D materials exposed to high intensity ultrashort pulses. Results from simulations solving the proposed model demonstrate howmore » electron–electron scattering relaxes the non-equilibrium electron distribution on the femtosecond time scale.« less

Identifying influences on model uncertainty: an application using a forest carbon budget model

Treesearch

James E. Smith; Linda S. Heath

2001-01-01

Uncertainty is an important consideration for both developers and users of environmental simulation models. Establishing quantitative estimates of uncertainty for deterministic models can be difficult when the underlying bases for such information are scarce. We demonstrate an application of probabilistic uncertainty analysis that provides for refinements in...

Compartmental and Spatial Rule-Based Modeling with Virtual Cell.

PubMed

Blinov, Michael L; Schaff, James C; Vasilescu, Dan; Moraru, Ion I; Bloom, Judy E; Loew, Leslie M

2017-10-03

In rule-based modeling, molecular interactions are systematically specified in the form of reaction rules that serve as generators of reactions. This provides a way to account for all the potential molecular complexes and interactions among multivalent or multistate molecules. Recently, we introduced rule-based modeling into the Virtual Cell (VCell) modeling framework, permitting graphical specification of rules and merger of networks generated automatically (using the BioNetGen modeling engine) with hand-specified reaction networks. VCell provides a number of ordinary differential equation and stochastic numerical solvers for single-compartment simulations of the kinetic systems derived from these networks, and agent-based network-free simulation of the rules. In this work, compartmental and spatial modeling of rule-based models has been implemented within VCell. To enable rule-based deterministic and stochastic spatial simulations and network-free agent-based compartmental simulations, the BioNetGen and NFSim engines were each modified to support compartments. In the new rule-based formalism, every reactant and product pattern and every reaction rule are assigned locations. We also introduce the rule-based concept of molecular anchors. This assures that any species that has a molecule anchored to a predefined compartment will remain in this compartment. Importantly, in addition to formulation of compartmental models, this now permits VCell users to seamlessly connect reaction networks derived from rules to explicit geometries to automatically generate a system of reaction-diffusion equations. These may then be simulated using either the VCell partial differential equations deterministic solvers or the Smoldyn stochastic simulator. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Jitter and phase noise of ADPLL due to PSN with deterministic frequency

NASA Astrophysics Data System (ADS)

Deng, Xiaoying; Yang, Jun; Wu, Jianhui

2011-09-01

In this article, jitter and phase noise of all-digital phase-locked loop due to power supply noise (PSN) with deterministic frequency are analysed. It leads to the conclusion that jitter and phase noise heavily depend on the noise frequency. Compared with jitter, phase noise is much less affected by the deterministic PSN. Our method is utilised to study a CMOS ADPLL designed and simulated in SMIC 0.13 µm standard CMOS process. A comparison between the results obtained by our method and those obtained by simulation and measurement proves the accuracy of the predicted model. When the digital controlled oscillator was corrupted by PSN with 100 mVpk-pk, the measured jitters were 33.9 ps at the rate of fG = 192 MHz and 148.5 ps at the rate of fG = 40 MHz. However, the measured phase noise was exactly the same except for two impulses appearing at 192 and 40 MHz, respectively.

Simulation of earthquake ground motions in the eastern United States using deterministic physics‐based and site‐based stochastic approaches

USGS Publications Warehouse

Rezaeian, Sanaz; Hartzell, Stephen; Sun, Xiaodan; Mendoza, Carlos

2017-01-01

Earthquake ground‐motion recordings are scarce in the central and eastern United States (CEUS) for large‐magnitude events and at close distances. We use two different simulation approaches, a deterministic physics‐based method and a site‐based stochastic method, to simulate ground motions over a wide range of magnitudes. Drawing on previous results for the modeling of recordings from the 2011 Mw 5.8 Mineral, Virginia, earthquake and using the 2001 Mw 7.6 Bhuj, India, earthquake as a tectonic analog for a large magnitude CEUS event, we are able to calibrate the two simulation methods over this magnitude range. Both models show a good fit to the Mineral and Bhuj observations from 0.1 to 10 Hz. Model parameters are then adjusted to obtain simulations for Mw 6.5, 7.0, and 7.6 events in the CEUS. Our simulations are compared with the 2014 U.S. Geological Survey weighted combination of existing ground‐motion prediction equations in the CEUS. The physics‐based simulations show comparable response spectral amplitudes and a fairly similar attenuation with distance. The site‐based stochastic simulations suggest a slightly faster attenuation of the response spectral amplitudes with distance for larger magnitude events and, as a result, slightly lower amplitudes at distances greater than 200 km. Both models are plausible alternatives and, given the few available data points in the CEUS, can be used to represent the epistemic uncertainty in modeling of postulated CEUS large‐magnitude events.

Modeling the spreading of large-scale wildland fires

Treesearch

Mohamed Drissi

2015-01-01

The objective of the present study is twofold. First, the last developments and validation results of a hybrid model designed to simulate fire patterns in heterogeneous landscapes are presented. The model combines the features of a stochastic small-world network model with those of a deterministic semi-physical model of the interaction between burning and non-burning...

Oscillatory Regulation of Hes1: Discrete Stochastic Delay Modelling and Simulation

PubMed Central

Barrio, Manuel; Burrage, Kevin; Leier, André; Tian, Tianhai

2006-01-01

Discrete stochastic simulations are a powerful tool for understanding the dynamics of chemical kinetics when there are small-to-moderate numbers of certain molecular species. In this paper we introduce delays into the stochastic simulation algorithm, thus mimicking delays associated with transcription and translation. We then show that this process may well explain more faithfully than continuous deterministic models the observed sustained oscillations in expression levels of hes1 mRNA and Hes1 protein. PMID:16965175

ML-Space: Hybrid Spatial Gillespie and Particle Simulation of Multi-Level Rule-Based Models in Cell Biology.

PubMed

Bittig, Arne T; Uhrmacher, Adelinde M

2017-01-01

Spatio-temporal dynamics of cellular processes can be simulated at different levels of detail, from (deterministic) partial differential equations via the spatial Stochastic Simulation algorithm to tracking Brownian trajectories of individual particles. We present a spatial simulation approach for multi-level rule-based models, which includes dynamically hierarchically nested cellular compartments and entities. Our approach ML-Space combines discrete compartmental dynamics, stochastic spatial approaches in discrete space, and particles moving in continuous space. The rule-based specification language of ML-Space supports concise and compact descriptions of models and to adapt the spatial resolution of models easily.

Estimate Tsunami Flow Conditions and Large-Debris Tracks for the Design of Coastal Infrastructures along Coastlines of the U.S. Pacific Northwest

NASA Astrophysics Data System (ADS)

Wei, Y.; Thomas, S.; Zhou, H.; Arcas, D.; Titov, V. V.

2017-12-01

The increasing potential tsunami hazards pose great challenges for infrastructures along the coastlines of the U.S. Pacific Northwest. Tsunami impact at a coastal site is usually assessed from deterministic scenarios based on 10,000 years of geological records in the Cascadia Subduction Zone (CSZ). Aside from these deterministic methods, the new ASCE 7-16 tsunami provisions provide engineering design criteria of tsunami loads on buildings based on a probabilistic approach. This work develops a site-specific model near Newport, OR using high-resolution grids, and compute tsunami inundation depth and velocities at the study site resulted from credible probabilistic and deterministic earthquake sources in the Cascadia Subduction Zone. Three Cascadia scenarios, two deterministic scenarios, XXL1 and L1, and a 2,500-yr probabilistic scenario compliant with the new ASCE 7-16 standard, are simulated using combination of a depth-averaged shallow water model for offshore propagation and a Boussinesq-type model for onshore inundation. We speculate on the methods and procedure to obtain the 2,500-year probabilistic scenario for Newport that is compliant with the ASCE 7-16 tsunami provisions. We provide details of model results, particularly the inundation depth and flow speed for a new building, which will also be designated as a tsunami vertical evacuation shelter, at Newport, Oregon. We show that the ASCE 7-16 consistent hazards are between those obtained from deterministic L1 and XXL1 scenarios, and the greatest impact on the building may come from later waves. As a further step, we utilize the inundation model results to numerically compute tracks of large vessels in the vicinity of the building site and estimate if these vessels will impact on the building site during the extreme XXL1 and ASCE 7-16 hazard-consistent scenarios. Two-step study is carried out first to study tracks of massless particles and then large vessels with assigned mass considering drag force, inertial force, ship grounding and mooring. The simulation results show that none of the large vessels will impact on the building site in all tested scenarios.

Positive dwell time algorithm with minimum equal extra material removal in deterministic optical surfacing technology.

PubMed

Li, Longxiang; Xue, Donglin; Deng, Weijie; Wang, Xu; Bai, Yang; Zhang, Feng; Zhang, Xuejun

2017-11-10

In deterministic computer-controlled optical surfacing, accurate dwell time execution by computer numeric control machines is crucial in guaranteeing a high-convergence ratio for the optical surface error. It is necessary to consider the machine dynamics limitations in the numerical dwell time algorithms. In this paper, these constraints on dwell time distribution are analyzed, and a model of the equal extra material removal is established. A positive dwell time algorithm with minimum equal extra material removal is developed. Results of simulations based on deterministic magnetorheological finishing demonstrate the necessity of considering machine dynamics performance and illustrate the validity of the proposed algorithm. Indeed, the algorithm effectively facilitates the determinacy of sub-aperture optical surfacing processes.

Spatial delineation, fluid-lithology characterization, and petrophysical modeling of deepwater Gulf of Mexico reservoirs though joint AVA deterministic and stochastic inversion of three-dimensional partially-stacked seismic amplitude data and well logs

NASA Astrophysics Data System (ADS)

Contreras, Arturo Javier

This dissertation describes a novel Amplitude-versus-Angle (AVA) inversion methodology to quantitatively integrate pre-stack seismic data, well logs, geologic data, and geostatistical information. Deterministic and stochastic inversion algorithms are used to characterize flow units of deepwater reservoirs located in the central Gulf of Mexico. A detailed fluid/lithology sensitivity analysis was conducted to assess the nature of AVA effects in the study area. Standard AVA analysis indicates that the shale/sand interface represented by the top of the hydrocarbon-bearing turbidite deposits generate typical Class III AVA responses. Layer-dependent Biot-Gassmann analysis shows significant sensitivity of the P-wave velocity and density to fluid substitution, indicating that presence of light saturating fluids clearly affects the elastic response of sands. Accordingly, AVA deterministic and stochastic inversions, which combine the advantages of AVA analysis with those of inversion, have provided quantitative information about the lateral continuity of the turbidite reservoirs based on the interpretation of inverted acoustic properties and fluid-sensitive modulus attributes (P-Impedance, S-Impedance, density, and LambdaRho, in the case of deterministic inversion; and P-velocity, S-velocity, density, and lithotype (sand-shale) distributions, in the case of stochastic inversion). The quantitative use of rock/fluid information through AVA seismic data, coupled with the implementation of co-simulation via lithotype-dependent multidimensional joint probability distributions of acoustic/petrophysical properties, provides accurate 3D models of petrophysical properties such as porosity, permeability, and water saturation. Pre-stack stochastic inversion provides more realistic and higher-resolution results than those obtained from analogous deterministic techniques. Furthermore, 3D petrophysical models can be more accurately co-simulated from AVA stochastic inversion results. By combining AVA sensitivity analysis techniques with pre-stack stochastic inversion, geologic data, and awareness of inversion pitfalls, it is possible to substantially reduce the risk in exploration and development of conventional and non-conventional reservoirs. From the final integration of deterministic and stochastic inversion results with depositional models and analogous examples, the M-series reservoirs have been interpreted as stacked terminal turbidite lobes within an overall fan complex (the Miocene MCAVLU Submarine Fan System); this interpretation is consistent with previous core data interpretations and regional stratigraphic/depositional studies.

Failed rib region prediction in a human body model during crash events with precrash braking.

PubMed

Guleyupoglu, B; Koya, B; Barnard, R; Gayzik, F S

2018-02-28

The objective of this study is 2-fold. We used a validated human body finite element model to study the predicted chest injury (focusing on rib fracture as a function of element strain) based on varying levels of simulated precrash braking. Furthermore, we compare deterministic and probabilistic methods of rib injury prediction in the computational model. The Global Human Body Models Consortium (GHBMC) M50-O model was gravity settled in the driver position of a generic interior equipped with an advanced 3-point belt and airbag. Twelve cases were investigated with permutations for failure, precrash braking system, and crash severity. The severities used were median (17 kph), severe (34 kph), and New Car Assessment Program (NCAP; 56.4 kph). Cases with failure enabled removed rib cortical bone elements once 1.8% effective plastic strain was exceeded. Alternatively, a probabilistic framework found in the literature was used to predict rib failure. Both the probabilistic and deterministic methods take into consideration location (anterior, lateral, and posterior). The deterministic method is based on a rubric that defines failed rib regions dependent on a threshold for contiguous failed elements. The probabilistic method depends on age-based strain and failure functions. Kinematics between both methods were similar (peak max deviation: ΔX head = 17 mm; ΔZ head = 4 mm; ΔX thorax = 5 mm; ΔZ thorax = 1 mm). Seat belt forces at the time of probabilistic failed region initiation were lower than those at deterministic failed region initiation. The probabilistic method for rib fracture predicted more failed regions in the rib (an analog for fracture) than the deterministic method in all but 1 case where they were equal. The failed region patterns between models are similar; however, there are differences that arise due to stress reduced from element elimination that cause probabilistic failed regions to continue to rise after no deterministic failed region would be predicted. Both the probabilistic and deterministic methods indicate similar trends with regards to the effect of precrash braking; however, there are tradeoffs. The deterministic failed region method is more spatially sensitive to failure and is more sensitive to belt loads. The probabilistic failed region method allows for increased capability in postprocessing with respect to age. The probabilistic failed region method predicted more failed regions than the deterministic failed region method due to force distribution differences.

Down to the roughness scale assessment of piston-ring/liner contacts

NASA Astrophysics Data System (ADS)

Checo, H. M.; Jaramillo, A.; Ausas, R. F.; Jai, M.; Buscaglia, G. C.

2017-02-01

The effects of surface roughness in hydrodynamic bearings been accounted for through several approaches, the most widely used being averaging or stochastic techniques. With these the surface is not treated “as it is”, but by means of an assumed probability distribution for the roughness. The so called direct, deterministic or measured-surface simulation) solve the lubrication problem with realistic surfaces down to the roughness scale. This leads to expensive computational problems. Most researchers have tackled this problem considering non-moving surfaces and neglecting the ring dynamics to reduce the computational burden. What is proposed here is to solve the fully-deterministic simulation both in space and in time, so that the actual movement of the surfaces and the rings dynamics are taken into account. This simulation is much more complex than previous ones, as it is intrinsically transient. The feasibility of these fully-deterministic simulations is illustrated two cases: fully deterministic simulation of liner surfaces with diverse finishings (honed and coated bores) with constant piston velocity and load on the ring and also in real engine conditions.

Disentangling mechanisms that mediate the balance between stochastic and deterministic processes in microbial succession.

PubMed

Dini-Andreote, Francisco; Stegen, James C; van Elsas, Jan Dirk; Salles, Joana Falcão

2015-03-17

Ecological succession and the balance between stochastic and deterministic processes are two major themes within microbial ecology, but these conceptual domains have mostly developed independent of each other. Here we provide a framework that integrates shifts in community assembly processes with microbial primary succession to better understand mechanisms governing the stochastic/deterministic balance. Synthesizing previous work, we devised a conceptual model that links ecosystem development to alternative hypotheses related to shifts in ecological assembly processes. Conceptual model hypotheses were tested by coupling spatiotemporal data on soil bacterial communities with environmental conditions in a salt marsh chronosequence spanning 105 years of succession. Analyses within successional stages showed community composition to be initially governed by stochasticity, but as succession proceeded, there was a progressive increase in deterministic selection correlated with increasing sodium concentration. Analyses of community turnover among successional stages--which provide a larger spatiotemporal scale relative to within stage analyses--revealed that changes in the concentration of soil organic matter were the main predictor of the type and relative influence of determinism. Taken together, these results suggest scale-dependency in the mechanisms underlying selection. To better understand mechanisms governing these patterns, we developed an ecological simulation model that revealed how changes in selective environments cause shifts in the stochastic/deterministic balance. Finally, we propose an extended--and experimentally testable--conceptual model integrating ecological assembly processes with primary and secondary succession. This framework provides a priori hypotheses for future experiments, thereby facilitating a systematic approach to understand assembly and succession in microbial communities across ecosystems.

Disentangling mechanisms that mediate the balance between stochastic and deterministic processes in microbial succession

PubMed Central

Dini-Andreote, Francisco; Stegen, James C.; van Elsas, Jan Dirk; Salles, Joana Falcão

2015-01-01

Ecological succession and the balance between stochastic and deterministic processes are two major themes within microbial ecology, but these conceptual domains have mostly developed independent of each other. Here we provide a framework that integrates shifts in community assembly processes with microbial primary succession to better understand mechanisms governing the stochastic/deterministic balance. Synthesizing previous work, we devised a conceptual model that links ecosystem development to alternative hypotheses related to shifts in ecological assembly processes. Conceptual model hypotheses were tested by coupling spatiotemporal data on soil bacterial communities with environmental conditions in a salt marsh chronosequence spanning 105 years of succession. Analyses within successional stages showed community composition to be initially governed by stochasticity, but as succession proceeded, there was a progressive increase in deterministic selection correlated with increasing sodium concentration. Analyses of community turnover among successional stages—which provide a larger spatiotemporal scale relative to within stage analyses—revealed that changes in the concentration of soil organic matter were the main predictor of the type and relative influence of determinism. Taken together, these results suggest scale-dependency in the mechanisms underlying selection. To better understand mechanisms governing these patterns, we developed an ecological simulation model that revealed how changes in selective environments cause shifts in the stochastic/deterministic balance. Finally, we propose an extended—and experimentally testable—conceptual model integrating ecological assembly processes with primary and secondary succession. This framework provides a priori hypotheses for future experiments, thereby facilitating a systematic approach to understand assembly and succession in microbial communities across ecosystems. PMID:25733885

A stochastic chemostat model with an inhibitor and noise independent of population sizes

NASA Astrophysics Data System (ADS)

Sun, Shulin; Zhang, Xiaolu

2018-02-01

In this paper, a stochastic chemostat model with an inhibitor is considered, here the inhibitor is input from an external source and two organisms in chemostat compete for a nutrient. Firstly, we show that the system has a unique global positive solution. Secondly, by constructing some suitable Lyapunov functions, we investigate that the average in time of the second moment of the solutions of the stochastic model is bounded for a relatively small noise. That is, the asymptotic behaviors of the stochastic system around the equilibrium points of the deterministic system are studied. However, the sufficient large noise can make the microorganisms become extinct with probability one, although the solutions to the original deterministic model may be persistent. Finally, the obtained analytical results are illustrated by computer simulations.

Watershed scale response to climate change--Trout Lake Basin, Wisconsin

USGS Publications Warehouse

Walker, John F.; Hunt, Randall J.; Hay, Lauren E.; Markstrom, Steven L.

2012-01-01

Fourteen basins for which the Precipitation Runoff Modeling System has been calibrated and evaluated were selected as study sites. Precipitation Runoff Modeling System is a deterministic, distributed parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land use on streamflow and general basin hydrology. Output from five General Circulation Model simulations and four emission scenarios were used to develop an ensemble of climate-change scenarios for each basin. These ensembles were simulated with the corresponding Precipitation Runoff Modeling System model. This fact sheet summarizes the hydrologic effect and sensitivity of the Precipitation Runoff Modeling System simulations to climate change for the Trout River Basin at Trout Lake in northern Wisconsin.

Watershed scale response to climate change--Clear Creek Basin, Iowa

USGS Publications Warehouse

Christiansen, Daniel E.; Hay, Lauren E.; Markstrom, Steven L.

2012-01-01

Fourteen basins for which the Precipitation Runoff Modeling System has been calibrated and evaluated were selected as study sites. Precipitation Runoff Modeling System is a deterministic, distributed parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land use on streamflow and general basin hydrology. Output from five General Circulation Model simulations and four emission scenarios were used to develop an ensemble of climate-change scenarios for each basin. These ensembles were simulated with the corresponding Precipitation Runoff Modeling System model. This fact sheet summarizes the hydrologic effect and sensitivity of the Precipitation Runoff Modeling System simulations to climate change for the Clear Creek Basin, near Coralville, Iowa.

Watershed scale response to climate change--Feather River Basin, California

USGS Publications Warehouse

Koczot, Kathryn M.; Markstrom, Steven L.; Hay, Lauren E.

2012-01-01

Fourteen basins for which the Precipitation Runoff Modeling System has been calibrated and evaluated were selected as study sites. Precipitation Runoff Modeling System is a deterministic, distributed parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land use on streamflow and general basin hydrology. Output from five General Circulation Model simulations and four emission scenarios were used to develop an ensemble of climate-change scenarios for each basin. These ensembles were simulated with the corresponding Precipitation Runoff Modeling System model. This fact sheet summarizes the hydrologic effect and sensitivity of the Precipitation Runoff Modeling System simulations to climate change for the Feather River Basin, California.

Watershed scale response to climate change--South Fork Flathead River Basin, Montana

USGS Publications Warehouse

Chase, Katherine J.; Hay, Lauren E.; Markstrom, Steven L.

2012-01-01

Fourteen basins for which the Precipitation Runoff Modeling System has been calibrated and evaluated were selected as study sites. Precipitation Runoff Modeling System is a deterministic, distributed parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land use on streamflow and general basin hydrology. Output from five General Circulation Model simulations and four emission scenarios were used to develop an ensemble of climate-change scenarios for each basin. These ensembles were simulated with the corresponding Precipitation Runoff Modeling System model. This fact sheet summarizes the hydrologic effect and sensitivity of the Precipitation Runoff Modeling System simulations to climate change for the South Fork Flathead River Basin, Montana.

Watershed scale response to climate change--Cathance Stream Basin, Maine

USGS Publications Warehouse

Dudley, Robert W.; Hay, Lauren E.; Markstrom, Steven L.; Hodgkins, Glenn A.

2012-01-01

Fourteen basins for which the Precipitation Runoff Modeling System has been calibrated and evaluated were selected as study sites. Precipitation Runoff Modeling System is a deterministic, distributed parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land use on streamflow and general basin hydrology. Output from five General Circulation Model simulations and four emission scenarios were used to develop an ensemble of climate-change scenarios for each basin. These ensembles were simulated with the corresponding Precipitation Runoff Modeling System model. This fact sheet summarizes the hydrologic effect and sensitivity of the Precipitation Runoff Modeling System simulations to climate change for the Cathance Stream Basin, Maine.

Watershed scale response to climate change--Pomperaug River Watershed, Connecticut

USGS Publications Warehouse

Bjerklie, David M.; Hay, Lauren E.; Markstrom, Steven L.

2012-01-01

Fourteen basins for which the Precipitation Runoff Modeling System has been calibrated and evaluated were selected as study sites. Precipitation Runoff Modeling System is a deterministic, distributed parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land use on streamflow and general basin hydrology. Output from five General Circulation Model simulations and four emission scenarios were used to develop an ensemble of climate-change scenarios for each basin. These ensembles were simulated with the corresponding Precipitation Runoff Modeling System model. This fact sheet summarizes the hydrologic effect and sensitivity of the Precipitation Runoff Modeling System simulations to climate change for the Pomperaug River Basin at Southbury, Connecticut.

Watershed scale response to climate change--Starkweather Coulee Basin, North Dakota

USGS Publications Warehouse

Vining, Kevin C.; Hay, Lauren E.; Markstrom, Steven L.

2012-01-01

Fourteen basins for which the Precipitation Runoff Modeling System has been calibrated and evaluated were selected as study sites. Precipitation Runoff Modeling System is a deterministic, distributed parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land use on streamflow and general basin hydrology. Output from five General Circulation Model simulations and four emission scenarios were used to develop an ensemble of climate-change scenarios for each basin. These ensembles were simulated with the corresponding Precipitation Runoff Modeling System model. This fact sheet summarizes the hydrologic effect and sensitivity of the Precipitation Runoff Modeling System simulations to climate change for the Starkweather Coulee Basin near Webster, North Dakota.

Watershed scale response to climate change--Sagehen Creek Basin, California

USGS Publications Warehouse

Markstrom, Steven L.; Hay, Lauren E.; Regan, R. Steven

2012-01-01

Fourteen basins for which the Precipitation Runoff Modeling System has been calibrated and evaluated were selected as study sites. Precipitation Runoff Modeling System is a deterministic, distributed parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land use on streamflow and general basin hydrology. Output from five General Circulation Model simulations and four emission scenarios were used to develop an ensemble of climate-change scenarios for each basin. These ensembles were simulated with the corresponding Precipitation Runoff Modeling System model. This fact sheet summarizes the hydrologic effect and sensitivity of the Precipitation Runoff Modeling System simulations to climate change for the Sagehen Creek Basin near Truckee, California.

Watershed scale response to climate change--Sprague River Basin, Oregon

USGS Publications Warehouse

Risley, John; Hay, Lauren E.; Markstrom, Steven L.

2012-01-01

Fourteen basins for which the Precipitation Runoff Modeling System has been calibrated and evaluated were selected as study sites. Precipitation Runoff Modeling System is a deterministic, distributed parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land use on streamflow and general basin hydrology. Output from five General Circulation Model simulations and four emission scenarios were used to develop an ensemble of climate-change scenarios for each basin. These ensembles were simulated with the corresponding Precipitation Runoff Modeling System model. This fact sheet summarizes the hydrologic effect and sensitivity of the Precipitation Runoff Modeling System simulations to climate change for the Sprague River Basin near Chiloquin, Oregon.

Watershed scale response to climate change--Black Earth Creek Basin, Wisconsin

USGS Publications Warehouse

Hunt, Randall J.; Walker, John F.; Westenbroek, Steven M.; Hay, Lauren E.; Markstrom, Steven L.

2012-01-01

Fourteen basins for which the Precipitation Runoff Modeling System has been calibrated and evaluated were selected as study sites. Precipitation Runoff Modeling System is a deterministic, distributed parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land use on streamflow and general basin hydrology. Output from five General Circulation Model simulations and four emission scenarios were used to develop an ensemble of climate-change scenarios for each basin. These ensembles were simulated with the corresponding Precipitation Runoff Modeling System model. This fact sheet summarizes the hydrologic effect and sensitivity of the Precipitation Runoff Modeling System simulations to climate change for the Black Earth Creek Basin, Wisconsin.

Watershed scale response to climate change--East River Basin, Colorado

USGS Publications Warehouse

Battaglin, William A.; Hay, Lauren E.; Markstrom, Steven L.

2012-01-01

Fourteen basins for which the Precipitation Runoff Modeling System has been calibrated and evaluated were selected as study sites. Precipitation Runoff Modeling System is a deterministic, distributed parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land use on streamflow and general basin hydrology. Output from five General Circulation Model simulations and four emission scenarios were used to develop an ensemble of climate-change scenarios for each basin. These ensembles were simulated with the corresponding Precipitation Runoff Modeling System model. This fact sheet summarizes the hydrologic effect and sensitivity of the Precipitation Runoff Modeling System simulations to climate change for the East River Basin, Colorado.

Watershed scale response to climate change--Naches River Basin, Washington

USGS Publications Warehouse

Mastin, Mark C.; Hay, Lauren E.; Markstrom, Steven L.

2012-01-01

Fourteen basins for which the Precipitation Runoff Modeling System has been calibrated and evaluated were selected as study sites. Precipitation Runoff Modeling System is a deterministic, distributed parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land use on streamflow and general basin hydrology. Output from five General Circulation Model simulations and four emission scenarios were used to develop an ensemble of climate-change scenarios for each basin. These ensembles were simulated with the corresponding Precipitation Runoff Modeling System model. This fact sheet summarizes the hydrologic effect and sensitivity of the Precipitation Runoff Modeling System simulations to climate change for the Naches River Basin below Tieton River in Washington.

Watershed scale response to climate change--Flint River Basin, Georgia

USGS Publications Warehouse

Hay, Lauren E.; Markstrom, Steven L.

2012-01-01

Fourteen basins for which the Precipitation Runoff Modeling System has been calibrated and evaluated were selected as study sites. Precipitation Runoff Modeling System is a deterministic, distributed parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land use on streamflow and general basin hydrology. Output from five General Circulation Model simulations and four emission scenarios were used to develop an ensemble of climate-change scenarios for each basin. These ensembles were simulated with the corresponding Precipitation Runoff Modeling System model. This fact sheet summarizes the hydrologic effect and sensitivity of the Precipitation Runoff Modeling System simulations to climate change for the Flint River Basin at Montezuma, Georgia.

Construction of dynamic stochastic simulation models using knowledge-based techniques

NASA Technical Reports Server (NTRS)

Williams, M. Douglas; Shiva, Sajjan G.

1990-01-01

Over the past three decades, computer-based simulation models have proven themselves to be cost-effective alternatives to the more structured deterministic methods of systems analysis. During this time, many techniques, tools and languages for constructing computer-based simulation models have been developed. More recently, advances in knowledge-based system technology have led many researchers to note the similarities between knowledge-based programming and simulation technologies and to investigate the potential application of knowledge-based programming techniques to simulation modeling. The integration of conventional simulation techniques with knowledge-based programming techniques is discussed to provide a development environment for constructing knowledge-based simulation models. A comparison of the techniques used in the construction of dynamic stochastic simulation models and those used in the construction of knowledge-based systems provides the requirements for the environment. This leads to the design and implementation of a knowledge-based simulation development environment. These techniques were used in the construction of several knowledge-based simulation models including the Advanced Launch System Model (ALSYM).

Assessment of effectiveness of geologic isolation systems. Geologic-simulation model for a hypothetical site in the Columbia Plateau. Volume 2: results

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

Foley, M.G.; Petrie, G.M.; Baldwin, A.J.

1982-06-01

This report contains the input data and computer results for the Geologic Simulation Model. This model is described in detail in the following report: Petrie, G.M., et. al. 1981. Geologic Simulation Model for a Hypothetical Site in the Columbia Plateau, Pacific Northwest Laboratory, Richland, Washington. The Geologic Simulation Model is a quasi-deterministic process-response model which simulates, for a million years into the future, the development of the geologic and hydrologic systems of the ground-water basin containing the Pasco Basin. Effects of natural processes on the ground-water hydrologic system are modeled principally by rate equations. The combined effects and synergistic interactionsmore » of different processes are approximated by linear superposition of their effects during discrete time intervals in a stepwise-integration approach.« less

Stochastic Watershed Models for Risk Based Decision Making

NASA Astrophysics Data System (ADS)

Vogel, R. M.

2017-12-01

Over half a century ago, the Harvard Water Program introduced the field of operational or synthetic hydrology providing stochastic streamflow models (SSMs), which could generate ensembles of synthetic streamflow traces useful for hydrologic risk management. The application of SSMs, based on streamflow observations alone, revolutionized water resources planning activities, yet has fallen out of favor due, in part, to their inability to account for the now nearly ubiquitous anthropogenic influences on streamflow. This commentary advances the modern equivalent of SSMs, termed `stochastic watershed models' (SWMs) useful as input to nearly all modern risk based water resource decision making approaches. SWMs are deterministic watershed models implemented using stochastic meteorological series, model parameters and model errors, to generate ensembles of streamflow traces that represent the variability in possible future streamflows. SWMs combine deterministic watershed models, which are ideally suited to accounting for anthropogenic influences, with recent developments in uncertainty analysis and principles of stochastic simulation

Simulation of the M13 life cycle I: Assembly of a genetically-structured deterministic chemical kinetic simulation.

PubMed

Smeal, Steven W; Schmitt, Margaret A; Pereira, Ronnie Rodrigues; Prasad, Ashok; Fisk, John D

2017-01-01

To expand the quantitative, systems level understanding and foster the expansion of the biotechnological applications of the filamentous bacteriophage M13, we have unified the accumulated quantitative information on M13 biology into a genetically-structured, experimentally-based computational simulation of the entire phage life cycle. The deterministic chemical kinetic simulation explicitly includes the molecular details of DNA replication, mRNA transcription, protein translation and particle assembly, as well as the competing protein-protein and protein-nucleic acid interactions that control the timing and extent of phage production. The simulation reproduces the holistic behavior of M13, closely matching experimentally reported values of the intracellular levels of phage species and the timing of events in the M13 life cycle. The computational model provides a quantitative description of phage biology, highlights gaps in the present understanding of M13, and offers a framework for exploring alternative mechanisms of regulation in the context of the complete M13 life cycle. Copyright © 2016 Elsevier Inc. All rights reserved.

Chaos and simple determinism in reversed field pinch plasmas: Nonlinear analysis of numerical simulation and experimental data

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

Watts, Christopher A.

In this dissertation the possibility that chaos and simple determinism are governing the dynamics of reversed field pinch (RFP) plasmas is investigated. To properly assess this possibility, data from both numerical simulations and experiment are analyzed. A large repertoire of nonlinear analysis techniques is used to identify low dimensional chaos in the data. These tools include phase portraits and Poincare sections, correlation dimension, the spectrum of Lyapunov exponents and short term predictability. In addition, nonlinear noise reduction techniques are applied to the experimental data in an attempt to extract any underlying deterministic dynamics. Two model systems are used to simulatemore » the plasma dynamics. These are the DEBS code, which models global RFP dynamics, and the dissipative trapped electron mode (DTEM) model, which models drift wave turbulence. Data from both simulations show strong indications of low dimensional chaos and simple determinism. Experimental date were obtained from the Madison Symmetric Torus RFP and consist of a wide array of both global and local diagnostic signals. None of the signals shows any indication of low dimensional chaos or low simple determinism. Moreover, most of the analysis tools indicate the experimental system is very high dimensional with properties similar to noise. Nonlinear noise reduction is unsuccessful at extracting an underlying deterministic system.« less

The viability of ADVANTG deterministic method for synthetic radiography generation

NASA Astrophysics Data System (ADS)

Bingham, Andrew; Lee, Hyoung K.

2018-07-01

Fast simulation techniques to generate synthetic radiographic images of high resolution are helpful when new radiation imaging systems are designed. However, the standard stochastic approach requires lengthy run time with poorer statistics at higher resolution. The investigation of the viability of a deterministic approach to synthetic radiography image generation was explored. The aim was to analyze a computational time decrease over the stochastic method. ADVANTG was compared to MCNP in multiple scenarios including a small radiography system prototype, to simulate high resolution radiography images. By using ADVANTG deterministic code to simulate radiography images the computational time was found to decrease 10 to 13 times compared to the MCNP stochastic approach while retaining image quality.

Sampled-Data Consensus of Linear Multi-agent Systems With Packet Losses.

PubMed

Zhang, Wenbing; Tang, Yang; Huang, Tingwen; Kurths, Jurgen

In this paper, the consensus problem is studied for a class of multi-agent systems with sampled data and packet losses, where random and deterministic packet losses are considered, respectively. For random packet losses, a Bernoulli-distributed white sequence is used to describe packet dropouts among agents in a stochastic way. For deterministic packet losses, a switched system with stable and unstable subsystems is employed to model packet dropouts in a deterministic way. The purpose of this paper is to derive consensus criteria, such that linear multi-agent systems with sampled-data and packet losses can reach consensus. By means of the Lyapunov function approach and the decomposition method, the design problem of a distributed controller is solved in terms of convex optimization. The interplay among the allowable bound of the sampling interval, the probability of random packet losses, and the rate of deterministic packet losses are explicitly derived to characterize consensus conditions. The obtained criteria are closely related to the maximum eigenvalue of the Laplacian matrix versus the second minimum eigenvalue of the Laplacian matrix, which reveals the intrinsic effect of communication topologies on consensus performance. Finally, simulations are given to show the effectiveness of the proposed results.In this paper, the consensus problem is studied for a class of multi-agent systems with sampled data and packet losses, where random and deterministic packet losses are considered, respectively. For random packet losses, a Bernoulli-distributed white sequence is used to describe packet dropouts among agents in a stochastic way. For deterministic packet losses, a switched system with stable and unstable subsystems is employed to model packet dropouts in a deterministic way. The purpose of this paper is to derive consensus criteria, such that linear multi-agent systems with sampled-data and packet losses can reach consensus. By means of the Lyapunov function approach and the decomposition method, the design problem of a distributed controller is solved in terms of convex optimization. The interplay among the allowable bound of the sampling interval, the probability of random packet losses, and the rate of deterministic packet losses are explicitly derived to characterize consensus conditions. The obtained criteria are closely related to the maximum eigenvalue of the Laplacian matrix versus the second minimum eigenvalue of the Laplacian matrix, which reveals the intrinsic effect of communication topologies on consensus performance. Finally, simulations are given to show the effectiveness of the proposed results.

Influence of the feedback loops in the trp operon of B. subtilis on the system dynamic response and noise amplitude.

PubMed

Zamora-Chimal, Criseida; Santillán, Moisés; Rodríguez-González, Jesús

2012-10-07

In this paper we introduce a mathematical model for the tryptophan operon regulatory pathway in Bacillus subtilis. This model considers the transcription-attenuation, and the enzyme-inhibition regulatory mechanisms. Special attention is paid to the estimation of all the model parameters from reported experimental data. With the aid of this model we investigate, from a mathematical-modeling point of view, whether the existing multiplicity of regulatory feedback loops is advantageous in some sense, regarding the dynamic response and the biochemical noise in the system. The tryptophan operon dynamic behavior is studied by means of deterministic numeric simulations, while the biochemical noise is analyzed with the aid of stochastic simulations. The model feasibility is tested comparing its stochastic and deterministic results with experimental reports. Our results for the wildtype and for a couple of mutant bacterial strains suggest that the enzyme-inhibition feedback loop, dynamically accelerates the operon response, and plays a major role in the reduction of biochemical noise. Also, the transcription-attenuation feedback loop makes the trp operon sensitive to changes in the endogenous tryptophan level, and increases the amplitude of the biochemical noise. Copyright © 2012 Elsevier Ltd. All rights reserved.

Deterministic absorbed dose estimation in computed tomography using a discrete ordinates method

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

Norris, Edward T.; Liu, Xin, E-mail: xinliu@mst.edu; Hsieh, Jiang

Purpose: Organ dose estimation for a patient undergoing computed tomography (CT) scanning is very important. Although Monte Carlo methods are considered gold-standard in patient dose estimation, the computation time required is formidable for routine clinical calculations. Here, the authors instigate a deterministic method for estimating an absorbed dose more efficiently. Methods: Compared with current Monte Carlo methods, a more efficient approach to estimating the absorbed dose is to solve the linear Boltzmann equation numerically. In this study, an axial CT scan was modeled with a software package, Denovo, which solved the linear Boltzmann equation using the discrete ordinates method. Themore » CT scanning configuration included 16 x-ray source positions, beam collimators, flat filters, and bowtie filters. The phantom was the standard 32 cm CT dose index (CTDI) phantom. Four different Denovo simulations were performed with different simulation parameters, including the number of quadrature sets and the order of Legendre polynomial expansions. A Monte Carlo simulation was also performed for benchmarking the Denovo simulations. A quantitative comparison was made of the simulation results obtained by the Denovo and the Monte Carlo methods. Results: The difference in the simulation results of the discrete ordinates method and those of the Monte Carlo methods was found to be small, with a root-mean-square difference of around 2.4%. It was found that the discrete ordinates method, with a higher order of Legendre polynomial expansions, underestimated the absorbed dose near the center of the phantom (i.e., low dose region). Simulations of the quadrature set 8 and the first order of the Legendre polynomial expansions proved to be the most efficient computation method in the authors’ study. The single-thread computation time of the deterministic simulation of the quadrature set 8 and the first order of the Legendre polynomial expansions was 21 min on a personal computer. Conclusions: The simulation results showed that the deterministic method can be effectively used to estimate the absorbed dose in a CTDI phantom. The accuracy of the discrete ordinates method was close to that of a Monte Carlo simulation, and the primary benefit of the discrete ordinates method lies in its rapid computation speed. It is expected that further optimization of this method in routine clinical CT dose estimation will improve its accuracy and speed.« less

On the applicability of low-dimensional models for convective flow reversals at extreme Prandtl numbers

NASA Astrophysics Data System (ADS)

Mannattil, Manu; Pandey, Ambrish; Verma, Mahendra K.; Chakraborty, Sagar

2017-12-01

Constructing simpler models, either stochastic or deterministic, for exploring the phenomenon of flow reversals in fluid systems is in vogue across disciplines. Using direct numerical simulations and nonlinear time series analysis, we illustrate that the basic nature of flow reversals in convecting fluids can depend on the dimensionless parameters describing the system. Specifically, we find evidence of low-dimensional behavior in flow reversals occurring at zero Prandtl number, whereas we fail to find such signatures for reversals at infinite Prandtl number. Thus, even in a single system, as one varies the system parameters, one can encounter reversals that are fundamentally different in nature. Consequently, we conclude that a single general low-dimensional deterministic model cannot faithfully characterize flow reversals for every set of parameter values.

Probabilistic safety assessment of the design of a tall buildings under the extreme load

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

Králik, Juraj, E-mail: juraj.kralik@stuba.sk

2016-06-08

The paper describes some experiences from the deterministic and probabilistic analysis of the safety of the tall building structure. There are presented the methods and requirements of Eurocode EN 1990, standard ISO 2394 and JCSS. The uncertainties of the model and resistance of the structures are considered using the simulation methods. The MONTE CARLO, LHS and RSM probabilistic methods are compared with the deterministic results. On the example of the probability analysis of the safety of the tall buildings is demonstrated the effectiveness of the probability design of structures using Finite Element Methods.

Probabilistic safety assessment of the design of a tall buildings under the extreme load

NASA Astrophysics Data System (ADS)

Králik, Juraj

2016-06-01

The paper describes some experiences from the deterministic and probabilistic analysis of the safety of the tall building structure. There are presented the methods and requirements of Eurocode EN 1990, standard ISO 2394 and JCSS. The uncertainties of the model and resistance of the structures are considered using the simulation methods. The MONTE CARLO, LHS and RSM probabilistic methods are compared with the deterministic results. On the example of the probability analysis of the safety of the tall buildings is demonstrated the effectiveness of the probability design of structures using Finite Element Methods.

How synapses can enhance sensibility of a neural network

NASA Astrophysics Data System (ADS)

Protachevicz, P. R.; Borges, F. S.; Iarosz, K. C.; Caldas, I. L.; Baptista, M. S.; Viana, R. L.; Lameu, E. L.; Macau, E. E. N.; Batista, A. M.

2018-02-01

In this work, we study the dynamic range in a neural network modelled by cellular automaton. We consider deterministic and non-deterministic rules to simulate electrical and chemical synapses. Chemical synapses have an intrinsic time-delay and are susceptible to parameter variations guided by learning Hebbian rules of behaviour. The learning rules are related to neuroplasticity that describes change to the neural connections in the brain. Our results show that chemical synapses can abruptly enhance sensibility of the neural network, a manifestation that can become even more predominant if learning rules of evolution are applied to the chemical synapses.

A Hybrid Method of Moment Equations and Rate Equations to Modeling Gas-Grain Chemistry

NASA Astrophysics Data System (ADS)

Pei, Y.; Herbst, E.

2011-05-01

Grain surfaces play a crucial role in catalyzing many important chemical reactions in the interstellar medium (ISM). The deterministic rate equation (RE) method has often been used to simulate the surface chemistry. But this method becomes inaccurate when the number of reacting particles per grain is typically less than one, which can occur in the ISM. In this condition, stochastic approaches such as the master equations are adopted. However, these methods have mostly been constrained to small chemical networks due to the large amounts of processor time and computer power required. In this study, we present a hybrid method consisting of the moment equation approximation to the stochastic master equation approach and deterministic rate equations to treat a gas-grain model of homogeneous cold cloud cores with time-independent physical conditions. In this model, we use the standard OSU gas phase network (version OSU2006V3) which involves 458 gas phase species and more than 4000 reactions, and treat it by deterministic rate equations. A medium-sized surface reaction network which consists of 21 species and 19 reactions accounts for the productions of stable molecules such as H_2O, CO, CO_2, H_2CO, CH_3OH, NH_3 and CH_4. These surface reactions are treated by a hybrid method of moment equations (Barzel & Biham 2007) and rate equations: when the abundance of a surface species is lower than a specific threshold, say one per grain, we use the ``stochastic" moment equations to simulate the evolution; when its abundance goes above this threshold, we use the rate equations. A continuity technique is utilized to secure a smooth transition between these two methods. We have run chemical simulations for a time up to 10^8 yr at three temperatures: 10 K, 15 K, and 20 K. The results will be compared with those generated from (1) a completely deterministic model that uses rate equations for both gas phase and grain surface chemistry, (2) the method of modified rate equations (Garrod 2008), which partially takes into account the stochastic effect for surface reactions, and (3) the master equation approach solved using a Monte Carlo technique. At 10 K and standard grain sizes, our model results agree well with the above three methods, while discrepancies appear at higher temperatures and smaller grain sizes.

Crosstalk in an FDM Laboratory Setup and the Athena X-IFU End-to-End Simulator

NASA Astrophysics Data System (ADS)

den Hartog, R.; Kirsch, C.; de Vries, C.; Akamatsu, H.; Dauser, T.; Peille, P.; Cucchetti, E.; Jackson, B.; Bandler, S.; Smith, S.; Wilms, J.

2018-04-01

The impact of various crosstalk mechanisms on the performance of the Athena X-IFU instrument has been assessed with detailed end-to-end simulations. For the crosstalk in the electrical circuit, a detailed model has been developed. In this contribution, we test this model against measurements made with an FDM laboratory setup and discuss the assumption of deterministic crosstalk in the context of the weak link effect in the detectors. We conclude that crosstalk levels predicted by the model are conservative with respect to the observed levels.

Multi-Scale Modeling of the Gamma Radiolysis of Nitrate Solutions.

PubMed

Horne, Gregory P; Donoclift, Thomas A; Sims, Howard E; Orr, Robin M; Pimblott, Simon M

2016-11-17

A multiscale modeling approach has been developed for the extended time scale long-term radiolysis of aqueous systems. The approach uses a combination of stochastic track structure and track chemistry as well as deterministic homogeneous chemistry techniques and involves four key stages: radiation track structure simulation, the subsequent physicochemical processes, nonhomogeneous diffusion-reaction kinetic evolution, and homogeneous bulk chemistry modeling. The first three components model the physical and chemical evolution of an isolated radiation chemical track and provide radiolysis yields, within the extremely low dose isolated track paradigm, as the input parameters for a bulk deterministic chemistry model. This approach to radiation chemical modeling has been tested by comparison with the experimentally observed yield of nitrite from the gamma radiolysis of sodium nitrate solutions. This is a complex radiation chemical system which is strongly dependent on secondary reaction processes. The concentration of nitrite is not just dependent upon the evolution of radiation track chemistry and the scavenging of the hydrated electron and its precursors but also on the subsequent reactions of the products of these scavenging reactions with other water radiolysis products. Without the inclusion of intratrack chemistry, the deterministic component of the multiscale model is unable to correctly predict experimental data, highlighting the importance of intratrack radiation chemistry in the chemical evolution of the irradiated system.

Linear regression metamodeling as a tool to summarize and present simulation model results.

PubMed

Jalal, Hawre; Dowd, Bryan; Sainfort, François; Kuntz, Karen M

2013-10-01

Modelers lack a tool to systematically and clearly present complex model results, including those from sensitivity analyses. The objective was to propose linear regression metamodeling as a tool to increase transparency of decision analytic models and better communicate their results. We used a simplified cancer cure model to demonstrate our approach. The model computed the lifetime cost and benefit of 3 treatment options for cancer patients. We simulated 10,000 cohorts in a probabilistic sensitivity analysis (PSA) and regressed the model outcomes on the standardized input parameter values in a set of regression analyses. We used the regression coefficients to describe measures of sensitivity analyses, including threshold and parameter sensitivity analyses. We also compared the results of the PSA to deterministic full-factorial and one-factor-at-a-time designs. The regression intercept represented the estimated base-case outcome, and the other coefficients described the relative parameter uncertainty in the model. We defined simple relationships that compute the average and incremental net benefit of each intervention. Metamodeling produced outputs similar to traditional deterministic 1-way or 2-way sensitivity analyses but was more reliable since it used all parameter values. Linear regression metamodeling is a simple, yet powerful, tool that can assist modelers in communicating model characteristics and sensitivity analyses.

Optimum spaceborne computer system design by simulation

NASA Technical Reports Server (NTRS)

Williams, T.; Weatherbee, J. E.; Taylor, D. S.

1972-01-01

A deterministic digital simulation model is described which models the Automatically Reconfigurable Modular Multiprocessor System (ARMMS), a candidate computer system for future manned and unmanned space missions. Use of the model as a tool in configuring a minimum computer system for a typical mission is demonstrated. The configuration which is developed as a result of studies with the simulator is optimal with respect to the efficient use of computer system resources, i.e., the configuration derived is a minimal one. Other considerations such as increased reliability through the use of standby spares would be taken into account in the definition of a practical system for a given mission.

Factors Affecting the Item Parameter Estimation and Classification Accuracy of the DINA Model

ERIC Educational Resources Information Center

de la Torre, Jimmy; Hong, Yuan; Deng, Weiling

2010-01-01

To better understand the statistical properties of the deterministic inputs, noisy "and" gate cognitive diagnosis (DINA) model, the impact of several factors on the quality of the item parameter estimates and classification accuracy was investigated. Results of the simulation study indicate that the fully Bayes approach is most accurate when the…

Comparative Petrographic Maturity of River and Beach Sand, and Origin of Quartz Arenites.

ERIC Educational Resources Information Center

Ferree, Rob A.; And Others

1988-01-01

Describes a deterministic computer model that incorporates: (1) initial framework composition; (2) abrasion factors for quartz, feldspar, and rock fragments; and (3) a fragmentation ratio for rock fragments to simulate the recycling of coastal sands by rivers and beaches. (TW)

Design and Analysis of a Low Latency Deterministic Network MAC for Wireless Sensor Networks

PubMed Central

Sahoo, Prasan Kumar; Pattanaik, Sudhir Ranjan; Wu, Shih-Lin

2017-01-01

The IEEE 802.15.4e standard has four different superframe structures for different applications. Use of a low latency deterministic network (LLDN) superframe for the wireless sensor network is one of them, which can operate in a star topology. In this paper, a new channel access mechanism for IEEE 802.15.4e-based LLDN shared slots is proposed, and analytical models are designed based on this channel access mechanism. A prediction model is designed to estimate the possible number of retransmission slots based on the number of failed transmissions. Performance analysis in terms of data transmission reliability, delay, throughput and energy consumption are provided based on our proposed designs. Our designs are validated for simulation and analytical results, and it is observed that the simulation results well match with the analytical ones. Besides, our designs are compared with the IEEE 802.15.4 MAC mechanism, and it is shown that ours outperforms in terms of throughput, energy consumption, delay and reliability. PMID:28937632

Design and Analysis of a Low Latency Deterministic Network MAC for Wireless Sensor Networks.

PubMed

Sahoo, Prasan Kumar; Pattanaik, Sudhir Ranjan; Wu, Shih-Lin

2017-09-22

The IEEE 802.15.4e standard has four different superframe structures for different applications. Use of a low latency deterministic network (LLDN) superframe for the wireless sensor network is one of them, which can operate in a star topology. In this paper, a new channel access mechanism for IEEE 802.15.4e-based LLDN shared slots is proposed, and analytical models are designed based on this channel access mechanism. A prediction model is designed to estimate the possible number of retransmission slots based on the number of failed transmissions. Performance analysis in terms of data transmission reliability, delay, throughput and energy consumption are provided based on our proposed designs. Our designs are validated for simulation and analytical results, and it is observed that the simulation results well match with the analytical ones. Besides, our designs are compared with the IEEE 802.15.4 MAC mechanism, and it is shown that ours outperforms in terms of throughput, energy consumption, delay and reliability.

Analyzing simulation-based PRA data through traditional and topological clustering: A BWR station blackout case study

DOE PAGES

Maljovec, D.; Liu, S.; Wang, B.; ...

2015-07-14

Here, dynamic probabilistic risk assessment (DPRA) methodologies couple system simulator codes (e.g., RELAP and MELCOR) with simulation controller codes (e.g., RAVEN and ADAPT). Whereas system simulator codes model system dynamics deterministically, simulation controller codes introduce both deterministic (e.g., system control logic and operating procedures) and stochastic (e.g., component failures and parameter uncertainties) elements into the simulation. Typically, a DPRA is performed by sampling values of a set of parameters and simulating the system behavior for that specific set of parameter values. For complex systems, a major challenge in using DPRA methodologies is to analyze the large number of scenarios generated,more » where clustering techniques are typically employed to better organize and interpret the data. In this paper, we focus on the analysis of two nuclear simulation datasets that are part of the risk-informed safety margin characterization (RISMC) boiling water reactor (BWR) station blackout (SBO) case study. We provide the domain experts a software tool that encodes traditional and topological clustering techniques within an interactive analysis and visualization environment, for understanding the structures of such high-dimensional nuclear simulation datasets. We demonstrate through our case study that both types of clustering techniques complement each other for enhanced structural understanding of the data.« less

Cross-frequency and band-averaged response variance prediction in the hybrid deterministic-statistical energy analysis method

NASA Astrophysics Data System (ADS)

Reynders, Edwin P. B.; Langley, Robin S.

2018-08-01

The hybrid deterministic-statistical energy analysis method has proven to be a versatile framework for modeling built-up vibro-acoustic systems. The stiff system components are modeled deterministically, e.g., using the finite element method, while the wave fields in the flexible components are modeled as diffuse. In the present paper, the hybrid method is extended such that not only the ensemble mean and variance of the harmonic system response can be computed, but also of the band-averaged system response. This variance represents the uncertainty that is due to the assumption of a diffuse field in the flexible components of the hybrid system. The developments start with a cross-frequency generalization of the reciprocity relationship between the total energy in a diffuse field and the cross spectrum of the blocked reverberant loading at the boundaries of that field. By making extensive use of this generalization in a first-order perturbation analysis, explicit expressions are derived for the cross-frequency and band-averaged variance of the vibrational energies in the diffuse components and for the cross-frequency and band-averaged variance of the cross spectrum of the vibro-acoustic field response of the deterministic components. These expressions are extensively validated against detailed Monte Carlo analyses of coupled plate systems in which diffuse fields are simulated by randomly distributing small point masses across the flexible components, and good agreement is found.

Dual Roles for Spike Signaling in Cortical Neural Populations

PubMed Central

Ballard, Dana H.; Jehee, Janneke F. M.

2011-01-01

A prominent feature of signaling in cortical neurons is that of randomness in the action potential. The output of a typical pyramidal cell can be well fit with a Poisson model, and variations in the Poisson rate repeatedly have been shown to be correlated with stimuli. However while the rate provides a very useful characterization of neural spike data, it may not be the most fundamental description of the signaling code. Recent data showing γ frequency range multi-cell action potential correlations, together with spike timing dependent plasticity, are spurring a re-examination of the classical model, since precise timing codes imply that the generation of spikes is essentially deterministic. Could the observed Poisson randomness and timing determinism reflect two separate modes of communication, or do they somehow derive from a single process? We investigate in a timing-based model whether the apparent incompatibility between these probabilistic and deterministic observations may be resolved by examining how spikes could be used in the underlying neural circuits. The crucial component of this model draws on dual roles for spike signaling. In learning receptive fields from ensembles of inputs, spikes need to behave probabilistically, whereas for fast signaling of individual stimuli, the spikes need to behave deterministically. Our simulations show that this combination is possible if deterministic signals using γ latency coding are probabilistically routed through different members of a cortical cell population at different times. This model exhibits standard features characteristic of Poisson models such as orientation tuning and exponential interval histograms. In addition, it makes testable predictions that follow from the γ latency coding. PMID:21687798

A stochastic spatiotemporal model of a response-regulator network in the Caulobacter crescentus cell cycle

NASA Astrophysics Data System (ADS)

Li, Fei; Subramanian, Kartik; Chen, Minghan; Tyson, John J.; Cao, Yang

2016-06-01

The asymmetric cell division cycle in Caulobacter crescentus is controlled by an elaborate molecular mechanism governing the production, activation and spatial localization of a host of interacting proteins. In previous work, we proposed a deterministic mathematical model for the spatiotemporal dynamics of six major regulatory proteins. In this paper, we study a stochastic version of the model, which takes into account molecular fluctuations of these regulatory proteins in space and time during early stages of the cell cycle of wild-type Caulobacter cells. We test the stochastic model with regard to experimental observations of increased variability of cycle time in cells depleted of the divJ gene product. The deterministic model predicts that overexpression of the divK gene blocks cell cycle progression in the stalked stage; however, stochastic simulations suggest that a small fraction of the mutants cells do complete the cell cycle normally.

Stochastic Simulation Service: Bridging the Gap between the Computational Expert and the Biologist

PubMed Central

Banerjee, Debjani; Bellesia, Giovanni; Daigle, Bernie J.; Douglas, Geoffrey; Gu, Mengyuan; Gupta, Anand; Hellander, Stefan; Horuk, Chris; Nath, Dibyendu; Takkar, Aviral; Lötstedt, Per; Petzold, Linda R.

2016-01-01

We present StochSS: Stochastic Simulation as a Service, an integrated development environment for modeling and simulation of both deterministic and discrete stochastic biochemical systems in up to three dimensions. An easy to use graphical user interface enables researchers to quickly develop and simulate a biological model on a desktop or laptop, which can then be expanded to incorporate increasing levels of complexity. StochSS features state-of-the-art simulation engines. As the demand for computational power increases, StochSS can seamlessly scale computing resources in the cloud. In addition, StochSS can be deployed as a multi-user software environment where collaborators share computational resources and exchange models via a public model repository. We demonstrate the capabilities and ease of use of StochSS with an example of model development and simulation at increasing levels of complexity. PMID:27930676

Stochastic Simulation Service: Bridging the Gap between the Computational Expert and the Biologist

DOE PAGES

Drawert, Brian; Hellander, Andreas; Bales, Ben; ...

2016-12-08

We present StochSS: Stochastic Simulation as a Service, an integrated development environment for modeling and simulation of both deterministic and discrete stochastic biochemical systems in up to three dimensions. An easy to use graphical user interface enables researchers to quickly develop and simulate a biological model on a desktop or laptop, which can then be expanded to incorporate increasing levels of complexity. StochSS features state-of-the-art simulation engines. As the demand for computational power increases, StochSS can seamlessly scale computing resources in the cloud. In addition, StochSS can be deployed as a multi-user software environment where collaborators share computational resources andmore » exchange models via a public model repository. We also demonstrate the capabilities and ease of use of StochSS with an example of model development and simulation at increasing levels of complexity.« less

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

Highlights of advances in the field of hydrometeorological research brought about by the DRIHM project

NASA Astrophysics Data System (ADS)

Caumont, Olivier; Hally, Alan; Garrote, Luis; Richard, Évelyne; Weerts, Albrecht; Delogu, Fabio; Fiori, Elisabetta; Rebora, Nicola; Parodi, Antonio; Mihalović, Ana; Ivković, Marija; Dekić, Ljiljana; van Verseveld, Willem; Nuissier, Olivier; Ducrocq, Véronique; D'Agostino, Daniele; Galizia, Antonella; Danovaro, Emanuele; Clematis, Andrea

2015-04-01

The FP7 DRIHM (Distributed Research Infrastructure for Hydro-Meteorology, http://www.drihm.eu, 2011-2015) project intends to develop a prototype e-Science environment to facilitate the collaboration between meteorologists, hydrologists, and Earth science experts for accelerated scientific advances in Hydro-Meteorology Research (HMR). As the project comes to its end, this presentation will summarize the HMR results that have been obtained in the framework of DRIHM. The vision shaped and implemented in the framework of the DRIHM project enables the production and interpretation of numerous, complex compositions of hydrometeorological simulations of flood events from rainfall, either simulated or modelled, down to discharge. Each element of a composition is drawn from a set of various state-of-the-art models. Atmospheric simulations providing high-resolution rainfall forecasts involve different global and limited-area convection-resolving models, the former being used as boundary conditions for the latter. Some of these models can be run as ensembles, i.e. with perturbed boundary conditions, initial conditions and/or physics, thus sampling the probability density function of rainfall forecasts. In addition, a stochastic downscaling algorithm can be used to create high-resolution rainfall ensemble forecasts from deterministic lower-resolution forecasts. All these rainfall forecasts may be used as input to various rainfall-discharge hydrological models that compute the resulting stream flows for catchments of interest. In some hydrological simulations, physical parameters are perturbed to take into account model errors. As a result, six different kinds of rainfall data (either deterministic or probabilistic) can currently be compared with each other and combined with three different hydrological model engines running either in deterministic or probabilistic mode. HMR topics which are allowed or facilitated by such unprecedented sets of hydrometerological forecasts include: physical process studies, intercomparison of models and ensembles, sensitivity studies to a particular component of the forecasting chain, and design of flash-flood early-warning systems. These benefits will be illustrated with the different key cases that have been under investigation in the course of the project. These are four catastrophic cases of flooding, namely the case of 4 November 2011 in Genoa, Italy, 6 November 2011 in Catalonia, Spain, 13-16 May 2014 in eastern Europe, and 9 October 2014, again in Genoa, Italy.

Modelling the interaction between flooding events and economic growth

NASA Astrophysics Data System (ADS)

Grames, J.; Prskawetz, A.; Grass, D.; Blöschl, G.

2015-06-01

Socio-hydrology describes the interaction between the socio-economy and water. Recent models analyze the interplay of community risk-coping culture, flooding damage and economic growth (Di Baldassarre et al., 2013; Viglione et al., 2014). These models descriptively explain the feedbacks between socio-economic development and natural disasters like floods. Contrary to these descriptive models, our approach develops an optimization model, where the intertemporal decision of an economic agent interacts with the hydrological system. In order to build this first economic growth model describing the interaction between the consumption and investment decisions of an economic agent and the occurrence of flooding events, we transform an existing descriptive stochastic model into an optimal deterministic model. The intermediate step is to formulate and simulate a descriptive deterministic model. We develop a periodic water function to approximate the former discrete stochastic time series of rainfall events. Due to the non-autonomous exogenous periodic rainfall function the long-term path of consumption and investment will be periodic.

P2S--Coupled simulation with the Precipitation-Runoff Modeling System (PRMS) and the Stream Temperature Network (SNTemp) Models

USGS Publications Warehouse

Markstrom, Steven L.

2012-01-01

A software program, called P2S, has been developed which couples the daily stream temperature simulation capabilities of the U.S. Geological Survey Stream Network Temperature model with the watershed hydrology simulation capabilities of the U.S. Geological Survey Precipitation-Runoff Modeling System. The Precipitation-Runoff Modeling System is a modular, deterministic, distributed-parameter, physical-process watershed model that simulates hydrologic response to various combinations of climate and land use. Stream Network Temperature was developed to help aquatic biologists and engineers predict the effects of changes that hydrology and energy have on water temperatures. P2S will allow scientists and watershed managers to evaluate the effects of historical climate and projected climate change, landscape evolution, and resource management scenarios on watershed hydrology and in-stream water temperature.

Spatial modeling of cell signaling networks.

PubMed

Cowan, Ann E; Moraru, Ion I; Schaff, James C; Slepchenko, Boris M; Loew, Leslie M

2012-01-01

The shape of a cell, the sizes of subcellular compartments, and the spatial distribution of molecules within the cytoplasm can all control how molecules interact to produce a cellular behavior. This chapter describes how these spatial features can be included in mechanistic mathematical models of cell signaling. The Virtual Cell computational modeling and simulation software is used to illustrate the considerations required to build a spatial model. An explanation of how to appropriately choose between physical formulations that implicitly or explicitly account for cell geometry and between deterministic versus stochastic formulations for molecular dynamics is provided, along with a discussion of their respective strengths and weaknesses. As a first step toward constructing a spatial model, the geometry needs to be specified and associated with the molecules, reactions, and membrane flux processes of the network. Initial conditions, diffusion coefficients, velocities, and boundary conditions complete the specifications required to define the mathematics of the model. The numerical methods used to solve reaction-diffusion problems both deterministically and stochastically are then described and some guidance is provided in how to set up and run simulations. A study of cAMP signaling in neurons ends the chapter, providing an example of the insights that can be gained in interpreting experimental results through the application of spatial modeling. Copyright © 2012 Elsevier Inc. All rights reserved.

A comparison of geospatially modeled fire behavior and fire management utility of three data sources in the southeastern United States

Treesearch

LaWen T. Hollingsworth; Laurie L. Kurth; Bernard R. Parresol; Roger D. Ottmar; Susan J. Prichard

2012-01-01

Landscape-scale fire behavior analyses are important to inform decisions on resource management projects that meet land management objectives and protect values from adverse consequences of fire. Deterministic and probabilistic geospatial fire behavior analyses are conducted with various modeling systems including FARSITE, FlamMap, FSPro, and Large Fire Simulation...

Probabilistic sensitivity analysis incorporating the bootstrap: an example comparing treatments for the eradication of Helicobacter pylori.

PubMed

Pasta, D J; Taylor, J L; Henning, J M

1999-01-01

Decision-analytic models are frequently used to evaluate the relative costs and benefits of alternative therapeutic strategies for health care. Various types of sensitivity analysis are used to evaluate the uncertainty inherent in the models. Although probabilistic sensitivity analysis is more difficult theoretically and computationally, the results can be much more powerful and useful than deterministic sensitivity analysis. The authors show how a Monte Carlo simulation can be implemented using standard software to perform a probabilistic sensitivity analysis incorporating the bootstrap. The method is applied to a decision-analytic model evaluating the cost-effectiveness of Helicobacter pylori eradication. The necessary steps are straightforward and are described in detail. The use of the bootstrap avoids certain difficulties encountered with theoretical distributions. The probabilistic sensitivity analysis provided insights into the decision-analytic model beyond the traditional base-case and deterministic sensitivity analyses and should become the standard method for assessing sensitivity.

The Stochastic Parcel Model: A deterministic parameterization of stochastically entraining convection

DOE PAGES

Romps, David M.

2016-03-01

Convective entrainment is a process that is poorly represented in existing convective parameterizations. By many estimates, convective entrainment is the leading source of error in global climate models. As a potential remedy, an Eulerian implementation of the Stochastic Parcel Model (SPM) is presented here as a convective parameterization that treats entrainment in a physically realistic and computationally efficient way. Drawing on evidence that convecting clouds comprise air parcels subject to Poisson-process entrainment events, the SPM calculates the deterministic limit of an infinite number of such parcels. For computational efficiency, the SPM groups parcels at each height by their purity, whichmore » is a measure of their total entrainment up to that height. This reduces the calculation of convective fluxes to a sequence of matrix multiplications. The SPM is implemented in a single-column model and compared with a large-eddy simulation of deep convection.« less

An ensemble-based dynamic Bayesian averaging approach for discharge simulations using multiple global precipitation products and hydrological models

NASA Astrophysics Data System (ADS)

Qi, Wei; Liu, Junguo; Yang, Hong; Sweetapple, Chris

2018-03-01

Global precipitation products are very important datasets in flow simulations, especially in poorly gauged regions. Uncertainties resulting from precipitation products, hydrological models and their combinations vary with time and data magnitude, and undermine their application to flow simulations. However, previous studies have not quantified these uncertainties individually and explicitly. This study developed an ensemble-based dynamic Bayesian averaging approach (e-Bay) for deterministic discharge simulations using multiple global precipitation products and hydrological models. In this approach, the joint probability of precipitation products and hydrological models being correct is quantified based on uncertainties in maximum and mean estimation, posterior probability is quantified as functions of the magnitude and timing of discharges, and the law of total probability is implemented to calculate expected discharges. Six global fine-resolution precipitation products and two hydrological models of different complexities are included in an illustrative application. e-Bay can effectively quantify uncertainties and therefore generate better deterministic discharges than traditional approaches (weighted average methods with equal and varying weights and maximum likelihood approach). The mean Nash-Sutcliffe Efficiency values of e-Bay are up to 0.97 and 0.85 in training and validation periods respectively, which are at least 0.06 and 0.13 higher than traditional approaches. In addition, with increased training data, assessment criteria values of e-Bay show smaller fluctuations than traditional approaches and its performance becomes outstanding. The proposed e-Bay approach bridges the gap between global precipitation products and their pragmatic applications to discharge simulations, and is beneficial to water resources management in ungauged or poorly gauged regions across the world.

Modelling and simulating reaction-diffusion systems using coloured Petri nets.

PubMed

Liu, Fei; Blätke, Mary-Ann; Heiner, Monika; Yang, Ming

2014-10-01

Reaction-diffusion systems often play an important role in systems biology when developmental processes are involved. Traditional methods of modelling and simulating such systems require substantial prior knowledge of mathematics and/or simulation algorithms. Such skills may impose a challenge for biologists, when they are not equally well-trained in mathematics and computer science. Coloured Petri nets as a high-level and graphical language offer an attractive alternative, which is easily approachable. In this paper, we investigate a coloured Petri net framework integrating deterministic, stochastic and hybrid modelling formalisms and corresponding simulation algorithms for the modelling and simulation of reaction-diffusion processes that may be closely coupled with signalling pathways, metabolic reactions and/or gene expression. Such systems often manifest multiscaleness in time, space and/or concentration. We introduce our approach by means of some basic diffusion scenarios, and test it against an established case study, the Brusselator model. Copyright © 2014 Elsevier Ltd. All rights reserved.

Stability analysis and application of a mathematical cholera model.

PubMed

Liao, Shu; Wang, Jin

2011-07-01

In this paper, we conduct a dynamical analysis of the deterministic cholera model proposed in [9]. We study the stability of both the disease-free and endemic equilibria so as to explore the complex epidemic and endemic dynamics of the disease. We demonstrate a real-world application of this model by investigating the recent cholera outbreak in Zimbabwe. Meanwhile, we present numerical simulation results to verify the analytical predictions.

Modeling of Magnetoelastic Nanostructures with a Fully-coupled Mechanical-Micromagnetic Model and Its Applications

NASA Astrophysics Data System (ADS)

Liang, Cheng-Yen

Micromagnetic simulations of magnetoelastic nanostructures traditionally rely on either the Stoner-Wohlfarth model or the Landau-Lifshitz-Gilbert (LLG) model assuming uniform strain (and/or assuming uniform magnetization). While the uniform strain assumption is reasonable when modeling magnetoelastic thin films, this constant strain approach becomes increasingly inaccurate for smaller in-plane nanoscale structures. In this dissertation, a fully-coupled finite element micromagnetic method is developed. The method deals with the micromagnetics, elastodynamics, and piezoelectric effects. The dynamics of magnetization, non-uniform strain distribution, and electric fields are iteratively solved. This more sophisticated modeling technique is critical for guiding the design process of the nanoscale strain-mediated multiferroic elements such as those needed in multiferroic systems. In this dissertation, we will study magnetic property changes (e.g., hysteresis, coercive field, and spin states) due to strain effects in nanostructures. in addition, a multiferroic memory device is studied. The electric-field-driven magnetization switching by applying voltage on patterned electrodes simulation in a nickel memory device is shown in this work. The deterministic control law for the magnetization switching in a nanoring with electric field applied to the patterned electrodes is investigated. Using the patterned electrodes, we show that strain-induced anisotropy is able to be controlled, which changes the magnetization deterministically in a nano-ring.

Developing a stochastic conflict resolution model for urban runoff quality management: Application of info-gap and bargaining theories

NASA Astrophysics Data System (ADS)

Ghodsi, Seyed Hamed; Kerachian, Reza; Estalaki, Siamak Malakpour; Nikoo, Mohammad Reza; Zahmatkesh, Zahra

2016-02-01

In this paper, two deterministic and stochastic multilateral, multi-issue, non-cooperative bargaining methodologies are proposed for urban runoff quality management. In the proposed methodologies, a calibrated Storm Water Management Model (SWMM) is used to simulate stormwater runoff quantity and quality for different urban stormwater runoff management scenarios, which have been defined considering several Low Impact Development (LID) techniques. In the deterministic methodology, the best management scenario, representing location and area of LID controls, is identified using the bargaining model. In the stochastic methodology, uncertainties of some key parameters of SWMM are analyzed using the info-gap theory. For each water quality management scenario, robustness and opportuneness criteria are determined based on utility functions of different stakeholders. Then, to find the best solution, the bargaining model is performed considering a combination of robustness and opportuneness criteria for each scenario based on utility function of each stakeholder. The results of applying the proposed methodology in the Velenjak urban watershed located in the northeastern part of Tehran, the capital city of Iran, illustrate its practical utility for conflict resolution in urban water quantity and quality management. It is shown that the solution obtained using the deterministic model cannot outperform the result of the stochastic model considering the robustness and opportuneness criteria. Therefore, it can be concluded that the stochastic model, which incorporates the main uncertainties, could provide more reliable results.

UAV Swarm Tactics: An Agent-Based Simulation and Markov Process Analysis

DTIC Science & Technology

2013-06-01

CRN Common Random Numbers CSV Comma Separated Values DoE Design of Experiment GLM Generalized Linear Model HVT High Value Target JAR Java ARchive JMF... Java Media Framework JRE Java runtime environment Mason Multi-Agent Simulator Of Networks MOE Measure Of Effectiveness MOP Measures Of Performance...with every set several times, and to write a CSV file with the results. Rather than scripting the agent behavior deterministically, the agents should

Tests of peak flow scaling in simulated self-similar river networks

USGS Publications Warehouse

Menabde, M.; Veitzer, S.; Gupta, V.; Sivapalan, M.

2001-01-01

The effect of linear flow routing incorporating attenuation and network topology on peak flow scaling exponent is investigated for an instantaneously applied uniform runoff on simulated deterministic and random self-similar channel networks. The flow routing is modelled by a linear mass conservation equation for a discrete set of channel links connected in parallel and series, and having the same topology as the channel network. A quasi-analytical solution for the unit hydrograph is obtained in terms of recursion relations. The analysis of this solution shows that the peak flow has an asymptotically scaling dependence on the drainage area for deterministic Mandelbrot-Vicsek (MV) and Peano networks, as well as for a subclass of random self-similar channel networks. However, the scaling exponent is shown to be different from that predicted by the scaling properties of the maxima of the width functions. ?? 2001 Elsevier Science Ltd. All rights reserved.

Kinetics of Thermal Unimolecular Decomposition of Acetic Anhydride: An Integrated Deterministic and Stochastic Model.

PubMed

Mai, Tam V-T; Duong, Minh V; Nguyen, Hieu T; Lin, Kuang C; Huynh, Lam K

2017-04-27

An integrated deterministic and stochastic model within the master equation/Rice-Ramsperger-Kassel-Marcus (ME/RRKM) framework was first used to characterize temperature- and pressure-dependent behaviors of thermal decomposition of acetic anhydride in a wide range of conditions (i.e., 300-1500 K and 0.001-100 atm). Particularly, using potential energy surface and molecular properties obtained from high-level electronic structure calculations at CCSD(T)/CBS, macroscopic thermodynamic properties and rate coefficients of the title reaction were derived with corrections for hindered internal rotation and tunneling treatments. Being in excellent agreement with the scattered experimental data, the results from deterministic and stochastic frameworks confirmed and complemented each other to reveal that the main decomposition pathway proceeds via a 6-membered-ring transition state with the 0 K barrier of 35.2 kcal·mol -1 . This observation was further understood and confirmed by the sensitivity analysis on the time-resolved species profiles and the derived rate coefficients with respect to the ab initio barriers. Such an agreement suggests the integrated model can be confidently used for a wide range of conditions as a powerful postfacto and predictive tool in detailed chemical kinetic modeling and simulation for the title reaction and thus can be extended to complex chemical reactions.

An adaptive modeling and simulation environment for combined-cycle data reconciliation and degradation estimation

NASA Astrophysics Data System (ADS)

Lin, Tsungpo

Performance engineers face the major challenge in modeling and simulation for the after-market power system due to system degradation and measurement errors. Currently, the majority in power generation industries utilizes the deterministic data matching method to calibrate the model and cascade system degradation, which causes significant calibration uncertainty and also the risk of providing performance guarantees. In this research work, a maximum-likelihood based simultaneous data reconciliation and model calibration (SDRMC) is used for power system modeling and simulation. By replacing the current deterministic data matching with SDRMC one can reduce the calibration uncertainty and mitigate the error propagation to the performance simulation. A modeling and simulation environment for a complex power system with certain degradation has been developed. In this environment multiple data sets are imported when carrying out simultaneous data reconciliation and model calibration. Calibration uncertainties are estimated through error analyses and populated to performance simulation by using principle of error propagation. System degradation is then quantified by performance comparison between the calibrated model and its expected new & clean status. To mitigate smearing effects caused by gross errors, gross error detection (GED) is carried out in two stages. The first stage is a screening stage, in which serious gross errors are eliminated in advance. The GED techniques used in the screening stage are based on multivariate data analysis (MDA), including multivariate data visualization and principal component analysis (PCA). Subtle gross errors are treated at the second stage, in which the serial bias compensation or robust M-estimator is engaged. To achieve a better efficiency in the combined scheme of the least squares based data reconciliation and the GED technique based on hypotheses testing, the Levenberg-Marquardt (LM) algorithm is utilized as the optimizer. To reduce the computation time and stabilize the problem solving for a complex power system such as a combined cycle power plant, meta-modeling using the response surface equation (RSE) and system/process decomposition are incorporated with the simultaneous scheme of SDRMC. The goal of this research work is to reduce the calibration uncertainties and, thus, the risks of providing performance guarantees arisen from uncertainties in performance simulation.

Composite load spectra for select space propulsion structural components

NASA Technical Reports Server (NTRS)

Newell, J. F.; Kurth, R. E.; Ho, H.

1991-01-01

The objective of this program is to develop generic load models with multiple levels of progressive sophistication to simulate the composite (combined) load spectra that are induced in space propulsion system components, representative of Space Shuttle Main Engines (SSME), such as transfer ducts, turbine blades, and liquid oxygen posts and system ducting. The first approach will consist of using state of the art probabilistic methods to describe the individual loading conditions and combinations of these loading conditions to synthesize the composite load spectra simulation. The second approach will consist of developing coupled models for composite load spectra simulation which combine the deterministic models for composite load dynamic, acoustic, high pressure, and high rotational speed, etc., load simulation using statistically varying coefficients. These coefficients will then be determined using advanced probabilistic simulation methods with and without strategically selected experimental data.

Composite Load Spectra for Select Space Propulsion Structural Components

NASA Technical Reports Server (NTRS)

Ho, Hing W.; Newell, James F.

1994-01-01

Generic load models are described with multiple levels of progressive sophistication to simulate the composite (combined) load spectra (CLS) that are induced in space propulsion system components, representative of Space Shuttle Main Engines (SSME), such as transfer ducts, turbine blades and liquid oxygen (LOX) posts. These generic (coupled) models combine the deterministic models for composite load dynamic, acoustic, high-pressure and high rotational speed, etc., load simulation using statistically varying coefficients. These coefficients are then determined using advanced probabilistic simulation methods with and without strategically selected experimental data. The entire simulation process is included in a CLS computer code. Applications of the computer code to various components in conjunction with the PSAM (Probabilistic Structural Analysis Method) to perform probabilistic load evaluation and life prediction evaluations are also described to illustrate the effectiveness of the coupled model approach.

A deterministic model of nettle caterpillar life cycle

NASA Astrophysics Data System (ADS)

Syukriyah, Y.; Nuraini, N.; Handayani, D.

2018-03-01

Palm oil is an excellent product in the plantation sector in Indonesia. The level of palm oil productivity is very potential to increase every year. However, the level of palm oil productivity is lower than its potential. Pests and diseases are the main factors that can reduce production levels by up to 40%. The existence of pests in plants can be caused by various factors, so the anticipation in controlling pest attacks should be prepared as early as possible. Caterpillars are the main pests in oil palm. The nettle caterpillars are leaf eaters that can significantly decrease palm productivity. We construct a deterministic model that describes the life cycle of the caterpillar and its mitigation by using a caterpillar predator. The equilibrium points of the model are analyzed. The numerical simulations are constructed to give a representation how the predator as the natural enemies affects the nettle caterpillar life cycle.

Computing the apparent centroid of radar targets

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

Lee, C.E.

1996-12-31

A high-frequency multibounce radar scattering code was used as a simulation platform for demonstrating an algorithm to compute the ARC of specific radar targets. To illustrate this simulation process, several targets models were used. Simulation results for a sphere model were used to determine the errors of approximation associated with the simulation; verifying the process. The severity of glint induced tracking errors was also illustrated using a model of an F-15 aircraft. It was shown, in a deterministic manner, that the ARC of a target can fall well outside its physical extent. Finally, the apparent radar centroid simulation based onmore » a ray casting procedure is well suited for use on most massively parallel computing platforms and could lead to the development of a near real-time radar tracking simulation for applications such as endgame fuzing, survivability, and vulnerability analyses using specific radar targets and fuze algorithms.« less

Monte Carlo capabilities of the SCALE code system

DOE PAGES

Rearden, Bradley T.; Petrie, Jr., Lester M.; Peplow, Douglas E.; ...

2014-09-12

SCALE is a broadly used suite of tools for nuclear systems modeling and simulation that provides comprehensive, verified and validated, user-friendly capabilities for criticality safety, reactor physics, radiation shielding, and sensitivity and uncertainty analysis. For more than 30 years, regulators, licensees, and research institutions around the world have used SCALE for nuclear safety analysis and design. SCALE provides a “plug-and-play” framework that includes three deterministic and three Monte Carlo radiation transport solvers that can be selected based on the desired solution, including hybrid deterministic/Monte Carlo simulations. SCALE includes the latest nuclear data libraries for continuous-energy and multigroup radiation transport asmore » well as activation, depletion, and decay calculations. SCALE’s graphical user interfaces assist with accurate system modeling, visualization, and convenient access to desired results. SCALE 6.2 will provide several new capabilities and significant improvements in many existing features, especially with expanded continuous-energy Monte Carlo capabilities for criticality safety, shielding, depletion, and sensitivity and uncertainty analysis. Finally, an overview of the Monte Carlo capabilities of SCALE is provided here, with emphasis on new features for SCALE 6.2.« less

Studies on a Novel Neuro-dynamic Model for Prediction Learning of Fluctuated Data Streams: Beyond Dichotomy between Probabilistic and Deterministic Models

DTIC Science & Technology

2014-11-04

learning by robots as well as video image understanding by accumulated learning of the exemplars are discussed. 15. SUBJECT TERMS Cognitive ...learning to predict perceptual streams or encountering events by acquiring internal models is indispensable for intelligent or cognitive systems because...various cognitive functions are based on this compentency including goal-directed planning, mental simulation and recognition of the current situation

3D Dynamic Rupture Simulations along Dipping Faults, with a focus on the Wasatch Fault Zone, Utah

NASA Astrophysics Data System (ADS)

Withers, K.; Moschetti, M. P.

2017-12-01

We study dynamic rupture and ground motion from dip-slip faults in regions that have high-seismic hazard, such as the Wasatch fault zone, Utah. Previous numerical simulations have modeled deterministic ground motion along segments of this fault in the heavily populated regions near Salt Lake City but were restricted to low frequencies ( 1 Hz). We seek to better understand the rupture process and assess broadband ground motions and variability from the Wasatch Fault Zone by extending deterministic ground motion prediction to higher frequencies (up to 5 Hz). We perform simulations along a dipping normal fault (40 x 20 km along strike and width, respectively) with characteristics derived from geologic observations to generate a suite of ruptures > Mw 6.5. This approach utilizes dynamic simulations (fully physics-based models, where the initial stress drop and friction law are imposed) using a summation by parts (SBP) method. The simulations include rough-fault topography following a self-similar fractal distribution (over length scales from 100 m to the size of the fault) in addition to off-fault plasticity. Energy losses from heat and other mechanisms, modeled as anelastic attenuation, are also included, as well as free-surface topography, which can significantly affect ground motion patterns. We compare the effect of material structure and both rate and state and slip-weakening friction laws have on rupture propagation. The simulations show reduced slip and moment release in the near surface with the inclusion of plasticity, better agreeing with observations of shallow slip deficit. Long-wavelength fault geometry imparts a non-uniform stress distribution along both dip and strike, influencing the preferred rupture direction and hypocenter location, potentially important for seismic hazard estimation.

A Model for the Epigenetic Switch Linking Inflammation to Cell Transformation: Deterministic and Stochastic Approaches

PubMed Central

Gérard, Claude; Gonze, Didier; Lemaigre, Frédéric; Novák, Béla

2014-01-01

Recently, a molecular pathway linking inflammation to cell transformation has been discovered. This molecular pathway rests on a positive inflammatory feedback loop between NF-κB, Lin28, Let-7 microRNA and IL6, which leads to an epigenetic switch allowing cell transformation. A transient activation of an inflammatory signal, mediated by the oncoprotein Src, activates NF-κB, which elicits the expression of Lin28. Lin28 decreases the expression of Let-7 microRNA, which results in higher level of IL6 than achieved directly by NF-κB. In turn, IL6 can promote NF-κB activation. Finally, IL6 also elicits the synthesis of STAT3, which is a crucial activator for cell transformation. Here, we propose a computational model to account for the dynamical behavior of this positive inflammatory feedback loop. By means of a deterministic model, we show that an irreversible bistable switch between a transformed and a non-transformed state of the cell is at the core of the dynamical behavior of the positive feedback loop linking inflammation to cell transformation. The model indicates that inhibitors (tumor suppressors) or activators (oncogenes) of this positive feedback loop regulate the occurrence of the epigenetic switch by modulating the threshold of inflammatory signal (Src) needed to promote cell transformation. Both stochastic simulations and deterministic simulations of a heterogeneous cell population suggest that random fluctuations (due to molecular noise or cell-to-cell variability) are able to trigger cell transformation. Moreover, the model predicts that oncogenes/tumor suppressors respectively decrease/increase the robustness of the non-transformed state of the cell towards random fluctuations. Finally, the model accounts for the potential effect of competing endogenous RNAs, ceRNAs, on the dynamics of the epigenetic switch. Depending on their microRNA targets, the model predicts that ceRNAs could act as oncogenes or tumor suppressors by regulating the occurrence of cell transformation. PMID:24499937

An efficient deterministic-probabilistic approach to modeling regional groundwater flow: 1. Theory

USGS Publications Warehouse

Yen, Chung-Cheng; Guymon, Gary L.

1990-01-01

An efficient probabilistic model is developed and cascaded with a deterministic model for predicting water table elevations in regional aquifers. The objective is to quantify model uncertainty where precise estimates of water table elevations may be required. The probabilistic model is based on the two-point probability method which only requires prior knowledge of uncertain variables mean and coefficient of variation. The two-point estimate method is theoretically developed and compared with the Monte Carlo simulation method. The results of comparisons using hypothetical determinisitic problems indicate that the two-point estimate method is only generally valid for linear problems where the coefficients of variation of uncertain parameters (for example, storage coefficient and hydraulic conductivity) is small. The two-point estimate method may be applied to slightly nonlinear problems with good results, provided coefficients of variation are small. In such cases, the two-point estimate method is much more efficient than the Monte Carlo method provided the number of uncertain variables is less than eight.

An Efficient Deterministic-Probabilistic Approach to Modeling Regional Groundwater Flow: 1. Theory

NASA Astrophysics Data System (ADS)

Yen, Chung-Cheng; Guymon, Gary L.

1990-07-01

An efficient probabilistic model is developed and cascaded with a deterministic model for predicting water table elevations in regional aquifers. The objective is to quantify model uncertainty where precise estimates of water table elevations may be required. The probabilistic model is based on the two-point probability method which only requires prior knowledge of uncertain variables mean and coefficient of variation. The two-point estimate method is theoretically developed and compared with the Monte Carlo simulation method. The results of comparisons using hypothetical determinisitic problems indicate that the two-point estimate method is only generally valid for linear problems where the coefficients of variation of uncertain parameters (for example, storage coefficient and hydraulic conductivity) is small. The two-point estimate method may be applied to slightly nonlinear problems with good results, provided coefficients of variation are small. In such cases, the two-point estimate method is much more efficient than the Monte Carlo method provided the number of uncertain variables is less than eight.

Stochastic Modelling, Analysis, and Simulations of the Solar Cycle Dynamic Process

NASA Astrophysics Data System (ADS)

Turner, Douglas C.; Ladde, Gangaram S.

2018-03-01

Analytical solutions, discretization schemes and simulation results are presented for the time delay deterministic differential equation model of the solar dynamo presented by Wilmot-Smith et al. In addition, this model is extended under stochastic Gaussian white noise parametric fluctuations. The introduction of stochastic fluctuations incorporates variables affecting the dynamo process in the solar interior, estimation error of parameters, and uncertainty of the α-effect mechanism. Simulation results are presented and analyzed to exhibit the effects of stochastic parametric volatility-dependent perturbations. The results generalize and extend the work of Hazra et al. In fact, some of these results exhibit the oscillatory dynamic behavior generated by the stochastic parametric additative perturbations in the absence of time delay. In addition, the simulation results of the modified stochastic models influence the change in behavior of the very recently developed stochastic model of Hazra et al.

Deterministic generation of remote entanglement with active quantum feedback

DOE PAGES

Martin, Leigh; Motzoi, Felix; Li, Hanhan; ...

2015-12-10

We develop and study protocols for deterministic remote entanglement generation using quantum feedback, without relying on an entangling Hamiltonian. In order to formulate the most effective experimentally feasible protocol, we introduce the notion of average-sense locally optimal feedback protocols, which do not require real-time quantum state estimation, a difficult component of real-time quantum feedback control. We use this notion of optimality to construct two protocols that can deterministically create maximal entanglement: a semiclassical feedback protocol for low-efficiency measurements and a quantum feedback protocol for high-efficiency measurements. The latter reduces to direct feedback in the continuous-time limit, whose dynamics can bemore » modeled by a Wiseman-Milburn feedback master equation, which yields an analytic solution in the limit of unit measurement efficiency. Our formalism can smoothly interpolate between continuous-time and discrete-time descriptions of feedback dynamics and we exploit this feature to derive a superior hybrid protocol for arbitrary nonunit measurement efficiency that switches between quantum and semiclassical protocols. Lastly, we show using simulations incorporating experimental imperfections that deterministic entanglement of remote superconducting qubits may be achieved with current technology using the continuous-time feedback protocol alone.« less

User's manual for a parameter identification technique. [with options for model simulation for fixed input forcing functions and identification from wind tunnel and flight measurements

NASA Technical Reports Server (NTRS)

Kanning, G.

1975-01-01

A digital computer program written in FORTRAN is presented that implements the system identification theory for deterministic systems using input-output measurements. The user supplies programs simulating the mathematical model of the physical plant whose parameters are to be identified. The user may choose any one of three options. The first option allows for a complete model simulation for fixed input forcing functions. The second option identifies up to 36 parameters of the model from wind tunnel or flight measurements. The third option performs a sensitivity analysis for up to 36 parameters. The use of each option is illustrated with an example using input-output measurements for a helicopter rotor tested in a wind tunnel.

Influence of the hypercycle on the error threshold: a stochastic approach.

PubMed

García-Tejedor, A; Sanz-Nuño, J C; Olarrea, J; Javier de la Rubia, F; Montero, F

1988-10-21

The role of fluctuations on the error threshold of the hypercycle has been studied by a stochastic approach on a very simplified model. For this model, the master equation was derived and its unique steady state calculated. This state implies the extinction of the system. But the actual time necessary to reach the steady state may be astronomically long whereas for times of experimental interest the system could be near some quasi-stationary states. In order to explore this possibility a Gillespie simulation of the stochastic process has been carried out. These quasi-stationary states correspond to the deterministic steady states of the system. The error threshold shifts towards higher values of the quality factor Q. Moreover, information about the fluctuations around the quasi-stationary states is obtained. The results are discussed in relation to the deterministic states.

Numerical simulations of piecewise deterministic Markov processes with an application to the stochastic Hodgkin-Huxley model.

PubMed

Ding, Shaojie; Qian, Min; Qian, Hong; Zhang, Xuejuan

2016-12-28

The stochastic Hodgkin-Huxley model is one of the best-known examples of piecewise deterministic Markov processes (PDMPs), in which the electrical potential across a cell membrane, V(t), is coupled with a mesoscopic Markov jump process representing the stochastic opening and closing of ion channels embedded in the membrane. The rates of the channel kinetics, in turn, are voltage-dependent. Due to this interdependence, an accurate and efficient sampling of the time evolution of the hybrid stochastic systems has been challenging. The current exact simulation methods require solving a voltage-dependent hitting time problem for multiple path-dependent intensity functions with random thresholds. This paper proposes a simulation algorithm that approximates an alternative representation of the exact solution by fitting the log-survival function of the inter-jump dwell time, H(t), with a piecewise linear one. The latter uses interpolation points that are chosen according to the time evolution of the H(t), as the numerical solution to the coupled ordinary differential equations of V(t) and H(t). This computational method can be applied to all PDMPs. Pathwise convergence of the approximated sample trajectories to the exact solution is proven, and error estimates are provided. Comparison with a previous algorithm that is based on piecewise constant approximation is also presented.

Microcanonical model for interface formation

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

Rucklidge, A.; Zaleski, S.

1988-04-01

We describe a new cellular automaton model which allows us to simulate separation of phases. The model is an extension of existing cellular automata for the Ising model, such as Q2R. It conserves particle number and presents the qualitative features of spinodal decomposition. The dynamics is deterministic and does not require random number generators. The spins exchange energy with small local reservoirs or demons. The rate of relaxation to equilibrium is investigated, and the results are compared to the Lifshitz-Slyozov theory.

Modeling seasonal measles transmission in China

NASA Astrophysics Data System (ADS)

Bai, Zhenguo; Liu, Dan

2015-08-01

A discrete-time deterministic measles model with periodic transmission rate is formulated and studied. The basic reproduction number R0 is defined and used as the threshold parameter in determining the dynamics of the model. It is shown that the disease will die out if R0 < 1 , and the disease will persist in the population if R0 > 1 . Parameters in the model are estimated on the basis of demographic and epidemiological data. Numerical simulations are presented to describe the seasonal fluctuation of measles infection in China.

PRMS-IV, the precipitation-runoff modeling system, version 4

USGS Publications Warehouse

Markstrom, Steven L.; Regan, R. Steve; Hay, Lauren E.; Viger, Roland J.; Webb, Richard M.; Payn, Robert A.; LaFontaine, Jacob H.

2015-01-01

Computer models that simulate the hydrologic cycle at a watershed scale facilitate assessment of variability in climate, biota, geology, and human activities on water availability and flow. This report describes an updated version of the Precipitation-Runoff Modeling System. The Precipitation-Runoff Modeling System is a deterministic, distributed-parameter, physical-process-based modeling system developed to evaluate the response of various combinations of climate and land use on streamflow and general watershed hydrology. Several new model components were developed, and all existing components were updated, to enhance performance and supportability. This report describes the history, application, concepts, organization, and mathematical formulation of the Precipitation-Runoff Modeling System and its model components. This updated version provides improvements in (1) system flexibility for integrated science, (2) verification of conservation of water during simulation, (3) methods for spatial distribution of climate boundary conditions, and (4) methods for simulation of soil-water flow and storage.

PCEMCAN - Probabilistic Ceramic Matrix Composites Analyzer: User's Guide, Version 1.0

NASA Technical Reports Server (NTRS)

Shah, Ashwin R.; Mital, Subodh K.; Murthy, Pappu L. N.

1998-01-01

PCEMCAN (Probabalistic CEramic Matrix Composites ANalyzer) is an integrated computer code developed at NASA Lewis Research Center that simulates uncertainties associated with the constituent properties, manufacturing process, and geometric parameters of fiber reinforced ceramic matrix composites and quantifies their random thermomechanical behavior. The PCEMCAN code can perform the deterministic as well as probabilistic analyses to predict thermomechanical properties. This User's guide details the step-by-step procedure to create input file and update/modify the material properties database required to run PCEMCAN computer code. An overview of the geometric conventions, micromechanical unit cell, nonlinear constitutive relationship and probabilistic simulation methodology is also provided in the manual. Fast probability integration as well as Monte-Carlo simulation methods are available for the uncertainty simulation. Various options available in the code to simulate probabilistic material properties and quantify sensitivity of the primitive random variables have been described. The description of deterministic as well as probabilistic results have been described using demonstration problems. For detailed theoretical description of deterministic and probabilistic analyses, the user is referred to the companion documents "Computational Simulation of Continuous Fiber-Reinforced Ceramic Matrix Composite Behavior," NASA TP-3602, 1996 and "Probabilistic Micromechanics and Macromechanics for Ceramic Matrix Composites", NASA TM 4766, June 1997.

Insights into the deterministic skill of air quality ensembles ...

EPA Pesticide Factsheets

Simulations from chemical weather models are subject to uncertainties in the input data (e.g. emission inventory, initial and boundary conditions) as well as those intrinsic to the model (e.g. physical parameterization, chemical mechanism). Multi-model ensembles can improve the forecast skill, provided that certain mathematical conditions are fulfilled. In this work, four ensemble methods were applied to two different datasets, and their performance was compared for ozone (O3), nitrogen dioxide (NO2) and particulate matter (PM10). Apart from the unconditional ensemble average, the approach behind the other three methods relies on adding optimum weights to members or constraining the ensemble to those members that meet certain conditions in time or frequency domain. The two different datasets were created for the first and second phase of the Air Quality Model Evaluation International Initiative (AQMEII). The methods are evaluated against ground level observations collected from the EMEP (European Monitoring and Evaluation Programme) and AirBase databases. The goal of the study is to quantify to what extent we can extract predictable signals from an ensemble with superior skill over the single models and the ensemble mean. Verification statistics show that the deterministic models simulate better O3 than NO2 and PM10, linked to different levels of complexity in the represented processes. The unconditional ensemble mean achieves higher skill compared to each stati

Adequacy assessment of composite generation and transmission systems incorporating wind energy conversion systems

NASA Astrophysics Data System (ADS)

Gao, Yi

The development and utilization of wind energy for satisfying electrical demand has received considerable attention in recent years due to its tremendous environmental, social and economic benefits, together with public support and government incentives. Electric power generation from wind energy behaves quite differently from that of conventional sources. The fundamentally different operating characteristics of wind energy facilities therefore affect power system reliability in a different manner than those of conventional systems. The reliability impact of such a highly variable energy source is an important aspect that must be assessed when the wind power penetration is significant. The focus of the research described in this thesis is on the utilization of state sampling Monte Carlo simulation in wind integrated bulk electric system reliability analysis and the application of these concepts in system planning and decision making. Load forecast uncertainty is an important factor in long range planning and system development. This thesis describes two approximate approaches developed to reduce the number of steps in a load duration curve which includes load forecast uncertainty, and to provide reasonably accurate generating and bulk system reliability index predictions. The developed approaches are illustrated by application to two composite test systems. A method of generating correlated random numbers with uniform distributions and a specified correlation coefficient in the state sampling method is proposed and used to conduct adequacy assessment in generating systems and in bulk electric systems containing correlated wind farms in this thesis. The studies described show that it is possible to use the state sampling Monte Carlo simulation technique to quantitatively assess the reliability implications associated with adding wind power to a composite generation and transmission system including the effects of multiple correlated wind sites. This is an important development as it permits correlated wind farms to be incorporated in large practical system studies without requiring excessive increases in computer solution time. The procedures described in this thesis for creating monthly and seasonal wind farm models should prove useful in situations where time period models are required to incorporate scheduled maintenance of generation and transmission facilities. There is growing interest in combining deterministic considerations with probabilistic assessment in order to evaluate the quantitative system risk and conduct bulk power system planning. A relatively new approach that incorporates deterministic and probabilistic considerations in a single risk assessment framework has been designated as the joint deterministic-probabilistic approach. The research work described in this thesis illustrates that the joint deterministic-probabilistic approach can be effectively used to integrate wind power in bulk electric system planning. The studies described in this thesis show that the application of the joint deterministic-probabilistic method provides more stringent results for a system with wind power than the traditional deterministic N-1 method because the joint deterministic-probabilistic technique is driven by the deterministic N-1 criterion with an added probabilistic perspective which recognizes the power output characteristics of a wind turbine generator.

Assimilation of lightning data by nudging tropospheric water vapor and applications to numerical forecasts of convective events

NASA Astrophysics Data System (ADS)

Dixon, Kenneth

A lightning data assimilation technique is developed for use with observations from the World Wide Lightning Location Network (WWLLN). The technique nudges the water vapor mixing ratio toward saturation within 10 km of a lightning observation. This technique is applied to deterministic forecasts of convective events on 29 June 2012, 17 November 2013, and 19 April 2011 as well as an ensemble forecast of the 29 June 2012 event using the Weather Research and Forecasting (WRF) model. Lightning data are assimilated over the first 3 hours of the forecasts, and the subsequent impact on forecast quality is evaluated. The nudged deterministic simulations for all events produce composite reflectivity fields that are closer to observations. For the ensemble forecasts of the 29 June 2012 event, the improvement in forecast quality from lightning assimilation is more subtle than for the deterministic forecasts, suggesting that the lightning assimilation may improve ensemble convective forecasts where conventional observations (e.g., aircraft, surface, radiosonde, satellite) are less dense or unavailable.

Modeling and Simulation for Mission Operations Work System Design

NASA Technical Reports Server (NTRS)

Sierhuis, Maarten; Clancey, William J.; Seah, Chin; Trimble, Jay P.; Sims, Michael H.

2003-01-01

Work System analysis and design is complex and non-deterministic. In this paper we describe Brahms, a multiagent modeling and simulation environment for designing complex interactions in human-machine systems. Brahms was originally conceived as a business process design tool that simulates work practices, including social systems of work. We describe our modeling and simulation method for mission operations work systems design, based on a research case study in which we used Brahms to design mission operations for a proposed discovery mission to the Moon. We then describe the results of an actual method application project-the Brahms Mars Exploration Rover. Space mission operations are similar to operations of traditional organizations; we show that the application of Brahms for space mission operations design is relevant and transferable to other types of business processes in organizations.

Acceptability of the Kalman filter to monitor pronghorn population size

Treesearch

Raymond L. Czaplewski

1986-01-01

Pronghorn antelope are important components of grassland and steppe ecosystems in Wyoming. Monitoring data on the size and population dynamics of these herds are expensive and gathered only a few times each year. Reliable data include estimates of animals harvested and proportion of bucks, does, and fawns. A deterministic simulation model has been used to improve...

Controlled deterministic implantation by nanostencil lithography at the limit of ion-aperture straggling

NASA Astrophysics Data System (ADS)

Alves, A. D. C.; Newnham, J.; van Donkelaar, J. A.; Rubanov, S.; McCallum, J. C.; Jamieson, D. N.

2013-04-01

Solid state electronic devices fabricated in silicon employ many ion implantation steps in their fabrication. In nanoscale devices deterministic implants of dopant atoms with high spatial precision will be needed to overcome problems with statistical variations in device characteristics and to open new functionalities based on controlled quantum states of single atoms. However, to deterministically place a dopant atom with the required precision is a significant technological challenge. Here we address this challenge with a strategy based on stepped nanostencil lithography for the construction of arrays of single implanted atoms. We address the limit on spatial precision imposed by ion straggling in the nanostencil—fabricated with the readily available focused ion beam milling technique followed by Pt deposition. Two nanostencils have been fabricated; a 60 nm wide aperture in a 3 μm thick Si cantilever and a 30 nm wide aperture in a 200 nm thick Si3N4 membrane. The 30 nm wide aperture demonstrates the fabricating process for sub-50 nm apertures while the 60 nm aperture was characterized with 500 keV He+ ion forward scattering to measure the effect of ion straggling in the collimator and deduce a model for its internal structure using the GEANT4 ion transport code. This model is then applied to simulate collimation of a 14 keV P+ ion beam in a 200 nm thick Si3N4 membrane nanostencil suitable for the implantation of donors in silicon. We simulate collimating apertures with widths in the range of 10-50 nm because we expect the onset of J-coupling in a device with 30 nm donor spacing. We find that straggling in the nanostencil produces mis-located implanted ions with a probability between 0.001 and 0.08 depending on the internal collimator profile and the alignment with the beam direction. This result is favourable for the rapid prototyping of a proof-of-principle device containing multiple deterministically implanted dopants.

Hands-on-Entropy, Energy Balance with Biological Relevance

NASA Astrophysics Data System (ADS)

Reeves, Mark

2015-03-01

Entropy changes underlie the physics that dominates biological interactions. Indeed, introductory biology courses often begin with an exploration of the qualities of water that are important to living systems. However, one idea that is not explicitly addressed in most introductory physics or biology textbooks is important contribution of the entropy in driving fundamental biological processes towards equilibrium. From diffusion to cell-membrane formation, to electrostatic binding in protein folding, to the functioning of nerve cells, entropic effects often act to counterbalance deterministic forces such as electrostatic attraction and in so doing, allow for effective molecular signaling. A small group of biology, biophysics and computer science faculty have worked together for the past five years to develop curricular modules (based on SCALEUP pedagogy). This has enabled students to create models of stochastic and deterministic processes. Our students are first-year engineering and science students in the calculus-based physics course and they are not expected to know biology beyond the high-school level. In our class, they learn to reduce complex biological processes and structures in order model them mathematically to account for both deterministic and probabilistic processes. The students test these models in simulations and in laboratory experiments that are biologically relevant such as diffusion, ionic transport, and ligand-receptor binding. Moreover, the students confront random forces and traditional forces in problems, simulations, and in laboratory exploration throughout the year-long course as they move from traditional kinematics through thermodynamics to electrostatic interactions. This talk will present a number of these exercises, with particular focus on the hands-on experiments done by the students, and will give examples of the tangible material that our students work with throughout the two-semester sequence of their course on introductory physics with a bio focus. Supported by NSF DUE.

Hybrid approaches for multiple-species stochastic reaction–diffusion models

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

Spill, Fabian, E-mail: fspill@bu.edu; Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139; Guerrero, Pilar

2015-10-15

Reaction–diffusion models are used to describe systems in fields as diverse as physics, chemistry, ecology and biology. The fundamental quantities in such models are individual entities such as atoms and molecules, bacteria, cells or animals, which move and/or react in a stochastic manner. If the number of entities is large, accounting for each individual is inefficient, and often partial differential equation (PDE) models are used in which the stochastic behaviour of individuals is replaced by a description of the averaged, or mean behaviour of the system. In some situations the number of individuals is large in certain regions and smallmore » in others. In such cases, a stochastic model may be inefficient in one region, and a PDE model inaccurate in another. To overcome this problem, we develop a scheme which couples a stochastic reaction–diffusion system in one part of the domain with its mean field analogue, i.e. a discretised PDE model, in the other part of the domain. The interface in between the two domains occupies exactly one lattice site and is chosen such that the mean field description is still accurate there. In this way errors due to the flux between the domains are small. Our scheme can account for multiple dynamic interfaces separating multiple stochastic and deterministic domains, and the coupling between the domains conserves the total number of particles. The method preserves stochastic features such as extinction not observable in the mean field description, and is significantly faster to simulate on a computer than the pure stochastic model. - Highlights: • A novel hybrid stochastic/deterministic reaction–diffusion simulation method is given. • Can massively speed up stochastic simulations while preserving stochastic effects. • Can handle multiple reacting species. • Can handle moving boundaries.« less

Deterministic quantum dense coding networks

NASA Astrophysics Data System (ADS)

Roy, Saptarshi; Chanda, Titas; Das, Tamoghna; Sen(De), Aditi; Sen, Ujjwal

2018-07-01

We consider the scenario of deterministic classical information transmission between multiple senders and a single receiver, when they a priori share a multipartite quantum state - an attempt towards building a deterministic dense coding network. Specifically, we prove that in the case of two or three senders and a single receiver, generalized Greenberger-Horne-Zeilinger (gGHZ) states are not beneficial for sending classical information deterministically beyond the classical limit, except when the shared state is the GHZ state itself. On the other hand, three- and four-qubit generalized W (gW) states with specific parameters as well as the four-qubit Dicke states can provide a quantum advantage of sending the information in deterministic dense coding. Interestingly however, numerical simulations in the three-qubit scenario reveal that the percentage of states from the GHZ-class that are deterministic dense codeable is higher than that of states from the W-class.

Automated Calibration For Numerical Models Of Riverflow

NASA Astrophysics Data System (ADS)

Fernandez, Betsaida; Kopmann, Rebekka; Oladyshkin, Sergey

2017-04-01

Calibration of numerical models is fundamental since the beginning of all types of hydro system modeling, to approximate the parameters that can mimic the overall system behavior. Thus, an assessment of different deterministic and stochastic optimization methods is undertaken to compare their robustness, computational feasibility, and global search capacity. Also, the uncertainty of the most suitable methods is analyzed. These optimization methods minimize the objective function that comprises synthetic measurements and simulated data. Synthetic measurement data replace the observed data set to guarantee an existing parameter solution. The input data for the objective function derivate from a hydro-morphological dynamics numerical model which represents an 180-degree bend channel. The hydro- morphological numerical model shows a high level of ill-posedness in the mathematical problem. The minimization of the objective function by different candidate methods for optimization indicates a failure in some of the gradient-based methods as Newton Conjugated and BFGS. Others reveal partial convergence, such as Nelder-Mead, Polak und Ribieri, L-BFGS-B, Truncated Newton Conjugated, and Trust-Region Newton Conjugated Gradient. Further ones indicate parameter solutions that range outside the physical limits, such as Levenberg-Marquardt and LeastSquareRoot. Moreover, there is a significant computational demand for genetic optimization methods, such as Differential Evolution and Basin-Hopping, as well as for Brute Force methods. The Deterministic Sequential Least Square Programming and the scholastic Bayes Inference theory methods present the optimal optimization results. keywords: Automated calibration of hydro-morphological dynamic numerical model, Bayesian inference theory, deterministic optimization methods.

A hybrid algorithm for coupling partial differential equation and compartment-based dynamics.

PubMed

Harrison, Jonathan U; Yates, Christian A

2016-09-01

Stochastic simulation methods can be applied successfully to model exact spatio-temporally resolved reaction-diffusion systems. However, in many cases, these methods can quickly become extremely computationally intensive with increasing particle numbers. An alternative description of many of these systems can be derived in the diffusive limit as a deterministic, continuum system of partial differential equations (PDEs). Although the numerical solution of such PDEs is, in general, much more efficient than the full stochastic simulation, the deterministic continuum description is generally not valid when copy numbers are low and stochastic effects dominate. Therefore, to take advantage of the benefits of both of these types of models, each of which may be appropriate in different parts of a spatial domain, we have developed an algorithm that can be used to couple these two types of model together. This hybrid coupling algorithm uses an overlap region between the two modelling regimes. By coupling fluxes at one end of the interface and using a concentration-matching condition at the other end, we ensure that mass is appropriately transferred between PDE- and compartment-based regimes. Our methodology gives notable reductions in simulation time in comparison with using a fully stochastic model, while maintaining the important stochastic features of the system and providing detail in appropriate areas of the domain. We test our hybrid methodology robustly by applying it to several biologically motivated problems including diffusion and morphogen gradient formation. Our analysis shows that the resulting error is small, unbiased and does not grow over time. © 2016 The Authors.

A hybrid algorithm for coupling partial differential equation and compartment-based dynamics

PubMed Central

Yates, Christian A.

2016-01-01

Stochastic simulation methods can be applied successfully to model exact spatio-temporally resolved reaction–diffusion systems. However, in many cases, these methods can quickly become extremely computationally intensive with increasing particle numbers. An alternative description of many of these systems can be derived in the diffusive limit as a deterministic, continuum system of partial differential equations (PDEs). Although the numerical solution of such PDEs is, in general, much more efficient than the full stochastic simulation, the deterministic continuum description is generally not valid when copy numbers are low and stochastic effects dominate. Therefore, to take advantage of the benefits of both of these types of models, each of which may be appropriate in different parts of a spatial domain, we have developed an algorithm that can be used to couple these two types of model together. This hybrid coupling algorithm uses an overlap region between the two modelling regimes. By coupling fluxes at one end of the interface and using a concentration-matching condition at the other end, we ensure that mass is appropriately transferred between PDE- and compartment-based regimes. Our methodology gives notable reductions in simulation time in comparison with using a fully stochastic model, while maintaining the important stochastic features of the system and providing detail in appropriate areas of the domain. We test our hybrid methodology robustly by applying it to several biologically motivated problems including diffusion and morphogen gradient formation. Our analysis shows that the resulting error is small, unbiased and does not grow over time. PMID:27628171

The impact of ARM on climate modeling

DOE PAGES

Randall, David A.; Del Genio, Anthony D.; Donner, Lee J.; ...

2016-07-15

Climate models are among humanity’s most ambitious and elaborate creations. They are designed to simulate the interactions of the atmosphere, ocean, land surface, and cryosphere on time scales far beyond the limits of deterministic predictability and including the effects of time-dependent external forcings. The processes involved include radiative transfer, fluid dynamics, microphysics, and some aspects of geochemistry, biology, and ecology. The models explicitly simulate processes on spatial scales ranging from the circumference of Earth down to 100 km or smaller and implicitly include the effects of processes on even smaller scales down to a micron or so. In addition, themore » atmospheric component of a climate model can be called an atmospheric global circulation model (AGCM).« less

Impacts of DNAPL Source Treatment: Experimental and Modeling Assessment of the Benefits of Partial DNAPL Source Removal

DTIC Science & Technology

2009-09-01

nuclear industry for conducting performance assessment calculations. The analytical FORTRAN code for the DNAPL source function, REMChlor, was...project. The first was to apply existing deterministic codes , such as T2VOC and UTCHEM, to the DNAPL source zone to simulate the remediation processes...but describe the spatial variability of source zones unlike one-dimensional flow and transport codes that assume homogeneity. The Lagrangian models

Optimum spaceborne computer system design by simulation

NASA Technical Reports Server (NTRS)

Williams, T.; Kerner, H.; Weatherbee, J. E.; Taylor, D. S.; Hodges, B.

1973-01-01

A deterministic simulator is described which models the Automatically Reconfigurable Modular Multiprocessor System (ARMMS), a candidate computer system for future manned and unmanned space missions. Its use as a tool to study and determine the minimum computer system configuration necessary to satisfy the on-board computational requirements of a typical mission is presented. The paper describes how the computer system configuration is determined in order to satisfy the data processing demand of the various shuttle booster subsytems. The configuration which is developed as a result of studies with the simulator is optimal with respect to the efficient use of computer system resources.

Development of three-dimensional patient face model that enables real-time collision detection and cutting operation for a dental simulator.

PubMed

Yamaguchi, Satoshi; Yamada, Yuya; Yoshida, Yoshinori; Noborio, Hiroshi; Imazato, Satoshi

2012-01-01

The virtual reality (VR) simulator is a useful tool to develop dental hand skill. However, VR simulations with reactions of patients have limited computational time to reproduce a face model. Our aim was to develop a patient face model that enables real-time collision detection and cutting operation by using stereolithography (STL) and deterministic finite automaton (DFA) data files. We evaluated dependence of computational cost and constructed the patient face model using the optimum condition for combining STL and DFA data files, and assessed the computational costs for operation in do-nothing, collision, cutting, and combination of collision and cutting. The face model was successfully constructed with low computational costs of 11.3, 18.3, 30.3, and 33.5 ms for do-nothing, collision, cutting, and collision and cutting, respectively. The patient face model could be useful for developing dental hand skill with VR.

Analytical approximations for spatial stochastic gene expression in single cells and tissues

PubMed Central

Smith, Stephen; Cianci, Claudia; Grima, Ramon

2016-01-01

Gene expression occurs in an environment in which both stochastic and diffusive effects are significant. Spatial stochastic simulations are computationally expensive compared with their deterministic counterparts, and hence little is currently known of the significance of intrinsic noise in a spatial setting. Starting from the reaction–diffusion master equation (RDME) describing stochastic reaction–diffusion processes, we here derive expressions for the approximate steady-state mean concentrations which are explicit functions of the dimensionality of space, rate constants and diffusion coefficients. The expressions have a simple closed form when the system consists of one effective species. These formulae show that, even for spatially homogeneous systems, mean concentrations can depend on diffusion coefficients: this contradicts the predictions of deterministic reaction–diffusion processes, thus highlighting the importance of intrinsic noise. We confirm our theory by comparison with stochastic simulations, using the RDME and Brownian dynamics, of two models of stochastic and spatial gene expression in single cells and tissues. PMID:27146686

An error-dependent model of instrument-scanning behavior in commercial airline pilots. Ph.D. Thesis - May 1983

NASA Technical Reports Server (NTRS)

Jones, D. H.

1985-01-01

A new flexible model of pilot instrument scanning behavior is presented which assumes that the pilot uses a set of deterministic scanning patterns on the pilot's perception of error in the state of the aircraft, and the pilot's knowledge of the interactive nature of the aircraft's systems. Statistical analyses revealed that a three stage Markov process composed of the pilot's three predicted lookpoints (LP), occurring 1/30, 2/30, and 3/30 of a second prior to each LP, accurately modelled the scanning behavior of 14 commercial airline pilots while flying steep turn maneuvers in a Boeing 737 flight simulator. The modelled scanning data for each pilot were not statistically different from the observed scanning data in comparisons of mean dwell time, entropy, and entropy rate. These findings represent the first direct evidence that pilots are using deterministic scanning patterns during instrument flight. The results are interpreted as direct support for the error dependent model and suggestions are made for further research that could allow for identification of the specific scanning patterns suggested by the model.

Dynamics of a stochastic multi-strain SIS epidemic model driven by Lévy noise

NASA Astrophysics Data System (ADS)

Chen, Can; Kang, Yanmei

2017-01-01

A stochastic multi-strain SIS epidemic model is formulated by introducing Lévy noise into the disease transmission rate of each strain. First, we prove that the stochastic model admits a unique global positive solution, and, by the comparison theorem, we show that the solution remains within a positively invariant set almost surely. Next we investigate stochastic stability of the disease-free equilibrium, including stability in probability and pth moment asymptotic stability. Then sufficient conditions for persistence in the mean of the disease are established. Finally, based on an Euler scheme for Lévy-driven stochastic differential equations, numerical simulations for a stochastic two-strain model are carried out to verify the theoretical results. Moreover, numerical comparison results of the stochastic two-strain model and the deterministic version are also given. Lévy noise can cause the two strains to become extinct almost surely, even though there is a dominant strain that persists in the deterministic model. It can be concluded that the introduction of Lévy noise reduces the disease extinction threshold, which indicates that Lévy noise may suppress the disease outbreak.

The past, present and future of cyber-physical systems: a focus on models.

PubMed

Lee, Edward A

2015-02-26

This paper is about better engineering of cyber-physical systems (CPSs) through better models. Deterministic models have historically proven extremely useful and arguably form the kingpin of the industrial revolution and the digital and information technology revolutions. Key deterministic models that have proven successful include differential equations, synchronous digital logic and single-threaded imperative programs. Cyber-physical systems, however, combine these models in such a way that determinism is not preserved. Two projects show that deterministic CPS models with faithful physical realizations are possible and practical. The first project is PRET, which shows that the timing precision of synchronous digital logic can be practically made available at the software level of abstraction. The second project is Ptides (programming temporally-integrated distributed embedded systems), which shows that deterministic models for distributed cyber-physical systems have practical faithful realizations. These projects are existence proofs that deterministic CPS models are possible and practical.

The Past, Present and Future of Cyber-Physical Systems: A Focus on Models

PubMed Central

Lee, Edward A.

2015-01-01

This paper is about better engineering of cyber-physical systems (CPSs) through better models. Deterministic models have historically proven extremely useful and arguably form the kingpin of the industrial revolution and the digital and information technology revolutions. Key deterministic models that have proven successful include differential equations, synchronous digital logic and single-threaded imperative programs. Cyber-physical systems, however, combine these models in such a way that determinism is not preserved. Two projects show that deterministic CPS models with faithful physical realizations are possible and practical. The first project is PRET, which shows that the timing precision of synchronous digital logic can be practically made available at the software level of abstraction. The second project is Ptides (programming temporally-integrated distributed embedded systems), which shows that deterministic models for distributed cyber-physical systems have practical faithful realizations. These projects are existence proofs that deterministic CPS models are possible and practical. PMID:25730486

Delayed-feedback chimera states: Forced multiclusters and stochastic resonance

NASA Astrophysics Data System (ADS)

Semenov, V.; Zakharova, A.; Maistrenko, Y.; Schöll, E.

2016-07-01

A nonlinear oscillator model with negative time-delayed feedback is studied numerically under external deterministic and stochastic forcing. It is found that in the unforced system complex partial synchronization patterns like chimera states as well as salt-and-pepper-like solitary states arise on the route from regular dynamics to spatio-temporal chaos. The control of the dynamics by external periodic forcing is demonstrated by numerical simulations. It is shown that one-cluster and multi-cluster chimeras can be achieved by adjusting the external forcing frequency to appropriate resonance conditions. If a stochastic component is superimposed to the deterministic external forcing, chimera states can be induced in a way similar to stochastic resonance, they appear, therefore, in regimes where they do not exist without noise.

Particle simulation of Coulomb collisions: Comparing the methods of Takizuka and Abe and Nanbu

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

Wang Chiaming; Lin, Tungyou; Caflisch, Russel

2008-04-20

The interactions of charged particles in a plasma are governed by long-range Coulomb collision. We compare two widely used Monte Carlo models for Coulomb collisions. One was developed by Takizuka and Abe in 1977, the other was developed by Nanbu in 1997. We perform deterministic and statistical error analysis with respect to particle number and time step. The two models produce similar stochastic errors, but Nanbu's model gives smaller time step errors. Error comparisons between these two methods are presented.

Stochastic inference with spiking neurons in the high-conductance state

NASA Astrophysics Data System (ADS)

Petrovici, Mihai A.; Bill, Johannes; Bytschok, Ilja; Schemmel, Johannes; Meier, Karlheinz

2016-10-01

The highly variable dynamics of neocortical circuits observed in vivo have been hypothesized to represent a signature of ongoing stochastic inference but stand in apparent contrast to the deterministic response of neurons measured in vitro. Based on a propagation of the membrane autocorrelation across spike bursts, we provide an analytical derivation of the neural activation function that holds for a large parameter space, including the high-conductance state. On this basis, we show how an ensemble of leaky integrate-and-fire neurons with conductance-based synapses embedded in a spiking environment can attain the correct firing statistics for sampling from a well-defined target distribution. For recurrent networks, we examine convergence toward stationarity in computer simulations and demonstrate sample-based Bayesian inference in a mixed graphical model. This points to a new computational role of high-conductance states and establishes a rigorous link between deterministic neuron models and functional stochastic dynamics on the network level.

Detection of "noisy" chaos in a time series

NASA Technical Reports Server (NTRS)

Chon, K. H.; Kanters, J. K.; Cohen, R. J.; Holstein-Rathlou, N. H.

1997-01-01

Time series from biological system often displays fluctuations in the measured variables. Much effort has been directed at determining whether this variability reflects deterministic chaos, or whether it is merely "noise". The output from most biological systems is probably the result of both the internal dynamics of the systems, and the input to the system from the surroundings. This implies that the system should be viewed as a mixed system with both stochastic and deterministic components. We present a method that appears to be useful in deciding whether determinism is present in a time series, and if this determinism has chaotic attributes. The method relies on fitting a nonlinear autoregressive model to the time series followed by an estimation of the characteristic exponents of the model over the observed probability distribution of states for the system. The method is tested by computer simulations, and applied to heart rate variability data.

Data-driven gradient algorithm for high-precision quantum control

NASA Astrophysics Data System (ADS)

Wu, Re-Bing; Chu, Bing; Owens, David H.; Rabitz, Herschel

2018-04-01

In the quest to achieve scalable quantum information processing technologies, gradient-based optimal control algorithms (e.g., grape) are broadly used for implementing high-precision quantum gates, but their performance is often hindered by deterministic or random errors in the system model and the control electronics. In this paper, we show that grape can be taught to be more effective by jointly learning from the design model and the experimental data obtained from process tomography. The resulting data-driven gradient optimization algorithm (d-grape) can in principle correct all deterministic gate errors, with a mild efficiency loss. The d-grape algorithm may become more powerful with broadband controls that involve a large number of control parameters, while other algorithms usually slow down due to the increased size of the search space. These advantages are demonstrated by simulating the implementation of a two-qubit controlled-not gate.

Robust Audio Watermarking Scheme Based on Deterministic Plus Stochastic Model

NASA Astrophysics Data System (ADS)

Dhar, Pranab Kumar; Kim, Cheol Hong; Kim, Jong-Myon

Digital watermarking has been widely used for protecting digital contents from unauthorized duplication. This paper proposes a new watermarking scheme based on spectral modeling synthesis (SMS) for copyright protection of digital contents. SMS defines a sound as a combination of deterministic events plus a stochastic component that makes it possible for a synthesized sound to attain all of the perceptual characteristics of the original sound. In our proposed scheme, watermarks are embedded into the highest prominent peak of the magnitude spectrum of each non-overlapping frame in peak trajectories. Simulation results indicate that the proposed watermarking scheme is highly robust against various kinds of attacks such as noise addition, cropping, re-sampling, re-quantization, and MP3 compression and achieves similarity values ranging from 17 to 22. In addition, our proposed scheme achieves signal-to-noise ratio (SNR) values ranging from 29 dB to 30 dB.

Exact and approximate stochastic simulation of intracellular calcium dynamics.

PubMed

Wieder, Nicolas; Fink, Rainer H A; Wegner, Frederic von

2011-01-01

In simulations of chemical systems, the main task is to find an exact or approximate solution of the chemical master equation (CME) that satisfies certain constraints with respect to computation time and accuracy. While Brownian motion simulations of single molecules are often too time consuming to represent the mesoscopic level, the classical Gillespie algorithm is a stochastically exact algorithm that provides satisfying results in the representation of calcium microdomains. Gillespie's algorithm can be approximated via the tau-leap method and the chemical Langevin equation (CLE). Both methods lead to a substantial acceleration in computation time and a relatively small decrease in accuracy. Elimination of the noise terms leads to the classical, deterministic reaction rate equations (RRE). For complex multiscale systems, hybrid simulations are increasingly proposed to combine the advantages of stochastic and deterministic algorithms. An often used exemplary cell type in this context are striated muscle cells (e.g., cardiac and skeletal muscle cells). The properties of these cells are well described and they express many common calcium-dependent signaling pathways. The purpose of the present paper is to provide an overview of the aforementioned simulation approaches and their mutual relationships in the spectrum ranging from stochastic to deterministic algorithms.

A deterministic computer simulation model of life-cycle lamb and wool production.

PubMed

Wang, C T; Dickerson, G E

1991-11-01

A deterministic mathematical computer model was developed to simulate effects on life-cycle efficiency of lamb and wool production from genetic improvement of performance traits under alternative management systems. Genetic input parameters can be varied for age at puberty, length of anestrus, fertility, precocity of fertility, number born, milk yield, mortality, growth rate, body fat, and wool growth. Management options include mating systems, lambing intervals, feeding levels, creep feeding, weaning age, marketing age or weight, and culling policy. Simulated growth of animals is linear from birth to inflection point, then slows asymptotically to specified mature empty BW and fat content when nutrition is not limiting. The ME intake requirement to maintain normal condition is calculated daily or weekly for maintenance, protein and fat deposition, wool growth, gestation, and lactation. Simulated feed intake is the minimum of availability, DM physical limit, or ME physiological limit. Tissue catabolism occurs when intake is below the requirement for essential functions. Mortality increases when BW is depressed. Equations developed for calculations of biological functions were validated with published and unpublished experimental data. Lifetime totals are accumulated for TDN, DM, and protein intake and for market lamb equivalent output values of empty body or carcass lean and wool from both lambs and ewes. These measures of efficiency for combinations of genetic, management, and marketing variables can provide the relative economic weighting of traits needed to derive optimal criteria for genetic selection among and within breeds under defined industry production systems.

Probabilistic Analysis of a Composite Crew Module

NASA Technical Reports Server (NTRS)

Mason, Brian H.; Krishnamurthy, Thiagarajan

2011-01-01

An approach for conducting reliability-based analysis (RBA) of a Composite Crew Module (CCM) is presented. The goal is to identify and quantify the benefits of probabilistic design methods for the CCM and future space vehicles. The coarse finite element model from a previous NASA Engineering and Safety Center (NESC) project is used as the baseline deterministic analysis model to evaluate the performance of the CCM using a strength-based failure index. The first step in the probabilistic analysis process is the determination of the uncertainty distributions for key parameters in the model. Analytical data from water landing simulations are used to develop an uncertainty distribution, but such data were unavailable for other load cases. The uncertainty distributions for the other load scale factors and the strength allowables are generated based on assumed coefficients of variation. Probability of first-ply failure is estimated using three methods: the first order reliability method (FORM), Monte Carlo simulation, and conditional sampling. Results for the three methods were consistent. The reliability is shown to be driven by first ply failure in one region of the CCM at the high altitude abort load set. The final predicted probability of failure is on the order of 10-11 due to the conservative nature of the factors of safety on the deterministic loads.

Combining deterministic and stochastic velocity fields in the analysis of deep crustal seismic data

NASA Astrophysics Data System (ADS)

Larkin, Steven Paul

Standard crustal seismic modeling obtains deterministic velocity models which ignore the effects of wavelength-scale heterogeneity, known to exist within the Earth's crust. Stochastic velocity models are a means to include wavelength-scale heterogeneity in the modeling. These models are defined by statistical parameters obtained from geologic maps of exposed crystalline rock, and are thus tied to actual geologic structures. Combining both deterministic and stochastic velocity models into a single model allows a realistic full wavefield (2-D) to be computed. By comparing these simulations to recorded seismic data, the effects of wavelength-scale heterogeneity can be investigated. Combined deterministic and stochastic velocity models are created for two datasets, the 1992 RISC seismic experiment in southeastern California and the 1986 PASSCAL seismic experiment in northern Nevada. The RISC experiment was located in the transition zone between the Salton Trough and the southern Basin and Range province. A high-velocity body previously identified beneath the Salton Trough is constrained to pinch out beneath the Chocolate Mountains to the northeast. The lateral extent of this body is evidence for the ephemeral nature of rifting loci as a continent is initially rifted. Stochastic modeling of wavelength-scale structures above this body indicate that little more than 5% mafic intrusion into a more felsic continental crust is responsible for the observed reflectivity. Modeling of the wide-angle RISC data indicates that coda waves following PmP are initially dominated by diffusion of energy out of the near-surface basin as the wavefield reverberates within this low-velocity layer. At later times, this coda consists of scattered body waves and P to S conversions. Surface waves do not play a significant role in this coda. Modeling of the PASSCAL dataset indicates that a high-gradient crust-mantle transition zone or a rough Moho interface is necessary to reduce precritical PmP energy. Possibly related, inconsistencies in published velocity models are rectified by hypothesizing the existence of large, elongate, high-velocity bodies at the base of the crust oriented to and of similar scale as the basins and ranges at the surface. This structure would result in an anisotropic lower crust.

LEGEND, a LEO-to-GEO Environment Debris Model

NASA Technical Reports Server (NTRS)

Liou, Jer Chyi; Hall, Doyle T.

2013-01-01

LEGEND (LEO-to-GEO Environment Debris model) is a three-dimensional orbital debris evolutionary model that is capable of simulating the historical and future debris populations in the near-Earth environment. The historical component in LEGEND adopts a deterministic approach to mimic the known historical populations. Launched rocket bodies, spacecraft, and mission-related debris (rings, bolts, etc.) are added to the simulated environment. Known historical breakup events are reproduced, and fragments down to 1 mm in size are created. The LEGEND future projection component adopts a Monte Carlo approach and uses an innovative pair-wise collision probability evaluation algorithm to simulate the future breakups and the growth of the debris populations. This algorithm is based on a new "random sampling in time" approach that preserves characteristics of the traditional approach and captures the rapidly changing nature of the orbital debris environment. LEGEND is a Fortran 90-based numerical simulation program. It operates in a UNIX/Linux environment.

Calibration of semi-stochastic procedure for simulating high-frequency ground motions

USGS Publications Warehouse

Seyhan, Emel; Stewart, Jonathan P.; Graves, Robert

2013-01-01

Broadband ground motion simulation procedures typically utilize physics-based modeling at low frequencies, coupled with semi-stochastic procedures at high frequencies. The high-frequency procedure considered here combines deterministic Fourier amplitude spectra (dependent on source, path, and site models) with random phase. Previous work showed that high-frequency intensity measures from this simulation methodology attenuate faster with distance and have lower intra-event dispersion than in empirical equations. We address these issues by increasing crustal damping (Q) to reduce distance attenuation bias and by introducing random site-to-site variations to Fourier amplitudes using a lognormal standard deviation ranging from 0.45 for Mw < 7 to zero for Mw 8. Ground motions simulated with the updated parameterization exhibit significantly reduced distance attenuation bias and revised dispersion terms are more compatible with those from empirical models but remain lower at large distances (e.g., > 100 km).

An Evaluation of the Predictability of Austral Summer Season Precipitation over South America.

NASA Astrophysics Data System (ADS)

Misra, Vasubandhu

2004-03-01

In this study predictability of austral summer seasonal precipitation over South America is investigated using a 12-yr set of a 3.5-month range (seasonal) and a 17-yr range (continuous multiannual) five-member ensemble integrations of the Center for Ocean Land Atmosphere Studies (COLA) atmospheric general circulation model (AGCM). These integrations were performed with prescribed observed sea surface temperature (SST); therefore, skill attained represents an estimate of the upper bound of the skill achievable by COLA AGCM with predicted SST. The seasonal runs outperform the multiannual model integrations both in deterministic and probabilistic skill. The simulation of the January February March (JFM) seasonal climatology of precipitation is vastly superior in the seasonal runs except over the Nordeste region where the multiannual runs show a marginal improvement. The teleconnection of the ensemble mean JFM precipitation over tropical South America with global contemporaneous observed sea surface temperature in the seasonal runs conforms more closely to observations than in the multiannual runs. Both the sets of runs clearly beat persistence in predicting the interannual precipitation anomalies over the Amazon River basin, Nordeste, South Atlantic convergence zone, and subtropical South America. However, both types of runs display poorer simulations over subtropical regions than the tropical areas of South America. The examination of probabilistic skill of precipitation supports the conclusions from deterministic skill analysis that the seasonal runs yield superior simulations than the multiannual-type runs.

Multimodel Ensemble Methods for Prediction of Wake-Vortex Transport and Decay Originating NASA

NASA Technical Reports Server (NTRS)

Korner, Stephan; Ahmad, Nashat N.; Holzapfel, Frank; VanValkenburg, Randal L.

2017-01-01

Several multimodel ensemble methods are selected and further developed to improve the deterministic and probabilistic prediction skills of individual wake-vortex transport and decay models. The different multimodel ensemble methods are introduced, and their suitability for wake applications is demonstrated. The selected methods include direct ensemble averaging, Bayesian model averaging, and Monte Carlo simulation. The different methodologies are evaluated employing data from wake-vortex field measurement campaigns conducted in the United States and Germany.

Stress Rupture Life Reliability Measures for Composite Overwrapped Pressure Vessels

NASA Technical Reports Server (NTRS)

Murthy, Pappu L. N.; Thesken, John C.; Phoenix, S. Leigh; Grimes-Ledesma, Lorie

2007-01-01

Composite Overwrapped Pressure Vessels (COPVs) are often used for storing pressurant gases onboard spacecraft. Kevlar (DuPont), glass, carbon and other more recent fibers have all been used as overwraps. Due to the fact that overwraps are subjected to sustained loads for an extended period during a mission, stress rupture failure is a major concern. It is therefore important to ascertain the reliability of these vessels by analysis, since the testing of each flight design cannot be completed on a practical time scale. The present paper examines specifically a Weibull statistics based stress rupture model and considers the various uncertainties associated with the model parameters. The paper also examines several reliability estimate measures that would be of use for the purpose of recertification and for qualifying flight worthiness of these vessels. Specifically, deterministic values for a point estimate, mean estimate and 90/95 percent confidence estimates of the reliability are all examined for a typical flight quality vessel under constant stress. The mean and the 90/95 percent confidence estimates are computed using Monte-Carlo simulation techniques by assuming distribution statistics of model parameters based also on simulation and on the available data, especially the sample sizes represented in the data. The data for the stress rupture model are obtained from the Lawrence Livermore National Laboratories (LLNL) stress rupture testing program, carried out for the past 35 years. Deterministic as well as probabilistic sensitivities are examined.

Deterministic ripple-spreading model for complex networks.

PubMed

Hu, Xiao-Bing; Wang, Ming; Leeson, Mark S; Hines, Evor L; Di Paolo, Ezequiel

2011-04-01

This paper proposes a deterministic complex network model, which is inspired by the natural ripple-spreading phenomenon. The motivations and main advantages of the model are the following: (i) The establishment of many real-world networks is a dynamic process, where it is often observed that the influence of a few local events spreads out through nodes, and then largely determines the final network topology. Obviously, this dynamic process involves many spatial and temporal factors. By simulating the natural ripple-spreading process, this paper reports a very natural way to set up a spatial and temporal model for such complex networks. (ii) Existing relevant network models are all stochastic models, i.e., with a given input, they cannot output a unique topology. Differently, the proposed ripple-spreading model can uniquely determine the final network topology, and at the same time, the stochastic feature of complex networks is captured by randomly initializing ripple-spreading related parameters. (iii) The proposed model can use an easily manageable number of ripple-spreading related parameters to precisely describe a network topology, which is more memory efficient when compared with traditional adjacency matrix or similar memory-expensive data structures. (iv) The ripple-spreading model has a very good potential for both extensions and applications.

A Reliability Estimation in Modeling Watershed Runoff With Uncertainties

NASA Astrophysics Data System (ADS)

Melching, Charles S.; Yen, Ben Chie; Wenzel, Harry G., Jr.

1990-10-01

The reliability of simulation results produced by watershed runoff models is a function of uncertainties in nature, data, model parameters, and model structure. A framework is presented here for using a reliability analysis method (such as first-order second-moment techniques or Monte Carlo simulation) to evaluate the combined effect of the uncertainties on the reliability of output hydrographs from hydrologic models. For a given event the prediction reliability can be expressed in terms of the probability distribution of the estimated hydrologic variable. The peak discharge probability for a watershed in Illinois using the HEC-1 watershed model is given as an example. The study of the reliability of predictions from watershed models provides useful information on the stochastic nature of output from deterministic models subject to uncertainties and identifies the relative contribution of the various uncertainties to unreliability of model predictions.

Dynamics Under Location Uncertainty: Model Derivation, Modified Transport and Uncertainty Quantification

NASA Astrophysics Data System (ADS)

Resseguier, V.; Memin, E.; Chapron, B.; Fox-Kemper, B.

2017-12-01

In order to better observe and predict geophysical flows, ensemble-based data assimilation methods are of high importance. In such methods, an ensemble of random realizations represents the variety of the simulated flow's likely behaviors. For this purpose, randomness needs to be introduced in a suitable way and physically-based stochastic subgrid parametrizations are promising paths. This talk will propose a new kind of such a parametrization referred to as modeling under location uncertainty. The fluid velocity is decomposed into a resolved large-scale component and an aliased small-scale one. The first component is possibly random but time-correlated whereas the second is white-in-time but spatially-correlated and possibly inhomogeneous and anisotropic. With such a velocity, the material derivative of any - possibly active - tracer is modified. Three new terms appear: a correction of the large-scale advection, a multiplicative noise and a possibly heterogeneous and anisotropic diffusion. This parameterization naturally ensures attractive properties such as energy conservation for each realization. Additionally, this stochastic material derivative and the associated Reynolds' transport theorem offer a systematic method to derive stochastic models. In particular, we will discuss the consequences of the Quasi-Geostrophic assumptions in our framework. Depending on the turbulence amount, different models with different physical behaviors are obtained. Under strong turbulence assumptions, a simplified diagnosis of frontolysis and frontogenesis at the surface of the ocean is possible in this framework. A Surface Quasi-Geostrophic (SQG) model with a weaker noise influence has also been simulated. A single realization better represents small scales than a deterministic SQG model at the same resolution. Moreover, an ensemble accurately predicts extreme events, bifurcations as well as the amplitudes and the positions of the simulation errors. Figure 1 highlights this last result and compares it to the strong error underestimation of an ensemble simulated from the deterministic dynamic with random initial conditions.

Deterministic analysis of processes at corroding metal surfaces and the study of electrochemical noise in these systems

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

Latanision, R.M.

1990-12-01

Electrochemical corrosion is pervasive in virtually all engineering systems and in virtually all industrial circumstances. Although engineers now understand how to design systems to minimize corrosion in many instances, many fundamental questions remain poorly understood and, therefore, the development of corrosion control strategies is based more on empiricism than on a deep understanding of the processes by which metals corrode in electrolytes. Fluctuations in potential, or current, in electrochemical systems have been observed for many years. To date, all investigations of this phenomenon have utilized non-deterministic analyses. In this work it is proposed to study electrochemical noise from a deterministicmore » viewpoint by comparison of experimental parameters, such as first and second order moments (non-deterministic), with computer simulation of corrosion at metal surfaces. In this way it is proposed to analyze the origins of these fluctuations and to elucidate the relationship between these fluctuations and kinetic parameters associated with metal dissolution and cathodic reduction reactions. This research program addresses in essence two areas of interest: (a) computer modeling of corrosion processes in order to study the electrochemical processes on an atomistic scale, and (b) experimental investigations of fluctuations in electrochemical systems and correlation of experimental results with computer modeling. In effect, the noise generated by mathematical modeling will be analyzed and compared to experimental noise in electrochemical systems. 1 fig.« less

Location of coating defects and assessment of level of cathodic protection on underground pipelines using AC impedance, deterministic and non-deterministic models

NASA Astrophysics Data System (ADS)

Castaneda-Lopez, Homero

A methodology for detecting and locating defects or discontinuities on the outside covering of coated metal underground pipelines subjected to cathodic protection has been addressed. On the basis of wide range AC impedance signals for various frequencies applied to a steel-coated pipeline system and by measuring its corresponding transfer function under several laboratory simulation scenarios, a physical laboratory setup of an underground cathodic-protected, coated pipeline was built. This model included different variables and elements that exist under real conditions, such as soil resistivity, soil chemical composition, defect (holiday) location in the pipeline covering, defect area and geometry, and level of cathodic protection. The AC impedance data obtained under different working conditions were used to fit an electrical transmission line model. This model was then used as a tool to fit the impedance signal for different experimental conditions and to establish trends in the impedance behavior without the necessity of further experimental work. However, due to the chaotic nature of the transfer function response of this system under several conditions, it is believed that non-deterministic models based on pattern recognition algorithms are suitable for field condition analysis. A non-deterministic approach was used for experimental analysis by applying an artificial neural network (ANN) algorithm based on classification analysis capable of studying the pipeline system and differentiating the variables that can change impedance conditions. These variables include level of cathodic protection, location of discontinuities (holidays), and severity of corrosion. This work demonstrated a proof-of-concept for a well-known technique and a novel algorithm capable of classifying impedance data for experimental results to predict the exact location of the active holidays and defects on the buried pipelines. Laboratory findings from this procedure are promising, and efforts to develop it for field conditions should continue.

Research on Intelligent Synthesis Environments

NASA Technical Reports Server (NTRS)

Noor, Ahmed K.; Lobeck, William E.

2002-01-01

Four research activities related to Intelligent Synthesis Environment (ISE) have been performed under this grant. The four activities are: 1) non-deterministic approaches that incorporate technologies such as intelligent software agents, visual simulations and other ISE technologies; 2) virtual labs that leverage modeling, simulation and information technologies to create an immersive, highly interactive virtual environment tailored to the needs of researchers and learners; 3) advanced learning modules that incorporate advanced instructional, user interface and intelligent agent technologies; and 4) assessment and continuous improvement of engineering team effectiveness in distributed collaborative environments.

Research on Intelligent Synthesis Environments

NASA Astrophysics Data System (ADS)

Noor, Ahmed K.; Loftin, R. Bowen

2002-12-01

Four research activities related to Intelligent Synthesis Environment (ISE) have been performed under this grant. The four activities are: 1) non-deterministic approaches that incorporate technologies such as intelligent software agents, visual simulations and other ISE technologies; 2) virtual labs that leverage modeling, simulation and information technologies to create an immersive, highly interactive virtual environment tailored to the needs of researchers and learners; 3) advanced learning modules that incorporate advanced instructional, user interface and intelligent agent technologies; and 4) assessment and continuous improvement of engineering team effectiveness in distributed collaborative environments.

Ensemble Bayesian forecasting system Part I: Theory and algorithms

NASA Astrophysics Data System (ADS)

Herr, Henry D.; Krzysztofowicz, Roman

2015-05-01

The ensemble Bayesian forecasting system (EBFS), whose theory was published in 2001, is developed for the purpose of quantifying the total uncertainty about a discrete-time, continuous-state, non-stationary stochastic process such as a time series of stages, discharges, or volumes at a river gauge. The EBFS is built of three components: an input ensemble forecaster (IEF), which simulates the uncertainty associated with random inputs; a deterministic hydrologic model (of any complexity), which simulates physical processes within a river basin; and a hydrologic uncertainty processor (HUP), which simulates the hydrologic uncertainty (an aggregate of all uncertainties except input). It works as a Monte Carlo simulator: an ensemble of time series of inputs (e.g., precipitation amounts) generated by the IEF is transformed deterministically through a hydrologic model into an ensemble of time series of outputs, which is next transformed stochastically by the HUP into an ensemble of time series of predictands (e.g., river stages). Previous research indicated that in order to attain an acceptable sampling error, the ensemble size must be on the order of hundreds (for probabilistic river stage forecasts and probabilistic flood forecasts) or even thousands (for probabilistic stage transition forecasts). The computing time needed to run the hydrologic model this many times renders the straightforward simulations operationally infeasible. This motivates the development of the ensemble Bayesian forecasting system with randomization (EBFSR), which takes full advantage of the analytic meta-Gaussian HUP and generates multiple ensemble members after each run of the hydrologic model; this auxiliary randomization reduces the required size of the meteorological input ensemble and makes it operationally feasible to generate a Bayesian ensemble forecast of large size. Such a forecast quantifies the total uncertainty, is well calibrated against the prior (climatic) distribution of predictand, possesses a Bayesian coherence property, constitutes a random sample of the predictand, and has an acceptable sampling error-which makes it suitable for rational decision making under uncertainty.

Fast estimation of first-order scattering in a medical x-ray computed tomography scanner using a ray-tracing technique.

PubMed

Liu, Xin

2014-01-01

This study describes a deterministic method for simulating the first-order scattering in a medical computed tomography scanner. The method was developed based on a physics model of x-ray photon interactions with matter and a ray tracing technique. The results from simulated scattering were compared to the ones from an actual scattering measurement. Two phantoms with homogeneous and heterogeneous material distributions were used in the scattering simulation and measurement. It was found that the simulated scatter profile was in agreement with the measurement result, with an average difference of 25% or less. Finally, tomographic images with artifacts caused by scatter were corrected based on the simulated scatter profiles. The image quality improved significantly.

Inferring Fitness Effects from Time-Resolved Sequence Data with a Delay-Deterministic Model

PubMed Central

Nené, Nuno R.; Dunham, Alistair S.; Illingworth, Christopher J. R.

2018-01-01

A common challenge arising from the observation of an evolutionary system over time is to infer the magnitude of selection acting upon a specific genetic variant, or variants, within the population. The inference of selection may be confounded by the effects of genetic drift in a system, leading to the development of inference procedures to account for these effects. However, recent work has suggested that deterministic models of evolution may be effective in capturing the effects of selection even under complex models of demography, suggesting the more general application of deterministic approaches to inference. Responding to this literature, we here note a case in which a deterministic model of evolution may give highly misleading inferences, resulting from the nondeterministic properties of mutation in a finite population. We propose an alternative approach that acts to correct for this error, and which we denote the delay-deterministic model. Applying our model to a simple evolutionary system, we demonstrate its performance in quantifying the extent of selection acting within that system. We further consider the application of our model to sequence data from an evolutionary experiment. We outline scenarios in which our model may produce improved results for the inference of selection, noting that such situations can be easily identified via the use of a regular deterministic model. PMID:29500183

Development of TIF based figuring algorithm for deterministic pitch tool polishing

NASA Astrophysics Data System (ADS)

Yi, Hyun-Su; Kim, Sug-Whan; Yang, Ho-Soon; Lee, Yun-Woo

2007-12-01

Pitch is perhaps the oldest material used for optical polishing, leaving superior surface texture, and has been used widely in the optics shop floor. However, for its unpredictable controllability of removal characteristics, the pitch tool polishing has been rarely analysed quantitatively and many optics shops rely heavily on optician's "feel" even today. In order to bring a degree of process controllability to the pitch tool polishing, we added motorized tool motions to the conventional Draper type polishing machine and modelled the tool path in the absolute machine coordinate. We then produced a number of Tool Influence Function (TIF) both from an analytical model and a series of experimental polishing runs using the pitch tool. The theoretical TIFs agreed well with the experimental TIFs to the profile accuracy of 79 % in terms of its shape. The surface figuring algorithm was then developed in-house utilizing both theoretical and experimental TIFs. We are currently undertaking a series of trial figuring experiments to prove the performance of the polishing algorithm, and the early results indicate that the highly deterministic material removal control with the pitch tool can be achieved to a certain level of form error. The machine renovation, TIF theory and experimental confirmation, figuring simulation results are reported together with implications to deterministic polishing.

Integrated Arrival and Departure Schedule Optimization Under Uncertainty

NASA Technical Reports Server (NTRS)

Xue, Min; Zelinski, Shannon

2014-01-01

In terminal airspace, integrating arrivals and departures with shared waypoints provides the potential of improving operational efficiency by allowing direct routes when possible. Incorporating stochastic evaluation as a post-analysis process of deterministic optimization, and imposing a safety buffer in deterministic optimization, are two ways to learn and alleviate the impact of uncertainty and to avoid unexpected outcomes. This work presents a third and direct way to take uncertainty into consideration during the optimization. The impact of uncertainty was incorporated into cost evaluations when searching for the optimal solutions. The controller intervention count was computed using a heuristic model and served as another stochastic cost besides total delay. Costs under uncertainty were evaluated using Monte Carlo simulations. The Pareto fronts that contain a set of solutions were identified and the trade-off between delays and controller intervention count was shown. Solutions that shared similar delays but had different intervention counts were investigated. The results showed that optimization under uncertainty could identify compromise solutions on Pareto fonts, which is better than deterministic optimization with extra safety buffers. It helps decision-makers reduce controller intervention while achieving low delays.

Modelling the protocol stack in NCS with deterministic and stochastic petri net

NASA Astrophysics Data System (ADS)

Hui, Chen; Chunjie, Zhou; Weifeng, Zhu

2011-06-01

Protocol stack is the basis of the networked control systems (NCS). Full or partial reconfiguration of protocol stack offers both optimised communication service and system performance. Nowadays, field testing is unrealistic to determine the performance of reconfigurable protocol stack; and the Petri net formal description technique offers the best combination of intuitive representation, tool support and analytical capabilities. Traditionally, separation between the different layers of the OSI model has been a common practice. Nevertheless, such a layered modelling analysis framework of protocol stack leads to the lack of global optimisation for protocol reconfiguration. In this article, we proposed a general modelling analysis framework for NCS based on the cross-layer concept, which is to establish an efficiency system scheduling model through abstracting the time constraint, the task interrelation, the processor and the bus sub-models from upper and lower layers (application, data link and physical layer). Cross-layer design can help to overcome the inadequacy of global optimisation based on information sharing between protocol layers. To illustrate the framework, we take controller area network (CAN) as a case study. The simulation results of deterministic and stochastic Petri-net (DSPN) model can help us adjust the message scheduling scheme and obtain better system performance.

Modeling a SI epidemic with stochastic transmission: hyperbolic incidence rate.

PubMed

Christen, Alejandra; Maulén-Yañez, M Angélica; González-Olivares, Eduardo; Curé, Michel

2018-03-01

In this paper a stochastic susceptible-infectious (SI) epidemic model is analysed, which is based on the model proposed by Roberts and Saha (Appl Math Lett 12: 37-41, 1999), considering a hyperbolic type nonlinear incidence rate. Assuming the proportion of infected population varies with time, our new model is described by an ordinary differential equation, which is analogous to the equation that describes the double Allee effect. The limit of the solution of this equation (deterministic model) is found when time tends to infinity. Then, the asymptotic behaviour of a stochastic fluctuation due to the environmental variation in the coefficient of disease transmission is studied. Thus a stochastic differential equation (SDE) is obtained and the existence of a unique solution is proved. Moreover, the SDE is analysed through the associated Fokker-Planck equation to obtain the invariant measure when the proportion of the infected population reaches steady state. An explicit expression for invariant measure is found and we study some of its properties. The long time behaviour of deterministic and stochastic models are compared by simulations. According to our knowledge this incidence rate has not been previously used for this type of epidemic models.

Mapping flood hazards under uncertainty through probabilistic flood inundation maps

NASA Astrophysics Data System (ADS)

Stephens, T.; Bledsoe, B. P.; Miller, A. J.; Lee, G.

2017-12-01

Changing precipitation, rapid urbanization, and population growth interact to create unprecedented challenges for flood mitigation and management. Standard methods for estimating risk from flood inundation maps generally involve simulations of floodplain hydraulics for an established regulatory discharge of specified frequency. Hydraulic model results are then geospatially mapped and depicted as a discrete boundary of flood extents and a binary representation of the probability of inundation (in or out) that is assumed constant over a project's lifetime. Consequently, existing methods utilized to define flood hazards and assess risk management are hindered by deterministic approaches that assume stationarity in a nonstationary world, failing to account for spatio-temporal variability of climate and land use as they translate to hydraulic models. This presentation outlines novel techniques for portraying flood hazards and the results of multiple flood inundation maps spanning hydroclimatic regions. Flood inundation maps generated through modeling of floodplain hydraulics are probabilistic reflecting uncertainty quantified through Monte-Carlo analyses of model inputs and parameters under current and future scenarios. The likelihood of inundation and range of variability in flood extents resulting from Monte-Carlo simulations are then compared with deterministic evaluations of flood hazards from current regulatory flood hazard maps. By facilitating alternative approaches of portraying flood hazards, the novel techniques described in this presentation can contribute to a shifting paradigm in flood management that acknowledges the inherent uncertainty in model estimates and the nonstationary behavior of land use and climate.

Expansion or extinction: deterministic and stochastic two-patch models with Allee effects.

PubMed

Kang, Yun; Lanchier, Nicolas

2011-06-01

We investigate the impact of Allee effect and dispersal on the long-term evolution of a population in a patchy environment. Our main focus is on whether a population already established in one patch either successfully invades an adjacent empty patch or undergoes a global extinction. Our study is based on the combination of analytical and numerical results for both a deterministic two-patch model and a stochastic counterpart. The deterministic model has either two, three or four attractors. The existence of a regime with exactly three attractors only appears when patches have distinct Allee thresholds. In the presence of weak dispersal, the analysis of the deterministic model shows that a high-density and a low-density populations can coexist at equilibrium in nearby patches, whereas the analysis of the stochastic model indicates that this equilibrium is metastable, thus leading after a large random time to either a global expansion or a global extinction. Up to some critical dispersal, increasing the intensity of the interactions leads to an increase of both the basin of attraction of the global extinction and the basin of attraction of the global expansion. Above this threshold, for both the deterministic and the stochastic models, the patches tend to synchronize as the intensity of the dispersal increases. This results in either a global expansion or a global extinction. For the deterministic model, there are only two attractors, while the stochastic model no longer exhibits a metastable behavior. In the presence of strong dispersal, the limiting behavior is entirely determined by the value of the Allee thresholds as the global population size in the deterministic and the stochastic models evolves as dictated by their single-patch counterparts. For all values of the dispersal parameter, Allee effects promote global extinction in terms of an expansion of the basin of attraction of the extinction equilibrium for the deterministic model and an increase of the probability of extinction for the stochastic model.

INCREASING HEAVY OIL RESERVES IN THE WILMINGTON OIL FIELD THROUGH ADVANCED RESERVOIR CHARACTERIZATION AND THERMAL PRODUCTION TECHNOLOGIES

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

Scott Hara

2000-02-18

The project involves using advanced reservoir characterization and thermal production technologies to improve thermal recovery techniques and lower operating and capital costs in a slope and basin clastic (SBC) reservoir in the Wilmington field, Los Angeles Co., CA. Through March 1999, project work has been completed related to data preparation, basic reservoir engineering, developing a deterministic three dimensional (3-D) geologic model, a 3-D deterministic reservoir simulation model, and a rock-log model, well drilling and completions, and surface facilities. Work is continuing on the stochastic geologic model, developing a 3-D stochastic thermal reservoir simulation model of the Fault Block IIA Tarmore » (Tar II-A) Zone, and operational work and research studies to prevent thermal-related formation compaction. Thermal-related formation compaction is a concern of the project team due to observed surface subsidence in the local area above the steamflood project. Last quarter on January 12, the steamflood project lost its inexpensive steam source from the Harbor Cogeneration Plant as a result of the recent deregulation of electrical power rates in California. An operational plan was developed and implemented to mitigate the effects of the two situations. Seven water injection wells were placed in service in November and December 1998 on the flanks of the Phase 1 steamflood area to pressure up the reservoir to fill up the existing steam chest. Intensive reservoir engineering and geomechanics studies are continuing to determine the best ways to shut down the steamflood operations in Fault Block II while minimizing any future surface subsidence. The new 3-D deterministic thermal reservoir simulator model is being used to provide sensitivity cases to optimize production, steam injection, future flank cold water injection and reservoir temperature and pressure. According to the model, reservoir fill up of the steam chest at the current injection rate of 28,000 BPD and gross and net oil production rates of 7,700 BPD and 750 BOPD (injection to production ratio of 4) will occur in October 1999. At that time, the reservoir should act more like a waterflood and production and cold water injection can be operated at lower net injection rates to be determined. Modeling runs developed this quarter found that varying individual well injection rates to meet added production and local pressure problems by sub-zone could reduce steam chest fill-up by up to one month.« less

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

Buechler, Elizabeth D.; Pallin, Simon B.; Boudreaux, Philip R.

The indoor air temperature and relative humidity in residential buildings significantly affect material moisture durability, HVAC system performance, and occupant comfort. Therefore, indoor climate data is generally required to define boundary conditions in numerical models that evaluate envelope durability and equipment performance. However, indoor climate data obtained from field studies is influenced by weather, occupant behavior and internal loads, and is generally unrepresentative of the residential building stock. Likewise, whole-building simulation models typically neglect stochastic variables and yield deterministic results that are applicable to only a single home in a specific climate. The

Comparison of two optimization algorithms for fuzzy finite element model updating for damage detection in a wind turbine blade

NASA Astrophysics Data System (ADS)

Turnbull, Heather; Omenzetter, Piotr

2018-03-01

vDifficulties associated with current health monitoring and inspection practices combined with harsh, often remote, operational environments of wind turbines highlight the requirement for a non-destructive evaluation system capable of remotely monitoring the current structural state of turbine blades. This research adopted a physics based structural health monitoring methodology through calibration of a finite element model using inverse techniques. A 2.36m blade from a 5kW turbine was used as an experimental specimen, with operational modal analysis techniques utilised to realize the modal properties of the system. Modelling the experimental responses as fuzzy numbers using the sub-level technique, uncertainty in the response parameters was propagated back through the model and into the updating parameters. Initially, experimental responses of the blade were obtained, with a numerical model of the blade created and updated. Deterministic updating was carried out through formulation and minimisation of a deterministic objective function using both firefly algorithm and virus optimisation algorithm. Uncertainty in experimental responses were modelled using triangular membership functions, allowing membership functions of updating parameters (Young's modulus and shear modulus) to be obtained. Firefly algorithm and virus optimisation algorithm were again utilised, however, this time in the solution of fuzzy objective functions. This enabled uncertainty associated with updating parameters to be quantified. Varying damage location and severity was simulated experimentally through addition of small masses to the structure intended to cause a structural alteration. A damaged model was created, modelling four variable magnitude nonstructural masses at predefined points and updated to provide a deterministic damage prediction and information in relation to the parameters uncertainty via fuzzy updating.

Particle Simulation of Coulomb Collisions: Comparing the Methods of Takizuka & Abe and Nanbu

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

Wang, C; Lin, T; Caflisch, R

2007-05-22

The interactions of charged particles in a plasma are in a plasma is governed by the long-range Coulomb collision. We compare two widely used Monte Carlo models for Coulomb collisions. One was developed by Takizuka and Abe in 1977, the other was developed by Nanbu in 1997. We perform deterministic and stochastic error analysis with respect to particle number and time step. The two models produce similar stochastic errors, but Nanbu's model gives smaller time step errors. Error comparisons between these two methods are presented.

Psychological effect on single-species population models in a polluted environment.

PubMed

Wei, Fengying; Chen, Lihong

2017-08-01

We formulate and investigate the psychological effect of single-species population models in a polluted environment in this paper. For the deterministic single-species population model, the conditions that guarantee the local extinction and persistence in the mean are derived firstly. We then show that, around the pollution-free equilibrium, the stochastic single-species population is weakly persistent in the mean, and is stochastically permanent under some conditions. As a consequence, some numerical simulations demonstrate the efficiency of the main results. Copyright © 2017 Elsevier Inc. All rights reserved.

Reduced order surrogate modelling (ROSM) of high dimensional deterministic simulations

NASA Astrophysics Data System (ADS)

Mitry, Mina

Often, computationally expensive engineering simulations can prohibit the engineering design process. As a result, designers may turn to a less computationally demanding approximate, or surrogate, model to facilitate their design process. However, owing to the the curse of dimensionality, classical surrogate models become too computationally expensive for high dimensional data. To address this limitation of classical methods, we develop linear and non-linear Reduced Order Surrogate Modelling (ROSM) techniques. Two algorithms are presented, which are based on a combination of linear/kernel principal component analysis and radial basis functions. These algorithms are applied to subsonic and transonic aerodynamic data, as well as a model for a chemical spill in a channel. The results of this thesis show that ROSM can provide a significant computational benefit over classical surrogate modelling, sometimes at the expense of a minor loss in accuracy.

Stochastic effects in a seasonally forced epidemic model

NASA Astrophysics Data System (ADS)

Rozhnova, G.; Nunes, A.

2010-10-01

The interplay of seasonality, the system’s nonlinearities and intrinsic stochasticity, is studied for a seasonally forced susceptible-exposed-infective-recovered stochastic model. The model is explored in the parameter region that corresponds to childhood infectious diseases such as measles. The power spectrum of the stochastic fluctuations around the attractors of the deterministic system that describes the model in the thermodynamic limit is computed analytically and validated by stochastic simulations for large system sizes. Size effects are studied through additional simulations. Other effects such as switching between coexisting attractors induced by stochasticity often mentioned in the literature as playing an important role in the dynamics of childhood infectious diseases are also investigated. The main conclusion is that stochastic amplification, rather than these effects, is the key ingredient to understand the observed incidence patterns.

Stochastic mixed-mode oscillations in a three-species predator-prey model

NASA Astrophysics Data System (ADS)

Sadhu, Susmita; Kuehn, Christian

2018-03-01

The effect of demographic stochasticity, in the form of Gaussian white noise, in a predator-prey model with one fast and two slow variables is studied. We derive the stochastic differential equations (SDEs) from a discrete model. For suitable parameter values, the deterministic drift part of the model admits a folded node singularity and exhibits a singular Hopf bifurcation. We focus on the parameter regime near the Hopf bifurcation, where small amplitude oscillations exist as stable dynamics in the absence of noise. In this regime, the stochastic model admits noise-driven mixed-mode oscillations (MMOs), which capture the intermediate dynamics between two cycles of population outbreaks. We perform numerical simulations to calculate the distribution of the random number of small oscillations between successive spikes for varying noise intensities and distance to the Hopf bifurcation. We also study the effect of noise on a suitable Poincaré map. Finally, we prove that the stochastic model can be transformed into a normal form near the folded node, which can be linked to recent results on the interplay between deterministic and stochastic small amplitude oscillations. The normal form can also be used to study the parameter influence on the noise level near folded singularities.

Cognitive diagnosis modelling incorporating item response times.

PubMed

Zhan, Peida; Jiao, Hong; Liao, Dandan

2018-05-01

To provide more refined diagnostic feedback with collateral information in item response times (RTs), this study proposed joint modelling of attributes and response speed using item responses and RTs simultaneously for cognitive diagnosis. For illustration, an extended deterministic input, noisy 'and' gate (DINA) model was proposed for joint modelling of responses and RTs. Model parameter estimation was explored using the Bayesian Markov chain Monte Carlo (MCMC) method. The PISA 2012 computer-based mathematics data were analysed first. These real data estimates were treated as true values in a subsequent simulation study. A follow-up simulation study with ideal testing conditions was conducted as well to further evaluate model parameter recovery. The results indicated that model parameters could be well recovered using the MCMC approach. Further, incorporating RTs into the DINA model would improve attribute and profile correct classification rates and result in more accurate and precise estimation of the model parameters. © 2017 The British Psychological Society.

NASA-STD-7009 Guidance Document for Human Health and Performance Models and Simulations

NASA Technical Reports Server (NTRS)

Walton, Marlei; Mulugeta, Lealem; Nelson, Emily S.; Myers, Jerry G.

2014-01-01

Rigorous verification, validation, and credibility (VVC) processes are imperative to ensure that models and simulations (MS) are sufficiently reliable to address issues within their intended scope. The NASA standard for MS, NASA-STD-7009 (7009) [1] was a resultant outcome of the Columbia Accident Investigation Board (CAIB) to ensure MS are developed, applied, and interpreted appropriately for making decisions that may impact crew or mission safety. Because the 7009 focus is engineering systems, a NASA-STD-7009 Guidance Document is being developed to augment the 7009 and provide information, tools, and techniques applicable to the probabilistic and deterministic biological MS more prevalent in human health and performance (HHP) and space biomedical research and operations.

Inferring Fitness Effects from Time-Resolved Sequence Data with a Delay-Deterministic Model.

PubMed

Nené, Nuno R; Dunham, Alistair S; Illingworth, Christopher J R

2018-05-01

A common challenge arising from the observation of an evolutionary system over time is to infer the magnitude of selection acting upon a specific genetic variant, or variants, within the population. The inference of selection may be confounded by the effects of genetic drift in a system, leading to the development of inference procedures to account for these effects. However, recent work has suggested that deterministic models of evolution may be effective in capturing the effects of selection even under complex models of demography, suggesting the more general application of deterministic approaches to inference. Responding to this literature, we here note a case in which a deterministic model of evolution may give highly misleading inferences, resulting from the nondeterministic properties of mutation in a finite population. We propose an alternative approach that acts to correct for this error, and which we denote the delay-deterministic model. Applying our model to a simple evolutionary system, we demonstrate its performance in quantifying the extent of selection acting within that system. We further consider the application of our model to sequence data from an evolutionary experiment. We outline scenarios in which our model may produce improved results for the inference of selection, noting that such situations can be easily identified via the use of a regular deterministic model. Copyright © 2018 Nené et al.

A hybrid deterministic-probabilistic approach to model the mechanical response of helically arranged hierarchical strands

NASA Astrophysics Data System (ADS)

Fraldi, M.; Perrella, G.; Ciervo, M.; Bosia, F.; Pugno, N. M.

2017-09-01

Very recently, a Weibull-based probabilistic strategy has been successfully applied to bundles of wires to determine their overall stress-strain behaviour, also capturing previously unpredicted nonlinear and post-elastic features of hierarchical strands. This approach is based on the so-called "Equal Load Sharing (ELS)" hypothesis by virtue of which, when a wire breaks, the load acting on the strand is homogeneously redistributed among the surviving wires. Despite the overall effectiveness of the method, some discrepancies between theoretical predictions and in silico Finite Element-based simulations or experimental findings might arise when more complex structures are analysed, e.g. helically arranged bundles. To overcome these limitations, an enhanced hybrid approach is proposed in which the probability of rupture is combined with a deterministic mechanical model of a strand constituted by helically-arranged and hierarchically-organized wires. The analytical model is validated comparing its predictions with both Finite Element simulations and experimental tests. The results show that generalized stress-strain responses - incorporating tension/torsion coupling - are naturally found and, once one or more elements break, the competition between geometry and mechanics of the strand microstructure, i.e. the different cross sections and helical angles of the wires in the different hierarchical levels of the strand, determines the no longer homogeneous stress redistribution among the surviving wires whose fate is hence governed by a "Hierarchical Load Sharing" criterion.

Heavy rain prediction using deterministic and probabilistic models - the flash flood cases of 11-13 October 2005 in Catalonia (NE Spain)

NASA Astrophysics Data System (ADS)

Barrera, A.; Altava-Ortiz, V.; Llasat, M. C.; Barnolas, M.

2007-09-01

Between the 11 and 13 October 2005 several flash floods were produced along the coast of Catalonia (NE Spain) due to a significant heavy rainfall event. Maximum rainfall achieved values up to 250 mm in 24 h. The total amount recorded during the event in some places was close to 350 mm. Barcelona city was also in the affected area where high rainfall intensities were registered, but just a few small floods occurred, thanks to the efficient urban drainage system of the city. Two forecasting methods have been applied in order to evaluate their capability of prediction regarding extreme events: the deterministic MM5 model and a probabilistic model based on the analogous method. The MM5 simulation allows analysing accurately the main meteorological features with a high spatial resolution (2 km), like the formation of some convergence lines over the region that partially explains the maximum precipitation location during the event. On the other hand, the analogous technique shows a good agreement among highest probability values and real affected areas, although a larger pluviometric rainfall database would be needed to improve the results. The comparison between the observed precipitation and from both QPF (quantitative precipitation forecast) methods shows that the analogous technique tends to underestimate the rainfall values and the MM5 simulation tends to overestimate them.

Magnified gradient function with deterministic weight modification in adaptive learning.

PubMed

Ng, Sin-Chun; Cheung, Chi-Chung; Leung, Shu-Hung

2004-11-01

This paper presents two novel approaches, backpropagation (BP) with magnified gradient function (MGFPROP) and deterministic weight modification (DWM), to speed up the convergence rate and improve the global convergence capability of the standard BP learning algorithm. The purpose of MGFPROP is to increase the convergence rate by magnifying the gradient function of the activation function, while the main objective of DWM is to reduce the system error by changing the weights of a multilayered feedforward neural network in a deterministic way. Simulation results show that the performance of the above two approaches is better than BP and other modified BP algorithms for a number of learning problems. Moreover, the integration of the above two approaches forming a new algorithm called MDPROP, can further improve the performance of MGFPROP and DWM. From our simulation results, the MDPROP algorithm always outperforms BP and other modified BP algorithms in terms of convergence rate and global convergence capability.

A Tidally Averaged Sediment-Transport Model for San Francisco Bay, California

USGS Publications Warehouse

Lionberger, Megan A.; Schoellhamer, David H.

2009-01-01

A tidally averaged sediment-transport model of San Francisco Bay was incorporated into a tidally averaged salinity box model previously developed and calibrated using salinity, a conservative tracer (Uncles and Peterson, 1995; Knowles, 1996). The Bay is represented in the model by 50 segments composed of two layers: one representing the channel (>5-meter depth) and the other the shallows (0- to 5-meter depth). Calculations are made using a daily time step and simulations can be made on the decadal time scale. The sediment-transport model includes an erosion-deposition algorithm, a bed-sediment algorithm, and sediment boundary conditions. Erosion and deposition of bed sediments are calculated explicitly, and suspended sediment is transported by implicitly solving the advection-dispersion equation. The bed-sediment model simulates the increase in bed strength with depth, owing to consolidation of fine sediments that make up San Francisco Bay mud. The model is calibrated to either net sedimentation calculated from bathymetric-change data or measured suspended-sediment concentration. Specified boundary conditions are the tributary fluxes of suspended sediment and suspended-sediment concentration in the Pacific Ocean. Results of model calibration and validation show that the model simulates the trends in suspended-sediment concentration associated with tidal fluctuations, residual velocity, and wind stress well, although the spring neap tidal suspended-sediment concentration variability was consistently underestimated. Model validation also showed poor simulation of seasonal sediment pulses from the Sacramento-San Joaquin River Delta at Point San Pablo because the pulses enter the Bay over only a few days and the fate of the pulses is determined by intra-tidal deposition and resuspension that are not included in this tidally averaged model. The model was calibrated to net-basin sedimentation to calculate budgets of sediment and sediment-associated contaminants. While simulated net sedimentation in the four basins that comprise San Francisco Bay was correct, the simulations incorrectly eroded shallows while channels deposited because model surface-layer boxes span both shallows and channels, and neglect lateral variability of suspended-sediment concentration. Validation with recent (1983-2005) net sedimentation in South San Francisco Bay was poor, perhaps owing to poorly quantified sediment supply, and to invasive species that altered erosion and deposition processes. This demonstrates that deterministically predicting future sedimentation is difficult in this or any estuary for which boundary conditions are not stationary. The model would best be used as a tool for developing past and present sediment budgets, and for creating scenarios of future sedimentation that are compared to one another rather than considered a deterministic prediction.

Stochasticity and determinism in models of hematopoiesis.

PubMed

Kimmel, Marek

2014-01-01

This chapter represents a novel view of modeling in hematopoiesis, synthesizing both deterministic and stochastic approaches. Whereas the stochastic models work in situations where chance dominates, for example when the number of cells is small, or under random mutations, the deterministic models are more important for large-scale, normal hematopoiesis. New types of models are on the horizon. These models attempt to account for distributed environments such as hematopoietic niches and their impact on dynamics. Mixed effects of such structures and chance events are largely unknown and constitute both a challenge and promise for modeling. Our discussion is presented under the separate headings of deterministic and stochastic modeling; however, the connections between both are frequently mentioned. Four case studies are included to elucidate important examples. We also include a primer of deterministic and stochastic dynamics for the reader's use.

Statistical performance and information content of time lag analysis and redundancy analysis in time series modeling.

PubMed

Angeler, David G; Viedma, Olga; Moreno, José M

2009-11-01

Time lag analysis (TLA) is a distance-based approach used to study temporal dynamics of ecological communities by measuring community dissimilarity over increasing time lags. Despite its increased use in recent years, its performance in comparison with other more direct methods (i.e., canonical ordination) has not been evaluated. This study fills this gap using extensive simulations and real data sets from experimental temporary ponds (true zooplankton communities) and landscape studies (landscape categories as pseudo-communities) that differ in community structure and anthropogenic stress history. Modeling time with a principal coordinate of neighborhood matrices (PCNM) approach, the canonical ordination technique (redundancy analysis; RDA) consistently outperformed the other statistical tests (i.e., TLAs, Mantel test, and RDA based on linear time trends) using all real data. In addition, the RDA-PCNM revealed different patterns of temporal change, and the strength of each individual time pattern, in terms of adjusted variance explained, could be evaluated, It also identified species contributions to these patterns of temporal change. This additional information is not provided by distance-based methods. The simulation study revealed better Type I error properties of the canonical ordination techniques compared with the distance-based approaches when no deterministic component of change was imposed on the communities. The simulation also revealed that strong emphasis on uniform deterministic change and low variability at other temporal scales is needed to result in decreased statistical power of the RDA-PCNM approach relative to the other methods. Based on the statistical performance of and information content provided by RDA-PCNM models, this technique serves ecologists as a powerful tool for modeling temporal change of ecological (pseudo-) communities.

Exploring a new method for the retrieval of urban thermophysical properties using thermal infrared remote sensing and deterministic modeling

NASA Astrophysics Data System (ADS)

De Ridder, K.; Bertrand, C.; Casanova, G.; Lefebvre, W.

2012-09-01

Increasingly, mesoscale meteorological and climate models are used to predict urban weather and climate. Yet, large uncertainties remain regarding values of some urban surface properties. In particular, information concerning urban values for thermal roughness length and thermal admittance is scarce. In this paper, we present a method to estimate values for thermal admittance in combination with an optimal scheme for thermal roughness length, based on METEOSAT-8/SEVIRI thermal infrared imagery in conjunction with a deterministic atmospheric model containing a simple urbanized land surface scheme. Given the spatial resolution of the SEVIRI sensor, the resulting parameter values are applicable at scales of the order of 5 km. As a study case we focused on the city of Paris, for the day of 29 June 2006. Land surface temperature was calculated from SEVIRI thermal radiances using a new split-window algorithm specifically designed to handle urban conditions, as described inAppendix A, including a correction for anisotropy effects. Land surface temperature was also calculated in an ensemble of simulations carried out with the ARPS mesoscale atmospheric model, combining different thermal roughness length parameterizations with a range of thermal admittance values. Particular care was taken to spatially match the simulated land surface temperature with the SEVIRI field of view, using the so-called point spread function of the latter. Using Bayesian inference, the best agreement between simulated and observed land surface temperature was obtained for the Zilitinkevich (1970) and Brutsaert (1975) thermal roughness length parameterizations, the latter with the coefficients obtained by Kanda et al. (2007). The retrieved thermal admittance values associated with either thermal roughness parameterization were, respectively, 1843 ± 108 J m-2 s-1/2 K-1 and 1926 ± 115 J m-2 s-1/2 K-1.

Analysis of the French insurance market exposure to floods: a stochastic model combining river overflow and surface runoff

NASA Astrophysics Data System (ADS)

Moncoulon, D.; Labat, D.; Ardon, J.; Onfroy, T.; Leblois, E.; Poulard, C.; Aji, S.; Rémy, A.; Quantin, A.

2013-07-01

The analysis of flood exposure at a national scale for the French insurance market must combine the generation of a probabilistic event set of all possible but not yet occurred flood situations with hazard and damage modeling. In this study, hazard and damage models are calibrated on a 1995-2012 historical event set, both for hazard results (river flow, flooded areas) and loss estimations. Thus, uncertainties in the deterministic estimation of a single event loss are known before simulating a probabilistic event set. To take into account at least 90% of the insured flood losses, the probabilistic event set must combine the river overflow (small and large catchments) with the surface runoff due to heavy rainfall, on the slopes of the watershed. Indeed, internal studies of CCR claim database has shown that approximately 45% of the insured flood losses are located inside the floodplains and 45% outside. 10% other percent are due to seasurge floods and groundwater rise. In this approach, two independent probabilistic methods are combined to create a single flood loss distribution: generation of fictive river flows based on the historical records of the river gauge network and generation of fictive rain fields on small catchments, calibrated on the 1958-2010 Météo-France rain database SAFRAN. All the events in the probabilistic event sets are simulated with the deterministic model. This hazard and damage distribution is used to simulate the flood losses at the national scale for an insurance company (MACIF) and to generate flood areas associated with hazard return periods. The flood maps concern river overflow and surface water runoff. Validation of these maps is conducted by comparison with the address located claim data on a small catchment (downstream Argens).

Bifurcation analysis of dengue transmission model in Baguio City, Philippines

NASA Astrophysics Data System (ADS)

Libatique, Criselda P.; Pajimola, Aprimelle Kris J.; Addawe, Joel M.

2017-11-01

In this study, we formulate a deterministic model for the transmission dynamics of dengue fever in Baguio City, Philippines. We analyzed the existence of the equilibria of the dengue model. We computed and obtained conditions for the existence of the equilibrium states. Stability analysis for the system is carried out for disease free equilibrium. We showed that the system becomes stable under certain conditions of the parameters. A particular parameter is taken and with the use of the Theory of Centre Manifold, the proposed model demonstrates a bifurcation phenomenon. We performed numerical simulation to verify the analytical results.

Simulating Conditional Deterministic Predictability within Ocean Frontogenesis

DTIC Science & Technology

2014-03-26

Prediction System (COAMPS; Hodur, 1997) across the inner domain. The surface wind stress is determined from the atmo- spheric model wind velocity...layers on the light side of the front. Increasing the strength of the down-front wind increases the frontogenesis. Mahadevan and Tandon (2006) showed...Filaments of shallow MLD, large frontogenesis and large surface divergence ( upwelling ) are found in the OSEs, but at different locations and strengths . The

Application of plurigaussian simulation to delineate the layout of alteration domains in Sungun copper deposit

NASA Astrophysics Data System (ADS)

Talebi, Hassan; Asghari, Omid; Emery, Xavier

2013-12-01

An accurate estimation of mineral grades in ore deposits with heterogeneous spatial variations requires defining geological domains that differentiate the types of mineralogy, alteration and lithology. Deterministic models define the layout of the domains based on the interpretation of the drill holes and do not take into account the uncertainty in areas with fewer data. Plurigaussian simulation (PGS) can be an alternative to generate multiple numerical models of the ore body, with the aim of assessing the uncertainty in the domain boundaries and improving the geological controls in the characterization of quantitative attributes. This study addresses the application of PGS to Sungun porphyry copper deposit (Iran), in order to simulate the layout of four hypogene alteration zones: potassic, phyllic, propylitic and argillic. The aim of this study is to construct numerical models in which the alteration structures reflect the evolution observed in the geology.

Updates to the Generation of Physics Data Inputs for MAMMOTH Simulations of the Transient Reactor Test Facility - FY2016

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

Ortensi, Javier; Baker, Benjamin Allen; Schunert, Sebastian

The INL is currently evolving the modeling and simulation (M&S) capability that will enable improved core operation as well as design and analysis of TREAT experiments. This M&S capability primarily uses MAMMOTH, a reactor physics application being developed under Multi-physics Object Oriented Simulation Environment (MOOSE) framework. MAMMOTH allows the coupling of a number of other MOOSE-based applications. This second year of work has been devoted to the generation of a deterministic reference solution for the full core, the preparation of anisotropic diffusion coefficients, the testing of the SPH equivalence method, and the improvement of the control rod modeling. In addition,more » this report includes the progress made in the modeling of the M8 core configuration and experiment vehicle since January of this year.« less

Simulations of Fluvial Landscapes

NASA Astrophysics Data System (ADS)

Cattan, D.; Birnir, B.

2013-12-01

The Smith-Bretherton-Birnir (SBB) model for fluvial landsurfaces consists of a pair of partial differential equations, one governing water flow and one governing the sediment flow. Numerical solutions of these equations have been shown to provide realistic models in the evolution of fluvial landscapes. Further analysis of these equations shows that they possess scaling laws (Hack's Law) that are known to exist in nature. However, the simulations are highly dependent on the numerical methods used; with implicit methods exhibiting the correct scaling laws, but the explicit methods fail to do so. These equations, and the resulting models, help to bridge the gap between the deterministic and the stochastic theories of landscape evolution. Slight modifications of the SBB equations make the results of the model more realistic. By modifying the sediment flow equation, the model obtains more pronounced meandering rivers. Typical landsurface with rivers.

A review of recent advances in the spherical harmonics expansion method for semiconductor device simulation.

PubMed

Rupp, K; Jungemann, C; Hong, S-M; Bina, M; Grasser, T; Jüngel, A

The Boltzmann transport equation is commonly considered to be the best semi-classical description of carrier transport in semiconductors, providing precise information about the distribution of carriers with respect to time (one dimension), location (three dimensions), and momentum (three dimensions). However, numerical solutions for the seven-dimensional carrier distribution functions are very demanding. The most common solution approach is the stochastic Monte Carlo method, because the gigabytes of memory requirements of deterministic direct solution approaches has not been available until recently. As a remedy, the higher accuracy provided by solutions of the Boltzmann transport equation is often exchanged for lower computational expense by using simpler models based on macroscopic quantities such as carrier density and mean carrier velocity. Recent developments for the deterministic spherical harmonics expansion method have reduced the computational cost for solving the Boltzmann transport equation, enabling the computation of carrier distribution functions even for spatially three-dimensional device simulations within minutes to hours. We summarize recent progress for the spherical harmonics expansion method and show that small currents, reasonable execution times, and rare events such as low-frequency noise, which are all hard or even impossible to simulate with the established Monte Carlo method, can be handled in a straight-forward manner. The applicability of the method for important practical applications is demonstrated for noise simulation, small-signal analysis, hot-carrier degradation, and avalanche breakdown.

Stochastic and Deterministic Approaches to Gas-grain Modeling of Interstellar Sources

NASA Astrophysics Data System (ADS)

Vasyunin, Anton; Herbst, Eric; Caselli, Paola

During the last decade, our understanding of the chemistry on surfaces of interstellar grains has been significantly enchanced. Extensive laboratory studies have revealed complex structure and dynamics in interstellar ice analogues, thus making our knowledge much more detailed. In addition, the first qualitative investigations of new processes were made, such as non-thermal chemical desorption of species from dust grains into the gas. Not surprisingly, the rapid growth of knowledge about the physics and chemistry of interstellar ices led to the development of a new generation of astrochemical models. The models are typically characterized by more detailed treatments of the ice physics and chemistry than previously. The utilized numerical approaches vary greatly from microscopic models, in which every single molecule is traced, to ``mean field'' macroscopic models, which simulate the evolution of averaged characteristics of interstellar ices, such as overall bulk composition. While microscopic models based on a stochastic Monte Carlo approach are potentially able to simulate the evolution of interstellar ices with an account of most subtle effects found in a laboratory, their use is often impractical due to limited knowledge about star-forming regions and huge computational demands. On the other hand, deterministic macroscopic models that often utilize kinetic rate equations are computationally efficient but experience difficulties in incorporation of such potentially important effects as ice segregation or discreteness of surface chemical reactions. In my talk, I will review the state of the art in the development of gas-grain astrochemical models. I will discuss how to incorporate key features of ice chemistry and dynamics in the gas-grain astrochemical models, and how the incorporation of recent laboratory findings into gas-grain models helps to better match observations.

Mathematical analysis of epidemiological models with heterogeneity

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

Van Ark, J.W.

1992-01-01

For many diseases in human populations the disease shows dissimilar characteristics in separate subgroups of the population; for example, the probability of disease transmission for gonorrhea or AIDS is much higher from male to female than from female to male. There is reason to construct and analyze epidemiological models which allow this heterogeneity of population, and to use these models to run computer simulations of the disease to predict the incidence and prevalence of the disease. In the models considered here the heterogeneous population is separated into subpopulations whose internal and external interactions are homogeneous in the sense that eachmore » person in the population can be assumed to have all average actions for the people of that subpopulation. The first model considered is an SIRS models; i.e., the Susceptible can become Infected, and if so he eventually Recovers with temporary immunity, and after a period of time becomes Susceptible again. Special cases allow for permanent immunity or other variations. This model is analyzed and threshold conditions are given which determine whether the disease dies out or persists. A deterministic model is presented; this model is constructed using difference equations, and it has been used in computer simulations for the AIDS epidemic in the homosexual population in San Francisco. The homogeneous version and the heterogeneous version of the differential-equations and difference-equations versions of the deterministic model are analyzed mathematically. In the analysis, equilibria are identified and threshold conditions are set forth for the disease to die out if the disease is below the threshold so that the disease-free equilibrium is globally asymptotically stable. Above the threshold the disease persists so that the disease-free equilibrium is unstable and there is a unique endemic equilibrium.« less

Stochastic dynamic modeling of regular and slow earthquakes

NASA Astrophysics Data System (ADS)

Aso, N.; Ando, R.; Ide, S.

2017-12-01

Both regular and slow earthquakes are slip phenomena on plate boundaries and are simulated by a (quasi-)dynamic modeling [Liu and Rice, 2005]. In these numerical simulations, spatial heterogeneity is usually considered not only for explaining real physical properties but also for evaluating the stability of the calculations or the sensitivity of the results on the condition. However, even though we discretize the model space with small grids, heterogeneity at smaller scales than the grid size is not considered in the models with deterministic governing equations. To evaluate the effect of heterogeneity at the smaller scales we need to consider stochastic interactions between slip and stress in a dynamic modeling. Tidal stress is known to trigger or affect both regular and slow earthquakes [Yabe et al., 2015; Ide et al., 2016], and such an external force with fluctuation can also be considered as a stochastic external force. A healing process of faults may also be stochastic, so we introduce stochastic friction law. In the present study, we propose a stochastic dynamic model to explain both regular and slow earthquakes. We solve mode III problem, which corresponds to the rupture propagation along the strike direction. We use BIEM (boundary integral equation method) scheme to simulate slip evolution, but we add stochastic perturbations in the governing equations, which is usually written in a deterministic manner. As the simplest type of perturbations, we adopt Gaussian deviations in the formulation of the slip-stress kernel, external force, and friction. By increasing the amplitude of perturbations of the slip-stress kernel, we reproduce complicated rupture process of regular earthquakes including unilateral and bilateral ruptures. By perturbing external force, we reproduce slow rupture propagation at a scale of km/day. The slow propagation generated by a combination of fast interaction at S-wave velocity is analogous to the kinetic theory of gasses: thermal diffusion appears much slower than the particle velocity of each molecule. The concept of stochastic triggering originates in the Brownian walk model [Ide, 2008], and the present study introduces the stochastic dynamics into dynamic simulations. The stochastic dynamic model has the potential to explain both regular and slow earthquakes more realistically.

Multiscale Mathematics for Biomass Conversion to Renewable Hydrogen

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

Plechac, Petr; Vlachos, Dionisios; Katsoulakis, Markos

2013-09-05

The overall objective of this project is to develop multiscale models for understanding and eventually designing complex processes for renewables. To the best of our knowledge, our work is the first attempt at modeling complex reacting systems, whose performance relies on underlying multiscale mathematics. Our specific application lies at the heart of biofuels initiatives of DOE and entails modeling of catalytic systems, to enable economic, environmentally benign, and efficient conversion of biomass into either hydrogen or valuable chemicals. Specific goals include: (i) Development of rigorous spatio-temporal coarse-grained kinetic Monte Carlo (KMC) mathematics and simulation for microscopic processes encountered in biomassmore » transformation. (ii) Development of hybrid multiscale simulation that links stochastic simulation to a deterministic partial differential equation (PDE) model for an entire reactor. (iii) Development of hybrid multiscale simulation that links KMC simulation with quantum density functional theory (DFT) calculations. (iv) Development of parallelization of models of (i)-(iii) to take advantage of Petaflop computing and enable real world applications of complex, multiscale models. In this NCE period, we continued addressing these objectives and completed the proposed work. Main initiatives, key results, and activities are outlined.« less

Stochastic Models for Precipitable Water in Convection

NASA Astrophysics Data System (ADS)

Leung, Kimberly

Atmospheric precipitable water vapor (PWV) is the amount of water vapor in the atmosphere within a vertical column of unit cross-sectional area and is a critically important parameter of precipitation processes. However, accurate high-frequency and long-term observations of PWV in the sky were impossible until the availability of modern instruments such as radar. The United States Department of Energy (DOE)'s Atmospheric Radiation Measurement (ARM) Program facility made the first systematic and high-resolution observations of PWV at Darwin, Australia since 2002. At a resolution of 20 seconds, this time series allowed us to examine the volatility of PWV, including fractal behavior with dimension equal to 1.9, higher than the Brownian motion dimension of 1.5. Such strong fractal behavior calls for stochastic differential equation modeling in an attempt to address some of the difficulties of convective parameterization in various kinds of climate models, ranging from general circulation models (GCM) to weather research forecasting (WRF) models. This important observed data at high resolution can capture the fractal behavior of PWV and enables stochastic exploration into the next generation of climate models which considers scales from micrometers to thousands of kilometers. As a first step, this thesis explores a simple stochastic differential equation model of water mass balance for PWV and assesses accuracy, robustness, and sensitivity of the stochastic model. A 1000-day simulation allows for the determination of the best-fitting 25-day period as compared to data from the TWP-ICE field campaign conducted out of Darwin, Australia in early 2006. The observed data and this portion of the simulation had a correlation coefficient of 0.6513 and followed similar statistics and low-resolution temporal trends. Building on the point model foundation, a similar algorithm was applied to the National Center for Atmospheric Research (NCAR)'s existing single-column model as a test-of-concept for eventual inclusion in a general circulation model. The stochastic scheme was designed to be coupled with the deterministic single-column simulation by modifying results of the existing convective scheme (Zhang-McFarlane) and was able to produce a 20-second resolution time series that effectively simulated observed PWV, as measured by correlation coefficient (0.5510), fractal dimension (1.9), statistics, and visual examination of temporal trends. Results indicate that simulation of a highly volatile time series of observed PWV is certainly achievable and has potential to improve prediction capabilities in climate modeling. Further, this study demonstrates the feasibility of adding a mathematics- and statistics-based stochastic scheme to an existing deterministic parameterization to simulate observed fractal behavior.

A Stochastic Differential Equation Model for the Spread of HIV amongst People Who Inject Drugs.

PubMed

Liang, Yanfeng; Greenhalgh, David; Mao, Xuerong

2016-01-01

We introduce stochasticity into the deterministic differential equation model for the spread of HIV amongst people who inject drugs (PWIDs) studied by Greenhalgh and Hay (1997). This was based on the original model constructed by Kaplan (1989) which analyses the behaviour of HIV/AIDS amongst a population of PWIDs. We derive a stochastic differential equation (SDE) for the fraction of PWIDs who are infected with HIV at time. The stochasticity is introduced using the well-known standard technique of parameter perturbation. We first prove that the resulting SDE for the fraction of infected PWIDs has a unique solution in (0, 1) provided that some infected PWIDs are initially present and next construct the conditions required for extinction and persistence. Furthermore, we show that there exists a stationary distribution for the persistence case. Simulations using realistic parameter values are then constructed to illustrate and support our theoretical results. Our results provide new insight into the spread of HIV amongst PWIDs. The results show that the introduction of stochastic noise into a model for the spread of HIV amongst PWIDs can cause the disease to die out in scenarios where deterministic models predict disease persistence.

Firing patterns in the adaptive exponential integrate-and-fire model.

PubMed

Naud, Richard; Marcille, Nicolas; Clopath, Claudia; Gerstner, Wulfram

2008-11-01

For simulations of large spiking neuron networks, an accurate, simple and versatile single-neuron modeling framework is required. Here we explore the versatility of a simple two-equation model: the adaptive exponential integrate-and-fire neuron. We show that this model generates multiple firing patterns depending on the choice of parameter values, and present a phase diagram describing the transition from one firing type to another. We give an analytical criterion to distinguish between continuous adaption, initial bursting, regular bursting and two types of tonic spiking. Also, we report that the deterministic model is capable of producing irregular spiking when stimulated with constant current, indicating low-dimensional chaos. Lastly, the simple model is fitted to real experiments of cortical neurons under step current stimulation. The results provide support for the suitability of simple models such as the adaptive exponential integrate-and-fire neuron for large network simulations.

A mathematical model of Staphylococcus aureus control in dairy herds.

PubMed Central

Zadoks, R. N.; Allore, H. G.; Hagenaars, T. J.; Barkema, H. W.; Schukken, Y. H.

2002-01-01

An ordinary differential equation model was developed to simulate dynamics of Staphylococcus aureus mastitis. Data to estimate model parameters were obtained from an 18-month observational study in three commercial dairy herds. A deterministic simulation model was constructed to estimate values of the basic (R0) and effective (Rt) reproductive number in each herd, and to examine the effect of management on mastitis control. In all herds R0 was below the threshold value 1, indicating control of contagious transmission. Rt was higher than R0 because recovered individuals were more susceptible to infection than individuals without prior infection history. Disease dynamics in two herds were well described by the model. Treatment of subclinical mastitis and prevention of influx of infected individuals contributed to decrease of S. aureus prevalence. For one herd, the model failed to mimic field observations. Explanations for the discrepancy are given in a discussion of current knowledge and model assumptions. PMID:12403116

Improving synoptic and intraseasonal variability in CFSv2 via stochastic representation of organized convection

NASA Astrophysics Data System (ADS)

Goswami, B. B.; Khouider, B.; Phani, R.; Mukhopadhyay, P.; Majda, A.

2017-01-01

To better represent organized convection in the Climate Forecast System version 2 (CFSv2), a stochastic multicloud model (SMCM) parameterization is adopted and a 15 year climate run is made. The last 10 years of simulations are analyzed here. While retaining an equally good mean state (if not better) as the parent model, the CFS-SMCM simulation shows significant improvement in the synoptic and intraseasonal variability. The CFS-SMCM provides a better account of convectively coupled equatorial waves and the Madden-Julian oscillation. The CFS-SMCM exhibits improvements in northward and eastward propagation of intraseasonal oscillation of convection including the MJO propagation beyond the maritime continent barrier, which is the Achilles Heel for coarse-resolution global climate models (GCMs). The distribution of precipitation events is better simulated in CFSsmcm and spreads naturally toward high-precipitation events. Deterministic GCMs tend to simulate a narrow distribution with too much drizzling precipitation and too little high-precipitation events.

A Bayesian model averaging method for the derivation of reservoir operating rules

NASA Astrophysics Data System (ADS)

Zhang, Jingwen; Liu, Pan; Wang, Hao; Lei, Xiaohui; Zhou, Yanlai

2015-09-01

Because the intrinsic dynamics among optimal decision making, inflow processes and reservoir characteristics are complex, functional forms of reservoir operating rules are always determined subjectively. As a result, the uncertainty of selecting form and/or model involved in reservoir operating rules must be analyzed and evaluated. In this study, we analyze the uncertainty of reservoir operating rules using the Bayesian model averaging (BMA) model. Three popular operating rules, namely piecewise linear regression, surface fitting and a least-squares support vector machine, are established based on the optimal deterministic reservoir operation. These individual models provide three-member decisions for the BMA combination, enabling the 90% release interval to be estimated by the Markov Chain Monte Carlo simulation. A case study of China's the Baise reservoir shows that: (1) the optimal deterministic reservoir operation, superior to any reservoir operating rules, is used as the samples to derive the rules; (2) the least-squares support vector machine model is more effective than both piecewise linear regression and surface fitting; (3) BMA outperforms any individual model of operating rules based on the optimal trajectories. It is revealed that the proposed model can reduce the uncertainty of operating rules, which is of great potential benefit in evaluating the confidence interval of decisions.

A downscaling scheme for atmospheric variables to drive soil-vegetation-atmosphere transfer models

NASA Astrophysics Data System (ADS)

Schomburg, A.; Venema, V.; Lindau, R.; Ament, F.; Simmer, C.

2010-09-01

For driving soil-vegetation-transfer models or hydrological models, high-resolution atmospheric forcing data is needed. For most applications the resolution of atmospheric model output is too coarse. To avoid biases due to the non-linear processes, a downscaling system should predict the unresolved variability of the atmospheric forcing. For this purpose we derived a disaggregation system consisting of three steps: (1) a bi-quadratic spline-interpolation of the low-resolution data, (2) a so-called `deterministic' part, based on statistical rules between high-resolution surface variables and the desired atmospheric near-surface variables and (3) an autoregressive noise-generation step. The disaggregation system has been developed and tested based on high-resolution model output (400m horizontal grid spacing). A novel automatic search-algorithm has been developed for deriving the deterministic downscaling rules of step 2. When applied to the atmospheric variables of the lowest layer of the atmospheric COSMO-model, the disaggregation is able to adequately reconstruct the reference fields. Applying downscaling step 1 and 2, root mean square errors are decreased. Step 3 finally leads to a close match of the subgrid variability and temporal autocorrelation with the reference fields. The scheme can be applied to the output of atmospheric models, both for stand-alone offline simulations, and a fully coupled model system.

Deterministic diffusion in flower-shaped billiards.

PubMed

Harayama, Takahisa; Klages, Rainer; Gaspard, Pierre

2002-08-01

We propose a flower-shaped billiard in order to study the irregular parameter dependence of chaotic normal diffusion. Our model is an open system consisting of periodically distributed obstacles in the shape of a flower, and it is strongly chaotic for almost all parameter values. We compute the parameter dependent diffusion coefficient of this model from computer simulations and analyze its functional form using different schemes, all generalizing the simple random walk approximation of Machta and Zwanzig. The improved methods we use are based either on heuristic higher-order corrections to the simple random walk model, on lattice gas simulation methods, or they start from a suitable Green-Kubo formula for diffusion. We show that dynamical correlations, or memory effects, are of crucial importance in reproducing the precise parameter dependence of the diffusion coefficent.

Simulation of unsteady flows by the DSMC macroscopic chemistry method

NASA Astrophysics Data System (ADS)

Goldsworthy, Mark; Macrossan, Michael; Abdel-jawad, Madhat

2009-03-01

In the Direct Simulation Monte-Carlo (DSMC) method, a combination of statistical and deterministic procedures applied to a finite number of 'simulator' particles are used to model rarefied gas-kinetic processes. In the macroscopic chemistry method (MCM) for DSMC, chemical reactions are decoupled from the specific particle pairs selected for collisions. Information from all of the particles within a cell, not just those selected for collisions, is used to determine a reaction rate coefficient for that cell. Unlike collision-based methods, MCM can be used with any viscosity or non-reacting collision models and any non-reacting energy exchange models. It can be used to implement any reaction rate formulations, whether these be from experimental or theoretical studies. MCM has been previously validated for steady flow DSMC simulations. Here we show how MCM can be used to model chemical kinetics in DSMC simulations of unsteady flow. Results are compared with a collision-based chemistry procedure for two binary reactions in a 1-D unsteady shock-expansion tube simulation. Close agreement is demonstrated between the two methods for instantaneous, ensemble-averaged profiles of temperature, density and species mole fractions, as well as for the accumulated number of net reactions per cell.

Simulation of glioblastoma multiforme (GBM) tumor cells using ising model on the Creutz Cellular Automaton

NASA Astrophysics Data System (ADS)

Züleyha, Artuç; Ziya, Merdan; Selçuk, Yeşiltaş; Kemal, Öztürk M.; Mesut, Tez

2017-11-01

Computational models for tumors have difficulties due to complexity of tumor nature and capacities of computational tools, however, these models provide visions to understand interactions between tumor and its micro environment. Moreover computational models have potential to develop strategies for individualized treatments for cancer. To observe a solid brain tumor, glioblastoma multiforme (GBM), we present a two dimensional Ising Model applied on Creutz cellular automaton (CCA). The aim of this study is to analyze avascular spherical solid tumor growth, considering transitions between non tumor cells and cancer cells are like phase transitions in physical system. Ising model on CCA algorithm provides a deterministic approach with discrete time steps and local interactions in position space to view tumor growth as a function of time. Our simulation results are given for fixed tumor radius and they are compatible with theoretical and clinic data.

Monte Carlo simulation of induction time and metastable zone width; stochastic or deterministic?

NASA Astrophysics Data System (ADS)

Kubota, Noriaki

2018-03-01

The induction time and metastable zone width (MSZW) measured for small samples (say 1 mL or less) both scatter widely. Thus, these two are observed as stochastic quantities. Whereas, for large samples (say 1000 mL or more), the induction time and MSZW are observed as deterministic quantities. The reason for such experimental differences is investigated with Monte Carlo simulation. In the simulation, the time (under isothermal condition) and supercooling (under polythermal condition) at which a first single crystal is detected are defined as the induction time t and the MSZW ΔT for small samples, respectively. The number of crystals just at the moment of t and ΔT is unity. A first crystal emerges at random due to the intrinsic nature of nucleation, accordingly t and ΔT become stochastic. For large samples, the time and supercooling at which the number density of crystals N/V reaches a detector sensitivity (N/V)det are defined as t and ΔT for isothermal and polythermal conditions, respectively. The points of t and ΔT are those of which a large number of crystals have accumulated. Consequently, t and ΔT become deterministic according to the law of large numbers. Whether t and ΔT may stochastic or deterministic in actual experiments should not be attributed to change in nucleation mechanisms in molecular level. It could be just a problem caused by differences in the experimental definition of t and ΔT.

Scoping analysis of the Advanced Test Reactor using SN2ND

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

Wolters, E.; Smith, M.; SC)

2012-07-26

A detailed set of calculations was carried out for the Advanced Test Reactor (ATR) using the SN2ND solver of the UNIC code which is part of the SHARP multi-physics code being developed under the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program in DOE-NE. The primary motivation of this work is to assess whether high fidelity deterministic transport codes can tackle coupled dynamics simulations of the ATR. The successful use of such codes in a coupled dynamics simulation can impact what experiments are performed and what power levels are permitted during those experiments at the ATR. The advantages of themore » SN2ND solver over comparable neutronics tools are its superior parallel performance and demonstrated accuracy on large scale homogeneous and heterogeneous reactor geometries. However, it should be noted that virtually no effort from this project was spent constructing a proper cross section generation methodology for the ATR usable in the SN2ND solver. While attempts were made to use cross section data derived from SCALE, the minimal number of compositional cross section sets were generated to be consistent with the reference Monte Carlo input specification. The accuracy of any deterministic transport solver is impacted by such an approach and clearly it causes substantial errors in this work. The reasoning behind this decision is justified given the overall funding dedicated to the task (two months) and the real focus of the work: can modern deterministic tools actually treat complex facilities like the ATR with heterogeneous geometry modeling. SN2ND has been demonstrated to solve problems with upwards of one trillion degrees of freedom which translates to tens of millions of finite elements, hundreds of angles, and hundreds of energy groups, resulting in a very high-fidelity model of the system unachievable by most deterministic transport codes today. A space-angle convergence study was conducted to determine the meshing and angular cubature requirements for the ATR, and also to demonstrate the feasibility of performing this analysis with a deterministic transport code capable of modeling heterogeneous geometries. The work performed indicates that a minimum of 260,000 linear finite elements combined with a L3T11 cubature (96 angles on the sphere) is required for both eigenvalue and flux convergence of the ATR. A critical finding was that the fuel meat and water channels must each be meshed with at least 3 'radial zones' for accurate flux convergence. A small number of 3D calculations were also performed to show axial mesh and eigenvalue convergence for a full core problem. Finally, a brief analysis was performed with different cross sections sets generated from DRAGON and SCALE, and the findings show that more effort will be required to improve the multigroup cross section generation process. The total number of degrees of freedom for a converged 27 group, 2D ATR problem is {approx}340 million. This number increases to {approx}25 billion for a 3D ATR problem. This scoping study shows that both 2D and 3D calculations are well within the capabilities of the current SN2ND solver, given the availability of a large-scale computing center such as BlueGene/P. However, dynamics calculations are not realistic without the implementation of improvements in the solver.« less

Statistics of Delta v magnitude for a trajectory correction maneuver containing deterministic and random components

NASA Technical Reports Server (NTRS)

Bollman, W. E.; Chadwick, C.

1982-01-01

A number of interplanetary missions now being planned involve placing deterministic maneuvers along the flight path to alter the trajectory. Lee and Boain (1973) examined the statistics of trajectory correction maneuver (TCM) magnitude with no deterministic ('bias') component. The Delta v vector magnitude statistics were generated for several values of random Delta v standard deviations using expansions in terms of infinite hypergeometric series. The present investigation uses a different technique (Monte Carlo simulation) to generate Delta v magnitude statistics for a wider selection of random Delta v standard deviations and also extends the analysis to the case of nonzero deterministic Delta v's. These Delta v magnitude statistics are plotted parametrically. The plots are useful in assisting the analyst in quickly answering questions about the statistics of Delta v magnitude for single TCM's consisting of both a deterministic and a random component. The plots provide quick insight into the nature of the Delta v magnitude distribution for the TCM.

Watershed scale response to climate change--Yampa River Basin, Colorado

USGS Publications Warehouse

Hay, Lauren E.; Battaglin, William A.; Markstrom, Steven L.

2012-01-01

General Circulation Model simulations of future climate through 2099 project a wide range of possible scenarios. To determine the sensitivity and potential effect of long-term climate change on the freshwater resources of the United States, the U.S. Geological Survey Global Change study, "An integrated watershed scale response to global change in selected basins across the United States" was started in 2008. The long-term goal of this national study is to provide the foundation for hydrologically based climate change studies across the nation. Fourteen basins for which the Precipitation Runoff Modeling System has been calibrated and evaluated were selected as study sites. Precipitation Runoff Modeling System is a deterministic, distributed parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land use on streamflow and general basin hydrology. Output from five General Circulation Model simulations and four emission scenarios were used to develop an ensemble of climate-change scenarios for each basin. These ensembles were simulated with the corresponding Precipitation Runoff Modeling System model. This fact sheet summarizes the hydrologic effect and sensitivity of the Precipitation Runoff Modeling System simulations to climate change for the Yampa River Basin at Steamboat Springs, Colorado.

Combining cellular automata and Lattice Boltzmann method to model multiscale avascular tumor growth coupled with nutrient diffusion and immune competition.

PubMed

Alemani, Davide; Pappalardo, Francesco; Pennisi, Marzio; Motta, Santo; Brusic, Vladimir

2012-02-28

In the last decades the Lattice Boltzmann method (LB) has been successfully used to simulate a variety of processes. The LB model describes the microscopic processes occurring at the cellular level and the macroscopic processes occurring at the continuum level with a unique function, the probability distribution function. Recently, it has been tried to couple deterministic approaches with probabilistic cellular automata (probabilistic CA) methods with the aim to model temporal evolution of tumor growths and three dimensional spatial evolution, obtaining hybrid methodologies. Despite the good results attained by CA-PDE methods, there is one important issue which has not been completely solved: the intrinsic stochastic nature of the interactions at the interface between cellular (microscopic) and continuum (macroscopic) level. CA methods are able to cope with the stochastic phenomena because of their probabilistic nature, while PDE methods are fully deterministic. Even if the coupling is mathematically correct, there could be important statistical effects that could be missed by the PDE approach. For such a reason, to be able to develop and manage a model that takes into account all these three level of complexity (cellular, molecular and continuum), we believe that PDE should be replaced with a statistic and stochastic model based on the numerical discretization of the Boltzmann equation: The Lattice Boltzmann (LB) method. In this work we introduce a new hybrid method to simulate tumor growth and immune system, by applying Cellular Automata Lattice Boltzmann (CA-LB) approach. Copyright © 2011 Elsevier B.V. All rights reserved.

A Model of Yeast Cell-Cycle Regulation Based on a Standard Component Modeling Strategy for Protein Regulatory Networks.

PubMed

Laomettachit, Teeraphan; Chen, Katherine C; Baumann, William T; Tyson, John J

2016-01-01

To understand the molecular mechanisms that regulate cell cycle progression in eukaryotes, a variety of mathematical modeling approaches have been employed, ranging from Boolean networks and differential equations to stochastic simulations. Each approach has its own characteristic strengths and weaknesses. In this paper, we propose a "standard component" modeling strategy that combines advantageous features of Boolean networks, differential equations and stochastic simulations in a framework that acknowledges the typical sorts of reactions found in protein regulatory networks. Applying this strategy to a comprehensive mechanism of the budding yeast cell cycle, we illustrate the potential value of standard component modeling. The deterministic version of our model reproduces the phenotypic properties of wild-type cells and of 125 mutant strains. The stochastic version of our model reproduces the cell-to-cell variability of wild-type cells and the partial viability of the CLB2-dbΔ clb5Δ mutant strain. Our simulations show that mathematical modeling with "standard components" can capture in quantitative detail many essential properties of cell cycle control in budding yeast.

A Model of Yeast Cell-Cycle Regulation Based on a Standard Component Modeling Strategy for Protein Regulatory Networks

PubMed Central

Laomettachit, Teeraphan; Chen, Katherine C.; Baumann, William T.

2016-01-01

To understand the molecular mechanisms that regulate cell cycle progression in eukaryotes, a variety of mathematical modeling approaches have been employed, ranging from Boolean networks and differential equations to stochastic simulations. Each approach has its own characteristic strengths and weaknesses. In this paper, we propose a “standard component” modeling strategy that combines advantageous features of Boolean networks, differential equations and stochastic simulations in a framework that acknowledges the typical sorts of reactions found in protein regulatory networks. Applying this strategy to a comprehensive mechanism of the budding yeast cell cycle, we illustrate the potential value of standard component modeling. The deterministic version of our model reproduces the phenotypic properties of wild-type cells and of 125 mutant strains. The stochastic version of our model reproduces the cell-to-cell variability of wild-type cells and the partial viability of the CLB2-dbΔ clb5Δ mutant strain. Our simulations show that mathematical modeling with “standard components” can capture in quantitative detail many essential properties of cell cycle control in budding yeast. PMID:27187804

Probabilistic modeling of the indoor climates of residential buildings using EnergyPlus

DOE PAGES

Buechler, Elizabeth D.; Pallin, Simon B.; Boudreaux, Philip R.; ...

2017-04-25

The indoor air temperature and relative humidity in residential buildings significantly affect material moisture durability, HVAC system performance, and occupant comfort. Therefore, indoor climate data is generally required to define boundary conditions in numerical models that evaluate envelope durability and equipment performance. However, indoor climate data obtained from field studies is influenced by weather, occupant behavior and internal loads, and is generally unrepresentative of the residential building stock. Likewise, whole-building simulation models typically neglect stochastic variables and yield deterministic results that are applicable to only a single home in a specific climate. The

Density waves in granular flow

NASA Astrophysics Data System (ADS)

Herrmann, H. J.; Flekkøy, E.; Nagel, K.; Peng, G.; Ristow, G.

Ample experimental evidence has shown the existence of spontaneous density waves in granular material flowing through pipes or hoppers. Using Molecular Dynamics Simulations we show that several types of waves exist and find that these density fluctuations follow a 1/f spectrum. We compare this behaviour to deterministic one-dimensional traffic models. If positions and velocities are continuous variables the model shows self-organized criticality driven by the slowest car. We also present Lattice Gas and Boltzmann Lattice Models which reproduce the experimentally observed effects. Density waves are spontaneously generated when the viscosity has a nonlinear dependence on density which characterizes granular flow.

Computer modeling of dynamic necking in bars

NASA Astrophysics Data System (ADS)

Partom, Yehuda; Lindenfeld, Avishay

2017-06-01

Necking of thin bodies (bars, plates, shells) is one form of strain localization in ductile materials that may lead to fracture. The phenomenon of necking has been studied extensively, initially for quasistatic loading and then also for dynamic loading. Nevertheless, many issues concerning necking are still unclear. Among these are: 1) is necking a random or deterministic process; 2) how does the specimen choose the final neck location; 3) to what extent do perturbations (material or geometrical) influence the neck forming process; and 4) how do various parameters (material, geometrical, loading) influence the neck forming process. Here we address these issues and others using computer simulations with a hydrocode. Among other things we find that: 1) neck formation is a deterministic process, and by changing one of the parameters influencing it monotonously, the final neck location moves monotonously as well; 2) the final neck location is sensitive to the radial velocity of the end boundaries, and as motion of these boundaries is not fully controlled in tests, this may be the reason why neck formation is sometimes regarded as a random process; and 3) neck formation is insensitive to small perturbations, which is probably why it is a deterministic process.

Understanding the transmission dynamics of respiratory syncytial virus using multiple time series and nested models.

PubMed

White, L J; Mandl, J N; Gomes, M G M; Bodley-Tickell, A T; Cane, P A; Perez-Brena, P; Aguilar, J C; Siqueira, M M; Portes, S A; Straliotto, S M; Waris, M; Nokes, D J; Medley, G F

2007-09-01

The nature and role of re-infection and partial immunity are likely to be important determinants of the transmission dynamics of human respiratory syncytial virus (hRSV). We propose a single model structure that captures four possible host responses to infection and subsequent reinfection: partial susceptibility, altered infection duration, reduced infectiousness and temporary immunity (which might be partial). The magnitude of these responses is determined by four homotopy parameters, and by setting some of these parameters to extreme values we generate a set of eight nested, deterministic transmission models. In order to investigate hRSV transmission dynamics, we applied these models to incidence data from eight international locations. Seasonality is included as cyclic variation in transmission. Parameters associated with the natural history of the infection were assumed to be independent of geographic location, while others, such as those associated with seasonality, were assumed location specific. Models incorporating either of the two extreme assumptions for immunity (none or solid and lifelong) were unable to reproduce the observed dynamics. Model fits with either waning or partial immunity to disease or both were visually comparable. The best fitting structure was a lifelong partial immunity to both disease and infection. Observed patterns were reproduced by stochastic simulations using the parameter values estimated from the deterministic models.

A model for Huanglongbing spread between citrus plants including delay times and human intervention

NASA Astrophysics Data System (ADS)

Vilamiu, Raphael G. d'A.; Ternes, Sonia; Braga, Guilherme A.; Laranjeira, Francisco F.

2012-09-01

The objective of this work was to present a compartmental deterministic mathematical model for representing the dynamics of HLB disease in a citrus orchard, including delay in the disease's incubation phase in the plants, and a delay period on the nymphal stage of Diaphorina citri, the most important HLB insect vector in Brazil. Numerical simulations were performed to assess the possible impacts of human detection efficiency of symptomatic plants, as well as the influence of a long incubation period of HLB in the plant.

Cognitive Diagnostic Analysis Using Hierarchically Structured Skills

ERIC Educational Resources Information Center

Su, Yu-Lan

2013-01-01

This dissertation proposes two modified cognitive diagnostic models (CDMs), the deterministic, inputs, noisy, "and" gate with hierarchy (DINA-H) model and the deterministic, inputs, noisy, "or" gate with hierarchy (DINO-H) model. Both models incorporate the hierarchical structures of the cognitive skills in the model estimation…

Stochastic blockmodeling of the modules and core of the Caenorhabditis elegans connectome.

PubMed

Pavlovic, Dragana M; Vértes, Petra E; Bullmore, Edward T; Schafer, William R; Nichols, Thomas E

2014-01-01

Recently, there has been much interest in the community structure or mesoscale organization of complex networks. This structure is characterised either as a set of sparsely inter-connected modules or as a highly connected core with a sparsely connected periphery. However, it is often difficult to disambiguate these two types of mesoscale structure or, indeed, to summarise the full network in terms of the relationships between its mesoscale constituents. Here, we estimate a community structure with a stochastic blockmodel approach, the Erdős-Rényi Mixture Model, and compare it to the much more widely used deterministic methods, such as the Louvain and Spectral algorithms. We used the Caenorhabditis elegans (C. elegans) nervous system (connectome) as a model system in which biological knowledge about each node or neuron can be used to validate the functional relevance of the communities obtained. The deterministic algorithms derived communities with 4-5 modules, defined by sparse inter-connectivity between all modules. In contrast, the stochastic Erdős-Rényi Mixture Model estimated a community with 9 blocks or groups which comprised a similar set of modules but also included a clearly defined core, made of 2 small groups. We show that the "core-in-modules" decomposition of the worm brain network, estimated by the Erdős-Rényi Mixture Model, is more compatible with prior biological knowledge about the C. elegans nervous system than the purely modular decomposition defined deterministically. We also show that the blockmodel can be used both to generate stochastic realisations (simulations) of the biological connectome, and to compress network into a small number of super-nodes and their connectivity. We expect that the Erdős-Rényi Mixture Model may be useful for investigating the complex community structures in other (nervous) systems.

Optimization strategies based on sequential quadratic programming applied for a fermentation process for butanol production.

PubMed

Pinto Mariano, Adriano; Bastos Borba Costa, Caliane; de Franceschi de Angelis, Dejanira; Maugeri Filho, Francisco; Pires Atala, Daniel Ibraim; Wolf Maciel, Maria Regina; Maciel Filho, Rubens

2009-11-01

In this work, the mathematical optimization of a continuous flash fermentation process for the production of biobutanol was studied. The process consists of three interconnected units, as follows: fermentor, cell-retention system (tangential microfiltration), and vacuum flash vessel (responsible for the continuous recovery of butanol from the broth). The objective of the optimization was to maximize butanol productivity for a desired substrate conversion. Two strategies were compared for the optimization of the process. In one of them, the process was represented by a deterministic model with kinetic parameters determined experimentally and, in the other, by a statistical model obtained using the factorial design technique combined with simulation. For both strategies, the problem was written as a nonlinear programming problem and was solved with the sequential quadratic programming technique. The results showed that despite the very similar solutions obtained with both strategies, the problems found with the strategy using the deterministic model, such as lack of convergence and high computational time, make the use of the optimization strategy with the statistical model, which showed to be robust and fast, more suitable for the flash fermentation process, being recommended for real-time applications coupling optimization and control.

Equilibrium reconstruction in an iron core tokamak using a deterministic magnetisation model

NASA Astrophysics Data System (ADS)

Appel, L. C.; Lupelli, I.; JET Contributors

2018-02-01

In many tokamaks ferromagnetic material, usually referred to as an iron-core, is present in order to improve the magnetic coupling between the solenoid and the plasma. The presence of the iron core in proximity to the plasma changes the magnetic topology with consequent effects on the magnetic field structure and the plasma boundary. This paper considers the problem of obtaining the free-boundary plasma equilibrium solution in the presence of ferromagnetic material based on measured constraints. The current approach employs a model described by O'Brien et al. (1992) in which the magnetisation currents at the iron-air boundary are represented by a set of free parameters and appropriate boundary conditions are enforced via a set of quasi-measurements on the material boundary. This can lead to the possibility of overfitting the data and hiding underlying issues with the measured signals. Although the model typically achieves good fits to measured magnetic signals there are significant discrepancies in the inferred magnetic topology compared with other plasma diagnostic measurements that are independent of the magnetic field. An alternative approach for equilibrium reconstruction in iron-core tokamaks, termed the deterministic magnetisation model is developed and implemented in EFIT++. The iron is represented by a boundary current with the gradients in the magnetisation dipole state generating macroscopic internal magnetisation currents. A model for the boundary magnetisation currents at the iron-air interface is developed using B-Splines enabling continuity to arbitrary order; internal magnetisation currents are allocated to triangulated regions within the iron, and a method to enable adaptive refinement is implemented. The deterministic model has been validated by comparing it with a synthetic 2-D electromagnetic model of JET. It is established that the maximum field discrepancy is less than 1.5 mT throughout the vacuum region enclosing the plasma. The discrepancies of simulated magnetic probe signals are accurate to within 1% for signals with absolute magnitude greater than 100mT; in all other cases agreement is to within 1mT. The effect of neglecting the internal magnetisation currents increases the maximum discrepancy in the vacuum region to >20mT, resulting in errors of 5%-10% in the simulated probe signals. The fact that the previous model neglects the internal magnetisation currents (and also has additional free parameters when fitting the measured data) makes it unsuitable for analysing data in the absence of plasma current. The discrepancy of the poloidal magnetic flux within the vacuum vessel is to within 0.1Wb. Finally the deterministic model is applied to an equilibrium force-balance solution of a JET discharge using experimental data. It is shown that the discrepancies of the outboard separatrix position, and the outer strike-point position inferred from Thomson Scattering and Infrared camera data are much improved beyond the routine equilibrium reconstruction, whereas the discrepancy of the inner strike-point position is similar.

From Random Walks to Brownian Motion, from Diffusion to Entropy: Statistical Principles in Introductory Physics

NASA Astrophysics Data System (ADS)

Reeves, Mark

2014-03-01

Entropy changes underlie the physics that dominates biological interactions. Indeed, introductory biology courses often begin with an exploration of the qualities of water that are important to living systems. However, one idea that is not explicitly addressed in most introductory physics or biology textbooks is dominant contribution of the entropy in driving important biological processes towards equilibrium. From diffusion to cell-membrane formation, to electrostatic binding in protein folding, to the functioning of nerve cells, entropic effects often act to counterbalance deterministic forces such as electrostatic attraction and in so doing, allow for effective molecular signaling. A small group of biology, biophysics and computer science faculty have worked together for the past five years to develop curricular modules (based on SCALEUP pedagogy) that enable students to create models of stochastic and deterministic processes. Our students are first-year engineering and science students in the calculus-based physics course and they are not expected to know biology beyond the high-school level. In our class, they learn to reduce seemingly complex biological processes and structures to be described by tractable models that include deterministic processes and simple probabilistic inference. The students test these models in simulations and in laboratory experiments that are biologically relevant. The students are challenged to bridge the gap between statistical parameterization of their data (mean and standard deviation) and simple model-building by inference. This allows the students to quantitatively describe realistic cellular processes such as diffusion, ionic transport, and ligand-receptor binding. Moreover, the students confront ``random'' forces and traditional forces in problems, simulations, and in laboratory exploration throughout the year-long course as they move from traditional kinematics through thermodynamics to electrostatic interactions. This talk will present a number of these exercises, with particular focus on the hands-on experiments done by the students, and will give examples of the tangible material that our students work with throughout the two-semester sequence of their course on introductory physics with a bio focus. Supported by NSF DUE.

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

PubMed

Subramanian, Swetha; Mast, T Douglas

2015-10-07

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

Finite Element Aircraft Simulation of Turbulence

NASA Technical Reports Server (NTRS)

McFarland, R. E.

1997-01-01

A turbulence model has been developed for realtime aircraft simulation that accommodates stochastic turbulence and distributed discrete gusts as a function of the terrain. This model is applicable to conventional aircraft, V/STOL aircraft, and disc rotor model helicopter simulations. Vehicle angular activity in response to turbulence is computed from geometrical and temporal relationships rather than by using the conventional continuum approximations that assume uniform gust immersion and low frequency responses. By using techniques similar to those recently developed for blade-element rotor models, the angular-rate filters of conventional turbulence models are not required. The model produces rotational rates as well as air mass translational velocities in response to both stochastic and deterministic disturbances, where the discrete gusts and turbulence magnitudes may be correlated with significant terrain features or ship models. Assuming isotropy, a two-dimensional vertical turbulence field is created. A novel Gaussian interpolation technique is used to distribute vertical turbulence on the wing span or lateral rotor disc, and this distribution is used to compute roll responses. Air mass velocities are applied at significant centers of pressure in the computation of the aircraft's pitch and roll responses.

Deterministic theory of Monte Carlo variance

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

Ueki, T.; Larsen, E.W.

1996-12-31

The theoretical estimation of variance in Monte Carlo transport simulations, particularly those using variance reduction techniques, is a substantially unsolved problem. In this paper, the authors describe a theory that predicts the variance in a variance reduction method proposed by Dwivedi. Dwivedi`s method combines the exponential transform with angular biasing. The key element of this theory is a new modified transport problem, containing the Monte Carlo weight w as an extra independent variable, which simulates Dwivedi`s Monte Carlo scheme. The (deterministic) solution of this modified transport problem yields an expression for the variance. The authors give computational results that validatemore » this theory.« less

Modelling Geomechanical Heterogeneity of Rock Masses Using Direct and Indirect Geostatistical Conditional Simulation Methods

NASA Astrophysics Data System (ADS)

Eivazy, Hesameddin; Esmaieli, Kamran; Jean, Raynald

2017-12-01

An accurate characterization and modelling of rock mass geomechanical heterogeneity can lead to more efficient mine planning and design. Using deterministic approaches and random field methods for modelling rock mass heterogeneity is known to be limited in simulating the spatial variation and spatial pattern of the geomechanical properties. Although the applications of geostatistical techniques have demonstrated improvements in modelling the heterogeneity of geomechanical properties, geostatistical estimation methods such as Kriging result in estimates of geomechanical variables that are not fully representative of field observations. This paper reports on the development of 3D models for spatial variability of rock mass geomechanical properties using geostatistical conditional simulation method based on sequential Gaussian simulation. A methodology to simulate the heterogeneity of rock mass quality based on the rock mass rating is proposed and applied to a large open-pit mine in Canada. Using geomechanical core logging data collected from the mine site, a direct and an indirect approach were used to model the spatial variability of rock mass quality. The results of the two modelling approaches were validated against collected field data. The study aims to quantify the risks of pit slope failure and provides a measure of uncertainties in spatial variability of rock mass properties in different areas of the pit.

D-VASim: an interactive virtual laboratory environment for the simulation and analysis of genetic circuits.

PubMed

Baig, Hasan; Madsen, Jan

2017-01-15

Simulation and behavioral analysis of genetic circuits is a standard approach of functional verification prior to their physical implementation. Many software tools have been developed to perform in silico analysis for this purpose, but none of them allow users to interact with the model during runtime. The runtime interaction gives the user a feeling of being in the lab performing a real world experiment. In this work, we present a user-friendly software tool named D-VASim (Dynamic Virtual Analyzer and Simulator), which provides a virtual laboratory environment to simulate and analyze the behavior of genetic logic circuit models represented in an SBML (Systems Biology Markup Language). Hence, SBML models developed in other software environments can be analyzed and simulated in D-VASim. D-VASim offers deterministic as well as stochastic simulation; and differs from other software tools by being able to extract and validate the Boolean logic from the SBML model. D-VASim is also capable of analyzing the threshold value and propagation delay of a genetic circuit model. D-VASim is available for Windows and Mac OS and can be downloaded from bda.compute.dtu.dk/downloads/. haba@dtu.dk, jama@dtu.dk. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Development and application of a crossbreeding simulation model for goat production systems in tropical regions.

PubMed

Tsukahara, Y; Oishi, K; Hirooka, H

2011-12-01

A deterministic simulation model was developed to estimate biological production efficiency and to evaluate goat crossbreeding systems under tropical conditions. The model involves 5 production systems: pure indigenous, first filial generations (F1), backcross (BC), composite breeds of F1 (CMP(F1)), and BC (CMP(BC)). The model first simulates growth, reproduction, lactation, and energy intakes of a doe and a kid on a 1-d time step at the individual level and thereafter the outputs are integrated into the herd dynamics program. The ability of the model to simulate individual performances was tested under a base situation. The simulation results represented daily BW changes, ME requirements, and milk yield and the estimates were within the range of published data. Two conventional goat production scenarios (an intensive milk production scenario and an integrated goat and oil palm production scenario) in Malaysia were examined. The simulation results of the intensive milk production scenario showed the greater production efficiency of the CMP(BC) and CMP(F1) systems and decreased production efficiency of the F1 and BC systems. The results of the integrated goat and oil palm production scenario showed that the production efficiency and stocking rate were greater for the indigenous goats than for the crossbreeding systems.

Hybrid models for chemical reaction networks: Multiscale theory and application to gene regulatory systems.

PubMed

Winkelmann, Stefanie; Schütte, Christof

2017-09-21

Well-mixed stochastic chemical kinetics are properly modeled by the chemical master equation (CME) and associated Markov jump processes in molecule number space. If the reactants are present in large amounts, however, corresponding simulations of the stochastic dynamics become computationally expensive and model reductions are demanded. The classical model reduction approach uniformly rescales the overall dynamics to obtain deterministic systems characterized by ordinary differential equations, the well-known mass action reaction rate equations. For systems with multiple scales, there exist hybrid approaches that keep parts of the system discrete while another part is approximated either using Langevin dynamics or deterministically. This paper aims at giving a coherent overview of the different hybrid approaches, focusing on their basic concepts and the relation between them. We derive a novel general description of such hybrid models that allows expressing various forms by one type of equation. We also check in how far the approaches apply to model extensions of the CME for dynamics which do not comply with the central well-mixed condition and require some spatial resolution. A simple but meaningful gene expression system with negative self-regulation is analysed to illustrate the different approximation qualities of some of the hybrid approaches discussed. Especially, we reveal the cause of error in the case of small volume approximations.

Hybrid models for chemical reaction networks: Multiscale theory and application to gene regulatory systems

NASA Astrophysics Data System (ADS)

Winkelmann, Stefanie; Schütte, Christof

2017-09-01

Well-mixed stochastic chemical kinetics are properly modeled by the chemical master equation (CME) and associated Markov jump processes in molecule number space. If the reactants are present in large amounts, however, corresponding simulations of the stochastic dynamics become computationally expensive and model reductions are demanded. The classical model reduction approach uniformly rescales the overall dynamics to obtain deterministic systems characterized by ordinary differential equations, the well-known mass action reaction rate equations. For systems with multiple scales, there exist hybrid approaches that keep parts of the system discrete while another part is approximated either using Langevin dynamics or deterministically. This paper aims at giving a coherent overview of the different hybrid approaches, focusing on their basic concepts and the relation between them. We derive a novel general description of such hybrid models that allows expressing various forms by one type of equation. We also check in how far the approaches apply to model extensions of the CME for dynamics which do not comply with the central well-mixed condition and require some spatial resolution. A simple but meaningful gene expression system with negative self-regulation is analysed to illustrate the different approximation qualities of some of the hybrid approaches discussed. Especially, we reveal the cause of error in the case of small volume approximations.

Discrete-State Stochastic Models of Calcium-Regulated Calcium Influx and Subspace Dynamics Are Not Well-Approximated by ODEs That Neglect Concentration Fluctuations

PubMed Central

Weinberg, Seth H.; Smith, Gregory D.

2012-01-01

Cardiac myocyte calcium signaling is often modeled using deterministic ordinary differential equations (ODEs) and mass-action kinetics. However, spatially restricted “domains” associated with calcium influx are small enough (e.g., 10−17 liters) that local signaling may involve 1–100 calcium ions. Is it appropriate to model the dynamics of subspace calcium using deterministic ODEs or, alternatively, do we require stochastic descriptions that account for the fundamentally discrete nature of these local calcium signals? To address this question, we constructed a minimal Markov model of a calcium-regulated calcium channel and associated subspace. We compared the expected value of fluctuating subspace calcium concentration (a result that accounts for the small subspace volume) with the corresponding deterministic model (an approximation that assumes large system size). When subspace calcium did not regulate calcium influx, the deterministic and stochastic descriptions agreed. However, when calcium binding altered channel activity in the model, the continuous deterministic description often deviated significantly from the discrete stochastic model, unless the subspace volume is unrealistically large and/or the kinetics of the calcium binding are sufficiently fast. This principle was also demonstrated using a physiologically realistic model of calmodulin regulation of L-type calcium channels introduced by Yue and coworkers. PMID:23509597

Hill functions for stochastic gene regulatory networks from master equations with split nodes and time-scale separation

NASA Astrophysics Data System (ADS)

Lipan, Ovidiu; Ferwerda, Cameron

2018-02-01

The deterministic Hill function depends only on the average values of molecule numbers. To account for the fluctuations in the molecule numbers, the argument of the Hill function needs to contain the means, the standard deviations, and the correlations. Here we present a method that allows for stochastic Hill functions to be constructed from the dynamical evolution of stochastic biocircuits with specific topologies. These stochastic Hill functions are presented in a closed analytical form so that they can be easily incorporated in models for large genetic regulatory networks. Using a repressive biocircuit as an example, we show by Monte Carlo simulations that the traditional deterministic Hill function inaccurately predicts time of repression by an order of two magnitudes. However, the stochastic Hill function was able to capture the fluctuations and thus accurately predicted the time of repression.

Stochastic Partial Differential Equation Solver for Hydroacoustic Modeling: Improvements to Paracousti Sound Propagation Solver

NASA Astrophysics Data System (ADS)

Preston, L. A.

2017-12-01

Marine hydrokinetic (MHK) devices offer a clean, renewable alternative energy source for the future. Responsible utilization of MHK devices, however, requires that the effects of acoustic noise produced by these devices on marine life and marine-related human activities be well understood. Paracousti is a 3-D full waveform acoustic modeling suite that can accurately propagate MHK noise signals in the complex bathymetry found in the near-shore to open ocean environment and considers real properties of the seabed, water column, and air-surface interface. However, this is a deterministic simulation that assumes the environment and source are exactly known. In reality, environmental and source characteristics are often only known in a statistical sense. Thus, to fully characterize the expected noise levels within the marine environment, this uncertainty in environmental and source factors should be incorporated into the acoustic simulations. One method is to use Monte Carlo (MC) techniques where simulation results from a large number of deterministic solutions are aggregated to provide statistical properties of the output signal. However, MC methods can be computationally prohibitive since they can require tens of thousands or more simulations to build up an accurate representation of those statistical properties. An alternative method, using the technique of stochastic partial differential equations (SPDE), allows computation of the statistical properties of output signals at a small fraction of the computational cost of MC. We are developing a SPDE solver for the 3-D acoustic wave propagation problem called Paracousti-UQ to help regulators and operators assess the statistical properties of environmental noise produced by MHK devices. In this presentation, we present the SPDE method and compare statistical distributions of simulated acoustic signals in simple models to MC simulations to show the accuracy and efficiency of the SPDE method. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia LLC, a wholly owned subsidiary of Honeywell International Inc. for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

Agent-based model of angiogenesis simulates capillary sprout initiation in multicellular networks

PubMed Central

Walpole, J.; Chappell, J.C.; Cluceru, J.G.; Mac Gabhann, F.; Bautch, V.L.; Peirce, S. M.

2015-01-01

Many biological processes are controlled by both deterministic and stochastic influences. However, efforts to model these systems often rely on either purely stochastic or purely rule-based methods. To better understand the balance between stochasticity and determinism in biological processes a computational approach that incorporates both influences may afford additional insight into underlying biological mechanisms that give rise to emergent system properties. We apply a combined approach to the simulation and study of angiogenesis, the growth of new blood vessels from existing networks. This complex multicellular process begins with selection of an initiating endothelial cell, or tip cell, which sprouts from the parent vessels in response to stimulation by exogenous cues. We have constructed an agent-based model of sprouting angiogenesis to evaluate endothelial cell sprout initiation frequency and location, and we have experimentally validated it using high-resolution time-lapse confocal microscopy. ABM simulations were then compared to a Monte Carlo model, revealing that purely stochastic simulations could not generate sprout locations as accurately as the rule-informed agent-based model. These findings support the use of rule-based approaches for modeling the complex mechanisms underlying sprouting angiogenesis over purely stochastic methods. PMID:26158406

Agent-based model of angiogenesis simulates capillary sprout initiation in multicellular networks.

PubMed

Walpole, J; Chappell, J C; Cluceru, J G; Mac Gabhann, F; Bautch, V L; Peirce, S M

2015-09-01

Many biological processes are controlled by both deterministic and stochastic influences. However, efforts to model these systems often rely on either purely stochastic or purely rule-based methods. To better understand the balance between stochasticity and determinism in biological processes a computational approach that incorporates both influences may afford additional insight into underlying biological mechanisms that give rise to emergent system properties. We apply a combined approach to the simulation and study of angiogenesis, the growth of new blood vessels from existing networks. This complex multicellular process begins with selection of an initiating endothelial cell, or tip cell, which sprouts from the parent vessels in response to stimulation by exogenous cues. We have constructed an agent-based model of sprouting angiogenesis to evaluate endothelial cell sprout initiation frequency and location, and we have experimentally validated it using high-resolution time-lapse confocal microscopy. ABM simulations were then compared to a Monte Carlo model, revealing that purely stochastic simulations could not generate sprout locations as accurately as the rule-informed agent-based model. These findings support the use of rule-based approaches for modeling the complex mechanisms underlying sprouting angiogenesis over purely stochastic methods.

Multi-Algorithm Particle Simulations with Spatiocyte.

PubMed

Arjunan, Satya N V; Takahashi, Koichi

2017-01-01

As quantitative biologists get more measurements of spatially regulated systems such as cell division and polarization, simulation of reaction and diffusion of proteins using the data is becoming increasingly relevant to uncover the mechanisms underlying the systems. Spatiocyte is a lattice-based stochastic particle simulator for biochemical reaction and diffusion processes. Simulations can be performed at single molecule and compartment spatial scales simultaneously. Molecules can diffuse and react in 1D (filament), 2D (membrane), and 3D (cytosol) compartments. The implications of crowded regions in the cell can be investigated because each diffusing molecule has spatial dimensions. Spatiocyte adopts multi-algorithm and multi-timescale frameworks to simulate models that simultaneously employ deterministic, stochastic, and particle reaction-diffusion algorithms. Comparison of light microscopy images to simulation snapshots is supported by Spatiocyte microscopy visualization and molecule tagging features. Spatiocyte is open-source software and is freely available at http://spatiocyte.org .

Quasi-continuous stochastic simulation framework for flood modelling

NASA Astrophysics Data System (ADS)

Moustakis, Yiannis; Kossieris, Panagiotis; Tsoukalas, Ioannis; Efstratiadis, Andreas

2017-04-01

Typically, flood modelling in the context of everyday engineering practices is addressed through event-based deterministic tools, e.g., the well-known SCS-CN method. A major shortcoming of such approaches is the ignorance of uncertainty, which is associated with the variability of soil moisture conditions and the variability of rainfall during the storm event.In event-based modeling, the sole expression of uncertainty is the return period of the design storm, which is assumed to represent the acceptable risk of all output quantities (flood volume, peak discharge, etc.). On the other hand, the varying antecedent soil moisture conditions across the basin are represented by means of scenarios (e.g., the three AMC types by SCS),while the temporal distribution of rainfall is represented through standard deterministic patterns (e.g., the alternative blocks method). In order to address these major inconsistencies,simultaneously preserving the simplicity and parsimony of the SCS-CN method, we have developed a quasi-continuous stochastic simulation approach, comprising the following steps: (1) generation of synthetic daily rainfall time series; (2) update of potential maximum soil moisture retention, on the basis of accumulated five-day rainfall; (3) estimation of daily runoff through the SCS-CN formula, using as inputs the daily rainfall and the updated value of soil moisture retention;(4) selection of extreme events and application of the standard SCS-CN procedure for each specific event, on the basis of synthetic rainfall.This scheme requires the use of two stochastic modelling components, namely the CastaliaR model, for the generation of synthetic daily data, and the HyetosMinute model, for the disaggregation of daily rainfall to finer temporal scales. Outcomes of this approach are a large number of synthetic flood events, allowing for expressing the design variables in statistical terms and thus properly evaluating the flood risk.

Stochastic Seismic Inversion and Migration for Offshore Site Investigation in the Northern Gulf of Mexico

NASA Astrophysics Data System (ADS)

Son, J.; Medina-Cetina, Z.

2017-12-01

We discuss the comparison between deterministic and stochastic optimization approaches to the nonlinear geophysical full-waveform inverse problem, based on the seismic survey data from Mississippi Canyon in the Northern Gulf of Mexico. Since the subsea engineering and offshore construction projects actively require reliable ground models from various site investigations, the primary goal of this study is to reconstruct the accurate subsurface information of the soil and rock material profiles under the seafloor. The shallow sediment layers have naturally formed heterogeneous formations which may cause unwanted marine landslides or foundation failures of underwater infrastructure. We chose the quasi-Newton and simulated annealing as deterministic and stochastic optimization algorithms respectively. Seismic forward modeling based on finite difference method with absorbing boundary condition implements the iterative simulations in the inverse modeling. We briefly report on numerical experiments using a synthetic data as an offshore ground model which contains shallow artificial target profiles of geomaterials under the seafloor. We apply the seismic migration processing and generate Voronoi tessellation on two-dimensional space-domain to improve the computational efficiency of the imaging stratigraphical velocity model reconstruction. We then report on the detail of a field data implementation, which shows the complex geologic structures in the Northern Gulf of Mexico. Lastly, we compare the new inverted image of subsurface site profiles in the space-domain with the previously processed seismic image in the time-domain at the same location. Overall, stochastic optimization for seismic inversion with migration and Voronoi tessellation show significant promise to improve the subsurface imaging of ground models and improve the computational efficiency required for the full waveform inversion. We anticipate that by improving the inversion process of shallow layers from geophysical data will better support the offshore site investigation.

A Comparison of Probabilistic and Deterministic Campaign Analysis for Human Space Exploration

NASA Technical Reports Server (NTRS)

Merrill, R. Gabe; Andraschko, Mark; Stromgren, Chel; Cirillo, Bill; Earle, Kevin; Goodliff, Kandyce

2008-01-01

Human space exploration is by its very nature an uncertain endeavor. Vehicle reliability, technology development risk, budgetary uncertainty, and launch uncertainty all contribute to stochasticity in an exploration scenario. However, traditional strategic analysis has been done in a deterministic manner, analyzing and optimizing the performance of a series of planned missions. History has shown that exploration scenarios rarely follow such a planned schedule. This paper describes a methodology to integrate deterministic and probabilistic analysis of scenarios in support of human space exploration. Probabilistic strategic analysis is used to simulate "possible" scenario outcomes, based upon the likelihood of occurrence of certain events and a set of pre-determined contingency rules. The results of the probabilistic analysis are compared to the nominal results from the deterministic analysis to evaluate the robustness of the scenario to adverse events and to test and optimize contingency planning.

Deterministic models for traffic jams

NASA Astrophysics Data System (ADS)

Nagel, Kai; Herrmann, Hans J.

1993-10-01

We study several deterministic one-dimensional traffic models. For integer positions and velocities we find the typical high and low density phases separated by a simple transition. If positions and velocities are continuous variables the model shows self-organized critically driven by the slowest car.

Neo-deterministic seismic hazard scenarios for India—a preventive tool for disaster mitigation

NASA Astrophysics Data System (ADS)

Parvez, Imtiyaz A.; Magrin, Andrea; Vaccari, Franco; Ashish; Mir, Ramees R.; Peresan, Antonella; Panza, Giuliano Francesco

2017-11-01

Current computational resources and physical knowledge of the seismic wave generation and propagation processes allow for reliable numerical and analytical models of waveform generation and propagation. From the simulation of ground motion, it is easy to extract the desired earthquake hazard parameters. Accordingly, a scenario-based approach to seismic hazard assessment has been developed, namely the neo-deterministic seismic hazard assessment (NDSHA), which allows for a wide range of possible seismic sources to be used in the definition of reliable scenarios by means of realistic waveforms modelling. Such reliable and comprehensive characterization of expected earthquake ground motion is essential to improve building codes, particularly for the protection of critical infrastructures and for land use planning. Parvez et al. (Geophys J Int 155:489-508, 2003) published the first ever neo-deterministic seismic hazard map of India by computing synthetic seismograms with input data set consisting of structural models, seismogenic zones, focal mechanisms and earthquake catalogues. As described in Panza et al. (Adv Geophys 53:93-165, 2012), the NDSHA methodology evolved with respect to the original formulation used by Parvez et al. (Geophys J Int 155:489-508, 2003): the computer codes were improved to better fit the need of producing realistic ground shaking maps and ground shaking scenarios, at different scale levels, exploiting the most significant pertinent progresses in data acquisition and modelling. Accordingly, the present study supplies a revised NDSHA map for India. The seismic hazard, expressed in terms of maximum displacement (Dmax), maximum velocity (Vmax) and design ground acceleration (DGA), has been extracted from the synthetic signals and mapped on a regular grid over the studied territory.

Effects of noise on a computational model for disease states of mood disorders

NASA Astrophysics Data System (ADS)

Tobias Huber, Martin; Krieg, Jürgen-Christian; Braun, Hans Albert; Moss, Frank

2000-03-01

Nonlinear dynamics are currently proposed to explain the progressive course of recurrent mood disorders starting with isolated episodes and ending with accelerated irregular (``chaotic") mood fluctuations. Such a low-dimensional disease model is attractive because of its principal accordance with biological disease models, i.e. the kindling and biological rhythms model. However, most natural systems are nonlinear and noisy and several studies in the neuro- and physical sciences have demonstrated interesting cooperative behaviors arising from interacting random and deterministic dynamics. Here, we consider the effects of noise on a recent neurodynamical model for the timecourse of affective disorders (Huber et al.: Biological Psychiatry 1999;46:256-262). We describe noise effects on temporal patterns and mean episode frequencies of various in computo disease states. Our simulations demonstrate that noise can cause unstructured randomness or can maximize periodic order. The frequency of episode occurence can increase with noise but it can also remain unaffected or even can decrease. We show further that noise can make visible bifurcations before they would normally occur under deterministic conditions and we quantify this behavior with a recently developed statistical method. All these effects depend critically on both, the dynamic state and the noise intensity. Implications for neurobiology and course of mood disorders are discussed.

The Creation and Statistical Evaluation of a Deterministic Model of the Human Bronchial Tree from HRCT Images.

PubMed

Montesantos, Spyridon; Katz, Ira; Pichelin, Marine; Caillibotte, Georges

2016-01-01

A quantitative description of the morphology of lung structure is essential prior to any form of predictive modeling of ventilation or aerosol deposition implemented within the lung. The human lung is a very complex organ, with airway structures that span two orders of magnitude and having a multitude of interfaces between air, tissue and blood. As such, current medical imaging protocols cannot provide medical practitioners and researchers with in-vivo knowledge of deeper lung structures. In this work a detailed algorithm for the generation of an individualized 3D deterministic model of the conducting part of the human tracheo-bronchial tree is described. Distinct initial conditions were obtained from the high-resolution computed tomography (HRCT) images of seven healthy volunteers. The algorithm developed is fractal in nature and is implemented as a self-similar space sub-division procedure. The expansion process utilizes physiologically realistic relationships and thresholds to produce an anatomically consistent human airway tree. The model was validated through extensive statistical analysis of the results and comparison of the most common morphological features with previously published morphometric studies and other equivalent models. The resulting trees were shown to be in good agreement with published human lung geometric characteristics and can be used to study, among other things, structure-function relationships in simulation studies.

Controllability of Deterministic Networks with the Identical Degree Sequence

PubMed Central

Ma, Xiujuan; Zhao, Haixing; Wang, Binghong

2015-01-01

Controlling complex network is an essential problem in network science and engineering. Recent advances indicate that the controllability of complex network is dependent on the network's topology. Liu and Barabási, et.al speculated that the degree distribution was one of the most important factors affecting controllability for arbitrary complex directed network with random link weights. In this paper, we analysed the effect of degree distribution to the controllability for the deterministic networks with unweighted and undirected. We introduce a class of deterministic networks with identical degree sequence, called (x,y)-flower. We analysed controllability of the two deterministic networks ((1, 3)-flower and (2, 2)-flower) by exact controllability theory in detail and give accurate results of the minimum number of driver nodes for the two networks. In simulation, we compare the controllability of (x,y)-flower networks. Our results show that the family of (x,y)-flower networks have the same degree sequence, but their controllability is totally different. So the degree distribution itself is not sufficient to characterize the controllability of deterministic networks with unweighted and undirected. PMID:26020920

A Deterministic Electron, Photon, Proton and Heavy Ion Radiation Transport Suite for the Study of the Jovian System

NASA Technical Reports Server (NTRS)

Norman, Ryan B.; Badavi, Francis F.; Blattnig, Steve R.; Atwell, William

2011-01-01

A deterministic suite of radiation transport codes, developed at NASA Langley Research Center (LaRC), which describe the transport of electrons, photons, protons, and heavy ions in condensed media is used to simulate exposures from spectral distributions typical of electrons, protons and carbon-oxygen-sulfur (C-O-S) trapped heavy ions in the Jovian radiation environment. The particle transport suite consists of a coupled electron and photon deterministic transport algorithm (CEPTRN) and a coupled light particle and heavy ion deterministic transport algorithm (HZETRN). The primary purpose for the development of the transport suite is to provide a means for the spacecraft design community to rapidly perform numerous repetitive calculations essential for electron, proton and heavy ion radiation exposure assessments in complex space structures. In this paper, the radiation environment of the Galilean satellite Europa is used as a representative boundary condition to show the capabilities of the transport suite. While the transport suite can directly access the output electron spectra of the Jovian environment as generated by the Jet Propulsion Laboratory (JPL) Galileo Interim Radiation Electron (GIRE) model of 2003; for the sake of relevance to the upcoming Europa Jupiter System Mission (EJSM), the 105 days at Europa mission fluence energy spectra provided by JPL is used to produce the corresponding dose-depth curve in silicon behind an aluminum shield of 100 mils ( 0.7 g/sq cm). The transport suite can also accept ray-traced thickness files from a computer-aided design (CAD) package and calculate the total ionizing dose (TID) at a specific target point. In that regard, using a low-fidelity CAD model of the Galileo probe, the transport suite was verified by comparing with Monte Carlo (MC) simulations for orbits JOI--J35 of the Galileo extended mission (1996-2001). For the upcoming EJSM mission with a potential launch date of 2020, the transport suite is used to compute the traditional aluminum-silicon dose-depth calculation as a standard shield-target combination output, as well as the shielding response of high charge (Z) shields such as tantalum (Ta). Finally, a shield optimization algorithm is used to guide the instrument designer with the choice of graded-Z shield analysis.

Hybrid Monte Carlo/Deterministic Methods for Accelerating Active Interrogation Modeling

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

Peplow, Douglas E.; Miller, Thomas Martin; Patton, Bruce W

2013-01-01

The potential for smuggling special nuclear material (SNM) into the United States is a major concern to homeland security, so federal agencies are investigating a variety of preventive measures, including detection and interdiction of SNM during transport. One approach for SNM detection, called active interrogation, uses a radiation source, such as a beam of neutrons or photons, to scan cargo containers and detect the products of induced fissions. In realistic cargo transport scenarios, the process of inducing and detecting fissions in SNM is difficult due to the presence of various and potentially thick materials between the radiation source and themore » SNM, and the practical limitations on radiation source strength and detection capabilities. Therefore, computer simulations are being used, along with experimental measurements, in efforts to design effective active interrogation detection systems. The computer simulations mostly consist of simulating radiation transport from the source to the detector region(s). Although the Monte Carlo method is predominantly used for these simulations, difficulties persist related to calculating statistically meaningful detector responses in practical computing times, thereby limiting their usefulness for design and evaluation of practical active interrogation systems. In previous work, the benefits of hybrid methods that use the results of approximate deterministic transport calculations to accelerate high-fidelity Monte Carlo simulations have been demonstrated for source-detector type problems. In this work, the hybrid methods are applied and evaluated for three example active interrogation problems. Additionally, a new approach is presented that uses multiple goal-based importance functions depending on a particle s relevance to the ultimate goal of the simulation. Results from the examples demonstrate that the application of hybrid methods to active interrogation problems dramatically increases their calculational efficiency.« less

Simulation of deterministic energy-balance particle agglomeration in turbulent liquid-solid flows

NASA Astrophysics Data System (ADS)

Njobuenwu, Derrick O.; Fairweather, Michael

2017-08-01

An efficient technique to simulate turbulent particle-laden flow at high mass loadings within the four-way coupled simulation regime is presented. The technique implements large-eddy simulation, discrete particle simulation, a deterministic treatment of inter-particle collisions, and an energy-balanced particle agglomeration model. The algorithm to detect inter-particle collisions is such that the computational costs scale linearly with the number of particles present in the computational domain. On detection of a collision, particle agglomeration is tested based on the pre-collision kinetic energy, restitution coefficient, and van der Waals' interactions. The performance of the technique developed is tested by performing parametric studies on the influence of the restitution coefficient (en = 0.2, 0.4, 0.6, and 0.8), particle size (dp = 60, 120, 200, and 316 μm), Reynolds number (Reτ = 150, 300, and 590), and particle concentration (αp = 5.0 × 10-4, 1.0 × 10-3, and 5.0 × 10-3) on particle-particle interaction events (collision and agglomeration). The results demonstrate that the collision frequency shows a linear dependency on the restitution coefficient, while the agglomeration rate shows an inverse dependence. Collisions among smaller particles are more frequent and efficient in forming agglomerates than those of coarser particles. The particle-particle interaction events show a strong dependency on the shear Reynolds number Reτ, while increasing the particle concentration effectively enhances particle collision and agglomeration whilst having only a minor influence on the agglomeration rate. Overall, the sensitivity of the particle-particle interaction events to the selected simulation parameters is found to influence the population and distribution of the primary particles and agglomerates formed.

A parallel implementation of an off-lattice individual-based model of multicellular populations

NASA Astrophysics Data System (ADS)

Harvey, Daniel G.; Fletcher, Alexander G.; Osborne, James M.; Pitt-Francis, Joe

2015-07-01

As computational models of multicellular populations include ever more detailed descriptions of biophysical and biochemical processes, the computational cost of simulating such models limits their ability to generate novel scientific hypotheses and testable predictions. While developments in microchip technology continue to increase the power of individual processors, parallel computing offers an immediate increase in available processing power. To make full use of parallel computing technology, it is necessary to develop specialised algorithms. To this end, we present a parallel algorithm for a class of off-lattice individual-based models of multicellular populations. The algorithm divides the spatial domain between computing processes and comprises communication routines that ensure the model is correctly simulated on multiple processors. The parallel algorithm is shown to accurately reproduce the results of a deterministic simulation performed using a pre-existing serial implementation. We test the scaling of computation time, memory use and load balancing as more processes are used to simulate a cell population of fixed size. We find approximate linear scaling of both speed-up and memory consumption on up to 32 processor cores. Dynamic load balancing is shown to provide speed-up for non-regular spatial distributions of cells in the case of a growing population.

Importance of vesicle release stochasticity in neuro-spike communication.

PubMed

Ramezani, Hamideh; Akan, Ozgur B

2017-07-01

Aim of this paper is proposing a stochastic model for vesicle release process, a part of neuro-spike communication. Hence, we study biological events occurring in this process and use microphysiological simulations to observe functionality of these events. Since the most important source of variability in vesicle release probability is opening of voltage dependent calcium channels (VDCCs) followed by influx of calcium ions through these channels, we propose a stochastic model for this event, while using a deterministic model for other variability sources. To capture the stochasticity of calcium influx to pre-synaptic neuron in our model, we study its statistics and find that it can be modeled by a distribution defined based on Normal and Logistic distributions.

Three-dimensional reconstructions come to life--interactive 3D PDF animations in functional morphology.

PubMed

van de Kamp, Thomas; dos Santos Rolo, Tomy; Vagovič, Patrik; Baumbach, Tilo; Riedel, Alexander

2014-01-01

Digital surface mesh models based on segmented datasets have become an integral part of studies on animal anatomy and functional morphology; usually, they are published as static images, movies or as interactive PDF files. We demonstrate the use of animated 3D models embedded in PDF documents, which combine the advantages of both movie and interactivity, based on the example of preserved Trigonopterus weevils. The method is particularly suitable to simulate joints with largely deterministic movements due to precise form closure. We illustrate the function of an individual screw-and-nut type hip joint and proceed to the complex movements of the entire insect attaining a defence position. This posture is achieved by a specific cascade of movements: Head and legs interlock mutually and with specific features of thorax and the first abdominal ventrite, presumably to increase the mechanical stability of the beetle and to maintain the defence position with minimal muscle activity. The deterministic interaction of accurately fitting body parts follows a defined sequence, which resembles a piece of engineering.

Three-Dimensional Reconstructions Come to Life – Interactive 3D PDF Animations in Functional Morphology

PubMed Central

van de Kamp, Thomas; dos Santos Rolo, Tomy; Vagovič, Patrik; Baumbach, Tilo; Riedel, Alexander

2014-01-01

Digital surface mesh models based on segmented datasets have become an integral part of studies on animal anatomy and functional morphology; usually, they are published as static images, movies or as interactive PDF files. We demonstrate the use of animated 3D models embedded in PDF documents, which combine the advantages of both movie and interactivity, based on the example of preserved Trigonopterus weevils. The method is particularly suitable to simulate joints with largely deterministic movements due to precise form closure. We illustrate the function of an individual screw-and-nut type hip joint and proceed to the complex movements of the entire insect attaining a defence position. This posture is achieved by a specific cascade of movements: Head and legs interlock mutually and with specific features of thorax and the first abdominal ventrite, presumably to increase the mechanical stability of the beetle and to maintain the defence position with minimal muscle activity. The deterministic interaction of accurately fitting body parts follows a defined sequence, which resembles a piece of engineering. PMID:25029366

Emergence of chaos in a spatially confined reactive system

NASA Astrophysics Data System (ADS)

Voorsluijs, Valérie; De Decker, Yannick

2016-11-01

In spatially restricted media, interactions between particles and local fluctuations of density can lead to important deviations of the dynamics from the unconfined, deterministic picture. In this context, we investigated how molecular crowding can affect the emergence of chaos in small reactive systems. We developed to this end an amended version of the Willamowski-Rössler model, where we account for the impenetrability of the reactive species. We analyzed the deterministic kinetics of this model and studied it with spatially-extended stochastic simulations in which the mobility of particles is included explicitly. We show that homogeneous fluctuations can lead to a destruction of chaos through a fluctuation-induced collision between chaotic trajectories and absorbing states. However, an interplay between the size of the system and the mobility of particles can counterbalance this effect so that chaos can indeed be found when particles diffuse slowly. This unexpected effect can be traced back to the emergence of spatial correlations which strongly affect the dynamics. The mobility of particles effectively acts as a new bifurcation parameter, enabling the system to switch from stationary states to absorbing states, oscillations or chaos.

A deterministic Lagrangian particle separation-based method for advective-diffusion problems

NASA Astrophysics Data System (ADS)

Wong, Ken T. M.; Lee, Joseph H. W.; Choi, K. W.

2008-12-01

A simple and robust Lagrangian particle scheme is proposed to solve the advective-diffusion transport problem. The scheme is based on relative diffusion concepts and simulates diffusion by regulating particle separation. This new approach generates a deterministic result and requires far less number of particles than the random walk method. For the advection process, particles are simply moved according to their velocity. The general scheme is mass conservative and is free from numerical diffusion. It can be applied to a wide variety of advective-diffusion problems, but is particularly suited for ecological and water quality modelling when definition of particle attributes (e.g., cell status for modelling algal blooms or red tides) is a necessity. The basic derivation, numerical stability and practical implementation of the NEighborhood Separation Technique (NEST) are presented. The accuracy of the method is demonstrated through a series of test cases which embrace realistic features of coastal environmental transport problems. Two field application examples on the tidal flushing of a fish farm and the dynamics of vertically migrating marine algae are also presented.

Detection of chaotic determinism in time series from randomly forced maps

NASA Technical Reports Server (NTRS)

Chon, K. H.; Kanters, J. K.; Cohen, R. J.; Holstein-Rathlou, N. H.

1997-01-01

Time series from biological system often display fluctuations in the measured variables. Much effort has been directed at determining whether this variability reflects deterministic chaos, or whether it is merely "noise". Despite this effort, it has been difficult to establish the presence of chaos in time series from biological sytems. The output from a biological system is probably the result of both its internal dynamics, and the input to the system from the surroundings. This implies that the system should be viewed as a mixed system with both stochastic and deterministic components. We present a method that appears to be useful in deciding whether determinism is present in a time series, and if this determinism has chaotic attributes, i.e., a positive characteristic exponent that leads to sensitivity to initial conditions. The method relies on fitting a nonlinear autoregressive model to the time series followed by an estimation of the characteristic exponents of the model over the observed probability distribution of states for the system. The method is tested by computer simulations, and applied to heart rate variability data.

Effects of delay and noise in a negative feedback regulatory motif

NASA Astrophysics Data System (ADS)

Palassini, Matteo; Dies, Marta

2009-03-01

The small copy number of the molecules involved in gene regulation can induce nontrivial stochastic phenomena such as noise-induced oscillations. An often neglected aspect of regulation dynamics are the delays involved in transcription and translation. Delays introduce analytical and computational complications because the dynamics is non-Markovian. We study the interplay of noise and delays in a negative feedback model of the p53 core regulatory network. Recent experiments have found pronounced oscillations in the concentrations of proteins p53 and Mdm2 in individual cells subjected to DNA damage. Similar oscillations occur in the Hes-1 and NK-kB systems, and in circadian rhythms. Several mechanisms have been proposed to explain this oscillatory behaviour, such as deterministic limit cycles, with and without delay, or noise-induced excursions in excitable models. We consider a generic delayed Master Equation incorporating the activation of Mdm2 by p53 and the Mdm2-promoted degradation of p53. In the deterministic limit and for large delays, the model shows a Hopf bifurcation. Via exact stochastic simulations, we find strong noise-induced oscillations well outside the limit-cycle region. We propose that this may be a generic mechanism for oscillations in gene regulatory systems.

Hybrid stochastic simulations of intracellular reaction-diffusion systems.

PubMed

Kalantzis, Georgios

2009-06-01

With the observation that stochasticity is important in biological systems, chemical kinetics have begun to receive wider interest. While the use of Monte Carlo discrete event simulations most accurately capture the variability of molecular species, they become computationally costly for complex reaction-diffusion systems with large populations of molecules. On the other hand, continuous time models are computationally efficient but they fail to capture any variability in the molecular species. In this study a hybrid stochastic approach is introduced for simulating reaction-diffusion systems. We developed an adaptive partitioning strategy in which processes with high frequency are simulated with deterministic rate-based equations, and those with low frequency using the exact stochastic algorithm of Gillespie. Therefore the stochastic behavior of cellular pathways is preserved while being able to apply it to large populations of molecules. We describe our method and demonstrate its accuracy and efficiency compared with the Gillespie algorithm for two different systems. First, a model of intracellular viral kinetics with two steady states and second, a compartmental model of the postsynaptic spine head for studying the dynamics of Ca+2 and NMDA receptors.

3D Dynamic Rupture Simulations along the Wasatch Fault, Utah, Incorporating Rough-fault Topography

NASA Astrophysics Data System (ADS)

Withers, Kyle; Moschetti, Morgan

2017-04-01

Studies have found that the Wasatch Fault has experienced successive large magnitude (>Mw 7.2) earthquakes, with an average recurrence interval near 350 years. To date, no large magnitude event has been recorded along the fault, with the last rupture along the Salt Lake City segment occurring 1300 years ago. Because of this, as well as the lack of strong ground motion records in basins and from normal-faulting earthquakes worldwide, seismic hazard in the region is not well constrained. Previous numerical simulations have modeled deterministic ground motion in the heavily populated regions of Utah, near Salt Lake City, but were primarily restricted to low frequencies ( 1 Hz). Our goal is to better assess broadband ground motions from the Wasatch Fault Zone. Here, we extend deterministic ground motion prediction to higher frequencies ( 5 Hz) in this region by using physics-based spontaneous dynamic rupture simulations along a normal fault with characteristics derived from geologic observations. We use a summation by parts finite difference code (Waveqlab3D) with rough-fault topography following a self-similar fractal distribution (over length scales from 100 m to the size of the fault) and include off-fault plasticity to simulate ruptures > Mw 6.5. Geometric complexity along fault planes has previously been shown to generate broadband sources with spectral energy matching that of observations. We investigate the impact of varying the hypocenter location, as well as the influence that multiple realizations of rough-fault topography have on the rupture process and resulting ground motion. We utilize Waveqlab3's computational efficiency to model wave-propagation to a significant distance from the fault with media heterogeneity at both long and short spatial wavelengths. These simulations generate a synthetic dataset of ground motions to compare with GMPEs, in terms of both the median and inter and intraevent variability.

Uncertainty Analysis and Parameter Estimation For Nearshore Hydrodynamic Models

NASA Astrophysics Data System (ADS)

Ardani, S.; Kaihatu, J. M.

2012-12-01

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

Noise induced phenomena in combustion

NASA Astrophysics Data System (ADS)

Liu, Hongliang

Quantitative models of combustion usually consist of systems of deterministic differential equations. However, there are reasons to suspect that noise may have a significant influence. In this thesis, our primary objective is to study the effect of noise on measurable quantities in the combustion process. Our first study involves combustion in a homogeneous gas. With a one step reaction model, we analytically estimate the requirements under which noise is important to create significant differences. Our simulation shows that a bi-modality phenomenon appears when appropriate parameters are applied, which agrees with our analytical result. Our second study involves steady planar flames. We use a relatively complete chemical model of the H2/air reaction system, which contains all eight reactive species (H2, O2, H, O, OH, H2O, HO2, H2O2) and N2. Our mathematical model for this system is a reacting flow model. We derive noise terms related to transport processes by a method advocated by Landau & Lifshitz, and we also derive noise terms related to chemical reactions. We develop a code to simulate this system. The numerical implementation relies on a good Riemann solver, suitable initial and boundary conditions, and so on. We also implement a code on a continuation method, which not only can be used to study approximate properties of laminar flames under deterministic governing equations, but also eliminates the difficulty of providing a suitable initial condition for governing equations with noise. With numerical experiments, we find the difference of flame speed exist when the noise is turned on or off although it is small when compared with the influence of other parameters, for example, the equivalence ratio. It will be a starting point for further studies to include noise in combustion.

Backward bifurcation and optimal control of Plasmodium Knowlesi malaria

NASA Astrophysics Data System (ADS)

Abdullahi, Mohammed Baba; Hasan, Yahya Abu; Abdullah, Farah Aini

2014-07-01

A deterministic model for the transmission dynamics of Plasmodium Knowlesi malaria with direct transmission is developed. The model is analyzed using dynamical system techniques and it shows that the backward bifurcation occurs for some range of parameters. The model is extended to assess the impact of time dependent preventive (biological and chemical control) against the mosquitoes and vaccination for susceptible humans, while treatment for infected humans. The existence of optimal control is established analytically by the use of optimal control theory. Numerical simulations of the problem, suggest that applying the four control measure can effectively reduce if not eliminate the spread of Plasmodium Knowlesi in a community.

The electrochemistry of carbon steel in simulated concrete pore water in boom clay repository environments

NASA Astrophysics Data System (ADS)

MacDonald, D. D.; Saleh, A.; Lee, S. K.; Azizi, O.; Rosas-Camacho, O.; Al-Marzooqi, A.; Taylor, M.

2011-04-01

The prediction of corrosion damage of canisters to experimentally inaccessible times is vitally important in assessing various concepts for the disposal of High Level Nuclear Waste. Such prediction can only be made using deterministic models, whose predictions are constrained by the time-invariant natural laws. In this paper, we describe the measurement of experimental electrochemical data that will allow the prediction of damage to the carbon steel overpack of the super container in Belgium's proposed Boom Clay repository by using the Point Defect Model (PDM). PDM parameter values are obtained by optimizing the model on experimental, wide-band electrochemical impedance spectroscopy data.

Microdose Induced Drain Leakage Effects in Power Trench MOSFETs: Experiment and Modeling

NASA Astrophysics Data System (ADS)

Zebrev, Gennady I.; Vatuev, Alexander S.; Useinov, Rustem G.; Emeliyanov, Vladimir V.; Anashin, Vasily S.; Gorbunov, Maxim S.; Turin, Valentin O.; Yesenkov, Kirill A.

2014-08-01

We study experimentally and theoretically the micro-dose induced drain-source leakage current in the trench power MOSFETs under irradiation with high-LET heavy ions. We found experimentally that cumulative increase of leakage current occurs by means of stochastic spikes corresponding to a strike of single heavy ion into the MOSFET gate oxide. We simulate this effect with the proposed analytic model allowing to describe (including Monte Carlo methods) both the deterministic (cumulative dose) and stochastic (single event) aspects of the problem. Based on this model the survival probability assessment in space heavy ion environment with high LETs was proposed.

Probabilistic risk assessment for CO2 storage in geological formations: robust design and support for decision making under uncertainty

NASA Astrophysics Data System (ADS)

Oladyshkin, Sergey; Class, Holger; Helmig, Rainer; Nowak, Wolfgang

2010-05-01

CO2 storage in geological formations is currently being discussed intensively as a technology for mitigating CO2 emissions. However, any large-scale application requires a thorough analysis of the potential risks. Current numerical simulation models are too expensive for probabilistic risk analysis and for stochastic approaches based on brute-force repeated simulation. Even single deterministic simulations may require parallel high-performance computing. The multiphase flow processes involved are too non-linear for quasi-linear error propagation and other simplified stochastic tools. As an alternative approach, we propose a massive stochastic model reduction based on the probabilistic collocation method. The model response is projected onto a orthogonal basis of higher-order polynomials to approximate dependence on uncertain parameters (porosity, permeability etc.) and design parameters (injection rate, depth etc.). This allows for a non-linear propagation of model uncertainty affecting the predicted risk, ensures fast computation and provides a powerful tool for combining design variables and uncertain variables into one approach based on an integrative response surface. Thus, the design task of finding optimal injection regimes explicitly includes uncertainty, which leads to robust designs of the non-linear system that minimize failure probability and provide valuable support for risk-informed management decisions. We validate our proposed stochastic approach by Monte Carlo simulation using a common 3D benchmark problem (Class et al. Computational Geosciences 13, 2009). A reasonable compromise between computational efforts and precision was reached already with second-order polynomials. In our case study, the proposed approach yields a significant computational speedup by a factor of 100 compared to Monte Carlo simulation. We demonstrate that, due to the non-linearity of the flow and transport processes during CO2 injection, including uncertainty in the analysis leads to a systematic and significant shift of predicted leakage rates towards higher values compared with deterministic simulations, affecting both risk estimates and the design of injection scenarios. This implies that, neglecting uncertainty can be a strong simplification for modeling CO2 injection, and the consequences can be stronger than when neglecting several physical phenomena (e.g. phase transition, convective mixing, capillary forces etc.). The authors would like to thank the German Research Foundation (DFG) for financial support of the project within the Cluster of Excellence in Simulation Technology (EXC 310/1) at the University of Stuttgart. Keywords: polynomial chaos; CO2 storage; multiphase flow; porous media; risk assessment; uncertainty; integrative response surfaces

A simulation of remote sensor systems and data processing algorithms for spectral feature classification

NASA Technical Reports Server (NTRS)

Arduini, R. F.; Aherron, R. M.; Samms, R. W.

1984-01-01

A computational model of the deterministic and stochastic processes involved in multispectral remote sensing was designed to evaluate the performance of sensor systems and data processing algorithms for spectral feature classification. Accuracy in distinguishing between categories of surfaces or between specific types is developed as a means to compare sensor systems and data processing algorithms. The model allows studies to be made of the effects of variability of the atmosphere and of surface reflectance, as well as the effects of channel selection and sensor noise. Examples of these effects are shown.

Modeling of contact tracing in social networks

NASA Astrophysics Data System (ADS)

Tsimring, Lev S.; Huerta, Ramón

2003-07-01

Spreading of certain infections in complex networks is effectively suppressed by using intelligent strategies for epidemic control. One such standard epidemiological strategy consists in tracing contacts of infected individuals. In this paper, we use a recently introduced generalization of the standard susceptible-infectious-removed stochastic model for epidemics in sparse random networks which incorporates an additional (traced) state. We describe a deterministic mean-field description which yields quantitative agreement with stochastic simulations on random graphs. We also discuss the role of contact tracing in epidemics control in small-world and scale-free networks. Effectiveness of contact tracing grows as the rewiring probability is reduced.

Fault Diagnosis System of Wind Turbine Generator Based on Petri Net

NASA Astrophysics Data System (ADS)

Zhang, Han

Petri net is an important tool for discrete event dynamic systems modeling and analysis. And it has great ability to handle concurrent phenomena and non-deterministic phenomena. Currently Petri nets used in wind turbine fault diagnosis have not participated in the actual system. This article will combine the existing fuzzy Petri net algorithms; build wind turbine control system simulation based on Siemens S7-1200 PLC, while making matlab gui interface for migration of the system to different platforms.

Dopant-controlled single-electron pumping through a metallic island

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

Wenz, Tobias, E-mail: tobias.wenz@ptb.de; Hohls, Frank, E-mail: frank.hohls@ptb.de; Jehl, Xavier

We investigate a hybrid metallic island/single dopant electron pump based on fully depleted silicon-on-insulator technology. Electron transfer between the central metallic island and the leads is controlled by resonant tunneling through single phosphorus dopants in the barriers. Top gates above the barriers are used to control the resonance conditions. Applying radio frequency signals to the gates, non-adiabatic quantized electron pumping is achieved. A simple deterministic model is presented and confirmed by comparing measurements with simulations.

Costing the satellite power system

NASA Technical Reports Server (NTRS)

Hazelrigg, G. A., Jr.

1978-01-01

The paper presents a methodology for satellite power system costing, places approximate limits on the accuracy possible in cost estimates made at this time, and outlines the use of probabilistic cost information in support of the decision-making process. Reasons for using probabilistic costing or risk analysis procedures instead of standard deterministic costing procedures are considered. Components of cost, costing estimating relationships, grass roots costing, and risk analysis are discussed. Risk analysis using a Monte Carlo simulation model is used to estimate future costs.

Application of constrained k-means clustering in ground motion simulation validation

NASA Astrophysics Data System (ADS)

Khoshnevis, N.; Taborda, R.

2017-12-01

The validation of ground motion synthetics has received increased attention over the last few years due to the advances in physics-based deterministic and hybrid simulation methods. Unlike for low frequency simulations (f ≤ 0.5 Hz), for which it has become reasonable to expect a good match between synthetics and data, in the case of high-frequency simulations (f ≥ 1 Hz) it is not possible to match results on a wiggle-by-wiggle basis. This is mostly due to the various complexities and uncertainties involved in earthquake ground motion modeling. Therefore, in order to compare synthetics with data we turn to different time series metrics, which are used as a means to characterize how the synthetics match the data on qualitative and statistical sense. In general, these metrics provide GOF scores that measure the level of similarity in the time and frequency domains. It is common for these scores to be scaled from 0 to 10, with 10 representing a perfect match. Although using individual metrics for particular applications is considered more adequate, there is no consensus or a unified method to classify the comparison between a set of synthetic and recorded seismograms when the various metrics offer different scores. We study the relationship among these metrics through a constrained k-means clustering approach. We define 4 hypothetical stations with scores 3, 5, 7, and 9 for all metrics. We put these stations in the category of cannot-link constraints. We generate the dataset through the validation of the results from a deterministic (physics-based) ground motion simulation for a moderate magnitude earthquake in the greater Los Angeles basin using three velocity models. The maximum frequency of the simulation is 4 Hz. The dataset involves over 300 stations and 11 metrics, or features, as they are understood in the clustering process, where the metrics form a multi-dimensional space. We address the high-dimensional feature effects with a subspace-clustering analysis, generate a final labeled dataset of stations, and discuss the within-class statistical characteristics of each metric. Labeling these stations is the first step towards developing a unified metric to evaluate ground motion simulations in an application-independent manner.

Analysis of the French insurance market exposure to floods: a stochastic model combining river overflow and surface runoff

NASA Astrophysics Data System (ADS)

Moncoulon, D.; Labat, D.; Ardon, J.; Leblois, E.; Onfroy, T.; Poulard, C.; Aji, S.; Rémy, A.; Quantin, A.

2014-09-01

The analysis of flood exposure at a national scale for the French insurance market must combine the generation of a probabilistic event set of all possible (but which have not yet occurred) flood situations with hazard and damage modeling. In this study, hazard and damage models are calibrated on a 1995-2010 historical event set, both for hazard results (river flow, flooded areas) and loss estimations. Thus, uncertainties in the deterministic estimation of a single event loss are known before simulating a probabilistic event set. To take into account at least 90 % of the insured flood losses, the probabilistic event set must combine the river overflow (small and large catchments) with the surface runoff, due to heavy rainfall, on the slopes of the watershed. Indeed, internal studies of the CCR (Caisse Centrale de Reassurance) claim database have shown that approximately 45 % of the insured flood losses are located inside the floodplains and 45 % outside. Another 10 % is due to sea surge floods and groundwater rise. In this approach, two independent probabilistic methods are combined to create a single flood loss distribution: a generation of fictive river flows based on the historical records of the river gauge network and a generation of fictive rain fields on small catchments, calibrated on the 1958-2010 Météo-France rain database SAFRAN. All the events in the probabilistic event sets are simulated with the deterministic model. This hazard and damage distribution is used to simulate the flood losses at the national scale for an insurance company (Macif) and to generate flood areas associated with hazard return periods. The flood maps concern river overflow and surface water runoff. Validation of these maps is conducted by comparison with the address located claim data on a small catchment (downstream Argens).

INTEGRATED PROBABILISTIC AND DETERMINISTIC MODELING TECHNIQUES IN ESTIMATING EXPOSURE TO WATER-BORNE CONTAMINANTS: PART 2 PHARMACOKINETIC MODELING

EPA Science Inventory

The Total Exposure Model (TEM) uses deterministic and stochastic methods to estimate the exposure of a person performing daily activities of eating, drinking, showering, and bathing. There were 250 time histories generated, by subject with activities, for the three exposure ro...

Refinement and evaluation of helicopter real-time self-adaptive active vibration controller algorithms

NASA Technical Reports Server (NTRS)

Davis, M. W.

1984-01-01

A Real-Time Self-Adaptive (RTSA) active vibration controller was used as the framework in developing a computer program for a generic controller that can be used to alleviate helicopter vibration. Based upon on-line identification of system parameters, the generic controller minimizes vibration in the fuselage by closed-loop implementation of higher harmonic control in the main rotor system. The new generic controller incorporates a set of improved algorithms that gives the capability to readily define many different configurations by selecting one of three different controller types (deterministic, cautious, and dual), one of two linear system models (local and global), and one or more of several methods of applying limits on control inputs (external and/or internal limits on higher harmonic pitch amplitude and rate). A helicopter rotor simulation analysis was used to evaluate the algorithms associated with the alternative controller types as applied to the four-bladed H-34 rotor mounted on the NASA Ames Rotor Test Apparatus (RTA) which represents the fuselage. After proper tuning all three controllers provide more effective vibration reduction and converge more quickly and smoothly with smaller control inputs than the initial RTSA controller (deterministic with external pitch-rate limiting). It is demonstrated that internal limiting of the control inputs a significantly improves the overall performance of the deterministic controller.

Vibration study of a vehicle suspension assembly with the finite element method

NASA Astrophysics Data System (ADS)

Cătălin Marinescu, Gabriel; Castravete, Ştefan-Cristian; Dumitru, Nicolae

2017-10-01

The main steps of the present work represent a methodology of analysing various vibration effects over suspension mechanical parts of a vehicle. A McPherson type suspension from an existing vehicle was created using CAD software. Using the CAD model as input, a finite element model of the suspension assembly was developed. Abaqus finite element analysis software was used to pre-process, solve, and post-process the results. Geometric nonlinearities are included in the model. Severe sources of nonlinearities such us friction and contact are also included in the model. The McPherson spring is modelled as linear spring. The analysis include several steps: preload, modal analysis, the reduction of the model to 200 generalized coordinates, a deterministic external excitation, a random excitation that comes from different types of roads. The vibration data used as an input for the simulation were previously obtained by experimental means. Mathematical expressions used for the simulation were also presented in the paper.

A model for HIV/AIDS pandemic with optimal control

NASA Astrophysics Data System (ADS)

Sule, Amiru; Abdullah, Farah Aini

2015-05-01

Human immunodeficiency virus and acquired immune deficiency syndrome (HIV/AIDS) is pandemic. It has affected nearly 60 million people since the detection of the disease in 1981 to date. In this paper basic deterministic HIV/AIDS model with mass action incidence function are developed. Stability analysis is carried out. And the disease free equilibrium of the basic model was found to be locally asymptotically stable whenever the threshold parameter (RO) value is less than one, and unstable otherwise. The model is extended by introducing two optimal control strategies namely, CD4 counts and treatment for the infective using optimal control theory. Numerical simulation was carried out in order to illustrate the analytic results.

Using Reputation Systems and Non-Deterministic Routing to Secure Wireless Sensor Networks

PubMed Central

Moya, José M.; Vallejo, Juan Carlos; Fraga, David; Araujo, Álvaro; Villanueva, Daniel; de Goyeneche, Juan-Mariano

2009-01-01

Security in wireless sensor networks is difficult to achieve because of the resource limitations of the sensor nodes. We propose a trust-based decision framework for wireless sensor networks coupled with a non-deterministic routing protocol. Both provide a mechanism to effectively detect and confine common attacks, and, unlike previous approaches, allow bad reputation feedback to the network. This approach has been extensively simulated, obtaining good results, even for unrealistically complex attack scenarios. PMID:22412345

Parallel and Preemptable Dynamically Dimensioned Search Algorithms for Single and Multi-objective Optimization in Water Resources

NASA Astrophysics Data System (ADS)

Tolson, B.; Matott, L. S.; Gaffoor, T. A.; Asadzadeh, M.; Shafii, M.; Pomorski, P.; Xu, X.; Jahanpour, M.; Razavi, S.; Haghnegahdar, A.; Craig, J. R.

2015-12-01

We introduce asynchronous parallel implementations of the Dynamically Dimensioned Search (DDS) family of algorithms including DDS, discrete DDS, PA-DDS and DDS-AU. These parallel algorithms are unique from most existing parallel optimization algorithms in the water resources field in that parallel DDS is asynchronous and does not require an entire population (set of candidate solutions) to be evaluated before generating and then sending a new candidate solution for evaluation. One key advance in this study is developing the first parallel PA-DDS multi-objective optimization algorithm. The other key advance is enhancing the computational efficiency of solving optimization problems (such as model calibration) by combining a parallel optimization algorithm with the deterministic model pre-emption concept. These two efficiency techniques can only be combined because of the asynchronous nature of parallel DDS. Model pre-emption functions to terminate simulation model runs early, prior to completely simulating the model calibration period for example, when intermediate results indicate the candidate solution is so poor that it will definitely have no influence on the generation of further candidate solutions. The computational savings of deterministic model preemption available in serial implementations of population-based algorithms (e.g., PSO) disappear in synchronous parallel implementations as these algorithms. In addition to the key advances above, we implement the algorithms across a range of computation platforms (Windows and Unix-based operating systems from multi-core desktops to a supercomputer system) and package these for future modellers within a model-independent calibration software package called Ostrich as well as MATLAB versions. Results across multiple platforms and multiple case studies (from 4 to 64 processors) demonstrate the vast improvement over serial DDS-based algorithms and highlight the important role model pre-emption plays in the performance of parallel, pre-emptable DDS algorithms. Case studies include single- and multiple-objective optimization problems in water resources model calibration and in many cases linear or near linear speedups are observed.

Model selection for integrated pest management with stochasticity.

PubMed

Akman, Olcay; Comar, Timothy D; Hrozencik, Daniel

2018-04-07

In Song and Xiang (2006), an integrated pest management model with periodically varying climatic conditions was introduced. In order to address a wider range of environmental effects, the authors here have embarked upon a series of studies resulting in a more flexible modeling approach. In Akman et al. (2013), the impact of randomly changing environmental conditions is examined by incorporating stochasticity into the birth pulse of the prey species. In Akman et al. (2014), the authors introduce a class of models via a mixture of two birth-pulse terms and determined conditions for the global and local asymptotic stability of the pest eradication solution. With this work, the authors unify the stochastic and mixture model components to create further flexibility in modeling the impacts of random environmental changes on an integrated pest management system. In particular, we first determine the conditions under which solutions of our deterministic mixture model are permanent. We then analyze the stochastic model to find the optimal value of the mixing parameter that minimizes the variance in the efficacy of the pesticide. Additionally, we perform a sensitivity analysis to show that the corresponding pesticide efficacy determined by this optimization technique is indeed robust. Through numerical simulations we show that permanence can be preserved in our stochastic model. Our study of the stochastic version of the model indicates that our results on the deterministic model provide informative conclusions about the behavior of the stochastic model. Copyright © 2017 Elsevier Ltd. All rights reserved.

Discretely Integrated Condition Event (DICE) Simulation for Pharmacoeconomics.

PubMed

Caro, J Jaime

2016-07-01

Several decision-analytic modeling techniques are in use for pharmacoeconomic analyses. Discretely integrated condition event (DICE) simulation is proposed as a unifying approach that has been deliberately designed to meet the modeling requirements in a straightforward transparent way, without forcing assumptions (e.g., only one transition per cycle) or unnecessary complexity. At the core of DICE are conditions that represent aspects that persist over time. They have levels that can change and many may coexist. Events reflect instantaneous occurrences that may modify some conditions or the timing of other events. The conditions are discretely integrated with events by updating their levels at those times. Profiles of determinant values allow for differences among patients in the predictors of the disease course. Any number of valuations (e.g., utility, cost, willingness-to-pay) of conditions and events can be applied concurrently in a single run. A DICE model is conveniently specified in a series of tables that follow a consistent format and the simulation can be implemented fully in MS Excel, facilitating review and validation. DICE incorporates both state-transition (Markov) models and non-resource-constrained discrete event simulation in a single formulation; it can be executed as a cohort or a microsimulation; and deterministically or stochastically.

Effect of Streamflow Forecast Uncertainty on Real-Time Reservoir Operation

NASA Astrophysics Data System (ADS)

Zhao, T.; Cai, X.; Yang, D.

2010-12-01

Various hydrological forecast products have been applied to real-time reservoir operation, including deterministic streamflow forecast (DSF), DSF-based probabilistic streamflow forecast (DPSF), and ensemble streamflow forecast (ESF), which represent forecast uncertainty in the form of deterministic forecast error, deterministic forecast error-based uncertainty distribution, and ensemble forecast errors, respectively. Compared to previous studies that treat these forecast products as ad hoc inputs for reservoir operation models, this paper attempts to model the uncertainties involved in the various forecast products and explores their effect on real-time reservoir operation decisions. In hydrology, there are various indices reflecting the magnitude of streamflow forecast uncertainty; meanwhile, few models illustrate the forecast uncertainty evolution process. This research introduces Martingale Model of Forecast Evolution (MMFE) from supply chain management and justifies its assumptions for quantifying the evolution of uncertainty in streamflow forecast as time progresses. Based on MMFE, this research simulates the evolution of forecast uncertainty in DSF, DPSF, and ESF, and applies the reservoir operation models (dynamic programming, DP; stochastic dynamic programming, SDP; and standard operation policy, SOP) to assess the effect of different forms of forecast uncertainty on real-time reservoir operation. Through a hypothetical single-objective real-time reservoir operation model, the results illustrate that forecast uncertainty exerts significant effects. Reservoir operation efficiency, as measured by a utility function, decreases as the forecast uncertainty increases. Meanwhile, these effects also depend on the type of forecast product being used. In general, the utility of reservoir operation with ESF is nearly as high as the utility obtained with a perfect forecast; the utilities of DSF and DPSF are similar to each other but not as efficient as ESF. Moreover, streamflow variability and reservoir capacity can change the magnitude of the effects of forecast uncertainty, but not the relative merit of DSF, DPSF, and ESF. Schematic diagram of the increase in forecast uncertainty with forecast lead-time and the dynamic updating property of real-time streamflow forecast

FRACOR-software toolbox for deterministic mapping of fracture corridors in oil fields on AutoCAD platform

NASA Astrophysics Data System (ADS)

Ozkaya, Sait I.

2018-03-01

Fracture corridors are interconnected large fractures in a narrow sub vertical tabular array, which usually traverse entire reservoir vertically and extended for several hundreds of meters laterally. Fracture corridors with their huge conductivities constitute an important element of many fractured reservoirs. Unlike small diffuse fractures, actual fracture corridors must be mapped deterministically for simulation or field development purposes. Fracture corridors can be identified and quantified definitely with borehole image logs and well testing. However, there are rarely sufficient image logs or well tests, and it is necessary to utilize various fracture corridor indicators with varying degrees of reliability. Integration of data from many different sources, in turn, requires a platform with powerful editing and layering capability. Available commercial reservoir characterization software packages, with layering and editing capabilities, can be cost intensive. CAD packages are far more affordable and may easily acquire the versatility and power of commercial software packages with addition of a small software toolbox. The objective of this communication is to present FRACOR, a software toolbox which enables deterministic 2D fracture corridor mapping and modeling on AutoCAD platform. The FRACOR toolbox is written in AutoLISPand contains several independent routines to import and integrate available fracture corridor data from an oil field, and export results as text files. The resulting fracture corridor maps consists mainly of fracture corridors with different confidence levels from combination of static and dynamic data and exclusion zones where no fracture corridor can exist. The exported text file of fracture corridors from FRACOR can be imported into an upscaling programs to generate fracture grid for dual porosity simulation or used for field development and well planning.

Stochastic simulation of reaction-diffusion systems: A fluctuating-hydrodynamics approach

NASA Astrophysics Data System (ADS)

Kim, Changho; Nonaka, Andy; Bell, John B.; Garcia, Alejandro L.; Donev, Aleksandar

2017-03-01

We develop numerical methods for stochastic reaction-diffusion systems based on approaches used for fluctuating hydrodynamics (FHD). For hydrodynamic systems, the FHD formulation is formally described by stochastic partial differential equations (SPDEs). In the reaction-diffusion systems we consider, our model becomes similar to the reaction-diffusion master equation (RDME) description when our SPDEs are spatially discretized and reactions are modeled as a source term having Poisson fluctuations. However, unlike the RDME, which becomes prohibitively expensive for an increasing number of molecules, our FHD-based description naturally extends from the regime where fluctuations are strong, i.e., each mesoscopic cell has few (reactive) molecules, to regimes with moderate or weak fluctuations, and ultimately to the deterministic limit. By treating diffusion implicitly, we avoid the severe restriction on time step size that limits all methods based on explicit treatments of diffusion and construct numerical methods that are more efficient than RDME methods, without compromising accuracy. Guided by an analysis of the accuracy of the distribution of steady-state fluctuations for the linearized reaction-diffusion model, we construct several two-stage (predictor-corrector) schemes, where diffusion is treated using a stochastic Crank-Nicolson method, and reactions are handled by the stochastic simulation algorithm of Gillespie or a weakly second-order tau leaping method. We find that an implicit midpoint tau leaping scheme attains second-order weak accuracy in the linearized setting and gives an accurate and stable structure factor for a time step size of an order of magnitude larger than the hopping time scale of diffusing molecules. We study the numerical accuracy of our methods for the Schlögl reaction-diffusion model both in and out of thermodynamic equilibrium. We demonstrate and quantify the importance of thermodynamic fluctuations to the formation of a two-dimensional Turing-like pattern and examine the effect of fluctuations on three-dimensional chemical front propagation. By comparing stochastic simulations to deterministic reaction-diffusion simulations, we show that fluctuations accelerate pattern formation in spatially homogeneous systems and lead to a qualitatively different disordered pattern behind a traveling wave.

Stochastic simulation of reaction-diffusion systems: A fluctuating-hydrodynamics approach

DOE PAGES

Kim, Changho; Nonaka, Andy; Bell, John B.; ...

2017-03-24

Here, we develop numerical methods for stochastic reaction-diffusion systems based on approaches used for fluctuating hydrodynamics (FHD). For hydrodynamic systems, the FHD formulation is formally described by stochastic partial differential equations (SPDEs). In the reaction-diffusion systems we consider, our model becomes similar to the reaction-diffusion master equation (RDME) description when our SPDEs are spatially discretized and reactions are modeled as a source term having Poisson fluctuations. However, unlike the RDME, which becomes prohibitively expensive for an increasing number of molecules, our FHD-based description naturally extends from the regime where fluctuations are strong, i.e., each mesoscopic cell has few (reactive) molecules,more » to regimes with moderate or weak fluctuations, and ultimately to the deterministic limit. By treating diffusion implicitly, we avoid the severe restriction on time step size that limits all methods based on explicit treatments of diffusion and construct numerical methods that are more efficient than RDME methods, without compromising accuracy. Guided by an analysis of the accuracy of the distribution of steady-state fluctuations for the linearized reaction-diffusion model, we construct several two-stage (predictor-corrector) schemes, where diffusion is treated using a stochastic Crank-Nicolson method, and reactions are handled by the stochastic simulation algorithm of Gillespie or a weakly second-order tau leaping method. We find that an implicit midpoint tau leaping scheme attains second-order weak accuracy in the linearized setting and gives an accurate and stable structure factor for a time step size of an order of magnitude larger than the hopping time scale of diffusing molecules. We study the numerical accuracy of our methods for the Schlögl reaction-diffusion model both in and out of thermodynamic equilibrium. We demonstrate and quantify the importance of thermodynamic fluctuations to the formation of a two-dimensional Turing-like pattern and examine the effect of fluctuations on three-dimensional chemical front propagation. Furthermore, by comparing stochastic simulations to deterministic reaction-diffusion simulations, we show that fluctuations accelerate pattern formation in spatially homogeneous systems and lead to a qualitatively different disordered pattern behind a traveling wave.« less

Pressure oscillations and instability of working processes in the combustion chambers of solid rocket motors

NASA Astrophysics Data System (ADS)

Emelyanov, V. N.; Teterina, I. V.; Volkov, K. N.; Garkushev, A. U.

2017-06-01

Metal particles are widely used in space engineering to increase specific impulse and to supress acoustic instability of intra-champber processes. A numerical analysis of the internal injection-driven turbulent gas-particle flows is performed to improve the current understanding and modeling capabilities of the complex flow characteristics in the combustion chambers of solid rocket motors (SRMs) in presence of forced pressure oscillations. The two-phase flow is simulated with a combined Eulerian-Lagrangian approach. The Reynolds-averaged Navier-Stokes equations and transport equations of k - ε model are solved numerically for the gas. The particulate phase is simulated through a Lagrangian deterministic and stochastic tracking models to provide particle trajectories and particle concentration. The results obtained highlight the crucial significance of the particle dispersion in turbulent flowfield and high potential of statistical methods. Strong coupling between acoustic oscillations, vortical motion, turbulent fluctuations and particle dynamics is observed.

Discussion of the paper ``the use of conditional simulation in nuclear waste site performance assessment,`` by Carol A. Gotway

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

Gilbert, R.O.; Doctor, P.G.

1993-08-01

First, we applaud Dr. Gotway for seeking via her paper to expose a wider audience of statisticians to the many interesting and challenging modeling and statistical problems in the environmental area. This well-written paper effective explains the WIPP and the context of the analysis. Dr. Gotway`s paper describes a geostatistical conditional simulation approach combined with deterministic modeling to estimate the cumulative distribution function (cdf) of groundwater travel time (GWTT), information that is needed for estimating the cumulative release of nuclear waste from the repository. We begin our discussion with comments and questions on modeling aspects of Dr. Gotway`s paper. Thenmore » we discuss uncertainty and sensitivity analyses and some of the problems inherent with implementing those techniques including correlations, elicitation of expert opinion, and planning to achieve specified Data Quality Objectives (DQOs).« less

Using stochastic models to incorporate spatial and temporal variability [Exercise 14

Treesearch

Carolyn Hull Sieg; Rudy M. King; Fred Van Dyke

2003-01-01

To this point, our analysis of population processes and viability in the western prairie fringed orchid has used only deterministic models. In this exercise, we conduct a similar analysis, using a stochastic model instead. This distinction is of great importance to population biology in general and to conservation biology in particular. In deterministic models,...

Binomial tau-leap spatial stochastic simulation algorithm for applications in chemical kinetics.

PubMed

Marquez-Lago, Tatiana T; Burrage, Kevin

2007-09-14

In cell biology, cell signaling pathway problems are often tackled with deterministic temporal models, well mixed stochastic simulators, and/or hybrid methods. But, in fact, three dimensional stochastic spatial modeling of reactions happening inside the cell is needed in order to fully understand these cell signaling pathways. This is because noise effects, low molecular concentrations, and spatial heterogeneity can all affect the cellular dynamics. However, there are ways in which important effects can be accounted without going to the extent of using highly resolved spatial simulators (such as single-particle software), hence reducing the overall computation time significantly. We present a new coarse grained modified version of the next subvolume method that allows the user to consider both diffusion and reaction events in relatively long simulation time spans as compared with the original method and other commonly used fully stochastic computational methods. Benchmarking of the simulation algorithm was performed through comparison with the next subvolume method and well mixed models (MATLAB), as well as stochastic particle reaction and transport simulations (CHEMCELL, Sandia National Laboratories). Additionally, we construct a model based on a set of chemical reactions in the epidermal growth factor receptor pathway. For this particular application and a bistable chemical system example, we analyze and outline the advantages of our presented binomial tau-leap spatial stochastic simulation algorithm, in terms of efficiency and accuracy, in scenarios of both molecular homogeneity and heterogeneity.

Spectral analysis of a two-species competition model: Determining the effects of extreme conditions on the color of noise generated from simulated time series

NASA Astrophysics Data System (ADS)

Golinski, M. R.

2006-07-01

Ecologists have observed that environmental noise affects population variance in the logistic equation for one-species growth. Interactions between deterministic and stochastic dynamics in a one-dimensional system result in increased variance in species population density over time. Since natural populations do not live in isolation, the present paper simulates a discrete-time two-species competition model with environmental noise to determine the type of colored population noise generated by extreme conditions in the long-term population dynamics of competing populations. Discrete Fourier analysis is applied to the simulation results and the calculated Hurst exponent ( H) is used to determine how the color of population noise for the two species corresponds to extreme conditions in population dynamics. To interpret the biological meaning of the color of noise generated by the two-species model, the paper determines the color of noise generated by three reference models: (1) A two-dimensional discrete-time white noise model (0⩽ H<1/2); (2) A two-dimensional fractional Brownian motion model (H=1/2); and (3) A two-dimensional discrete-time model with noise for unbounded growth of two uncoupled species (1/2< H⩽1).

Toward a Deterministic Model of Planetary Formation. II. The Formation and Retention of Gas Giant Planets around Stars with a Range of Metallicities

NASA Astrophysics Data System (ADS)

Ida, Shigeru; Lin, D. N. C.

2004-11-01

The apparent dependence of detection frequency of extrasolar planets on the metallicity of their host stars is investigated with Monte Carlo simulations using a deterministic core-accretion planet formation model. According to this model, gas giants formed and acquired their mass Mp through planetesimal coagulation followed by the emergence of cores onto which gas is accreted. These protoplanets migrate and attain their asymptotic semimajor axis a through tidal interaction with their nascent disk. Based on the observed properties of protostellar disks, we generate an Mp-a distribution. Our results reproduce the observed lack of planets with intermediate mass Mp=10-100 M⊕ and a<~3 AU and with large mass Mp>~103 M⊕ and a<~0.2 AU. Based on the simulated Mp-a distributions, we also evaluate the metallicity dependence of the fraction of stars harboring planets that are detectable with current radial velocity surveys. If protostellar disks attain the same fraction of heavy elements as contained in their host stars, the detection probability around metal-rich stars would be greatly enhanced because protoplanetary cores formed in them can grow to several Earth masses prior to their depletion. These large masses are required for the cores to initiate rapid gas accretion and to transform into giant planets. The theoretically extrapolated metallicity dependence is consistent with the observations. This correlation does not arise naturally in the gravitational-instability scenario. We also suggest other metallicity dependences of the planet distributions that can be tested by ongoing observations.

POD Model Reconstruction for Gray-Box Fault Detection

NASA Technical Reports Server (NTRS)

Park, Han; Zak, Michail

2007-01-01

Proper orthogonal decomposition (POD) is the mathematical basis of a method of constructing low-order mathematical models for the "gray-box" fault-detection algorithm that is a component of a diagnostic system known as beacon-based exception analysis for multi-missions (BEAM). POD has been successfully applied in reducing computational complexity by generating simple models that can be used for control and simulation for complex systems such as fluid flows. In the present application to BEAM, POD brings the same benefits to automated diagnosis. BEAM is a method of real-time or offline, automated diagnosis of a complex dynamic system.The gray-box approach makes it possible to utilize incomplete or approximate knowledge of the dynamics of the system that one seeks to diagnose. In the gray-box approach, a deterministic model of the system is used to filter a time series of system sensor data to remove the deterministic components of the time series from further examination. What is left after the filtering operation is a time series of residual quantities that represent the unknown (or at least unmodeled) aspects of the behavior of the system. Stochastic modeling techniques are then applied to the residual time series. The procedure for detecting abnormal behavior of the system then becomes one of looking for statistical differences between the residual time series and the predictions of the stochastic model.

Simple statistical bias correction techniques greatly improve moderate resolution air quality forecast at station level

NASA Astrophysics Data System (ADS)

Curci, Gabriele; Falasca, Serena

2017-04-01

Deterministic air quality forecast is routinely carried out at many local Environmental Agencies in Europe and throughout the world by means of eulerian chemistry-transport models. The skill of these models in predicting the ground-level concentrations of relevant pollutants (ozone, nitrogen dioxide, particulate matter) a few days ahead has greatly improved in recent years, but it is not yet always compliant with the required quality level for decision making (e.g. the European Commission has set a maximum uncertainty of 50% on daily values of relevant pollutants). Post-processing of deterministic model output is thus still regarded as a useful tool to make the forecast more reliable. In this work, we test several bias correction techniques applied to a long-term dataset of air quality forecasts over Europe and Italy. We used the WRF-CHIMERE modelling system, which provides operational experimental chemical weather forecast at CETEMPS (http://pumpkin.aquila.infn.it/forechem/), to simulate the years 2008-2012 at low resolution over Europe (0.5° x 0.5°) and moderate resolution over Italy (0.15° x 0.15°). We compared the simulated dataset with available observation from the European Environmental Agency database (AirBase) and characterized model skill and compliance with EU legislation using the Delta tool from FAIRMODE project (http://fairmode.jrc.ec.europa.eu/). The bias correction techniques adopted are, in order of complexity: (1) application of multiplicative factors calculated as the ratio of model-to-observed concentrations averaged over the previous days; (2) correction of the statistical distribution of model forecasts, in order to make it similar to that of the observations; (3) development and application of Model Output Statistics (MOS) regression equations. We illustrate differences and advantages/disadvantages of the three approaches. All the methods are relatively easy to implement for other modelling systems.

Appearance of deterministic mixing behavior from ensembles of fluctuating hydrodynamics simulations of the Richtmyer-Meshkov instability

NASA Astrophysics Data System (ADS)

Narayanan, Kiran; Samtaney, Ravi

2018-04-01

We obtain numerical solutions of the two-fluid fluctuating compressible Navier-Stokes (FCNS) equations, which consistently account for thermal fluctuations from meso- to macroscales, in order to study the effect of such fluctuations on the mixing behavior in the Richtmyer-Meshkov instability (RMI). The numerical method used was successfully verified in two stages: for the deterministic fluxes by comparison against air-SF6 RMI experiment, and for the stochastic terms by comparison against the direct simulation Monte Carlo results for He-Ar RMI. We present results from fluctuating hydrodynamic RMI simulations for three He-Ar systems having length scales with decreasing order of magnitude that span from macroscopic to mesoscopic, with different levels of thermal fluctuations characterized by a nondimensional Boltzmann number (Bo). For a multidimensional FCNS system on a regular Cartesian grid, when using a discretization of a space-time stochastic flux Z (x ,t ) of the form Z (x ,t ) →1 /√{h ▵ t }N (i h ,n Δ t ) for spatial interval h , time interval Δ t , h , and Gaussian noise N should be greater than h0, with h0 corresponding to a cell volume that contains a sufficient number of molecules of the fluid such that the fluctuations are physically meaningful and produce the right equilibrium spectrum. For the mesoscale RMI systems simulated, it was desirable to use a cell size smaller than this limit in order to resolve the viscous shock. This was achieved by using a modified regularization of the noise term via Z (h3,h03)>x ,t →1 /√ ▵ t max(i h ,n Δ t ) , with h0=ξ h ∀h

GPU accelerated simulations of 3D deterministic particle transport using discrete ordinates method

NASA Astrophysics Data System (ADS)

Gong, Chunye; Liu, Jie; Chi, Lihua; Huang, Haowei; Fang, Jingyue; Gong, Zhenghu

2011-07-01

Graphics Processing Unit (GPU), originally developed for real-time, high-definition 3D graphics in computer games, now provides great faculty in solving scientific applications. The basis of particle transport simulation is the time-dependent, multi-group, inhomogeneous Boltzmann transport equation. The numerical solution to the Boltzmann equation involves the discrete ordinates ( Sn) method and the procedure of source iteration. In this paper, we present a GPU accelerated simulation of one energy group time-independent deterministic discrete ordinates particle transport in 3D Cartesian geometry (Sweep3D). The performance of the GPU simulations are reported with the simulations of vacuum boundary condition. The discussion of the relative advantages and disadvantages of the GPU implementation, the simulation on multi GPUs, the programming effort and code portability are also reported. The results show that the overall performance speedup of one NVIDIA Tesla M2050 GPU ranges from 2.56 compared with one Intel Xeon X5670 chip to 8.14 compared with one Intel Core Q6600 chip for no flux fixup. The simulation with flux fixup on one M2050 is 1.23 times faster than on one X5670.

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

Pitarka, A.

In this project we developed GEN_SRF4 a computer program for generating kinematic rupture models, compatible with the SRF format, using Irikura and Miyake (2011) asperity-­based earthquake rupture model (IM2011, hereafter). IM2011, also known as Irkura’s recipe, has been widely used to model and simulate ground motion from earthquakes in Japan. An essential part of the method is its kinematic rupture generation technique, which is based on a deterministic rupture asperity modeling approach. The source model simplicity and efficiency of IM2011 at reproducing ground motion from earthquakes recorded in Japan makes it attractive to developers and users of the Southern Californiamore » Earthquake Center Broadband Platform (SCEC BB platform). Besides writing the code the objective of our study was to test the transportability of IM2011 to broadband simulation methods used by the SCEC BB platform. Here we test it using the Graves and Pitarka (2010) method, implemented in the platform. We performed broadband (0.1- -10 Hz) ground motion simulations for a M6.7 scenario earthquake using rupture models produced with both GEN_SRF4 and rupture generator of Graves and Pitarka (2016), (GP2016 hereafter). In the simulations we used the same Green’s functions, and same high frequency approach for calculating the low-­frequency and high-­frequency parts of ground motion, respectively.« less

Characterizing Uncertainty and Variability in PBPK Models ...

EPA Pesticide Factsheets

Mode-of-action based risk and safety assessments can rely upon tissue dosimetry estimates in animals and humans obtained from physiologically-based pharmacokinetic (PBPK) modeling. However, risk assessment also increasingly requires characterization of uncertainty and variability; such characterization for PBPK model predictions represents a continuing challenge to both modelers and users. Current practices show significant progress in specifying deterministic biological models and the non-deterministic (often statistical) models, estimating their parameters using diverse data sets from multiple sources, and using them to make predictions and characterize uncertainty and variability. The International Workshop on Uncertainty and Variability in PBPK Models, held Oct 31-Nov 2, 2006, sought to identify the state-of-the-science in this area and recommend priorities for research and changes in practice and implementation. For the short term, these include: (1) multidisciplinary teams to integrate deterministic and non-deterministic/statistical models; (2) broader use of sensitivity analyses, including for structural and global (rather than local) parameter changes; and (3) enhanced transparency and reproducibility through more complete documentation of the model structure(s) and parameter values, the results of sensitivity and other analyses, and supporting, discrepant, or excluded data. Longer-term needs include: (1) theoretic and practical methodological impro

Accurate modeling of switched reluctance machine based on hybrid trained WNN

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

Song, Shoujun, E-mail: sunnyway@nwpu.edu.cn; Ge, Lefei; Ma, Shaojie

2014-04-15

According to the strong nonlinear electromagnetic characteristics of switched reluctance machine (SRM), a novel accurate modeling method is proposed based on hybrid trained wavelet neural network (WNN) which combines improved genetic algorithm (GA) with gradient descent (GD) method to train the network. In the novel method, WNN is trained by GD method based on the initial weights obtained per improved GA optimization, and the global parallel searching capability of stochastic algorithm and local convergence speed of deterministic algorithm are combined to enhance the training accuracy, stability and speed. Based on the measured electromagnetic characteristics of a 3-phase 12/8-pole SRM, themore » nonlinear simulation model is built by hybrid trained WNN in Matlab. The phase current and mechanical characteristics from simulation under different working conditions meet well with those from experiments, which indicates the accuracy of the model for dynamic and static performance evaluation of SRM and verifies the effectiveness of the proposed modeling method.« less

Continuum and discrete approach in modeling biofilm development and structure: a review.

PubMed

Mattei, M R; Frunzo, L; D'Acunto, B; Pechaud, Y; Pirozzi, F; Esposito, G

2018-03-01

The scientific community has recognized that almost 99% of the microbial life on earth is represented by biofilms. Considering the impacts of their sessile lifestyle on both natural and human activities, extensive experimental activity has been carried out to understand how biofilms grow and interact with the environment. Many mathematical models have also been developed to simulate and elucidate the main processes characterizing the biofilm growth. Two main mathematical approaches for biomass representation can be distinguished: continuum and discrete. This review is aimed at exploring the main characteristics of each approach. Continuum models can simulate the biofilm processes in a quantitative and deterministic way. However, they require a multidimensional formulation to take into account the biofilm spatial heterogeneity, which makes the models quite complicated, requiring significant computational effort. Discrete models are more recent and can represent the typical multidimensional structural heterogeneity of biofilm reflecting the experimental expectations, but they generate computational results including elements of randomness and introduce stochastic effects into the solutions.

Uncertainty quantification-based robust aerodynamic optimization of laminar flow nacelle

NASA Astrophysics Data System (ADS)

Xiong, Neng; Tao, Yang; Liu, Zhiyong; Lin, Jun

2018-05-01

The aerodynamic performance of laminar flow nacelle is highly sensitive to uncertain working conditions, especially the surface roughness. An efficient robust aerodynamic optimization method on the basis of non-deterministic computational fluid dynamic (CFD) simulation and Efficient Global Optimization (EGO)algorithm was employed. A non-intrusive polynomial chaos method is used in conjunction with an existing well-verified CFD module to quantify the uncertainty propagation in the flow field. This paper investigates the roughness modeling behavior with the γ-Ret shear stress transport model including modeling flow transition and surface roughness effects. The roughness effects are modeled to simulate sand grain roughness. A Class-Shape Transformation-based parametrical description of the nacelle contour as part of an automatic design evaluation process is presented. A Design-of-Experiments (DoE) was performed and surrogate model by Kriging method was built. The new design nacelle process demonstrates that significant improvements of both mean and variance of the efficiency are achieved and the proposed method can be applied to laminar flow nacelle design successfully.

Impact of surface coupling grids on tropical cyclone extremes in high-resolution atmospheric simulations

DOE PAGES

Zarzycki, Colin M.; Reed, Kevin A.; Bacmeister, Julio T.; ...

2016-02-25

This article discusses the sensitivity of tropical cyclone climatology to surface coupling strategy in high-resolution configurations of the Community Earth System Model. Using two supported model setups, we demonstrate that the choice of grid on which the lowest model level wind stress and surface fluxes are computed may lead to differences in cyclone strength in multi-decadal climate simulations, particularly for the most intense cyclones. Using a deterministic framework, we show that when these surface quantities are calculated on an ocean grid that is coarser than the atmosphere, the computed frictional stress is misaligned with wind vectors in individual atmospheric gridmore » cells. This reduces the effective surface drag, and results in more intense cyclones when compared to a model configuration where the ocean and atmosphere are of equivalent resolution. Our results demonstrate that the choice of computation grid for atmosphere–ocean interactions is non-negligible when considering climate extremes at high horizontal resolution, especially when model components are on highly disparate grids.« less

Numerical simulation and characterization of trapping noise in InGaP-GaAs heterojunctions devices at high injection

NASA Astrophysics Data System (ADS)

Nallatamby, Jean-Christophe; Abdelhadi, Khaled; Jacquet, Jean-Claude; Prigent, Michel; Floriot, Didier; Delage, Sylvain; Obregon, Juan

2013-03-01

Commercially available simulators present considerable advantages in performing accurate DC, AC and transient simulations of semiconductor devices, including many fundamental and parasitic effects which are not generally taken into account in house-made simulators. Nevertheless, while the TCAD simulators of the public domain we have tested give accurate results for the simulation of diffusion noise, none of the tested simulators perform trap-assisted GR noise accurately. In order to overcome the aforementioned problem we propose a robust solution to accurately simulate GR noise due to traps. It is based on numerical processing of the output data of one of the simulators available in the public-domain, namely SENTAURUS (from Synopsys). We have linked together, through a dedicated Data Access Component (DAC), the deterministic output data available from SENTAURUS and a powerful, customizable post-processing tool developed on the mathematical SCILAB software package. Thus, robust simulations of GR noise in semiconductor devices can be performed by using GR Langevin sources associated to the scalar Green functions responses of the device. Our method takes advantage of the accuracy of the deterministic simulations of electronic devices obtained with SENTAURUS. A Comparison between 2-D simulations and measurements of low frequency noise on InGaP-GaAs heterojunctions, at low as well as high injection levels, demonstrates the validity of the proposed simulation tool.

SU-E-T-577: Commissioning of a Deterministic Algorithm for External Photon Beams

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

Zhu, T; Finlay, J; Mesina, C

Purpose: We report commissioning results for a deterministic algorithm for external photon beam treatment planning. A deterministic algorithm solves the radiation transport equations directly using a finite difference method, thus improve the accuracy of dose calculation, particularly under heterogeneous conditions with results similar to that of Monte Carlo (MC) simulation. Methods: Commissioning data for photon energies 6 – 15 MV includes the percentage depth dose (PDD) measured at SSD = 90 cm and output ratio in water (Spc), both normalized to 10 cm depth, for field sizes between 2 and 40 cm and depths between 0 and 40 cm. Off-axismore » ratio (OAR) for the same set of field sizes was used at 5 depths (dmax, 5, 10, 20, 30 cm). The final model was compared with the commissioning data as well as additional benchmark data. The benchmark data includes dose per MU determined for 17 points for SSD between 80 and 110 cm, depth between 5 and 20 cm, and lateral offset of up to 16.5 cm. Relative comparisons were made in a heterogeneous phantom made of cork and solid water. Results: Compared to the commissioning beam data, the agreement are generally better than 2% with large errors (up to 13%) observed in the buildup regions of the FDD and penumbra regions of the OAR profiles. The overall mean standard deviation is 0.04% when all data are taken into account. Compared to the benchmark data, the agreements are generally better than 2%. Relative comparison in heterogeneous phantom is in general better than 4%. Conclusion: A commercial deterministic algorithm was commissioned for megavoltage photon beams. In a homogeneous medium, the agreement between the algorithm and measurement at the benchmark points is generally better than 2%. The dose accuracy for a deterministic algorithm is better than a convolution algorithm in heterogeneous medium.« less

A deterministic partial differential equation model for dose calculation in electron radiotherapy.

PubMed

Duclous, R; Dubroca, B; Frank, M

2010-07-07

High-energy ionizing radiation is a prominent modality for the treatment of many cancers. The approaches to electron dose calculation can be categorized into semi-empirical models (e.g. Fermi-Eyges, convolution-superposition) and probabilistic methods (e.g.Monte Carlo). A third approach to dose calculation has only recently attracted attention in the medical physics community. This approach is based on the deterministic kinetic equations of radiative transfer. We derive a macroscopic partial differential equation model for electron transport in tissue. This model involves an angular closure in the phase space. It is exact for the free streaming and the isotropic regime. We solve it numerically by a newly developed HLLC scheme based on Berthon et al (2007 J. Sci. Comput. 31 347-89) that exactly preserves the key properties of the analytical solution on the discrete level. We discuss several test cases taken from the medical physics literature. A test case with an academic Henyey-Greenstein scattering kernel is considered. We compare our model to a benchmark discrete ordinate solution. A simplified model of electron interactions with tissue is employed to compute the dose of an electron beam in a water phantom, and a case of irradiation of the vertebral column. Here our model is compared to the PENELOPE Monte Carlo code. In the academic example, the fluences computed with the new model and a benchmark result differ by less than 1%. The depths at half maximum differ by less than 0.6%. In the two comparisons with Monte Carlo, our model gives qualitatively reasonable dose distributions. Due to the crude interaction model, these so far do not have the accuracy needed in clinical practice. However, the new model has a computational cost that is less than one-tenth of the cost of a Monte Carlo simulation. In addition, simulations can be set up in a similar way as a Monte Carlo simulation. If more detailed effects such as coupled electron-photon transport, bremsstrahlung, Compton scattering and the production of delta electrons are added to our model, the computation time will only slightly increase. Its margin of error, on the other hand, will decrease and should be within a few per cent of the actual dose. Therefore, the new model has the potential to become useful for dose calculations in clinical practice.

A deterministic partial differential equation model for dose calculation in electron radiotherapy

NASA Astrophysics Data System (ADS)

Duclous, R.; Dubroca, B.; Frank, M.

2010-07-01

High-energy ionizing radiation is a prominent modality for the treatment of many cancers. The approaches to electron dose calculation can be categorized into semi-empirical models (e.g. Fermi-Eyges, convolution-superposition) and probabilistic methods (e.g. Monte Carlo). A third approach to dose calculation has only recently attracted attention in the medical physics community. This approach is based on the deterministic kinetic equations of radiative transfer. We derive a macroscopic partial differential equation model for electron transport in tissue. This model involves an angular closure in the phase space. It is exact for the free streaming and the isotropic regime. We solve it numerically by a newly developed HLLC scheme based on Berthon et al (2007 J. Sci. Comput. 31 347-89) that exactly preserves the key properties of the analytical solution on the discrete level. We discuss several test cases taken from the medical physics literature. A test case with an academic Henyey-Greenstein scattering kernel is considered. We compare our model to a benchmark discrete ordinate solution. A simplified model of electron interactions with tissue is employed to compute the dose of an electron beam in a water phantom, and a case of irradiation of the vertebral column. Here our model is compared to the PENELOPE Monte Carlo code. In the academic example, the fluences computed with the new model and a benchmark result differ by less than 1%. The depths at half maximum differ by less than 0.6%. In the two comparisons with Monte Carlo, our model gives qualitatively reasonable dose distributions. Due to the crude interaction model, these so far do not have the accuracy needed in clinical practice. However, the new model has a computational cost that is less than one-tenth of the cost of a Monte Carlo simulation. In addition, simulations can be set up in a similar way as a Monte Carlo simulation. If more detailed effects such as coupled electron-photon transport, bremsstrahlung, Compton scattering and the production of δ electrons are added to our model, the computation time will only slightly increase. Its margin of error, on the other hand, will decrease and should be within a few per cent of the actual dose. Therefore, the new model has the potential to become useful for dose calculations in clinical practice.

Effect of sample volume on metastable zone width and induction time

NASA Astrophysics Data System (ADS)

Kubota, Noriaki

2012-04-01

The metastable zone width (MSZW) and the induction time, measured for a large sample (say>0.1 L) are reproducible and deterministic, while, for a small sample (say<1 mL), these values are irreproducible and stochastic. Such behaviors of MSZW and induction time were theoretically discussed both with stochastic and deterministic models. Equations for the distribution of stochastic MSZW and induction time were derived. The average values of stochastic MSZW and induction time both decreased with an increase in sample volume, while, the deterministic MSZW and induction time remained unchanged. Such different behaviors with variation in sample volume were explained in terms of detection sensitivity of crystallization events. The average values of MSZW and induction time in the stochastic model were compared with the deterministic MSZW and induction time, respectively. Literature data reported for paracetamol aqueous solution were explained theoretically with the presented models.

Modeling heterogeneous responsiveness of intrinsic apoptosis pathway

PubMed Central

2013-01-01

Background Apoptosis is a cell suicide mechanism that enables multicellular organisms to maintain homeostasis and to eliminate individual cells that threaten the organism’s survival. Dependent on the type of stimulus, apoptosis can be propagated by extrinsic pathway or intrinsic pathway. The comprehensive understanding of the molecular mechanism of apoptotic signaling allows for development of mathematical models, aiming to elucidate dynamical and systems properties of apoptotic signaling networks. There have been extensive efforts in modeling deterministic apoptosis network accounting for average behavior of a population of cells. Cellular networks, however, are inherently stochastic and significant cell-to-cell variability in apoptosis response has been observed at single cell level. Results To address the inevitable randomness in the intrinsic apoptosis mechanism, we develop a theoretical and computational modeling framework of intrinsic apoptosis pathway at single-cell level, accounting for both deterministic and stochastic behavior. Our deterministic model, adapted from the well-accepted Fussenegger model, shows that an additional positive feedback between the executioner caspase and the initiator caspase plays a fundamental role in yielding the desired property of bistability. We then examine the impact of intrinsic fluctuations of biochemical reactions, viewed as intrinsic noise, and natural variation of protein concentrations, viewed as extrinsic noise, on behavior of the intrinsic apoptosis network. Histograms of the steady-state output at varying input levels show that the intrinsic noise could elicit a wider region of bistability over that of the deterministic model. However, the system stochasticity due to intrinsic fluctuations, such as the noise of steady-state response and the randomness of response delay, shows that the intrinsic noise in general is insufficient to produce significant cell-to-cell variations at physiologically relevant level of molecular numbers. Furthermore, the extrinsic noise represented by random variations of two key apoptotic proteins, namely Cytochrome C and inhibitor of apoptosis proteins (IAP), is modeled separately or in combination with intrinsic noise. The resultant stochasticity in the timing of intrinsic apoptosis response shows that the fluctuating protein variations can induce cell-to-cell stochastic variability at a quantitative level agreeing with experiments. Finally, simulations illustrate that the mean abundance of fluctuating IAP protein is positively correlated with the degree of cellular stochasticity of the intrinsic apoptosis pathway. Conclusions Our theoretical and computational study shows that the pronounced non-genetic heterogeneity in intrinsic apoptosis responses among individual cells plausibly arises from extrinsic rather than intrinsic origin of fluctuations. In addition, it predicts that the IAP protein could serve as a potential therapeutic target for suppression of the cell-to-cell variation in the intrinsic apoptosis responsiveness. PMID:23875784

Stochastic foundations in nonlinear density-regulation growth

NASA Astrophysics Data System (ADS)

Méndez, Vicenç; Assaf, Michael; Horsthemke, Werner; Campos, Daniel

2017-08-01

In this work we construct individual-based models that give rise to the generalized logistic model at the mean-field deterministic level and that allow us to interpret the parameters of these models in terms of individual interactions. We also study the effect of internal fluctuations on the long-time dynamics for the different models that have been widely used in the literature, such as the theta-logistic and Savageau models. In particular, we determine the conditions for population extinction and calculate the mean time to extinction. If the population does not become extinct, we obtain analytical expressions for the population abundance distribution. Our theoretical results are based on WKB theory and the probability generating function formalism and are verified by numerical simulations.

Stochastic and deterministic model of microbial heat inactivation.

PubMed

Corradini, Maria G; Normand, Mark D; Peleg, Micha

2010-03-01

Microbial inactivation is described by a model based on the changing survival probabilities of individual cells or spores. It is presented in a stochastic and discrete form for small groups, and as a continuous deterministic model for larger populations. If the underlying mortality probability function remains constant throughout the treatment, the model generates first-order ("log-linear") inactivation kinetics. Otherwise, it produces survival patterns that include Weibullian ("power-law") with upward or downward concavity, tailing with a residual survival level, complete elimination, flat "shoulder" with linear or curvilinear continuation, and sigmoid curves. In both forms, the same algorithm or model equation applies to isothermal and dynamic heat treatments alike. Constructing the model does not require assuming a kinetic order or knowledge of the inactivation mechanism. The general features of its underlying mortality probability function can be deduced from the experimental survival curve's shape. Once identified, the function's coefficients, the survival parameters, can be estimated directly from the experimental survival ratios by regression. The model is testable in principle but matching the estimated mortality or inactivation probabilities with those of the actual cells or spores can be a technical challenge. The model is not intended to replace current models to calculate sterility. Its main value, apart from connecting the various inactivation patterns to underlying probabilities at the cellular level, might be in simulating the irregular survival patterns of small groups of cells and spores. In principle, it can also be used for nonthermal methods of microbial inactivation and their combination with heat.

Dynamic Stability of Uncertain Laminated Beams Under Subtangential Loads

NASA Technical Reports Server (NTRS)

Goyal, Vijay K.; Kapania, Rakesh K.; Adelman, Howard (Technical Monitor); Horta, Lucas (Technical Monitor)

2002-01-01

Because of the inherent complexity of fiber-reinforced laminated composites, it can be challenging to manufacture composite structures according to their exact design specifications, resulting in unwanted material and geometric uncertainties. In this research, we focus on the deterministic and probabilistic stability analysis of laminated structures subject to subtangential loading, a combination of conservative and nonconservative tangential loads, using the dynamic criterion. Thus a shear-deformable laminated beam element, including warping effects, is derived to study the deterministic and probabilistic response of laminated beams. This twenty-one degrees of freedom element can be used for solving both static and dynamic problems. In the first-order shear deformable model used here we have employed a more accurate method to obtain the transverse shear correction factor. The dynamic version of the principle of virtual work for laminated composites is expressed in its nondimensional form and the element tangent stiffness and mass matrices are obtained using analytical integration The stability is studied by giving the structure a small disturbance about an equilibrium configuration, and observing if the resulting response remains small. In order to study the dynamic behavior by including uncertainties into the problem, three models were developed: Exact Monte Carlo Simulation, Sensitivity Based Monte Carlo Simulation, and Probabilistic FEA. These methods were integrated into the developed finite element analysis. Also, perturbation and sensitivity analysis have been used to study nonconservative problems, as well as to study the stability analysis, using the dynamic criterion.

A hydro-meteorological ensemble prediction system for real-time flood forecasting purposes in the Milano area

NASA Astrophysics Data System (ADS)

Ravazzani, Giovanni; Amengual, Arnau; Ceppi, Alessandro; Romero, Romualdo; Homar, Victor; Mancini, Marco

2015-04-01

Analysis of forecasting strategies that can provide a tangible basis for flood early warning procedures and mitigation measures over the Western Mediterranean region is one of the fundamental motivations of the European HyMeX programme. Here, we examine a set of hydro-meteorological episodes that affected the Milano urban area for which the complex flood protection system of the city did not completely succeed before the occurred flash-floods. Indeed, flood damages have exponentially increased in the area during the last 60 years, due to industrial and urban developments. Thus, the improvement of the Milano flood control system needs a synergism between structural and non-structural approaches. The flood forecasting system tested in this work comprises the Flash-flood Event-based Spatially distributed rainfall-runoff Transformation, including Water Balance (FEST-WB) and the Weather Research and Forecasting (WRF) models, in order to provide a hydrological ensemble prediction system (HEPS). Deterministic and probabilistic quantitative precipitation forecasts (QPFs) have been provided by WRF model in a set of 48-hours experiments. HEPS has been generated by combining different physical parameterizations (i.e. cloud microphysics, moist convection and boundary-layer schemes) of the WRF model in order to better encompass the atmospheric processes leading to high precipitation amounts. We have been able to test the value of a probabilistic versus a deterministic framework when driving Quantitative Discharge Forecasts (QDFs). Results highlight (i) the benefits of using a high-resolution HEPS in conveying uncertainties for this complex orographic area and (ii) a better simulation of the most of extreme precipitation events, potentially enabling valuable probabilistic QDFs. Hence, the HEPS copes with the significant deficiencies found in the deterministic QPFs. These shortcomings would prevent to correctly forecast the location and timing of high precipitation rates and total amounts at the catchment scale, thus impacting heavily the deterministic QDFs. In contrast, early warnings would have been possible within a HEPS context for the Milano area, proving the suitability of such system for civil protection purposes.

Numerical approaches to combustion modeling. Progress in Astronautics and Aeronautics. Vol. 135

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

Oran, E.S.; Boris, J.P.

1991-01-01

Various papers on numerical approaches to combustion modeling are presented. The topics addressed include; ab initio quantum chemistry for combustion; rate coefficient calculations for combustion modeling; numerical modeling of combustion of complex hydrocarbons; combustion kinetics and sensitivity analysis computations; reduction of chemical reaction models; length scales in laminar and turbulent flames; numerical modeling of laminar diffusion flames; laminar flames in premixed gases; spectral simulations of turbulent reacting flows; vortex simulation of reacting shear flow; combustion modeling using PDF methods. Also considered are: supersonic reacting internal flow fields; studies of detonation initiation, propagation, and quenching; numerical modeling of heterogeneous detonations, deflagration-to-detonationmore » transition to reactive granular materials; toward a microscopic theory of detonations in energetic crystals; overview of spray modeling; liquid drop behavior in dense and dilute clusters; spray combustion in idealized configurations: parallel drop streams; comparisons of deterministic and stochastic computations of drop collisions in dense sprays; ignition and flame spread across solid fuels; numerical study of pulse combustor dynamics; mathematical modeling of enclosure fires; nuclear systems.« less

Groundwater flow and transport modeling

USGS Publications Warehouse

Konikow, Leonard F.; Mercer, J.W.

1988-01-01

Deterministic, distributed-parameter, numerical simulation models for analyzing groundwater flow and transport problems have come to be used almost routinely during the past decade. A review of the theoretical basis and practical use of groundwater flow and solute transport models is used to illustrate the state-of-the-art. Because of errors and uncertainty in defining model parameters, models must be calibrated to obtain a best estimate of the parameters. For flow modeling, data generally are sufficient to allow calibration. For solute-transport modeling, lack of data not only limits calibration, but also causes uncertainty in process description. Where data are available, model reliability should be assessed on the basis of sensitivity tests and measures of goodness-of-fit. Some of these concepts are demonstrated by using two case histories. ?? 1988.

Precipitation-runoff modeling system; user's manual

USGS Publications Warehouse

Leavesley, G.H.; Lichty, R.W.; Troutman, B.M.; Saindon, L.G.

1983-01-01

The concepts, structure, theoretical development, and data requirements of the precipitation-runoff modeling system (PRMS) are described. The precipitation-runoff modeling system is a modular-design, deterministic, distributed-parameter modeling system developed to evaluate the impacts of various combinations of precipitation, climate, and land use on streamflow, sediment yields, and general basin hydrology. Basin response to normal and extreme rainfall and snowmelt can be simulated to evaluate changes in water balance relationships, flow regimes, flood peaks and volumes, soil-water relationships, sediment yields, and groundwater recharge. Parameter-optimization and sensitivity analysis capabilites are provided to fit selected model parameters and evaluate their individual and joint effects on model output. The modular design provides a flexible framework for continued model system enhancement and hydrologic modeling research and development. (Author 's abstract)

NASA's Human Research Program at The Glenn Research Center: Progress and Opportunities

NASA Technical Reports Server (NTRS)

Nall, Marsha; Griffin, DeVon; Myers, Jerry; Perusek, Gail

2008-01-01

The NASA Human Research Program is aimed at correcting problems in critical areas that place NASA human spaceflight missions at risk due to shortfalls in astronaut health, safety and performance. The Glenn Research Center (GRC) and partners from Ohio are significant contributors to this effort. This presentation describes several areas of GRC emphasis, the first being NASA s path to creating exercise hardware requirements and protocols that mitigate the effects of long duration spaceflight. Computational simulations will be a second area that is discussed. This includes deterministic models that simulate the effects of spaceflight on the human body, as well as probabilistic models that bound and quantify the probability that adverse medical incidents will happen during an exploration mission. Medical technology development for exploration will be the final area to be discussed.

Incorporating GIS and remote sensing for census population disaggregation

NASA Astrophysics Data System (ADS)

Wu, Shuo-Sheng'derek'

Census data are the primary source of demographic data for a variety of researches and applications. For confidentiality issues and administrative purposes, census data are usually released to the public by aggregated areal units. In the United States, the smallest census unit is census blocks. Due to data aggregation, users of census data may have problems in visualizing population distribution within census blocks and estimating population counts for areas not coinciding with census block boundaries. The main purpose of this study is to develop methodology for estimating sub-block areal populations and assessing the estimation errors. The City of Austin, Texas was used as a case study area. Based on tax parcel boundaries and parcel attributes derived from ancillary GIS and remote sensing data, detailed urban land use classes were first classified using a per-field approach. After that, statistical models by land use classes were built to infer population density from other predictor variables, including four census demographic statistics (the Hispanic percentage, the married percentage, the unemployment rate, and per capita income) and three physical variables derived from remote sensing images and building footprints vector data (a landscape heterogeneity statistics, a building pattern statistics, and a building volume statistics). In addition to statistical models, deterministic models were proposed to directly infer populations from building volumes and three housing statistics, including the average space per housing unit, the housing unit occupancy rate, and the average household size. After population models were derived or proposed, how well the models predict populations for another set of sample blocks was assessed. The results show that deterministic models were more accurate than statistical models. Further, by simulating the base unit for modeling from aggregating blocks, I assessed how well the deterministic models estimate sub-unit-level populations. I also assessed the aggregation effects and the resealing effects on sub-unit estimates. Lastly, from another set of mixed-land-use sample blocks, a mixed-land-use model was derived and compared with a residential-land-use model. The results of per-field land use classification are satisfactory with a Kappa accuracy statistics of 0.747. Model Assessments by land use show that population estimates for multi-family land use areas have higher errors than those for single-family land use areas, and population estimates for mixed land use areas have higher errors than those for residential land use areas. The assessments of sub-unit estimates using a simulation approach indicate that smaller areas show higher estimation errors, estimation errors do not relate to the base unit size, and resealing improves all levels of sub-unit estimates.

Comparison of HSPF and PRMS model simulated flows using different temporal and spatial scales in the Black Hills, South Dakota

USGS Publications Warehouse

Chalise, D. R.; Haj, Adel E.; Fontaine, T.A.

2018-01-01

The hydrological simulation program Fortran (HSPF) [Hydrological Simulation Program Fortran version 12.2 (Computer software). USEPA, Washington, DC] and the precipitation runoff modeling system (PRMS) [Precipitation Runoff Modeling System version 4.0 (Computer software). USGS, Reston, VA] models are semidistributed, deterministic hydrological tools for simulating the impacts of precipitation, land use, and climate on basin hydrology and streamflow. Both models have been applied independently to many watersheds across the United States. This paper reports the statistical results assessing various temporal (daily, monthly, and annual) and spatial (small versus large watershed) scale biases in HSPF and PRMS simulations using two watersheds in the Black Hills, South Dakota. The Nash-Sutcliffe efficiency (NSE), Pearson correlation coefficient (r">rr), and coefficient of determination (R2">R2R2) statistics for the daily, monthly, and annual flows were used to evaluate the models’ performance. Results from the HSPF models showed that the HSPF consistently simulated the annual flows for both large and small basins better than the monthly and daily flows, and the simulated flows for the small watershed better than flows for the large watershed. In comparison, the PRMS model results show that the PRMS simulated the monthly flows for both the large and small watersheds better than the daily and annual flows, and the range of statistical error in the PRMS models was greater than that in the HSPF models. Moreover, it can be concluded that the statistical error in the HSPF and the PRMSdaily, monthly, and annual flow estimates for watersheds in the Black Hills was influenced by both temporal and spatial scale variability.

Balloon Ascent: 3-D Simulation Tool for the Ascent and Float of High-Altitude Balloons

NASA Technical Reports Server (NTRS)

Farley, Rodger E.

2005-01-01

The BalloonAscent balloon flight simulation code represents a from-scratch development using Visual Basic 5 as the software platform. The simulation code is a transient analysis of balloon flight, predicting the skin and gas temperatures along with the 3-D position and velocity in a time and spatially varying environment. There are manual and automated controls for gas valving and the dropping of ballast. Also, there are many handy calculators, such as appropriate free lift, and steady-state thermal solutions with temperature gradients. The strength of this simulation model over others in the past is that the infrared environment is deterministic rather than guessed at. The ground temperature is specified along with the emissivity, which creates a ground level IR environment that is then partially absorbed as it travels upward through the atmosphere to the altitude of the balloon.

Second Cancers After Fractionated Radiotherapy: Stochastic Population Dynamics Effects

NASA Technical Reports Server (NTRS)

Sachs, Rainer K.; Shuryak, Igor; Brenner, David; Fakir, Hatim; Hahnfeldt, Philip

2007-01-01

When ionizing radiation is used in cancer therapy it can induce second cancers in nearby organs. Mainly due to longer patient survival times, these second cancers have become of increasing concern. Estimating the risk of solid second cancers involves modeling: because of long latency times, available data is usually for older, obsolescent treatment regimens. Moreover, modeling second cancers gives unique insights into human carcinogenesis, since the therapy involves administering well characterized doses of a well studied carcinogen, followed by long-term monitoring. In addition to putative radiation initiation that produces pre-malignant cells, inactivation (i.e. cell killing), and subsequent cell repopulation by proliferation can be important at the doses relevant to second cancer situations. A recent initiation/inactivation/proliferation (IIP) model characterized quantitatively the observed occurrence of second breast and lung cancers, using a deterministic cell population dynamics approach. To analyze ifradiation-initiated pre-malignant clones become extinct before full repopulation can occur, we here give a stochastic version of this I I model. Combining Monte Carlo simulations with standard solutions for time-inhomogeneous birth-death equations, we show that repeated cycles of inactivation and repopulation, as occur during fractionated radiation therapy, can lead to distributions of pre-malignant cells per patient with variance >> mean, even when pre-malignant clones are Poisson-distributed. Thus fewer patients would be affected, but with a higher probability, than a deterministic model, tracking average pre-malignant cell numbers, would predict. Our results are applied to data on breast cancers after radiotherapy for Hodgkin disease. The stochastic IIP analysis, unlike the deterministic one, indicates: a) initiated, pre-malignant cells can have a growth advantage during repopulation, not just during the longer tumor latency period that follows; b) weekend treatment gaps during radiotherapy, apart from decreasing the probability of eradicating the primary cancer, substantially increase the risk of later second cancers.

Stochastic Blockmodeling of the Modules and Core of the Caenorhabditis elegans Connectome

PubMed Central

Pavlovic, Dragana M.; Vértes, Petra E.; Bullmore, Edward T.; Schafer, William R.; Nichols, Thomas E.

2014-01-01

Recently, there has been much interest in the community structure or mesoscale organization of complex networks. This structure is characterised either as a set of sparsely inter-connected modules or as a highly connected core with a sparsely connected periphery. However, it is often difficult to disambiguate these two types of mesoscale structure or, indeed, to summarise the full network in terms of the relationships between its mesoscale constituents. Here, we estimate a community structure with a stochastic blockmodel approach, the Erdős-Rényi Mixture Model, and compare it to the much more widely used deterministic methods, such as the Louvain and Spectral algorithms. We used the Caenorhabditis elegans (C. elegans) nervous system (connectome) as a model system in which biological knowledge about each node or neuron can be used to validate the functional relevance of the communities obtained. The deterministic algorithms derived communities with 4–5 modules, defined by sparse inter-connectivity between all modules. In contrast, the stochastic Erdős-Rényi Mixture Model estimated a community with 9 blocks or groups which comprised a similar set of modules but also included a clearly defined core, made of 2 small groups. We show that the “core-in-modules” decomposition of the worm brain network, estimated by the Erdős-Rényi Mixture Model, is more compatible with prior biological knowledge about the C. elegans nervous system than the purely modular decomposition defined deterministically. We also show that the blockmodel can be used both to generate stochastic realisations (simulations) of the biological connectome, and to compress network into a small number of super-nodes and their connectivity. We expect that the Erdős-Rényi Mixture Model may be useful for investigating the complex community structures in other (nervous) systems. PMID:24988196

Minimization for conditional simulation: Relationship to optimal transport

NASA Astrophysics Data System (ADS)

Oliver, Dean S.

2014-05-01

In this paper, we consider the problem of generating independent samples from a conditional distribution when independent samples from the prior distribution are available. Although there are exact methods for sampling from the posterior (e.g. Markov chain Monte Carlo or acceptance/rejection), these methods tend to be computationally demanding when evaluation of the likelihood function is expensive, as it is for most geoscience applications. As an alternative, in this paper we discuss deterministic mappings of variables distributed according to the prior to variables distributed according to the posterior. Although any deterministic mappings might be equally useful, we will focus our discussion on a class of algorithms that obtain implicit mappings by minimization of a cost function that includes measures of data mismatch and model variable mismatch. Algorithms of this type include quasi-linear estimation, randomized maximum likelihood, perturbed observation ensemble Kalman filter, and ensemble of perturbed analyses (4D-Var). When the prior pdf is Gaussian and the observation operators are linear, we show that these minimization-based simulation methods solve an optimal transport problem with a nonstandard cost function. When the observation operators are nonlinear, however, the mapping of variables from the prior to the posterior obtained from those methods is only approximate. Errors arise from neglect of the Jacobian determinant of the transformation and from the possibility of discontinuous mappings.

Advanced Information Technology in Simulation Based Life Cycle Design

NASA Technical Reports Server (NTRS)

Renaud, John E.

2003-01-01

In this research a Collaborative Optimization (CO) approach for multidisciplinary systems design is used to develop a decision based design framework for non-deterministic optimization. To date CO strategies have been developed for use in application to deterministic systems design problems. In this research the decision based design (DBD) framework proposed by Hazelrigg is modified for use in a collaborative optimization framework. The Hazelrigg framework as originally proposed provides a single level optimization strategy that combines engineering decisions with business decisions in a single level optimization. By transforming this framework for use in collaborative optimization one can decompose the business and engineering decision making processes. In the new multilevel framework of Decision Based Collaborative Optimization (DBCO) the business decisions are made at the system level. These business decisions result in a set of engineering performance targets that disciplinary engineering design teams seek to satisfy as part of subspace optimizations. The Decision Based Collaborative Optimization framework more accurately models the existing relationship between business and engineering in multidisciplinary systems design.

Estimating the probability of an extinction or major outbreak for an environmentally transmitted infectious disease.

PubMed

Lahodny, G E; Gautam, R; Ivanek, R

2015-01-01

Indirect transmission through the environment, pathogen shedding by infectious hosts, replication of free-living pathogens within the environment, and environmental decontamination are suspected to play important roles in the spread and control of environmentally transmitted infectious diseases. To account for these factors, the classic Susceptible-Infectious-Recovered-Susceptible epidemic model is modified to include a compartment representing the amount of free-living pathogen within the environment. The model accounts for host demography, direct and indirect transmission, replication of free-living pathogens in the environment, and removal of free-living pathogens by natural death or environmental decontamination. Based on the assumptions of the deterministic model, a continuous-time Markov chain model is developed. An estimate for the probability of disease extinction or a major outbreak is obtained by approximating the Markov chain with a multitype branching process. Numerical simulations illustrate important differences between the deterministic and stochastic counterparts, relevant for outbreak prevention, that depend on indirect transmission, pathogen shedding by infectious hosts, replication of free-living pathogens, and environmental decontamination. The probability of a major outbreak is computed for salmonellosis in a herd of dairy cattle as well as cholera in a human population. An explicit expression for the probability of disease extinction or a major outbreak in terms of the model parameters is obtained for systems with no direct transmission or replication of free-living pathogens.

Aspen succession in the Intermountain West: A deterministic model

Treesearch

Dale L. Bartos; Frederick R. Ward; George S. Innis

1983-01-01

A deterministic model of succession in aspen forests was developed using existing data and intuition. The degree of uncertainty, which was determined by allowing the parameter values to vary at random within limits, was larger than desired. This report presents results of an analysis of model sensitivity to changes in parameter values. These results have indicated...

Stochastic four-way coupling of gas-solid flows for Large Eddy Simulations

NASA Astrophysics Data System (ADS)

Curran, Thomas; Denner, Fabian; van Wachem, Berend

2017-11-01

The interaction of solid particles with turbulence has for long been a topic of interest for predicting the behavior of industrially relevant flows. For the turbulent fluid phase, Large Eddy Simulation (LES) methods are widely used for their low computational cost, leaving only the sub-grid scales (SGS) of turbulence to be modelled. Although LES has seen great success in predicting the behavior of turbulent single-phase flows, the development of LES for turbulent gas-solid flows is still in its infancy. This contribution aims at constructing a model to describe the four-way coupling of particles in an LES framework, by considering the role particles play in the transport of turbulent kinetic energy across the scales. Firstly, a stochastic model reconstructing the sub-grid velocities for the particle tracking is presented. Secondly, to solve particle-particle interaction, most models involve a deterministic treatment of the collisions. We finally introduce a stochastic model for estimating the collision probability. All results are validated against fully resolved DNS-DPS simulations. The final goal of this contribution is to propose a global stochastic method adapted to two-phase LES simulation where the number of particles considered can be significantly increased. Financial support from PetroBras is gratefully acknowledged.

Numerical simulation of dendrite growth in nickel-based superalloy and validated by in-situ observation using high temperature confocal laser scanning microscopy

NASA Astrophysics Data System (ADS)

Yan, Xuewei; Xu, Qingyan; Liu, Baicheng

2017-12-01

Dendritic structures are the predominant microstructural constituents of nickel-based superalloys, an understanding of the dendrite growth is required in order to obtain the desirable microstructure and improve the performance of castings. For this reason, numerical simulation method and an in-situ observation technology by employing high temperature confocal laser scanning microscopy (HT-CLSM) were used to investigate dendrite growth during solidification process. A combined cellular automaton-finite difference (CA-FD) model allowing for the prediction of dendrite growth of binary alloys was developed. The algorithm of cells capture was modified, and a deterministic cellular automaton (DCA) model was proposed to describe neighborhood tracking. The dendrite and detail morphology, especially hundreds of dendrites distribution at a large scale and three-dimensional (3-D) polycrystalline growth, were successfully simulated based on this model. The dendritic morphologies of samples before and after HT-CLSM were both observed by optical microscope (OM) and scanning electron microscope (SEM). The experimental observations presented a reasonable agreement with the simulation results. It was also found that primary or secondary dendrite arm spacing, and segregation pattern were significantly influenced by dendrite growth. Furthermore, the directional solidification (DS) dendritic evolution behavior and detail morphology were also simulated based on the proposed model, and the simulation results also agree well with experimental results.

Probabilistic volcanic hazard assessments of Pyroclastic Density Currents: ongoing practices and future perspectives

NASA Astrophysics Data System (ADS)

Tierz, Pablo; Sandri, Laura; Ramona Stefanescu, Elena; Patra, Abani; Marzocchi, Warner; Costa, Antonio; Sulpizio, Roberto

2014-05-01

Explosive volcanoes and, especially, Pyroclastic Density Currents (PDCs) pose an enormous threat to populations living in the surroundings of volcanic areas. Difficulties in the modeling of PDCs are related to (i) very complex and stochastic physical processes, intrinsic to their occurrence, and (ii) to a lack of knowledge about how these processes actually form and evolve. This means that there are deep uncertainties (namely, of aleatory nature due to point (i) above, and of epistemic nature due to point (ii) above) associated to the study and forecast of PDCs. Consequently, the assessment of their hazard is better described in terms of probabilistic approaches rather than by deterministic ones. What is actually done to assess probabilistic hazard from PDCs is to couple deterministic simulators with statistical techniques that can, eventually, supply probabilities and inform about the uncertainties involved. In this work, some examples of both PDC numerical simulators (Energy Cone and TITAN2D) and uncertainty quantification techniques (Monte Carlo sampling -MC-, Polynomial Chaos Quadrature -PCQ- and Bayesian Linear Emulation -BLE-) are presented, and their advantages, limitations and future potential are underlined. The key point in choosing a specific method leans on the balance between its related computational cost, the physical reliability of the simulator and the pursued target of the hazard analysis (type of PDCs considered, time-scale selected for the analysis, particular guidelines received from decision-making agencies, etc.). Although current numerical and statistical techniques have brought important advances in probabilistic volcanic hazard assessment from PDCs, some of them may be further applicable to more sophisticated simulators. In addition, forthcoming improvements could be focused on three main multidisciplinary directions: 1) Validate the simulators frequently used (through comparison with PDC deposits and other simulators), 2) Decrease simulator runtimes (whether by increasing the knowledge about the physical processes or by doing more efficient programming, parallelization, ...) and 3) Improve uncertainty quantification techniques.

Monte Carlo Simulations of the Formation Flying Dynamics for the Magnetospheric Multiscale (MMS) Mission

NASA Technical Reports Server (NTRS)

Schiff, Conrad; Dove, Edwin

2011-01-01

The MMS mission is an ambitious space physics mission that will fly 4 spacecraft in a tetrahedron formation in a series of highly elliptical orbits in order to study magnetic reconnection in the Earth's magnetosphere. The mission design is comprised of a combination of deterministic orbit adjust and random maintenance maneuvers distributed over the 2.5 year mission life. Formal verification of the requirements is achieved by analysis through the use of the End-to-End (ETE) code, which is a modular simulation of the maneuver operations over the entire mission duration. Error models for navigation accuracy (knowledge) and maneuver execution (control) are incorporated to realistically simulate the possible maneuver scenarios that might be realized These error models, coupled with the complex formation flying physics, lead to non-trivial effects that must be taken into account by the ETE automation. Using the ETE code, the MMS Flight Dynamics team was able to demonstrate that the current mission design satisfies the mission requirements.

Development of a framework for quantifying the environmental impacts of urban development and construction practices.

PubMed

Li, Ke; Zhang, Peng; Crittenden, John C; Guhathakurta, Subhrajit; Chen, Yongsheng; Fernando, Harindra; Sawhney, Anil; McCartney, Peter; Grimm, Nancy; Kahhat, Ramzy; Joshi, Himanshu; Konjevod, Goran; Choi, Yu-Jin; Fonseca, Ernesto; Allenby, Braden; Gerrity, Daniel; Torrens, Paul M

2007-07-15

To encourage sustainable development, engineers and scientists need to understand the interactions among social decision-making, development and redevelopment, land, energy and material use, and their environmental impacts. In this study, a framework that connects these interactions was proposed to guide more sustainable urban planning and construction practices. Focusing on the rapidly urbanizing setting of Phoenix, Arizona, complexity models and deterministic models were assembled as a metamodel, which is called Sustainable Futures 2100 and were used to predict land use and development, to quantify construction material demands, to analyze the life cycle environmental impacts, and to simulate future ground-level ozone formation.

Deterministic propagation model for RFID using site-specific and FDTD

NASA Astrophysics Data System (ADS)

Cunha de Azambuja, Marcelo; Passuelo Hessel, Fabiano; Luís Berz, Everton; Bauermann Porfírio, Leandro; Ruhnke Valério, Paula; De Pieri Baladei, Suely; Jung, Carlos Fernando

2015-06-01

The conduction of experiments to evaluate a tag orientation and its readability in a laboratory offers great potential for reducing time and costs for users. This article presents a novel methodology for developing simulation models for RFID (radio-frequency identification) environments. The main challenges in adopting this model are: (1) to find out how the properties of each one of the materials, on which the tag is applied, influence the read range and to determine the necessary power for tag reading and (2) to find out the power of the backscattered signal received by the tag when energised by the RF wave transmitted by the reader. The validation tests, performed in four different kinds of environments, with tags applied to six different kinds of materials, six different distances and with a reader configured with three different powers, showed achievements on the average of 95.3% accuracy in the best scenario and 87.0% in the worst scenario. The methodology can be easily duplicated to generate simulation models to other different RFID environments.

An open Markov chain scheme model for a credit consumption portfolio fed by ARIMA and SARMA processes

NASA Astrophysics Data System (ADS)

Esquível, Manuel L.; Fernandes, José Moniz; Guerreiro, Gracinda R.

2016-06-01

We introduce a schematic formalism for the time evolution of a random population entering some set of classes and such that each member of the population evolves among these classes according to a scheme based on a Markov chain model. We consider that the flow of incoming members is modeled by a time series and we detail the time series structure of the elements in each of the classes. We present a practical application to data from a credit portfolio of a Cape Verdian bank; after modeling the entering population in two different ways - namely as an ARIMA process and as a deterministic sigmoid type trend plus a SARMA process for the residues - we simulate the behavior of the population and compare the results. We get that the second method is more accurate in describing the behavior of the populations when compared to the observed values in a direct simulation of the Markov chain.

Using Discrete Event Simulation to Model Integrated Commodities Consumption for a Launch Campaign of the Space Launch System

NASA Technical Reports Server (NTRS)

Leonard, Daniel; Parsons, Jeremy W.; Cates, Grant

2014-01-01

In May 2013, NASA's GSDO Program requested a study to develop a discrete event simulation (DES) model that analyzes the launch campaign process of the Space Launch System (SLS) from an integrated commodities perspective. The scope of the study includes launch countdown and scrub turnaround and focuses on four core launch commodities: hydrogen, oxygen, nitrogen, and helium. Previously, the commodities were only analyzed individually and deterministically for their launch support capability, but this study was the first to integrate them to examine the impact of their interactions on a launch campaign as well as the effects of process variability on commodity availability. The study produced a validated DES model with Rockwell Arena that showed that Kennedy Space Center's ground systems were capable of supporting a 48-hour scrub turnaround for the SLS. The model will be maintained and updated to provide commodity consumption analysis of future ground system and SLS configurations.

Chemo-mechanical modeling of tumor growth in elastic epithelial tissue

NASA Astrophysics Data System (ADS)

Bratsun, Dmitry A.; Zakharov, Andrey P.; Pismen, Len

2016-08-01

We propose a multiscale chemo-mechanical model of the cancer tumor development in the epithelial tissue. The epithelium is represented by an elastic 2D array of polygonal cells with its own gene regulation dynamics. The model allows the simulation of the evolution of multiple cells interacting via the chemical signaling or mechanically induced strain. The algorithm includes the division and intercalation of cells as well as the transformation of normal cells into a cancerous state triggered by a local failure of the spatial synchronization of the cellular rhythms driven by transcription/translation processes. Both deterministic and stochastic descriptions of the system are given for chemical signaling. The transformation of cells means the modification of their respective parameters responsible for chemo-mechanical interactions. The simulations reproduce a distinct behavior of invasive and localized carcinoma. Generally, the model is designed in such a way that it can be readily modified to take account of any newly understood gene regulation processes and feedback mechanisms affecting chemo-mechanical properties of cells.

An Efficient Deterministic Approach to Model-based Prediction Uncertainty Estimation

NASA Technical Reports Server (NTRS)

Daigle, Matthew J.; Saxena, Abhinav; Goebel, Kai

2012-01-01

Prognostics deals with the prediction of the end of life (EOL) of a system. EOL is a random variable, due to the presence of process noise and uncertainty in the future inputs to the system. Prognostics algorithm must account for this inherent uncertainty. In addition, these algorithms never know exactly the state of the system at the desired time of prediction, or the exact model describing the future evolution of the system, accumulating additional uncertainty into the predicted EOL. Prediction algorithms that do not account for these sources of uncertainty are misrepresenting the EOL and can lead to poor decisions based on their results. In this paper, we explore the impact of uncertainty in the prediction problem. We develop a general model-based prediction algorithm that incorporates these sources of uncertainty, and propose a novel approach to efficiently handle uncertainty in the future input trajectories of a system by using the unscented transformation. Using this approach, we are not only able to reduce the computational load but also estimate the bounds of uncertainty in a deterministic manner, which can be useful to consider during decision-making. Using a lithium-ion battery as a case study, we perform several simulation-based experiments to explore these issues, and validate the overall approach using experimental data from a battery testbed.

Distributed modelling of hydrologic regime at three subcatchments of Kopaninský tok catchment

NASA Astrophysics Data System (ADS)

Žlábek, Pavel; Tachecí, Pavel; Kaplická, Markéta; Bystřický, Václav

2010-05-01

Kopaninský tok catchment is situated in crystalline area of Bohemo-Moravian highland hilly region, with cambisol cover and prevailing agricultural land use. It is a subject of long term (since 1980's) observation. Time series (discharge, precipitation, climatic parameters...) are nowadays available in 10 min. time step, water quality average daily composit samples plus samples during events are available. Soil survey resulting in reference soil hydraulic properties for horizons and vegetation cover survey incl. LAI measurement has been done. All parameters were analysed and used for establishing of distributed mathematical models of P6, P52 and P53 subcatchments, using MIKE SHE 2009 WM deterministic hydrologic modelling system. The aim is to simulate long-term hydrologic regime as well as rainfall-runoff events, serving the base for modelling of nitrate regime and agricultural management influence in the next step. Mentioned subcatchments differs in ratio of artificial drainage area, soil types, land use and slope angle. The models are set-up in a regular computational grid of 2 m size. Basic time step was set to 2 hrs, total simulated period covers 3 years. Runoff response and moisture regime is compared using spatially distributed simulation results. Sensitivity analysis revealed most important parameters influencing model response. Importance of spatial distribution of initial conditions was underlined. Further on, different runoff components in terms of their origin, flow paths and travel time were separated using a combination of two runoff separation techniques (a digital filter and a simple conceptual model GROUND) in 12 subcatchments of Kopaninský tok catchment. These two methods were chosen based on a number of methods testing. Ordinations diagrams performed with Canoco software were used to evaluate influence of different catchment parameters on different runoff components. A canonical ordination method analyses (RDA) was used to explain one data set (runoff components - either volumes of each runoff component or occurence of baseflow) with another data set (catchment parameters - proportion of arable land, proportion of forest, proportion of vulnerable zones with high infiltration capacity, average slope, topographic index and runoff coefficient). The influence was analysed both for long-term runoff balance and selected rainfall-runoff events. Keywords: small catchment, water balance modelling, rainfall-runoff modelling, distributed deterministic model, runoff separation, sensitivity analysis

Evaluation of Fast-Time Wake Vortex Models using Wake Encounter Flight Test Data

NASA Technical Reports Server (NTRS)

Ahmad, Nashat N.; VanValkenburg, Randal L.; Bowles, Roland L.; Limon Duparcmeur, Fanny M.; Gloudesman, Thijs; van Lochem, Sander; Ras, Eelco

2014-01-01

This paper describes a methodology for the integration and evaluation of fast-time wake models with flight data. The National Aeronautics and Space Administration conducted detailed flight tests in 1995 and 1997 under the Aircraft Vortex Spacing System Program to characterize wake vortex decay and wake encounter dynamics. In this study, data collected during Flight 705 were used to evaluate NASA's fast-time wake transport and decay models. Deterministic and Monte-Carlo simulations were conducted to define wake hazard bounds behind the wake generator. The methodology described in this paper can be used for further validation of fast-time wake models using en-route flight data, and for determining wake turbulence constraints in the design of air traffic management concepts.

Coupled multi-group neutron photon transport for the simulation of high-resolution gamma-ray spectroscopy applications

NASA Astrophysics Data System (ADS)

Burns, Kimberly Ann

The accurate and efficient simulation of coupled neutron-photon problems is necessary for several important radiation detection applications. Examples include the detection of nuclear threats concealed in cargo containers and prompt gamma neutron activation analysis for nondestructive determination of elemental composition of unknown samples. In these applications, high-resolution gamma-ray spectrometers are used to preserve as much information as possible about the emitted photon flux, which consists of both continuum and characteristic gamma rays with discrete energies. Monte Carlo transport is the most commonly used modeling tool for this type of problem, but computational times for many problems can be prohibitive. This work explores the use of coupled Monte Carlo-deterministic methods for the simulation of neutron-induced photons for high-resolution gamma-ray spectroscopy applications. RAdiation Detection Scenario Analysis Toolbox (RADSAT), a code which couples deterministic and Monte Carlo transport to perform radiation detection scenario analysis in three dimensions [1], was used as the building block for the methods derived in this work. RADSAT was capable of performing coupled deterministic-Monte Carlo simulations for gamma-only and neutron-only problems. The purpose of this work was to develop the methodology necessary to perform coupled neutron-photon calculations and add this capability to RADSAT. Performing coupled neutron-photon calculations requires four main steps: the deterministic neutron transport calculation, the neutron-induced photon spectrum calculation, the deterministic photon transport calculation, and the Monte Carlo detector response calculation. The necessary requirements for each of these steps were determined. A major challenge in utilizing multigroup deterministic transport methods for neutron-photon problems was maintaining the discrete neutron-induced photon signatures throughout the simulation. Existing coupled neutron-photon cross-section libraries and the methods used to produce neutron-induced photons were unsuitable for high-resolution gamma-ray spectroscopy applications. Central to this work was the development of a method for generating multigroup neutron-photon cross-sections in a way that separates the discrete and continuum photon emissions so the neutron-induced photon signatures were preserved. The RADSAT-NG cross-section library was developed as a specialized multigroup neutron-photon cross-section set for the simulation of high-resolution gamma-ray spectroscopy applications. The methodology and cross sections were tested using code-to-code comparison with MCNP5 [2] and NJOY [3]. A simple benchmark geometry was used for all cases compared with MCNP. The geometry consists of a cubical sample with a 252Cf neutron source on one side and a HPGe gamma-ray spectrometer on the opposing side. Different materials were examined in the cubical sample: polyethylene (C2H4), P, N, O, and Fe. The cross sections for each of the materials were compared to cross sections collapsed using NJOY. Comparisons of the volume-averaged neutron flux within the sample, volume-averaged photon flux within the detector, and high-purity gamma-ray spectrometer response (only for polyethylene) were completed using RADSAT and MCNP. The code-to-code comparisons show promising results for the coupled Monte Carlo-deterministic method. The RADSAT-NG cross-section production method showed good agreement with NJOY for all materials considered although some additional work is needed in the resonance region and in the first and last energy bin. Some cross section discrepancies existed in the lowest and highest energy bin, but the overall shape and magnitude of the two methods agreed. For the volume-averaged photon flux within the detector, typically the five most intense lines agree to within approximately 5% of the MCNP calculated flux for all of materials considered. The agreement in the code-to-code comparisons cases demonstrates a proof-of-concept of the method for use in RADSAT for coupled neutron-photon problems in high-resolution gamma-ray spectroscopy applications. One of the primary motivators for using the coupled method over pure Monte Carlo method is the potential for significantly lower computational times. For the code-to-code comparison cases, the run times for RADSAT were approximately 25--500 times shorter than for MCNP, as shown in Table 1. This was assuming a 40 mCi 252Cf neutron source and 600 seconds of "real-world" measurement time. The only variance reduction technique implemented in the MCNP calculation was forward biasing of the source toward the sample target. Improved MCNP runtimes could be achieved with the addition of more advanced variance reduction techniques.

Price-Dynamics of Shares and Bohmian Mechanics: Deterministic or Stochastic Model?

NASA Astrophysics Data System (ADS)

Choustova, Olga

2007-02-01

We apply the mathematical formalism of Bohmian mechanics to describe dynamics of shares. The main distinguishing feature of the financial Bohmian model is the possibility to take into account market psychology by describing expectations of traders by the pilot wave. We also discuss some objections (coming from conventional financial mathematics of stochastic processes) against the deterministic Bohmian model. In particular, the objection that such a model contradicts to the efficient market hypothesis which is the cornerstone of the modern market ideology. Another objection is of pure mathematical nature: it is related to the quadratic variation of price trajectories. One possibility to reply to this critique is to consider the stochastic Bohm-Vigier model, instead of the deterministic one. We do this in the present note.

Effects of Lead Exposure, Environmental Conditions, and Metapopulation Processes on Population Dynamics of Spectacled Eiders.

USGS Publications Warehouse

Flint, Paul L.; Grand, James B.; Petersen, Margaret; Rockwell, Robert F.

2016-01-01

Spectacled eider Somateria fischeri numbers have declined and they are considered threatened in accordance with the US Endangered Species Act throughout their range. We synthesized the available information for spectacled eiders to construct deterministic, stochastic, and metapopulation models for this species that incorporated current estimates of vital rates such as nest success, adult survival, and the impact of lead poisoning on survival. Elasticities of our deterministic models suggested that the populations would respond most dramatically to changes in adult female survival and that the reductions in adult female survival related to lead poisoning were locally important. We also examined the sensitivity of the population to changes in lead exposure rates. With the knowledge that some vital rates vary with environmental conditions, we cast stochastic models that mimicked observed variation in productivity. We also used the stochastic model to examine the probability that a specific population will persist for periods of up to 50 y. Elasticity analysis of these models was consistent with that for the deterministic models, with perturbations to adult female survival having the greatest effect on population projections. When used in single population models, demographic data for some localities predicted rapid declines that were inconsistent with our observations in the field. Thus, we constructed a metapopulation model and examined the predictions for local subpopulations and the metapopulation over a wide range of dispersal rates. Using the metapopulation model, we were able to simulate the observed stability of local subpopulations as well as that of the metapopulation. Finally, we developed a global metapopulation model that simulates periodic winter habitat limitation, similar to that which might be experienced in years of heavy sea ice in the core wintering area of spectacled eiders in the central Bering Sea. Our metapopulation analyses suggested that no subpopulation is independent and that future management actions may be improved through a metapopulation framework. For example, management actions could include displacement of breeding females from"sink" areas that reduce the growth potential of the population as a whole. However, this action is contingent upon dispersal among local populations, for which there is limited information. Thus, we recommend that researchers examine dispersal behavior among areas on the Yukon-Kuskokwim Delta in western Alaska. The metapopulation framework could also be applied at the rangewide scale to address the density-dependent limitation of available polynya habitat during winter that may limit the recovery of small subpopulations, such as that on the Yukon-Kuskokwim Delta. Reductions in other subpopulations may be necessary to ensure an increase in the Yukon-Kuskokwim Delta population. Thus, we recommend that managers consider the interpopulation dynamics of spectacled eiders at different spatial scales in future management actions.

Proton Lateral Broadening Distribution Comparisons Between GRNTRN, MCNPX, and Laboratory Beam Measurements

NASA Technical Reports Server (NTRS)

Mertens, Christopher J.; Moyers, Michael F.; Walker, Steven A.; Tweed, John

2010-01-01

Recent developments in NASA s deterministic High charge (Z) and Energy TRaNsport (HZETRN) code have included lateral broadening of primary ion beams due to small-angle multiple Coulomb scattering, and coupling of the ion-nuclear scattering interactions with energy loss and straggling. This new version of HZETRN is based on Green function methods, called GRNTRN, and is suitable for modeling transport with both space environment and laboratory boundary conditions. Multiple scattering processes are a necessary extension to GRNTRN in order to accurately model ion beam experiments, to simulate the physical and biological-effective radiation dose, and to develop new methods and strategies for light ion radiation therapy. In this paper we compare GRNTRN simulations of proton lateral broadening distributions with beam measurements taken at Loma Linda University Proton Therapy Facility. The simulated and measured lateral broadening distributions are compared for a 250 MeV proton beam on aluminum, polyethylene, polystyrene, bone substitute, iron, and lead target materials. The GRNTRN results are also compared to simulations from the Monte Carlo MCNPX code for the same projectile-target combinations described above.

A software tool for modeling and simulation of numerical P systems.

PubMed

Buiu, Catalin; Arsene, Octavian; Cipu, Corina; Patrascu, Monica

2011-03-01

A P system represents a distributed and parallel bio-inspired computing model in which basic data structures are multi-sets or strings. Numerical P systems have been recently introduced and they use numerical variables and local programs (or evolution rules), usually in a deterministic way. They may find interesting applications in areas such as computational biology, process control or robotics. The first simulator of numerical P systems (SNUPS) has been designed, implemented and made available to the scientific community by the authors of this paper. SNUPS allows a wide range of applications, from modeling and simulation of ordinary differential equations, to the use of membrane systems as computational blocks of cognitive architectures, and as controllers for autonomous mobile robots. This paper describes the functioning of a numerical P system and presents an overview of SNUPS capabilities together with an illustrative example. SNUPS is freely available to researchers as a standalone application and may be downloaded from a dedicated website, http://snups.ics.pub.ro/, which includes an user manual and sample membrane structures. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

SimAlba: A Spatial Microsimulation Approach to the Analysis of Health Inequalities

PubMed Central

Campbell, Malcolm; Ballas, Dimitris

2016-01-01

This paper presents applied geographical research based on a spatial microsimulation model, SimAlba, aimed at estimating geographically sensitive health variables in Scotland. SimAlba has been developed in order to answer a variety of “what-if” policy questions pertaining to health policy in Scotland. Using the SimAlba model, it is possible to simulate the distributions of previously unknown variables at the small area level such as smoking, alcohol consumption, mental well-being, and obesity. The SimAlba microdataset has been created by combining Scottish Health Survey and Census data using a deterministic reweighting spatial microsimulation algorithm developed for this purpose. The paper presents SimAlba outputs for Scotland’s largest city, Glasgow, and examines the spatial distribution of the simulated variables for small geographical areas in Glasgow as well as the effects on individuals of different policy scenario outcomes. In simulating previously unknown spatial data, a wealth of new perspectives can be examined and explored. This paper explores a small set of those potential avenues of research and shows the power of spatial microsimulation modeling in an urban context. PMID:27818989

Improved surface-roughness scattering and mobility models for multi-gate FETs with arbitrary cross-section and biasing scheme

NASA Astrophysics Data System (ADS)

Lizzit, D.; Badami, O.; Specogna, R.; Esseni, D.

2017-06-01

We present a new model for surface roughness (SR) scattering in n-type multi-gate FETs (MuGFETs) and gate-all-around nanowire FETs with fairly arbitrary cross-sections, its implementation in a complete device simulator, and the validation against experimental electron mobility data. The model describes the SR scattering matrix elements as non-linear transformations of interface fluctuations, which strongly influences the root mean square value of the roughness required to reproduce experimental mobility data. Mobility simulations are performed via the deterministic solution of the Boltzmann transport equation for a 1D-electron gas and including the most relevant scattering mechanisms for electronic transport, such as acoustic, polar, and non-polar optical phonon scattering, Coulomb scattering, and SR scattering. Simulation results show the importance of accounting for arbitrary cross-sections and biasing conditions when compared to experimental data. We also discuss how mobility is affected by the shape of the cross-section as well as by its area in gate-all-around and tri-gate MuGFETs.

Reliability-based trajectory optimization using nonintrusive polynomial chaos for Mars entry mission

NASA Astrophysics Data System (ADS)

Huang, Yuechen; Li, Haiyang

2018-06-01

This paper presents the reliability-based sequential optimization (RBSO) method to settle the trajectory optimization problem with parametric uncertainties in entry dynamics for Mars entry mission. First, the deterministic entry trajectory optimization model is reviewed, and then the reliability-based optimization model is formulated. In addition, the modified sequential optimization method, in which the nonintrusive polynomial chaos expansion (PCE) method and the most probable point (MPP) searching method are employed, is proposed to solve the reliability-based optimization problem efficiently. The nonintrusive PCE method contributes to the transformation between the stochastic optimization (SO) and the deterministic optimization (DO) and to the approximation of trajectory solution efficiently. The MPP method, which is used for assessing the reliability of constraints satisfaction only up to the necessary level, is employed to further improve the computational efficiency. The cycle including SO, reliability assessment and constraints update is repeated in the RBSO until the reliability requirements of constraints satisfaction are satisfied. Finally, the RBSO is compared with the traditional DO and the traditional sequential optimization based on Monte Carlo (MC) simulation in a specific Mars entry mission to demonstrate the effectiveness and the efficiency of the proposed method.

Study of selected phenotype switching strategies in time varying environment

NASA Astrophysics Data System (ADS)

Horvath, Denis; Brutovsky, Branislav

2016-03-01

Population heterogeneity plays an important role across many research, as well as the real-world, problems. The population heterogeneity relates to the ability of a population to cope with an environment change (or uncertainty) preventing its extinction. However, this ability is not always desirable as can be exemplified by an intratumor heterogeneity which positively correlates with the development of resistance to therapy. Causation of population heterogeneity is therefore in biology and medicine an intensively studied topic. In this paper the evolution of a specific strategy of population diversification, the phenotype switching, is studied at a conceptual level. The presented simulation model studies evolution of a large population of asexual organisms in a time-varying environment represented by a stochastic Markov process. Each organism disposes with a stochastic or nonlinear deterministic switching strategy realized by discrete-time models with evolvable parameters. We demonstrate that under rapidly varying exogenous conditions organisms operate in the vicinity of the bet-hedging strategy, while the deterministic patterns become relevant as the environmental variations are less frequent. Statistical characterization of the steady state regimes of the populations is done using the Hellinger and Kullback-Leibler functional distances and the Hamming distance.

Nonparametric estimates of drift and diffusion profiles via Fokker-Planck algebra.

PubMed

Lund, Steven P; Hubbard, Joseph B; Halter, Michael

2014-11-06

Diffusion processes superimposed upon deterministic motion play a key role in understanding and controlling the transport of matter, energy, momentum, and even information in physics, chemistry, material science, biology, and communications technology. Given functions defining these random and deterministic components, the Fokker-Planck (FP) equation is often used to model these diffusive systems. Many methods exist for estimating the drift and diffusion profiles from one or more identifiable diffusive trajectories; however, when many identical entities diffuse simultaneously, it may not be possible to identify individual trajectories. Here we present a method capable of simultaneously providing nonparametric estimates for both drift and diffusion profiles from evolving density profiles, requiring only the validity of Langevin/FP dynamics. This algebraic FP manipulation provides a flexible and robust framework for estimating stationary drift and diffusion coefficient profiles, is not based on fluctuation theory or solved diffusion equations, and may facilitate predictions for many experimental systems. We illustrate this approach on experimental data obtained from a model lipid bilayer system exhibiting free diffusion and electric field induced drift. The wide range over which this approach provides accurate estimates for drift and diffusion profiles is demonstrated through simulation.

Flood Simulation based on ArcGIS in the Ungauged Area from Fugu to Wubao of the middle Yellow River

NASA Astrophysics Data System (ADS)

Jin, Shuangyan; Yan, Yiqi; Jiang, Xinhui

2017-12-01

The Qingliangsigou and Jialuhe in the middle Yellow River are selected as the typical tributaries, history flood data in 1980-2013 and Horton infiltration capacity curve are used to calculate the stable infiltration rate and establish the model of runoff yield and concentration, the parameters are calibrated and applied in the ungauged area from Fugu to Wubao. The study area is divided into 20 units based on ArcGIS, Muskingum method parameters in each unit are calibrated, and typical floods of ungauged area from Fugu to Wubao are simulated. The results show that the simulation effects are good: the average error of peak time is about -0.4h, the error of peak discharge is in the forecasting allowable range, and the deterministic coefficient is 0.66.

Clocked Magnetostriction-Assisted Spintronic Device Design and Simulation

NASA Astrophysics Data System (ADS)

Mousavi Iraei, Rouhollah; Kani, Nickvash; Dutta, Sourav; Nikonov, Dmitri E.; Manipatruni, Sasikanth; Young, Ian A.; Heron, John T.; Naeemi, Azad

2018-05-01

We propose a heterostructure device comprised of magnets and piezoelectrics that significantly improves the delay and the energy dissipation of an all-spin logic (ASL) device. This paper studies and models the physics of the device, illustrates its operation, and benchmarks its performance using SPICE simulations. We show that the proposed device maintains low voltage operation, non-reciprocity, non-volatility, cascadability, and thermal reliability of the original ASL device. Moreover, by utilizing the deterministic switching of a magnet from the saddle point of the energy profile, the device is more efficient in terms of energy and delay and is robust to thermal fluctuations. The results of simulations show that compared to ASL devices, the proposed device achieves 21x shorter delay and 27x lower energy dissipation per bit for a 32-bit arithmetic-logic unit (ALU).

A building block for hardware belief networks.

PubMed

Behin-Aein, Behtash; Diep, Vinh; Datta, Supriyo

2016-07-21

Belief networks represent a powerful approach to problems involving probabilistic inference, but much of the work in this area is software based utilizing standard deterministic hardware based on the transistor which provides the gain and directionality needed to interconnect billions of them into useful networks. This paper proposes a transistor like device that could provide an analogous building block for probabilistic networks. We present two proof-of-concept examples of belief networks, one reciprocal and one non-reciprocal, implemented using the proposed device which is simulated using experimentally benchmarked models.

Stochastic Convection Parameterizations: The Eddy-Diffusivity/Mass-Flux (EDMF) Approach (Invited)

NASA Astrophysics Data System (ADS)

Teixeira, J.

2013-12-01

In this presentation it is argued that moist convection parameterizations need to be stochastic in order to be realistic - even in deterministic atmospheric prediction systems. A new unified convection and boundary layer parameterization (EDMF) that optimally combines the Eddy-Diffusivity (ED) approach for smaller-scale boundary layer mixing with the Mass-Flux (MF) approach for larger-scale plumes is discussed. It is argued that for realistic simulations stochastic methods have to be employed in this new unified EDMF. Positive results from the implementation of the EDMF approach in atmospheric models are presented.

LETTER TO THE EDITOR: Backbones of traffic jams

NASA Astrophysics Data System (ADS)

Shikhar Gupta, Himadri; Ramaswamy, Ramakrishna

1996-11-01

We study the jam phase of the deterministic traffic model in two dimensions. Within the jam phase, there is a phase transition, from a self-organized jam (formed by initial synchronization followed by jamming), to a random-jam structure. The backbone of the jam is defined and used to analyse self-organization in the jam. The fractal dimension and interparticle correlations on the backbone indicate a continous phase transition at density 0305-4470/29/21/003/img1 with critical exponent 0305-4470/29/21/003/img2, which are characterized through simulations.

Probabilistically modeling lava flows with MOLASSES

NASA Astrophysics Data System (ADS)

Richardson, J. A.; Connor, L.; Connor, C.; Gallant, E.

2017-12-01

Modeling lava flows through Cellular Automata methods enables a computationally inexpensive means to quickly forecast lava flow paths and ultimate areal extents. We have developed a lava flow simulator, MOLASSES, that forecasts lava flow inundation over an elevation model from a point source eruption. This modular code can be implemented in a deterministic fashion with given user inputs that will produce a single lava flow simulation. MOLASSES can also be implemented in a probabilistic fashion where given user inputs define parameter distributions that are randomly sampled to create many lava flow simulations. This probabilistic approach enables uncertainty in input data to be expressed in the model results and MOLASSES outputs a probability map of inundation instead of a determined lava flow extent. Since the code is comparatively fast, we use it probabilistically to investigate where potential vents are located that may impact specific sites and areas, as well as the unconditional probability of lava flow inundation of sites or areas from any vent. We have validated the MOLASSES code to community-defined benchmark tests and to the real world lava flows at Tolbachik (2012-2013) and Pico do Fogo (2014-2015). To determine the efficacy of the MOLASSES simulator at accurately and precisely mimicking the inundation area of real flows, we report goodness of fit using both model sensitivity and the Positive Predictive Value, the latter of which is a Bayesian posterior statistic. Model sensitivity is often used in evaluating lava flow simulators, as it describes how much of the lava flow was successfully modeled by the simulation. We argue that the positive predictive value is equally important in determining how good a simulator is, as it describes the percentage of the simulation space that was actually inundated by lava.

Impact Of Three-Phase Relative Permeability and Hysteresis Models On Forecasts of Storage Associated with CO2-EOR

NASA Astrophysics Data System (ADS)

Jia, W.; Pan, F.; McPherson, B. J. O. L.

2015-12-01

Due to the presence of multiple phases in a given system, CO2 sequestration with enhanced oil recovery (CO2-EOR) includes complex multiphase flow processes compared to CO2 sequestration in deep saline aquifers (no hydrocarbons). Two of the most important factors are three-phase relative permeability and hysteresis effects, both of which are difficult to measure and are usually represented by numerical interpolation models. The purposes of this study included quantification of impacts of different three-phase relative permeability models and hysteresis models on CO2 sequestration simulation results, and associated quantitative estimation of uncertainty. Four three-phase relative permeability models and three hysteresis models were applied to a model of an active CO2-EOR site, the SACROC unit located in western Texas. To eliminate possible bias of deterministic parameters on the evaluation, a sequential Gaussian simulation technique was utilized to generate 50 realizations to describe heterogeneity of porosity and permeability, initially obtained from well logs and seismic survey data. Simulation results of forecasted pressure distributions and CO2 storage suggest that (1) the choice of three-phase relative permeability model and hysteresis model have noticeable impacts on CO2 sequestration simulation results; (2) influences of both factors are observed in all 50 realizations; and (3) the specific choice of hysteresis model appears to be somewhat more important relative to the choice of three-phase relative permeability model in terms of model uncertainty.

Simulation environment and graphical visualization environment: a COPD use-case

PubMed Central

2014-01-01

Background Today, many different tools are developed to execute and visualize physiological models that represent the human physiology. Most of these tools run models written in very specific programming languages which in turn simplify the communication among models. Nevertheless, not all of these tools are able to run models written in different programming languages. In addition, interoperability between such models remains an unresolved issue. Results In this paper we present a simulation environment that allows, first, the execution of models developed in different programming languages and second the communication of parameters to interconnect these models. This simulation environment, developed within the Synergy-COPD project, aims at helping and supporting bio-researchers and medical students understand the internal mechanisms of the human body through the use of physiological models. This tool is composed of a graphical visualization environment, which is a web interface through which the user can interact with the models, and a simulation workflow management system composed of a control module and a data warehouse manager. The control module monitors the correct functioning of the whole system. The data warehouse manager is responsible for managing the stored information and supporting its flow among the different modules. This simulation environment has been validated with the integration of three models: two deterministic, i.e. based on linear and differential equations, and one probabilistic, i.e., based on probability theory. These models have been selected based on the disease under study in this project, i.e., chronic obstructive pulmonary disease. Conclusion It has been proved that the simulation environment presented here allows the user to research and study the internal mechanisms of the human physiology by the use of models via a graphical visualization environment. A new tool for bio-researchers is ready for deployment in various use cases scenarios. PMID:25471327

TU-AB-303-11: Predict Parotids Deformation Applying SIS Epidemiological Model in H&N Adaptive RT

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

Maffei, N; Guidi, G; University of Bologna, Bologna, Bologna

2015-06-15

Purpose: The aim is to investigate the use of epidemiological models to predict morphological variations in patients undergoing radiation therapy (RT). The susceptible-infected-susceptible (SIS) deterministic model was applied to simulate warping within a focused region of interest (ROI). Hypothesis is to consider each voxel like a single subject of the whole sample and to treat displacement vector fields like an infection. Methods: Using Raystation hybrid deformation algorithms and automatic re-contouring based on mesh grid, we post-processed 360 MVCT images of 12 H&N patients treated with Tomotherapy. Study focused on parotid glands, identified by literature and previous analysis, as ROI moremore » susceptible to warping in H&N region. Susceptible (S) and infectious (I) cases were identified in voxels with inter-fraction movement respectively under and over a set threshold. IronPython scripting allowed to export positions and displacement data of surface voxels for every fraction. A MATLAB homemade toolbox was developed to model the SIS. Results: SIS model was validated simulating organ motion on QUASAR phantom. Applying model in patients, within a [0–1cm] range, a single voxel movement of 0.4cm was selected as displacement threshold. SIS indexes were evaluated by MATLAB simulations. Dynamic time warping algorithm was used to assess matching between model and parotids behavior days of treatments. The best fit of the model was obtained with contact rate of 7.89±0.94 and recovery rate of 2.36±0.21. Conclusion: SIS model can follow daily structures evolutions, making possible to compare warping conditions and highlighting challenges due to abnormal variation and set-up errors. By epidemiology approach, organ motion could be assessed and predicted not in terms of average of the whole ROI, but in a voxel-by-voxel deterministic trend. Identifying anatomical region subjected to variations, would be possible to focus clinic controls within a cohort of pre-selected patients eligible for adaptive RT. The research is partially co-funded by the Italian Research Grant: Dose warping methods for IGRT and Adaptive RT: dose accumulation based on organ motion and anatomical variations of the patients during radiation therapy treatments,MoH (GR-2010-2318757) and Tecnologie Avanzate S.r.l.(Italy)« less

Tularosa Basin Play Fairway Analysis Data and Models

DOE Data Explorer

Nash, Greg

2017-07-11

This submission includes raster datasets for each layer of evidence used for weights of evidence analysis as well as the deterministic play fairway analysis (PFA). Data representative of heat, permeability and groundwater comprises some of the raster datasets. Additionally, the final deterministic PFA model is provided along with a certainty model. All of these datasets are best used with an ArcGIS software package, specifically Spatial Data Modeler.

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.

Simulations of hydrologic response in the Apalachicola-Chattahoochee-Flint River Basin, Southeastern United States

USGS Publications Warehouse

LaFontaine, Jacob H.; Jones, L. Elliott; Painter, Jaime A.

2017-12-29

A suite of hydrologic models has been developed for the Apalachicola-Chattahoochee-Flint River Basin (ACFB) as part of the National Water Census, a U.S. Geological Survey research program that focuses on developing new water accounting tools and assessing water availability and use at the regional and national scales. Seven hydrologic models were developed using the Precipitation-Runoff Modeling System (PRMS), a deterministic, distributed-parameter, process-based system that simulates the effects of precipitation, temperature, land cover, and water use on basin hydrology. A coarse-resolution PRMS model was developed for the entire ACFB, and six fine-resolution PRMS models were developed for six subbasins of the ACFB. The coarse-resolution model was loosely coupled with a groundwater model to better assess the effects of water use on streamflow in the lower ACFB, a complex geologic setting with karst features. The PRMS coarse-resolution model was used to provide inputs of recharge to the groundwater model, which in turn provide simulations of groundwater flow that were aggregated with PRMS-based simulations of surface runoff and shallow-subsurface flow. Simulations without the effects of water use were developed for each model for at least the calendar years 1982–2012 with longer periods for the Potato Creek subbasin (1942–2012) and the Spring Creek subbasin (1952–2012). Water-use-affected flows were simulated for 2008–12. Water budget simulations showed heterogeneous distributions of precipitation, actual evapotranspiration, recharge, runoff, and storage change across the ACFB. Streamflow volume differences between no-water-use and water-use simulations were largest along the main stem of the Apalachicola and Chattahoochee River Basins, with streamflow percentage differences largest in the upper Chattahoochee and Flint River Basins and Spring Creek in the lower Flint River Basin. Water-use information at a shorter time step and a fully coupled simulation in the lower ACFB may further improve water availability estimates and hydrologic simulations in the basin.

Assimilating MODIS-based albedo and snow cover fraction into the Common Land Model to improve snow depth simulation with direct insertion and deterministic ensemble Kalman filter methods

NASA Astrophysics Data System (ADS)

Xu, Jianhui; Shu, Hong

2014-09-01

This study assesses the analysis performance of assimilating the Moderate Resolution Imaging Spectroradiometer (MODIS)-based albedo and snow cover fraction (SCF) separately or jointly into the physically based Common Land Model (CoLM). A direct insertion method (DI) is proposed to assimilate the black and white-sky albedos into the CoLM. The MODIS-based albedo is calculated with the MODIS bidirectional reflectance distribution function (BRDF) model parameters product (MCD43B1) and the solar zenith angle as estimated in the CoLM for each time step. Meanwhile, the MODIS SCF (MOD10A1) is assimilated into the CoLM using the deterministic ensemble Kalman filter (DEnKF) method. A new DEnKF-albedo assimilation scheme for integrating the DI and DEnKF assimilation schemes is proposed. Our assimilation results are validated against in situ snow depth observations from November 2008 to March 2009 at five sites in the Altay region of China. The experimental results show that all three data assimilation schemes can improve snow depth simulations. But overall, the DEnKF-albedo assimilation shows the best analysis performance as it significantly reduces the bias and root-mean-square error (RMSE) during the snow accumulation and ablation periods at all sites except for the Fuyun site. The SCF assimilation via DEnKF produces better results than the albedo assimilation via DI, implying that the albedo assimilation that indirectly updates the snow depth state variable is less efficient than the direct SCF assimilation. For the Fuyun site, the DEnKF-albedo scheme tends to overestimate the snow depth accumulation with the maximum bias and RMSE values because of the large positive innovation (observation minus forecast).

Diffusion in Deterministic Interacting Lattice Systems

NASA Astrophysics Data System (ADS)

Medenjak, Marko; Klobas, Katja; Prosen, Tomaž

2017-09-01

We study reversible deterministic dynamics of classical charged particles on a lattice with hard-core interaction. It is rigorously shown that the system exhibits three types of transport phenomena, ranging from ballistic, through diffusive to insulating. By obtaining an exact expressions for the current time-autocorrelation function we are able to calculate the linear response transport coefficients, such as the diffusion constant and the Drude weight. Additionally, we calculate the long-time charge profile after an inhomogeneous quench and obtain diffusive profilewith the Green-Kubo diffusion constant. Exact analytical results are corroborated by Monte Carlo simulations.

Optimal entangling operations between deterministic blocks of qubits encoded into single photons

NASA Astrophysics Data System (ADS)

Smith, Jake A.; Kaplan, Lev

2018-01-01

Here, we numerically simulate probabilistic elementary entangling operations between rail-encoded photons for the purpose of scalable universal quantum computation or communication. We propose grouping logical qubits into single-photon blocks wherein single-qubit rotations and the controlled-not (cnot) gate are fully deterministic and simple to implement. Interblock communication is then allowed through said probabilistic entangling operations. We find a promising trend in the increasing probability of successful interblock communication as we increase the number of optical modes operated on by our elementary entangling operations.

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.

ShareSync: A Solution for Deterministic Data Sharing over Ethernet

NASA Technical Reports Server (NTRS)

Dunn, Daniel J., II; Koons, William A.; Kennedy, Richard D.; Davis, Philip A.

2007-01-01

As part of upgrading the Contact Dynamics Simulation Laboratory (CDSL) at the NASA Marshall Space Flight Center (MSFC), a simple, cost effective method was needed to communicate data among the networked simulation machines and I/O controllers used to run the facility. To fill this need and similar applicable situations, a generic protocol was developed, called ShareSync. ShareSync is a lightweight, real-time, publish-subscribe Ethernet protocol for simple and deterministic data sharing across diverse machines and operating systems. ShareSync provides a simple Application Programming Interface (API) for simulation programmers to incorporate into their code. The protocol is compatible with virtually all Ethernet-capable machines, is flexible enough to support a variety of applications, is fast enough to provide soft real-time determinism, and is a low-cost resource for distributed simulation development, deployment, and maintenance. The first design cycle iteration of ShareSync has been completed, and the protocol has undergone several testing procedures including endurance and benchmarking tests and approaches the 2001ts data synchronization design goal for the CDSL.

More reliable forecasts with less precise computations: a fast-track route to cloud-resolved weather and climate simulators?

PubMed Central

Palmer, T. N.

2014-01-01

This paper sets out a new methodological approach to solving the equations for simulating and predicting weather and climate. In this approach, the conventionally hard boundary between the dynamical core and the sub-grid parametrizations is blurred. This approach is motivated by the relatively shallow power-law spectrum for atmospheric energy on scales of hundreds of kilometres and less. It is first argued that, because of this, the closure schemes for weather and climate simulators should be based on stochastic–dynamic systems rather than deterministic formulae. Second, as high-wavenumber elements of the dynamical core will necessarily inherit this stochasticity during time integration, it is argued that the dynamical core will be significantly over-engineered if all computations, regardless of scale, are performed completely deterministically and if all variables are represented with maximum numerical precision (in practice using double-precision floating-point numbers). As the era of exascale computing is approached, an energy- and computationally efficient approach to cloud-resolved weather and climate simulation is described where determinism and numerical precision are focused on the largest scales only. PMID:24842038

More reliable forecasts with less precise computations: a fast-track route to cloud-resolved weather and climate simulators?

PubMed

Palmer, T N

2014-06-28

This paper sets out a new methodological approach to solving the equations for simulating and predicting weather and climate. In this approach, the conventionally hard boundary between the dynamical core and the sub-grid parametrizations is blurred. This approach is motivated by the relatively shallow power-law spectrum for atmospheric energy on scales of hundreds of kilometres and less. It is first argued that, because of this, the closure schemes for weather and climate simulators should be based on stochastic-dynamic systems rather than deterministic formulae. Second, as high-wavenumber elements of the dynamical core will necessarily inherit this stochasticity during time integration, it is argued that the dynamical core will be significantly over-engineered if all computations, regardless of scale, are performed completely deterministically and if all variables are represented with maximum numerical precision (in practice using double-precision floating-point numbers). As the era of exascale computing is approached, an energy- and computationally efficient approach to cloud-resolved weather and climate simulation is described where determinism and numerical precision are focused on the largest scales only.

History matching of a complex epidemiological model of human immunodeficiency virus transmission by using variance emulation.

PubMed

Andrianakis, I; Vernon, I; McCreesh, N; McKinley, T J; Oakley, J E; Nsubuga, R N; Goldstein, M; White, R G

2017-08-01

Complex stochastic models are commonplace in epidemiology, but their utility depends on their calibration to empirical data. History matching is a (pre)calibration method that has been applied successfully to complex deterministic models. In this work, we adapt history matching to stochastic models, by emulating the variance in the model outputs, and therefore accounting for its dependence on the model's input values. The method proposed is applied to a real complex epidemiological model of human immunodeficiency virus in Uganda with 22 inputs and 18 outputs, and is found to increase the efficiency of history matching, requiring 70% of the time and 43% fewer simulator evaluations compared with a previous variant of the method. The insight gained into the structure of the human immunodeficiency virus model, and the constraints placed on it, are then discussed.

Modelling the Impact of Different Tuberculosis Control Interventions on the Prevalence of Tuberculosis in an Overcrowded Prison.

PubMed

Naning, Herlianna; Al-Darraji, Haider Abdulrazzaq Abed; McDonald, Scott; Ismail, Noor Azina; Kamarulzaman, Adeeba

2018-04-01

The aim of this study was to simulate the effects of tuberculosis (TB) treatment strategies interventions in an overcrowded and poorly ventilated prison with both high (5 months) and low (3 years) turnover of inmates against improved environmental conditions. We used a deterministic transmission model to simulate the effects of treatment of latent TB infection and active TB, or the combination of both treatment strategies. Without any intervention, the TB prevalence is estimated to increase to 8.8% for a prison with low turnover of inmates but modestly stabilize at 5.8% for high-turnover prisons in a 10-year period. Reducing overcrowding from 6 to 4 inmates per housing cell and increasing the ventilation rate from 2 to 12 air changes per hour combined with any treatment strategy would further reduce the TB prevalence to as low as 0.98% for a prison with low inmate turnover.

Multiscale simulations of anisotropic particles combining molecular dynamics and Green's function reaction dynamics

NASA Astrophysics Data System (ADS)

Vijaykumar, Adithya; Ouldridge, Thomas E.; ten Wolde, Pieter Rein; Bolhuis, Peter G.

2017-03-01

The modeling of complex reaction-diffusion processes in, for instance, cellular biochemical networks or self-assembling soft matter can be tremendously sped up by employing a multiscale algorithm which combines the mesoscopic Green's Function Reaction Dynamics (GFRD) method with explicit stochastic Brownian, Langevin, or deterministic molecular dynamics to treat reactants at the microscopic scale [A. Vijaykumar, P. G. Bolhuis, and P. R. ten Wolde, J. Chem. Phys. 143, 214102 (2015)]. Here we extend this multiscale MD-GFRD approach to include the orientational dynamics that is crucial to describe the anisotropic interactions often prevalent in biomolecular systems. We present the novel algorithm focusing on Brownian dynamics only, although the methodology is generic. We illustrate the novel algorithm using a simple patchy particle model. After validation of the algorithm, we discuss its performance. The rotational Brownian dynamics MD-GFRD multiscale method will open up the possibility for large scale simulations of protein signalling networks.

Random walk-percolation-based modeling of two-phase flow in porous media: Breakthrough time and net to gross ratio estimation

NASA Astrophysics Data System (ADS)

Ganjeh-Ghazvini, Mostafa; Masihi, Mohsen; Ghaedi, Mojtaba

2014-07-01

Fluid flow modeling in porous media has many applications in waste treatment, hydrology and petroleum engineering. In any geological model, flow behavior is controlled by multiple properties. These properties must be known in advance of common flow simulations. When uncertainties are present, deterministic modeling often produces poor results. Percolation and Random Walk (RW) methods have recently been used in flow modeling. Their stochastic basis is useful in dealing with uncertainty problems. They are also useful in finding the relationship between porous media descriptions and flow behavior. This paper employs a simple methodology based on random walk and percolation techniques. The method is applied to a well-defined model reservoir in which the breakthrough time distributions are estimated. The results of this method and the conventional simulation are then compared. The effect of the net to gross ratio on the breakthrough time distribution is studied in terms of Shannon entropy. Use of the entropy plot allows one to assign the appropriate net to gross ratio to any porous medium.

Combining Deterministic structures and stochastic heterogeneity for transport modeling

NASA Astrophysics Data System (ADS)

Zech, Alraune; Attinger, Sabine; Dietrich, Peter; Teutsch, Georg

2017-04-01

Contaminant transport in highly heterogeneous aquifers is extremely challenging and subject of current scientific debate. Tracer plumes often show non-symmetric but highly skewed plume shapes. Predicting such transport behavior using the classical advection-dispersion-equation (ADE) in combination with a stochastic description of aquifer properties requires a dense measurement network. This is in contrast to the available information for most aquifers. A new conceptual aquifer structure model is presented which combines large-scale deterministic information and the stochastic approach for incorporating sub-scale heterogeneity. The conceptual model is designed to allow for a goal-oriented, site specific transport analysis making use of as few data as possible. Thereby the basic idea is to reproduce highly skewed tracer plumes in heterogeneous media by incorporating deterministic contrasts and effects of connectivity instead of using unimodal heterogeneous models with high variances. The conceptual model consists of deterministic blocks of mean hydraulic conductivity which might be measured by pumping tests indicating values differing in orders of magnitudes. A sub-scale heterogeneity is introduced within every block. This heterogeneity can be modeled as bimodal or log-normal distributed. The impact of input parameters, structure and conductivity contrasts is investigated in a systematic manor. Furthermore, some first successful implementation of the model was achieved for the well known MADE site.

Acceleration of discrete stochastic biochemical simulation using GPGPU.

PubMed

Sumiyoshi, Kei; Hirata, Kazuki; Hiroi, Noriko; Funahashi, Akira

2015-01-01

For systems made up of a small number of molecules, such as a biochemical network in a single cell, a simulation requires a stochastic approach, instead of a deterministic approach. The stochastic simulation algorithm (SSA) simulates the stochastic behavior of a spatially homogeneous system. Since stochastic approaches produce different results each time they are used, multiple runs are required in order to obtain statistical results; this results in a large computational cost. We have implemented a parallel method for using SSA to simulate a stochastic model; the method uses a graphics processing unit (GPU), which enables multiple realizations at the same time, and thus reduces the computational time and cost. During the simulation, for the purpose of analysis, each time course is recorded at each time step. A straightforward implementation of this method on a GPU is about 16 times faster than a sequential simulation on a CPU with hybrid parallelization; each of the multiple simulations is run simultaneously, and the computational tasks within each simulation are parallelized. We also implemented an improvement to the memory access and reduced the memory footprint, in order to optimize the computations on the GPU. We also implemented an asynchronous data transfer scheme to accelerate the time course recording function. To analyze the acceleration of our implementation on various sizes of model, we performed SSA simulations on different model sizes and compared these computation times to those for sequential simulations with a CPU. When used with the improved time course recording function, our method was shown to accelerate the SSA simulation by a factor of up to 130.

Acceleration of discrete stochastic biochemical simulation using GPGPU

PubMed Central

Sumiyoshi, Kei; Hirata, Kazuki; Hiroi, Noriko; Funahashi, Akira

2015-01-01

For systems made up of a small number of molecules, such as a biochemical network in a single cell, a simulation requires a stochastic approach, instead of a deterministic approach. The stochastic simulation algorithm (SSA) simulates the stochastic behavior of a spatially homogeneous system. Since stochastic approaches produce different results each time they are used, multiple runs are required in order to obtain statistical results; this results in a large computational cost. We have implemented a parallel method for using SSA to simulate a stochastic model; the method uses a graphics processing unit (GPU), which enables multiple realizations at the same time, and thus reduces the computational time and cost. During the simulation, for the purpose of analysis, each time course is recorded at each time step. A straightforward implementation of this method on a GPU is about 16 times faster than a sequential simulation on a CPU with hybrid parallelization; each of the multiple simulations is run simultaneously, and the computational tasks within each simulation are parallelized. We also implemented an improvement to the memory access and reduced the memory footprint, in order to optimize the computations on the GPU. We also implemented an asynchronous data transfer scheme to accelerate the time course recording function. To analyze the acceleration of our implementation on various sizes of model, we performed SSA simulations on different model sizes and compared these computation times to those for sequential simulations with a CPU. When used with the improved time course recording function, our method was shown to accelerate the SSA simulation by a factor of up to 130. PMID:25762936

Dynamic partitioning for hybrid simulation of the bistable HIV-1 transactivation network.

PubMed

Griffith, Mark; Courtney, Tod; Peccoud, Jean; Sanders, William H

2006-11-15

The stochastic kinetics of a well-mixed chemical system, governed by the chemical Master equation, can be simulated using the exact methods of Gillespie. However, these methods do not scale well as systems become more complex and larger models are built to include reactions with widely varying rates, since the computational burden of simulation increases with the number of reaction events. Continuous models may provide an approximate solution and are computationally less costly, but they fail to capture the stochastic behavior of small populations of macromolecules. In this article we present a hybrid simulation algorithm that dynamically partitions the system into subsets of continuous and discrete reactions, approximates the continuous reactions deterministically as a system of ordinary differential equations (ODE) and uses a Monte Carlo method for generating discrete reaction events according to a time-dependent propensity. Our approach to partitioning is improved such that we dynamically partition the system of reactions, based on a threshold relative to the distribution of propensities in the discrete subset. We have implemented the hybrid algorithm in an extensible framework, utilizing two rigorous ODE solvers to approximate the continuous reactions, and use an example model to illustrate the accuracy and potential speedup of the algorithm when compared with exact stochastic simulation. Software and benchmark models used for this publication can be made available upon request from the authors.

Discrete-Time Deterministic $Q$ -Learning: A Novel Convergence Analysis.

PubMed

Wei, Qinglai; Lewis, Frank L; Sun, Qiuye; Yan, Pengfei; Song, Ruizhuo

2017-05-01

In this paper, a novel discrete-time deterministic Q -learning algorithm is developed. In each iteration of the developed Q -learning algorithm, the iterative Q function is updated for all the state and control spaces, instead of updating for a single state and a single control in traditional Q -learning algorithm. A new convergence criterion is established to guarantee that the iterative Q function converges to the optimum, where the convergence criterion of the learning rates for traditional Q -learning algorithms is simplified. During the convergence analysis, the upper and lower bounds of the iterative Q function are analyzed to obtain the convergence criterion, instead of analyzing the iterative Q function itself. For convenience of analysis, the convergence properties for undiscounted case of the deterministic Q -learning algorithm are first developed. Then, considering the discounted factor, the convergence criterion for the discounted case is established. Neural networks are used to approximate the iterative Q function and compute the iterative control law, respectively, for facilitating the implementation of the deterministic Q -learning algorithm. Finally, simulation results and comparisons are given to illustrate the performance of the developed algorithm.

Seismic shaking scenarios in realistic 3D crustal model of Northern Italy

NASA Astrophysics Data System (ADS)

Molinari, I.; Morelli, A.; Basini, P.; Berbellini, A.

2013-12-01

Simulation of seismic wave propagation in realistic crustal structures is a fundamental tool to evaluate earthquake-generated ground shaking and assess seismic hazard. Current-generation numerical codes, and modern HPC infrastructures, allow for realistic simulations in complex 3D geologic structures. We apply such methodology to the Po Plain in Northern Italy -- a region with relatively rare earthquakes but having large property and industrial exposure, as it became clear during the two M~6 events of May 20-29, 2012. Historical seismicity is well known in this region, with maximum magnitudes estimates reaching M~7, and wave field amplitudes may be significantly amplified by the presence of the very thick sedimentary basin. Our goal is to produce estimates of expected ground shaking in Northern Italy through detailed deterministic simulations of ground motion due to expected earthquakes. We defined a three-dimensional model of the earth's crust using geo-statistical tools to merge the abundant information existing in the form of borehole data and seismic reflection profiles that had been shot in the '70s and the '80s for hydrocarbon exploration. Such information, that has been used by geologists to infer the deep structural setup, had never been merged to build a 3D model to be used for seismological simulations. We implement the model in SPECFEM3D_Cartesian and a hexahedral mesh with elements of ~2km, that allows us to simulate waves with minimum period of ~2 seconds. The model has then been optimized through comparison between simulated and recorded seismograms for the ~20 moderate-magnitude events (Mw > 4.5) that have been instrumentally recorded in the last 15 years. Realistic simulations in the frequency band of most common engineering relevance -- say, ~1 Hz -- at such a large scale would require an extremely detailed structural model, currently not available, and prohibitive computational resources. However, an interest is growing in longer period ground motion -- that impacts on the seismic response of taller structures (Cauzzi and Faccioli, 2008) -- and it is not unusual to consider the wave field up to 20s. In such period range, our Po Plain structural model has shown to be able to reproduce well basin resonance and amplification effects at stations boarding the sedimentary plain. We then simulate seismic shaking scenarios for possible sources tied to devastating historical earthquakes that are known to have occurred in the region --- such as the M~6 event that hit Modena in 1501; and the Verona, M~6.7 in 1117, quake that caused well-documented strong effects in an unusually wide area with radius of hundreds of kilometers. We explore different source geometries and rupture histories for each earthquake. We mainly focus our attention on the synthesis of the prominent surface waves that are highly amplified in deep sedimentary basin structures (e.g., Smerzini et al, 2011; Koketsu and Miyage, 2008). Such simulations hold high relevance because of the large local property exposure, due to extensive industrial and touristic infrastructure. We show that deterministic ground motion calculation can indeed provide information to be actively used to mitigate the effects of desctructive earthquakes on critical infrastructures.

Dilute suspensions in annular shear flow under gravity: simulation and experiment

NASA Astrophysics Data System (ADS)

Schröer, Kevin; Kurzeja, Patrick; Schulz, Stephan; Brockmann, Philipp; Hussong, Jeanette; Janas, Peter; Wlokas, Irenaeus; Kempf, Andreas; Wolf, Dietrich E.

2017-06-01

A dilute suspension in annular shear flow under gravity was simulated using multi-particle collision dynamics (MPC) and compared to experimental data. The focus of the analysis is the local particle velocity and density distribution under the influence of the rotational and gravitational forces. The results are further supported by a deterministic approximation of a single-particle trajectory and OpenFOAM CFD estimations of the overcritical frequency range. Good qualitative agreement is observed for single-particle trajectories between the statistical mean of MPC simulations and the deterministic approximation. Wall contact and detachment however occur earlier in the MPC simulation, which can be explained by the inherent thermal noise of the method. The multi-particle system is investigated at the point of highest particle accumulation that is found at 2/3 of the particle revolution, starting from the top of the annular gap. The combination of shear flow and a slowly rotating volumetric force leads to strong local accumulation in this section that increases the particle volume fraction from overall 0.7% to 4.7% at the outer boundary. MPC simulations and experimental observations agree well in terms of particle distribution and a close to linear velocity profile in radial direction.

Diffusion-Based Model for Synaptic Molecular Communication Channel.

PubMed

Khan, Tooba; Bilgin, Bilgesu A; Akan, Ozgur B

2017-06-01

Computational methods have been extensively used to understand the underlying dynamics of molecular communication methods employed by nature. One very effective and popular approach is to utilize a Monte Carlo simulation. Although it is very reliable, this method can have a very high computational cost, which in some cases renders the simulation impractical. Therefore, in this paper, for the special case of an excitatory synaptic molecular communication channel, we present a novel mathematical model for the diffusion and binding of neurotransmitters that takes into account the effects of synaptic geometry in 3-D space and re-absorption of neurotransmitters by the transmitting neuron. Based on this model we develop a fast deterministic algorithm, which calculates expected value of the output of this channel, namely, the amplitude of excitatory postsynaptic potential (EPSP), for given synaptic parameters. We validate our algorithm by a Monte Carlo simulation, which shows total agreement between the results of the two methods. Finally, we utilize our model to quantify the effects of variation in synaptic parameters, such as position of release site, receptor density, size of postsynaptic density, diffusion coefficient, uptake probability, and number of neurotransmitters in a vesicle, on maximum number of bound receptors that directly affect the peak amplitude of EPSP.

Contrasting support for alternative models of genomic variation based on microhabitat preference: species-specific effects of climate change in alpine sedges.

PubMed

Massatti, Rob; Knowles, L Lacey

2016-08-01

Deterministic processes may uniquely affect codistributed species' phylogeographic patterns such that discordant genetic variation among taxa is predicted. Yet, explicitly testing expectations of genomic discordance in a statistical framework remains challenging. Here, we construct spatially and temporally dynamic models to investigate the hypothesized effect of microhabitat preferences on the permeability of glaciated regions to gene flow in two closely related montane species. Utilizing environmental niche models from the Last Glacial Maximum and the present to inform demographic models of changes in habitat suitability over time, we evaluate the relative probabilities of two alternative models using approximate Bayesian computation (ABC) in which glaciated regions are either (i) permeable or (ii) a barrier to gene flow. Results based on the fit of the empirical data to data sets simulated using a spatially explicit coalescent under alternative models indicate that genomic data are consistent with predictions about the hypothesized role of microhabitat in generating discordant patterns of genetic variation among the taxa. Specifically, a model in which glaciated areas acted as a barrier was much more probable based on patterns of genomic variation in Carex nova, a wet-adapted species. However, in the dry-adapted Carex chalciolepis, the permeable model was more probable, although the difference in the support of the models was small. This work highlights how statistical inferences can be used to distinguish deterministic processes that are expected to result in discordant genomic patterns among species, including species-specific responses to climate change. © 2016 John Wiley & Sons Ltd.

Qualitative models and experimental investigation of chaotic NOR gates and set/reset flip-flops

NASA Astrophysics Data System (ADS)

Rahman, Aminur; Jordan, Ian; Blackmore, Denis

2018-01-01

It has been observed through experiments and SPICE simulations that logical circuits based upon Chua's circuit exhibit complex dynamical behaviour. This behaviour can be used to design analogues of more complex logic families and some properties can be exploited for electronics applications. Some of these circuits have been modelled as systems of ordinary differential equations. However, as the number of components in newer circuits increases so does the complexity. This renders continuous dynamical systems models impractical and necessitates new modelling techniques. In recent years, some discrete dynamical models have been developed using various simplifying assumptions. To create a robust modelling framework for chaotic logical circuits, we developed both deterministic and stochastic discrete dynamical models, which exploit the natural recurrence behaviour, for two chaotic NOR gates and a chaotic set/reset flip-flop. This work presents a complete applied mathematical investigation of logical circuits. Experiments on our own designs of the above circuits are modelled and the models are rigorously analysed and simulated showing surprisingly close qualitative agreement with the experiments. Furthermore, the models are designed to accommodate dynamics of similarly designed circuits. This will allow researchers to develop ever more complex chaotic logical circuits with a simple modelling framework.

Qualitative models and experimental investigation of chaotic NOR gates and set/reset flip-flops.

PubMed

Rahman, Aminur; Jordan, Ian; Blackmore, Denis

2018-01-01

It has been observed through experiments and SPICE simulations that logical circuits based upon Chua's circuit exhibit complex dynamical behaviour. This behaviour can be used to design analogues of more complex logic families and some properties can be exploited for electronics applications. Some of these circuits have been modelled as systems of ordinary differential equations. However, as the number of components in newer circuits increases so does the complexity. This renders continuous dynamical systems models impractical and necessitates new modelling techniques. In recent years, some discrete dynamical models have been developed using various simplifying assumptions. To create a robust modelling framework for chaotic logical circuits, we developed both deterministic and stochastic discrete dynamical models, which exploit the natural recurrence behaviour, for two chaotic NOR gates and a chaotic set/reset flip-flop. This work presents a complete applied mathematical investigation of logical circuits. Experiments on our own designs of the above circuits are modelled and the models are rigorously analysed and simulated showing surprisingly close qualitative agreement with the experiments. Furthermore, the models are designed to accommodate dynamics of similarly designed circuits. This will allow researchers to develop ever more complex chaotic logical circuits with a simple modelling framework.

An investigation of adaptive controllers for helicopter vibration and the development of a new dual controller

NASA Technical Reports Server (NTRS)

Mookerjee, P.; Molusis, J. A.; Bar-Shalom, Y.

1985-01-01

An investigation of the properties important for the design of stochastic adaptive controllers for the higher harmonic control of helicopter vibration is presented. Three different model types are considered for the transfer relationship between the helicopter higher harmonic control input and the vibration output: (1) nonlinear; (2) linear with slow time varying coefficients; and (3) linear with constant coefficients. The stochastic controller formulations and solutions are presented for a dual, cautious, and deterministic controller for both linear and nonlinear transfer models. Extensive simulations are performed with the various models and controllers. It is shown that the cautious adaptive controller can sometimes result in unacceptable vibration control. A new second order dual controller is developed which is shown to modify the cautious adaptive controller by adding numerator and denominator correction terms to the cautious control algorithm. The new dual controller is simulated on a simple single-control vibration example and is found to achieve excellent vibration reduction and significantly improves upon the cautious controller.

Toward Development of a Stochastic Wake Model: Validation Using LES and Turbine Loads

DOE PAGES

Moon, Jae; Manuel, Lance; Churchfield, Matthew; ...

2017-12-28

Wind turbines within an array do not experience free-stream undisturbed flow fields. Rather, the flow fields on internal turbines are influenced by wakes generated by upwind unit and exhibit different dynamic characteristics relative to the free stream. The International Electrotechnical Commission (IEC) standard 61400-1 for the design of wind turbines only considers a deterministic wake model for the design of a wind plant. This study is focused on the development of a stochastic model for waked wind fields. First, high-fidelity physics-based waked wind velocity fields are generated using Large-Eddy Simulation (LES). Stochastic characteristics of these LES waked wind velocity field,more » including mean and turbulence components, are analyzed. Wake-related mean and turbulence field-related parameters are then estimated for use with a stochastic model, using Multivariate Multiple Linear Regression (MMLR) with the LES data. To validate the simulated wind fields based on the stochastic model, wind turbine tower and blade loads are generated using aeroelastic simulation for utility-scale wind turbine models and compared with those based directly on the LES inflow. The study's overall objective is to offer efficient and validated stochastic approaches that are computationally tractable for assessing the performance and loads of turbines operating in wakes.« less

Toward Development of a Stochastic Wake Model: Validation Using LES and Turbine Loads

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

Moon, Jae; Manuel, Lance; Churchfield, Matthew

Wind turbines within an array do not experience free-stream undisturbed flow fields. Rather, the flow fields on internal turbines are influenced by wakes generated by upwind unit and exhibit different dynamic characteristics relative to the free stream. The International Electrotechnical Commission (IEC) standard 61400-1 for the design of wind turbines only considers a deterministic wake model for the design of a wind plant. This study is focused on the development of a stochastic model for waked wind fields. First, high-fidelity physics-based waked wind velocity fields are generated using Large-Eddy Simulation (LES). Stochastic characteristics of these LES waked wind velocity field,more » including mean and turbulence components, are analyzed. Wake-related mean and turbulence field-related parameters are then estimated for use with a stochastic model, using Multivariate Multiple Linear Regression (MMLR) with the LES data. To validate the simulated wind fields based on the stochastic model, wind turbine tower and blade loads are generated using aeroelastic simulation for utility-scale wind turbine models and compared with those based directly on the LES inflow. The study's overall objective is to offer efficient and validated stochastic approaches that are computationally tractable for assessing the performance and loads of turbines operating in wakes.« less

Improving Project Management with Simulation and Completion Distribution Functions

NASA Technical Reports Server (NTRS)

Cates, Grant R.

2004-01-01

Despite the critical importance of project completion timeliness, management practices in place today remain inadequate for addressing the persistent problem of project completion tardiness. A major culprit in late projects is uncertainty, which most, if not all, projects are inherently subject to. This uncertainty resides in the estimates for activity durations, the occurrence of unplanned and unforeseen events, and the availability of critical resources. In response to this problem, this research developed a comprehensive simulation based methodology for conducting quantitative project completion time risk analysis. It is called the Project Assessment by Simulation Technique (PAST). This new tool enables project stakeholders to visualize uncertainty or risk, i.e. the likelihood of their project completing late and the magnitude of the lateness, by providing them with a completion time distribution function of their projects. Discrete event simulation is used within PAST to determine the completion distribution function for the project of interest. The simulation is populated with both deterministic and stochastic elements. The deterministic inputs include planned project activities, precedence requirements, and resource requirements. The stochastic inputs include activity duration growth distributions, probabilities for events that can impact the project, and other dynamic constraints that may be placed upon project activities and milestones. These stochastic inputs are based upon past data from similar projects. The time for an entity to complete the simulation network, subject to both the deterministic and stochastic factors, represents the time to complete the project. Repeating the simulation hundreds or thousands of times allows one to create the project completion distribution function. The Project Assessment by Simulation Technique was demonstrated to be effective for the on-going NASA project to assemble the International Space Station. Approximately $500 million per month is being spent on this project, which is scheduled to complete by 2010. NASA project stakeholders participated in determining and managing completion distribution functions produced from PAST. The first result was that project stakeholders improved project completion risk awareness. Secondly, using PAST, mitigation options were analyzed to improve project completion performance and reduce total project cost.

Parameter Estimation in Epidemiology: from Simple to Complex Dynamics

NASA Astrophysics Data System (ADS)

Aguiar, Maíra; Ballesteros, Sebastién; Boto, João Pedro; Kooi, Bob W.; Mateus, Luís; Stollenwerk, Nico

2011-09-01

We revisit the parameter estimation framework for population biological dynamical systems, and apply it to calibrate various models in epidemiology with empirical time series, namely influenza and dengue fever. When it comes to more complex models like multi-strain dynamics to describe the virus-host interaction in dengue fever, even most recently developed parameter estimation techniques, like maximum likelihood iterated filtering, come to their computational limits. However, the first results of parameter estimation with data on dengue fever from Thailand indicate a subtle interplay between stochasticity and deterministic skeleton. The deterministic system on its own already displays complex dynamics up to deterministic chaos and coexistence of multiple attractors.

Two Different Template Replicators Coexisting in the Same Protocell: Stochastic Simulation of an Extended Chemoton Model

PubMed Central

Zachar, István; Fedor, Anna; Szathmáry, Eörs

2011-01-01

The simulation of complex biochemical systems, consisting of intertwined subsystems, is a challenging task in computational biology. The complex biochemical organization of the cell is effectively modeled by the minimal cell model called chemoton, proposed by Gánti. Since the chemoton is a system consisting of a large but fixed number of interacting molecular species, it can effectively be implemented in a process algebra-based language such as the BlenX programming language. The stochastic model behaves comparably to previous continuous deterministic models of the chemoton. Additionally to the well-known chemoton, we also implemented an extended version with two competing template cycles. The new insight from our study is that the coupling of reactions in the chemoton ensures that these templates coexist providing an alternative solution to Eigen's paradox. Our technical innovation involves the introduction of a two-state switch to control cell growth and division, thus providing an example for hybrid methods in BlenX. Further developments to the BlenX language are suggested in the Appendix. PMID:21818258

Two different template replicators coexisting in the same protocell: stochastic simulation of an extended chemoton model.

PubMed

Zachar, István; Fedor, Anna; Szathmáry, Eörs

2011-01-01

The simulation of complex biochemical systems, consisting of intertwined subsystems, is a challenging task in computational biology. The complex biochemical organization of the cell is effectively modeled by the minimal cell model called chemoton, proposed by Gánti. Since the chemoton is a system consisting of a large but fixed number of interacting molecular species, it can effectively be implemented in a process algebra-based language such as the BlenX programming language. The stochastic model behaves comparably to previous continuous deterministic models of the chemoton. Additionally to the well-known chemoton, we also implemented an extended version with two competing template cycles. The new insight from our study is that the coupling of reactions in the chemoton ensures that these templates coexist providing an alternative solution to Eigen's paradox. Our technical innovation involves the introduction of a two-state switch to control cell growth and division, thus providing an example for hybrid methods in BlenX. Further developments to the BlenX language are suggested in the Appendix.

Network approaches for expert decisions in sports.

PubMed

Glöckner, Andreas; Heinen, Thomas; Johnson, Joseph G; Raab, Markus

2012-04-01

This paper focuses on a model comparison to explain choices based on gaze behavior via simulation procedures. We tested two classes of models, a parallel constraint satisfaction (PCS) artificial neuronal network model and an accumulator model in a handball decision-making task from a lab experiment. Both models predict action in an option-generation task in which options can be chosen from the perspective of a playmaker in handball (i.e., passing to another player or shooting at the goal). Model simulations are based on a dataset of generated options together with gaze behavior measurements from 74 expert handball players for 22 pieces of video footage. We implemented both classes of models as deterministic vs. probabilistic models including and excluding fitted parameters. Results indicated that both classes of models can fit and predict participants' initially generated options based on gaze behavior data, and that overall, the classes of models performed about equally well. Early fixations were thereby particularly predictive for choices. We conclude that the analyses of complex environments via network approaches can be successfully applied to the field of experts' decision making in sports and provide perspectives for further theoretical developments. Copyright © 2011 Elsevier B.V. All rights reserved.

Vacuum effects over the closing of enterocutaneous fistulae: a mathematical modeling approach.

PubMed

Cattoni, D I; Chara, O

2008-01-01

Enterocutaneous fistulae are pathological communications between the intestinal lumen and the abdominal skin. Under surgery the mortality of this pathology is very high, therefore a vacuum applying system has been carried previously on attempting to close these fistulae. The objective of this article is the understanding of how these treatments might work through deterministic mathematical modelling. Four models are here proposed based on several assumptions involving: the conservation of the flow in the fistula, a low enough Reynolds number justifying a laminar flow, the use of Poiseuille law to model the movement of the fistulous liquid, as well as phenomenological equations including the fistula tissue and intermediate chamber compressibility. Interestingly, the four models show fistulae closing behaviour during experimental time (t<60 sec). To compare the models, both, simulations and pressure measurements, carried out on the vacuum connected to the patients, are performed. Time course of pressure are then simulated (from each model) and fitted to the experimental data. The model which best describes actual measurements shows exponential pumping flux kinetics. Applying this model, numerical relationship between the fistula compressibility and closure time is presented. The models here developed would contribute to clarify the treatment mechanism and, eventually, improve the fistulae treatment.

The Diffusion Model Is Not a Deterministic Growth Model: Comment on Jones and Dzhafarov (2014)

PubMed Central

Smith, Philip L.; Ratcliff, Roger; McKoon, Gail

2015-01-01

Jones and Dzhafarov (2014) claim that several current models of speeded decision making in cognitive tasks, including the diffusion model, can be viewed as special cases of other general models or model classes. The general models can be made to match any set of response time (RT) distribution and accuracy data exactly by a suitable choice of parameters and so are unfalsifiable. The implication of their claim is that models like the diffusion model are empirically testable only by artificially restricting them to exclude unfalsifiable instances of the general model. We show that Jones and Dzhafarov’s argument depends on enlarging the class of “diffusion” models to include models in which there is little or no diffusion. The unfalsifiable models are deterministic or near-deterministic growth models, from which the effects of within-trial variability have been removed or in which they are constrained to be negligible. These models attribute most or all of the variability in RT and accuracy to across-trial variability in the rate of evidence growth, which is permitted to be distributed arbitrarily and to vary freely across experimental conditions. In contrast, in the standard diffusion model, within-trial variability in evidence is the primary determinant of variability in RT. Across-trial variability, which determines the relative speed of correct responses and errors, is theoretically and empirically constrained. Jones and Dzhafarov’s attempt to include the diffusion model in a class of models that also includes deterministic growth models misrepresents and trivializes it and conveys a misleading picture of cognitive decision-making research. PMID:25347314

A hybrid CS-SA intelligent approach to solve uncertain dynamic facility layout problems considering dependency of demands

NASA Astrophysics Data System (ADS)

Moslemipour, Ghorbanali

2018-07-01

This paper aims at proposing a quadratic assignment-based mathematical model to deal with the stochastic dynamic facility layout problem. In this problem, product demands are assumed to be dependent normally distributed random variables with known probability density function and covariance that change from period to period at random. To solve the proposed model, a novel hybrid intelligent algorithm is proposed by combining the simulated annealing and clonal selection algorithms. The proposed model and the hybrid algorithm are verified and validated using design of experiment and benchmark methods. The results show that the hybrid algorithm has an outstanding performance from both solution quality and computational time points of view. Besides, the proposed model can be used in both of the stochastic and deterministic situations.

Dynamics of stochastic SEIS epidemic model with varying population size

NASA Astrophysics Data System (ADS)

Liu, Jiamin; Wei, Fengying

2016-12-01

We introduce the stochasticity into a deterministic model which has state variables susceptible-exposed-infected with varying population size in this paper. The infected individuals could return into susceptible compartment after recovering. We show that the stochastic model possesses a unique global solution under building up a suitable Lyapunov function and using generalized Itô's formula. The densities of the exposed and infected tend to extinction when some conditions are being valid. Moreover, the conditions of persistence to a global solution are derived when the parameters are subject to some simple criteria. The stochastic model admits a stationary distribution around the endemic equilibrium, which means that the disease will prevail. To check the validity of the main results, numerical simulations are demonstrated as end of this contribution.

Optimization Of Mean-Semivariance-Skewness Portfolio Selection Model In Fuzzy Random Environment

NASA Astrophysics Data System (ADS)

Chatterjee, Amitava; Bhattacharyya, Rupak; Mukherjee, Supratim; Kar, Samarjit

2010-10-01

The purpose of the paper is to construct a mean-semivariance-skewness portfolio selection model in fuzzy random environment. The objective is to maximize the skewness with predefined maximum risk tolerance and minimum expected return. Here the security returns in the objectives and constraints are assumed to be fuzzy random variables in nature and then the vagueness of the fuzzy random variables in the objectives and constraints are transformed into fuzzy variables which are similar to trapezoidal numbers. The newly formed fuzzy model is then converted into a deterministic optimization model. The feasibility and effectiveness of the proposed method is verified by numerical example extracted from Bombay Stock Exchange (BSE). The exact parameters of fuzzy membership function and probability density function are obtained through fuzzy random simulating the past dates.

Adaptive regularization network based neural modeling paradigm for nonlinear adaptive estimation of cerebral evoked potentials.

PubMed

Zhang, Jian-Hua; Böhme, Johann F

2007-11-01

In this paper we report an adaptive regularization network (ARN) approach to realizing fast blind separation of cerebral evoked potentials (EPs) from background electroencephalogram (EEG) activity with no need to make any explicit assumption on the statistical (or deterministic) signal model. The ARNs are proposed to construct nonlinear EEG and EP signal models. A novel adaptive regularization training (ART) algorithm is proposed to improve the generalization performance of the ARN. Two adaptive neural modeling methods based on the ARN are developed and their implementation and performance analysis are also presented. The computer experiments using simulated and measured visual evoked potential (VEP) data have shown that the proposed ARN modeling paradigm yields computationally efficient and more accurate VEP signal estimation owing to its intrinsic model-free and nonlinear processing characteristics.

Epidemic spreading on adaptively weighted scale-free networks.

PubMed

Sun, Mengfeng; Zhang, Haifeng; Kang, Huiyan; Zhu, Guanghu; Fu, Xinchu

2017-04-01

We introduce three modified SIS models on scale-free networks that take into account variable population size, nonlinear infectivity, adaptive weights, behavior inertia and time delay, so as to better characterize the actual spread of epidemics. We develop new mathematical methods and techniques to study the dynamics of the models, including the basic reproduction number, and the global asymptotic stability of the disease-free and endemic equilibria. We show the disease-free equilibrium cannot undergo a Hopf bifurcation. We further analyze the effects of local information of diseases and various immunization schemes on epidemic dynamics. We also perform some stochastic network simulations which yield quantitative agreement with the deterministic mean-field approach.

Effects of deterministic surface distortions on reflector antenna performance

NASA Technical Reports Server (NTRS)

Rahmat-Samii, Y.

1985-01-01

Systematic distortions of reflector antenna surfaces can cause antenna radiation patterns to be undesirably different from those of perfectly smooth reflector surfaces. In this paper, a simulation model for systematic distortions is described which permits an efficient computation of the effects of distortions in the reflector pattern. The model uses a vector diffraction physical optics analysis for the determination of both the co-polar and cross-polar fields. An interpolation scheme is also presented for the description of reflector surfaces which are prescribed by discrete points. Representative numerical results are presented for reflectors with sinusoidally and thermally distorted surfaces. Finally, comparisons are made between the measured and calculated patterns of a slowly-varying distorted offset parabolic reflector.

Developing and Implementing the Data Mining Algorithms in RAVEN

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

Sen, Ramazan Sonat; Maljovec, Daniel Patrick; Alfonsi, Andrea

The RAVEN code is becoming a comprehensive tool to perform probabilistic risk assessment, uncertainty quantification, and verification and validation. The RAVEN code is being developed to support many programs and to provide a set of methodologies and algorithms for advanced analysis. Scientific computer codes can generate enormous amounts of data. To post-process and analyze such data might, in some cases, take longer than the initial software runtime. Data mining algorithms/methods help in recognizing and understanding patterns in the data, and thus discover knowledge in databases. The methodologies used in the dynamic probabilistic risk assessment or in uncertainty and error quantificationmore » analysis couple system/physics codes with simulation controller codes, such as RAVEN. RAVEN introduces both deterministic and stochastic elements into the simulation while the system/physics code model the dynamics deterministically. A typical analysis is performed by sampling values of a set of parameter values. A major challenge in using dynamic probabilistic risk assessment or uncertainty and error quantification analysis for a complex system is to analyze the large number of scenarios generated. Data mining techniques are typically used to better organize and understand data, i.e. recognizing patterns in the data. This report focuses on development and implementation of Application Programming Interfaces (APIs) for different data mining algorithms, and the application of these algorithms to different databases.« less

({The) Solar System Large Planets influence on a new Maunder Miniμm}

NASA Astrophysics Data System (ADS)

Yndestad, Harald; Solheim, Jan-Erik

2016-04-01

In 1890´s G. Spörer and E. W. Maunder (1890) reported that the solar activity stopped in a period of 70 years from 1645 to 1715. Later a reconstruction of the solar activity confirms the grand minima Maunder (1640-1720), Spörer (1390-1550), Wolf (1270-1340), and the minima Oort (1010-1070) and Dalton (1785-1810) since the year 1000 A.D. (Usoskin et al. 2007). These minimum periods have been associated with less irradiation from the Sun and cold climate periods on Earth. An identification of a three grand Maunder type periods and two Dalton type periods in a period thousand years, indicates that sooner or later there will be a colder climate on Earth from a new Maunder- or Dalton- type period. The cause of these minimum periods, are not well understood. An expected new Maunder-type period is based on the properties of solar variability. If the solar variability has a deterministic element, we can estimate better a new Maunder grand minimum. A random solar variability can only explain the past. This investigation is based on the simple idea that if the solar variability has a deterministic property, it must have a deterministic source, as a first cause. If this deterministic source is known, we can compute better estimates the next expected Maunder grand minimum period. The study is based on a TSI ACRIM data series from 1700, a TSI ACRIM data series from 1000 A.D., sunspot data series from 1611 and a Solar Barycenter orbit data series from 1000. The analysis method is based on a wavelet spectrum analysis, to identify stationary periods, coincidence periods and their phase relations. The result shows that the TSI variability and the sunspots variability have deterministic oscillations, controlled by the large planets Jupiter, Uranus and Neptune, as the first cause. A deterministic model of TSI variability and sunspot variability confirms the known minimum and grand minimum periods since 1000. From this deterministic model we may expect a new Maunder type sunspot minimum period from about 2018 to 2055. The deterministic model of a TSI ACRIM data series from 1700 computes a new Maunder type grand minimum period from 2015 to 2071. A model of the longer TSI ACRIM data series from 1000 computes a new Dalton to Maunder type minimum irradiation period from 2047 to 2068.

Improved Climate Simulations through a Stochastic Parameterization of Ocean Eddies

NASA Astrophysics Data System (ADS)

Williams, Paul; Howe, Nicola; Gregory, Jonathan; Smith, Robin; Joshi, Manoj

2016-04-01

In climate simulations, the impacts of the sub-grid scales on the resolved scales are conventionally represented using deterministic closure schemes, which assume that the impacts are uniquely determined by the resolved scales. Stochastic parameterization relaxes this assumption, by sampling the sub-grid variability in a computationally inexpensive manner. This presentation shows that the simulated climatological state of the ocean is improved in many respects by implementing a simple stochastic parameterization of ocean eddies into a coupled atmosphere-ocean general circulation model. Simulations from a high-resolution, eddy-permitting ocean model are used to calculate the eddy statistics needed to inject realistic stochastic noise into a low-resolution, non-eddy-permitting version of the same model. A suite of four stochastic experiments is then run to test the sensitivity of the simulated climate to the noise definition, by varying the noise amplitude and decorrelation time within reasonable limits. The addition of zero-mean noise to the ocean temperature tendency is found to have a non-zero effect on the mean climate. Specifically, in terms of the ocean temperature and salinity fields both at the surface and at depth, the noise reduces many of the biases in the low-resolution model and causes it to more closely resemble the high-resolution model. The variability of the strength of the global ocean thermohaline circulation is also improved. It is concluded that stochastic ocean perturbations can yield reductions in climate model error that are comparable to those obtained by refining the resolution, but without the increased computational cost. Therefore, stochastic parameterizations of ocean eddies have the potential to significantly improve climate simulations. Reference PD Williams, NJ Howe, JM Gregory, RS Smith, and MM Joshi (2016) Improved Climate Simulations through a Stochastic Parameterization of Ocean Eddies. Journal of Climate, under revision.

Epidemic patch models applied to pandemic influenza: contact matrix, stochasticity, robustness of predictions.

PubMed

Lunelli, Antonella; Pugliese, Andrea; Rizzo, Caterina

2009-07-01

Due to the recent emergence of H5N1 virus, the modelling of pandemic influenza has become a relevant issue. Here we present an SEIR model formulated to simulate a possible outbreak in Italy, analysing its structure and, more generally, the effect of including specific details into a model. These details regard population heterogeneities, such as age and spatial distribution, as well as stochasticity, that regulates the epidemic dynamics when the number of infectives is low. We discuss and motivate the specific modelling choices made when building the model and investigate how the model details influence the predicted dynamics. Our analysis may help in deciding which elements of complexity are worth including in the design of a deterministic model for pandemic influenza, in a balance between, on the one hand, keeping the model computationally efficient and the number of parameters low and, on the other hand, maintaining the necessary realistic features.

Tsunamigenic scenarios for southern Peru and northern Chile seismic gap: Deterministic and probabilistic hybrid approach for hazard assessment

NASA Astrophysics Data System (ADS)

González-Carrasco, J. F.; Gonzalez, G.; Aránguiz, R.; Yanez, G. A.; Melgar, D.; Salazar, P.; Shrivastava, M. N.; Das, R.; Catalan, P. A.; Cienfuegos, R.

2017-12-01

Plausible worst-case tsunamigenic scenarios definition plays a relevant role in tsunami hazard assessment focused in emergency preparedness and evacuation planning for coastal communities. During the last decade, the occurrence of major and moderate tsunamigenic earthquakes along worldwide subduction zones has given clues about critical parameters involved in near-field tsunami inundation processes, i.e. slip spatial distribution, shelf resonance of edge waves and local geomorphology effects. To analyze the effects of these seismic and hydrodynamic variables over the epistemic uncertainty of coastal inundation, we implement a combined methodology using deterministic and probabilistic approaches to construct 420 tsunamigenic scenarios in a mature seismic gap of southern Peru and northern Chile, extended from 17ºS to 24ºS. The deterministic scenarios are calculated using a regional distribution of trench-parallel gravity anomaly (TPGA) and trench-parallel topography anomaly (TPTA), three-dimensional Slab 1.0 worldwide subduction zones geometry model and published interseismic coupling (ISC) distributions. As result, we find four higher slip deficit zones interpreted as major seismic asperities of the gap, used in a hierarchical tree scheme to generate ten tsunamigenic scenarios with seismic magnitudes fluctuates between Mw 8.4 to Mw 8.9. Additionally, we construct ten homogeneous slip scenarios as inundation baseline. For the probabilistic approach, we implement a Karhunen - Loève expansion to generate 400 stochastic tsunamigenic scenarios over the maximum extension of the gap, with the same magnitude range of the deterministic sources. All the scenarios are simulated through a non-hydrostatic tsunami model Neowave 2D, using a classical nesting scheme, for five coastal major cities in northern Chile (Arica, Iquique, Tocopilla, Mejillones and Antofagasta) obtaining high resolution data of inundation depth, runup, coastal currents and sea level elevation. The probabilistic kinematic tsunamigenic scenarios give a more realistic slip patterns, similar to maximum slip amount of major past earthquakes. For all studied sites, the peak of slip location and shelf resonance is a first order control for the observed coastal inundation depths results.

Statistically qualified neuro-analytic failure detection method and system

DOEpatents

Vilim, Richard B.; Garcia, Humberto E.; Chen, Frederick W.

2002-03-02

An apparatus and method for monitoring a process involve development and application of a statistically qualified neuro-analytic (SQNA) model to accurately and reliably identify process change. The development of the SQNA model is accomplished in two stages: deterministic model adaption and stochastic model modification of the deterministic model adaptation. Deterministic model adaption involves formulating an analytic model of the process representing known process characteristics, augmenting the analytic model with a neural network that captures unknown process characteristics, and training the resulting neuro-analytic model by adjusting the neural network weights according to a unique scaled equation error minimization technique. Stochastic model modification involves qualifying any remaining uncertainty in the trained neuro-analytic model by formulating a likelihood function, given an error propagation equation, for computing the probability that the neuro-analytic model generates measured process output. Preferably, the developed SQNA model is validated using known sequential probability ratio tests and applied to the process as an on-line monitoring system. Illustrative of the method and apparatus, the method is applied to a peristaltic pump system.

Probabilistic track coverage in cooperative sensor networks.

PubMed

Ferrari, Silvia; Zhang, Guoxian; Wettergren, Thomas A

2010-12-01

The quality of service of a network performing cooperative track detection is represented by the probability of obtaining multiple elementary detections over time along a target track. Recently, two different lines of research, namely, distributed-search theory and geometric transversals, have been used in the literature for deriving the probability of track detection as a function of random and deterministic sensors' positions, respectively. In this paper, we prove that these two approaches are equivalent under the same problem formulation. Also, we present a new performance function that is derived by extending the geometric-transversal approach to the case of random sensors' positions using Poisson flats. As a result, a unified approach for addressing track detection in both deterministic and probabilistic sensor networks is obtained. The new performance function is validated through numerical simulations and is shown to bring about considerable computational savings for both deterministic and probabilistic sensor networks.

Deterministic blade row interactions in a centrifugal compressor stage

NASA Technical Reports Server (NTRS)

Kirtley, K. R.; Beach, T. A.

1991-01-01

The three-dimensional viscous flow in a low speed centrifugal compressor stage is simulated using an average passage Navier-Stokes analysis. The impeller discharge flow is of the jet/wake type with low momentum fluid in the shroud-pressure side corner coincident with the tip leakage vortex. This nonuniformity introduces periodic unsteadiness in the vane frame of reference. The effect of such deterministic unsteadiness on the time-mean is included in the analysis through the average passage stress, which allows the analysis of blade row interactions. The magnitude of the divergence of the deterministic unsteady stress is of the order of the divergence of the Reynolds stress over most of the span, from the impeller trailing edge to the vane throat. Although the potential effects on the blade trailing edge from the diffuser vane are small, strong secondary flows generated by the impeller degrade the performance of the diffuser vanes.

Extraction of angle deterministic signals in the presence of stationary speed fluctuations with cyclostationary blind source separation

NASA Astrophysics Data System (ADS)

Delvecchio, S.; Antoni, J.

2012-02-01

This paper addresses the use of a cyclostationary blind source separation algorithm (namely RRCR) to extract angle deterministic signals from mechanical rotating machines in presence of stationary speed fluctuations. This means that only phase fluctuations while machine is running in steady-state conditions are considered while run-up or run-down speed variations are not taken into account. The machine is also supposed to run in idle conditions so non-stationary phenomena due to the load are not considered. It is theoretically assessed that in such operating conditions the deterministic (periodic) signal in the angle domain becomes cyclostationary at first and second orders in the time domain. This fact justifies the use of the RRCR algorithm, which is able to directly extract the angle deterministic signal from the time domain without performing any kind of interpolation. This is particularly valuable when angular resampling fails because of uncontrolled speed fluctuations. The capability of the proposed approach is verified by means of simulated and actual vibration signals captured on a pneumatic screwdriver handle. In this particular case not only the extraction of the angle deterministic part can be performed but also the separation of the main sources of excitation (i.e. motor shaft imbalance, epyciloidal gear meshing and air pressure forces) affecting the user hand during operations.

MONALISA for stochastic simulations of Petri net models of biochemical systems.

PubMed

Balazki, Pavel; Lindauer, Klaus; Einloft, Jens; Ackermann, Jörg; Koch, Ina

2015-07-10

The concept of Petri nets (PN) is widely used in systems biology and allows modeling of complex biochemical systems like metabolic systems, signal transduction pathways, and gene expression networks. In particular, PN allows the topological analysis based on structural properties, which is important and useful when quantitative (kinetic) data are incomplete or unknown. Knowing the kinetic parameters, the simulation of time evolution of such models can help to study the dynamic behavior of the underlying system. If the number of involved entities (molecules) is low, a stochastic simulation should be preferred against the classical deterministic approach of solving ordinary differential equations. The Stochastic Simulation Algorithm (SSA) is a common method for such simulations. The combination of the qualitative and semi-quantitative PN modeling and stochastic analysis techniques provides a valuable approach in the field of systems biology. Here, we describe the implementation of stochastic analysis in a PN environment. We extended MONALISA - an open-source software for creation, visualization and analysis of PN - by several stochastic simulation methods. The simulation module offers four simulation modes, among them the stochastic mode with constant firing rates and Gillespie's algorithm as exact and approximate versions. The simulator is operated by a user-friendly graphical interface and accepts input data such as concentrations and reaction rate constants that are common parameters in the biological context. The key features of the simulation module are visualization of simulation, interactive plotting, export of results into a text file, mathematical expressions for describing simulation parameters, and up to 500 parallel simulations of the same parameter sets. To illustrate the method we discuss a model for insulin receptor recycling as case study. We present a software that combines the modeling power of Petri nets with stochastic simulation of dynamic processes in a user-friendly environment supported by an intuitive graphical interface. The program offers a valuable alternative to modeling, using ordinary differential equations, especially when simulating single-cell experiments with low molecule counts. The ability to use mathematical expressions provides an additional flexibility in describing the simulation parameters. The open-source distribution allows further extensions by third-party developers. The software is cross-platform and is licensed under the Artistic License 2.0.

Emulation: A fast stochastic Bayesian method to eliminate model space

NASA Astrophysics Data System (ADS)

Roberts, Alan; Hobbs, Richard; Goldstein, Michael

2010-05-01

Joint inversion of large 3D datasets has been the goal of geophysicists ever since the datasets first started to be produced. There are two broad approaches to this kind of problem, traditional deterministic inversion schemes and more recently developed Bayesian search methods, such as MCMC (Markov Chain Monte Carlo). However, using both these kinds of schemes has proved prohibitively expensive, both in computing power and time cost, due to the normally very large model space which needs to be searched using forward model simulators which take considerable time to run. At the heart of strategies aimed at accomplishing this kind of inversion is the question of how to reliably and practicably reduce the size of the model space in which the inversion is to be carried out. Here we present a practical Bayesian method, known as emulation, which can address this issue. Emulation is a Bayesian technique used with considerable success in a number of technical fields, such as in astronomy, where the evolution of the universe has been modelled using this technique, and in the petroleum industry where history matching is carried out of hydrocarbon reservoirs. The method of emulation involves building a fast-to-compute uncertainty-calibrated approximation to a forward model simulator. We do this by modelling the output data from a number of forward simulator runs by a computationally cheap function, and then fitting the coefficients defining this function to the model parameters. By calibrating the error of the emulator output with respect to the full simulator output, we can use this to screen out large areas of model space which contain only implausible models. For example, starting with what may be considered a geologically reasonable prior model space of 10000 models, using the emulator we can quickly show that only models which lie within 10% of that model space actually produce output data which is plausibly similar in character to an observed dataset. We can thus much more tightly constrain the input model space for a deterministic inversion or MCMC method. By using this technique jointly on several datasets (specifically seismic, gravity, and magnetotelluric (MT) describing the same region), we can include in our modelling uncertainties in the data measurements, the relationships between the various physical parameters involved, as well as the model representation uncertainty, and at the same time further reduce the range of plausible models to several percent of the original model space. Being stochastic in nature, the output posterior parameter distributions also allow our understanding of/beliefs about a geological region can be objectively updated, with full assessment of uncertainties, and so the emulator is also an inversion-type tool in it's own right, with the advantage (as with any Bayesian method) that our uncertainties from all sources (both data and model) can be fully evaluated.

Dimensionality of Motion and Binding Valency Govern Receptor-Ligand Kinetics As Revealed by Agent-Based Modeling.

PubMed

Lehnert, Teresa; Figge, Marc Thilo

2017-01-01

Mathematical modeling and computer simulations have become an integral part of modern biological research. The strength of theoretical approaches is in the simplification of complex biological systems. We here consider the general problem of receptor-ligand binding in the context of antibody-antigen binding. On the one hand, we establish a quantitative mapping between macroscopic binding rates of a deterministic differential equation model and their microscopic equivalents as obtained from simulating the spatiotemporal binding kinetics by stochastic agent-based models. On the other hand, we investigate the impact of various properties of B cell-derived receptors-such as their dimensionality of motion, morphology, and binding valency-on the receptor-ligand binding kinetics. To this end, we implemented an algorithm that simulates antigen binding by B cell-derived receptors with a Y-shaped morphology that can move in different dimensionalities, i.e., either as membrane-anchored receptors or as soluble receptors. The mapping of the macroscopic and microscopic binding rates allowed us to quantitatively compare different agent-based model variants for the different types of B cell-derived receptors. Our results indicate that the dimensionality of motion governs the binding kinetics and that this predominant impact is quantitatively compensated by the bivalency of these receptors.

Dimensionality of Motion and Binding Valency Govern Receptor–Ligand Kinetics As Revealed by Agent-Based Modeling

PubMed Central

Lehnert, Teresa; Figge, Marc Thilo

2017-01-01

Mathematical modeling and computer simulations have become an integral part of modern biological research. The strength of theoretical approaches is in the simplification of complex biological systems. We here consider the general problem of receptor–ligand binding in the context of antibody–antigen binding. On the one hand, we establish a quantitative mapping between macroscopic binding rates of a deterministic differential equation model and their microscopic equivalents as obtained from simulating the spatiotemporal binding kinetics by stochastic agent-based models. On the other hand, we investigate the impact of various properties of B cell-derived receptors—such as their dimensionality of motion, morphology, and binding valency—on the receptor–ligand binding kinetics. To this end, we implemented an algorithm that simulates antigen binding by B cell-derived receptors with a Y-shaped morphology that can move in different dimensionalities, i.e., either as membrane-anchored receptors or as soluble receptors. The mapping of the macroscopic and microscopic binding rates allowed us to quantitatively compare different agent-based model variants for the different types of B cell-derived receptors. Our results indicate that the dimensionality of motion governs the binding kinetics and that this predominant impact is quantitatively compensated by the bivalency of these receptors. PMID:29250071

Necessary conditions for the emergence of homochirality via autocatalytic self-replication

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

Stich, Michael; Ribó, Josep M.; Blackmond, Donna G., E-mail: blackmond@scripps.edu

We analyze a recent proposal for spontaneous mirror symmetry breaking based on the coupling of first-order enantioselective autocatalysis and direct production of the enantiomers that invokes a critical role for intrinsic reaction noise. For isolated systems, the racemic state is the unique stable outcome for both stochastic and deterministic dynamics when the system is in compliance with the constraints dictated by the thermodynamics of chemical reaction processes. In open systems, the racemic outcome also results for both stochastic and deterministic dynamics when driving the autocatalysis unidirectionally by external reagents. Nonracemic states can result in the latter only if the reversemore » reactions are strictly zero: these are kinetically controlled outcomes for small populations and volumes, and can be simulated by stochastic dynamics. However, the stability of the thermodynamic limit proves that the racemic outcome is the unique stable state for strictly irreversible externally driven autocatalysis. These findings contradict the suggestion that the inhibition requirement of the Frank autocatalytic model for the emergence of homochirality may be relaxed in a noise-induced mechanism.« less

Simulated maximum likelihood method for estimating kinetic rates in gene expression.

PubMed

Tian, Tianhai; Xu, Songlin; Gao, Junbin; Burrage, Kevin

2007-01-01

Kinetic rate in gene expression is a key measurement of the stability of gene products and gives important information for the reconstruction of genetic regulatory networks. Recent developments in experimental technologies have made it possible to measure the numbers of transcripts and protein molecules in single cells. Although estimation methods based on deterministic models have been proposed aimed at evaluating kinetic rates from experimental observations, these methods cannot tackle noise in gene expression that may arise from discrete processes of gene expression, small numbers of mRNA transcript, fluctuations in the activity of transcriptional factors and variability in the experimental environment. In this paper, we develop effective methods for estimating kinetic rates in genetic regulatory networks. The simulated maximum likelihood method is used to evaluate parameters in stochastic models described by either stochastic differential equations or discrete biochemical reactions. Different types of non-parametric density functions are used to measure the transitional probability of experimental observations. For stochastic models described by biochemical reactions, we propose to use the simulated frequency distribution to evaluate the transitional density based on the discrete nature of stochastic simulations. The genetic optimization algorithm is used as an efficient tool to search for optimal reaction rates. Numerical results indicate that the proposed methods can give robust estimations of kinetic rates with good accuracy.

A spatial model to aggregate point-source and nonpoint-source water-quality data for large areas

USGS Publications Warehouse

White, D.A.; Smith, R.A.; Price, C.V.; Alexander, R.B.; Robinson, K.W.

1992-01-01

More objective and consistent methods are needed to assess water quality for large areas. A spatial model, one that capitalizes on the topologic relationships among spatial entities, to aggregate pollution sources from upstream drainage areas is described that can be implemented on land surfaces having heterogeneous water-pollution effects. An infrastructure of stream networks and drainage basins, derived from 1:250,000-scale digital-elevation models, define the hydrologic system in this spatial model. The spatial relationships between point- and nonpoint pollution sources and measurement locations are referenced to the hydrologic infrastructure with the aid of a geographic information system. A maximum-branching algorithm has been developed to simulate the effects of distance from a pollutant source to an arbitrary downstream location, a function traditionally employed in deterministic water quality models. ?? 1992.

An investigation of air transportation technology at the Massachusetts Institute of Technology, 1990-1991

NASA Technical Reports Server (NTRS)

Simpson, Robert W.

1991-01-01

Brief summaries are given of research activities at the Massachusetts Institute of Technology (MIT) under the sponsorship of the FAA/NASA Joint University Program. Topics covered include hazard assessment and cockpit presentation issues for microburst alerting systems; the situational awareness effect of automated air traffic control (ATC) datalink clearance amendments; a graphical simulation system for adaptive, automated approach spacing; an expert system for temporal planning with application to runway configuration management; deterministic multi-zone ice accretion modeling; alert generation and cockpit presentation for an integrated microburst alerting system; and passive infrared ice detection for helicopter applications.

Noise-induced transitions in a double-well oscillator with nonlinear dissipation.

PubMed

Semenov, Vladimir V; Neiman, Alexander B; Vadivasova, Tatyana E; Anishchenko, Vadim S

2016-05-01

We develop a model of bistable oscillator with nonlinear dissipation. Using a numerical simulation and an electronic circuit realization of this system we study its response to additive noise excitations. We show that depending on noise intensity the system undergoes multiple qualitative changes in the structure of its steady-state probability density function (PDF). In particular, the PDF exhibits two pitchfork bifurcations versus noise intensity, which we describe using an effective potential and corresponding normal form of the bifurcation. These stochastic effects are explained by the partition of the phase space by the nullclines of the deterministic oscillator.

Modeling and Properties of Nonlinear Stochastic Dynamical System of Continuous Culture

NASA Astrophysics Data System (ADS)

Wang, Lei; Feng, Enmin; Ye, Jianxiong; Xiu, Zhilong

The stochastic counterpart to the deterministic description of continuous fermentation with ordinary differential equation is investigated in the process of glycerol bio-dissimilation to 1,3-propanediol by Klebsiella pneumoniae. We briefly discuss the continuous fermentation process driven by three-dimensional Brownian motion and Lipschitz coefficients, which is suitable for the factual fermentation. Subsequently, we study the existence and uniqueness of solutions for the stochastic system as well as the boundedness of the Two-order Moment and the Markov property of the solution. Finally stochastic simulation is carried out under the Stochastic Euler-Maruyama method.

GPELab, a Matlab toolbox to solve Gross-Pitaevskii equations II: Dynamics and stochastic simulations

NASA Astrophysics Data System (ADS)

Antoine, Xavier; Duboscq, Romain

2015-08-01

GPELab is a free Matlab toolbox for modeling and numerically solving large classes of systems of Gross-Pitaevskii equations that arise in the physics of Bose-Einstein condensates. The aim of this second paper, which follows (Antoine and Duboscq, 2014), is to first present the various pseudospectral schemes available in GPELab for computing the deterministic and stochastic nonlinear dynamics of Gross-Pitaevskii equations (Antoine, et al., 2013). Next, the corresponding GPELab functions are explained in detail. Finally, some numerical examples are provided to show how the code works for the complex dynamics of BEC problems.

A 3-month long operational implementation of an ensemble prediction system of storm surge for the city of Venice

NASA Astrophysics Data System (ADS)

Mel, Riccardo; Lionello, Piero

2014-05-01

Advantages of an ensemble prediction forecast (EPF) technique that has been used for sea level (SL) prediction at the Northern Adriatic coast are investigated. The aims is to explore whether EPF is more precise than the traditional Deterministic Forecast (DF) and the value of the added information, mainly on forecast uncertainty. Improving the SL forecast for the city of Venice is of paramount importance for the management and maintenance of this historical city and for operating the movable barriers that are presently being built for its protection. The operational practice is simulated for three months from 1st October to 31st December 2010. The EPF is based on the HYPSE model, which is a standard single-layer nonlinear shallow water model, whose equations are derived from the depth averaged momentum equations and predicts the SL. A description of the model is available in the scientific literature. Forcing of HYPSE are provided by three different sets of 3-hourly ECMWF 10m-wind and MSLP fields: the high resolution meteorological forecast (which is used for the deterministic SL forecast, DF), the control run forecast (CRF, that differs from the DF forecast only for it lower meteorological fields resolution) and the 50 ensemble members of the ECMWF EPS (which are used for the SL-EPS. The resolution of DF fields is T1279 and resolution of both CRF and ECMWF EPS fields is T639 resolution. The 10m wind and MSLP fields have been downloaded at 0.125degs (DF) and 0.25degs(CRF and EPS) and linearly interpolated to the HYPSE grid (which is the same for all simulations). The version of HYPSE used in the SR EPS uses a rectangular mesh grid of variable size, which has the minimum grid step (0.03 degrees) in the northern part of the Adriatic Sea, from where grid step increases with a 1.01 factor in both latitude and longitude (In practice, resolution varies in the range from 3.3 to 7km). Results are analyzed considering the EPS spread, the rms of the simulations, the Brier Skill Score and are compared to observations at tide gauges distributed along the Croatian and Italian coast of the Adriatic Sea. It is shown that the ensemble spread is indeed a reliable indicator of the uncertainty of the storm surge prediction. Further, results show how uncertainty depends on the predicted value of sea level and how it increases with the forecast time range. The accuracy of the ensemble mean forecast is actually larger than that of the deterministic forecast, though the latter is produced by meteorological forcings at higher resolution

A deterministic electron, photon, proton and heavy ion transport suite for the study of the Jovian moon Europa

NASA Astrophysics Data System (ADS)

Badavi, Francis F.; Blattnig, Steve R.; Atwell, William; Nealy, John E.; Norman, Ryan B.

2011-02-01

A Langley research center (LaRC) developed deterministic suite of radiation transport codes describing the propagation of electron, photon, proton and heavy ion in condensed media is used to simulate the exposure from the spectral distribution of the aforementioned particles in the Jovian radiation environment. Based on the measurements by the Galileo probe (1995-2003) heavy ion counter (HIC), the choice of trapped heavy ions is limited to carbon, oxygen and sulfur (COS). The deterministic particle transport suite consists of a coupled electron photon algorithm (CEPTRN) and a coupled light heavy ion algorithm (HZETRN). The primary purpose for the development of the transport suite is to provide a means to the spacecraft design community to rapidly perform numerous repetitive calculations essential for electron, photon, proton and heavy ion exposure assessment in a complex space structure. In this paper, the reference radiation environment of the Galilean satellite Europa is used as a representative boundary condition to show the capabilities of the transport suite. While the transport suite can directly access the output electron and proton spectra of the Jovian environment as generated by the jet propulsion laboratory (JPL) Galileo interim radiation electron (GIRE) model of 2003; for the sake of relevance to the upcoming Europa Jupiter system mission (EJSM), the JPL provided Europa mission fluence spectrum, is used to produce the corresponding depth dose curve in silicon behind a default aluminum shield of 100 mils (˜0.7 g/cm2). The transport suite can also accept a geometry describing ray traced thickness file from a computer aided design (CAD) package and calculate the total ionizing dose (TID) at a specific target point within the interior of the vehicle. In that regard, using a low fidelity CAD model of the Galileo probe generated by the authors, the transport suite was verified versus Monte Carlo (MC) simulation for orbits JOI-J35 of the Galileo probe extended mission. For the upcoming EJSM mission with an expected launch date of 2020, the transport suite is used to compute the depth dose profile for the traditional aluminum silicon as a standard shield target combination, as well as simulating the shielding response of a high charge number (Z) material such as tantalum (Ta). Finally, a shield optimization algorithm is discussed which can guide the instrument designers and fabrication personnel with the choice of graded-Z shield selection and analysis.

Using cellular automata to simulate forest fire propagation in Portugal

NASA Astrophysics Data System (ADS)

Freire, Joana; daCamara, Carlos

2017-04-01

Wildfires in the Mediterranean region have severe damaging effects mainly due to large fire events [1, 2]. When restricting to Portugal, wildfires have burned over 1:4 million ha in the last decade. Considering the increasing tendency in the extent and severity of wildfires [1, 2], the availability of modeling tools of fire episodes is of crucial importance. Two main types of mathematical models are generally available, namely deterministic and stochastic models. Deterministic models attempt a description of fires, fuel and atmosphere as multiphase continua prescribing mass, momentum and energy conservation, which typically leads to systems of coupled PDEs to be solved numerically on a grid. Simpler descriptions, such as FARSITE, neglect the interaction with atmosphere and propagate the fire front using wave techniques. One of the most important stochastic models are Cellular Automata (CA), in which space is discretized into cells, and physical quantities take on a finite set of values at each cell. The cells evolve in discrete time according to a set of transition rules, and the states of the neighboring cells. In the present work, we implement and then improve a simple and fast CA model designed to operationally simulate wildfires in Portugal. The reference CA model chosen [3] has the advantage of having been applied successfully in other Mediterranean ecosystems, namely to historical fires in Greece. The model is defined on a square grid with propagation to 8 nearest and next-nearest neighbors, where each cell is characterized by 4 possible discrete states, corresponding to burning, not-yet burned, fuel-free and completely burned cells, with 4 possible rules of evolution which take into account fuel properties, meteorological conditions, and topography. As a CA model, it offers the possibility to run a very high number of simulations in order to verify and apply the model, and is easily modified by implementing additional variables and different rules for the evolution of the fire spread. We present and discuss the application of the CA model to the "Tavira wildfire" in which approximately 24,800ha were burned. The event took place in summer 2012, between July 18 and 21, and spread in the Tavira and São Brás de Alportel municipalities of Algarve, a province in the southern coast of Portugal. [1] DaCamara et. al. (2014), International Journal of Wildland Fire 23. [2] Amraoui et. al. (2013), Forest Ecology and Management 294. [3] Alexandridis et. al. (2008), Applied Mathematics and Computation 204.

Immersion freezing of supermicron mineral dust particles: freezing results, testing different schemes for describing ice nucleation, and ice nucleation active site densities.

PubMed

Wheeler, M J; Mason, R H; Steunenberg, K; Wagstaff, M; Chou, C; Bertram, A K

2015-05-14

Ice nucleation on mineral dust particles is known to be an important process in the atmosphere. To accurately implement ice nucleation on mineral dust particles in atmospheric simulations, a suitable theory or scheme is desirable to describe laboratory freezing data in atmospheric models. In the following, we investigated ice nucleation by supermicron mineral dust particles [kaolinite and Arizona Test Dust (ATD)] in the immersion mode. The median freezing temperature for ATD was measured to be approximately -30 °C compared with approximately -36 °C for kaolinite. The freezing results were then used to test four different schemes previously used to describe ice nucleation in atmospheric models. In terms of ability to fit the data (quantified by calculating the reduced chi-squared values), the following order was found for ATD (from best to worst): active site, pdf-α, deterministic, single-α. For kaolinite, the following order was found (from best to worst): active site, deterministic, pdf-α, single-α. The variation in the predicted median freezing temperature per decade change in the cooling rate for each of the schemes was also compared with experimental results from other studies. The deterministic model predicts the median freezing temperature to be independent of cooling rate, while experimental results show a weak dependence on cooling rate. The single-α, pdf-α, and active site schemes all agree with the experimental results within roughly a factor of 2. On the basis of our results and previous results where different schemes were tested, the active site scheme is recommended for describing the freezing of ATD and kaolinite particles. We also used our ice nucleation results to determine the ice nucleation active site (INAS) density for the supermicron dust particles tested. Using the data, we show that the INAS densities of supermicron kaolinite and ATD particles studied here are smaller than the INAS densities of submicron kaolinite and ATD particles previously reported in the literature.

Distinguishing between stochasticity and determinism: Examples from cell cycle duration variability.

PubMed

Pearl Mizrahi, Sivan; Sandler, Oded; Lande-Diner, Laura; Balaban, Nathalie Q; Simon, Itamar

2016-01-01

We describe a recent approach for distinguishing between stochastic and deterministic sources of variability, focusing on the mammalian cell cycle. Variability between cells is often attributed to stochastic noise, although it may be generated by deterministic components. Interestingly, lineage information can be used to distinguish between variability and determinism. Analysis of correlations within a lineage of the mammalian cell cycle duration revealed its deterministic nature. Here, we discuss the sources of such variability and the possibility that the underlying deterministic process is due to the circadian clock. Finally, we discuss the "kicked cell cycle" model and its implication on the study of the cell cycle in healthy and cancerous tissues. © 2015 WILEY Periodicals, Inc.

Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals.

PubMed

Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

2015-09-01

Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.

Scaling theory for the quasideterministic limit of continuous bifurcations.

PubMed

Kessler, David A; Shnerb, Nadav M

2012-05-01

Deterministic rate equations are widely used in the study of stochastic, interacting particles systems. This approach assumes that the inherent noise, associated with the discreteness of the elementary constituents, may be neglected when the number of particles N is large. Accordingly, it fails close to the extinction transition, when the amplitude of stochastic fluctuations is comparable with the size of the population. Here we present a general scaling theory of the transition regime for spatially extended systems. We demonstrate this through a detailed study of two fundamental models for out-of-equilibrium phase transitions: the Susceptible-Infected-Susceptible (SIS) that belongs to the directed percolation equivalence class and the Susceptible-Infected-Recovered (SIR) model belonging to the dynamic percolation class. Implementing the Ginzburg criteria we show that the width of the fluctuation-dominated region scales like N^{-κ}, where N is the number of individuals per site and κ=2/(d_{u}-d), d_{u} is the upper critical dimension. Other exponents that control the approach to the deterministic limit are shown to be calculable once κ is known. The theory is extended to include the corrections to the front velocity above the transition. It is supported by the results of extensive numerical simulations for systems of various dimensionalities.

Predicting morphological changes DS New Naga-Hammadi Barrage for extreme Nile flood flows: A Monte Carlo analysis

PubMed Central

Sattar, Ahmed M.A.; Raslan, Yasser M.

2013-01-01

While construction of the Aswan High Dam (AHD) has stopped concurrent flooding events, River Nile is still subject to low intensity flood waves resulting from controlled release of water from the dam reservoir. Analysis of flow released from New Naga-Hammadi Barrage, which is located at 3460 km downstream AHD indicated an increase in magnitude of flood released from the barrage in the past 10 years. A 2D numerical mobile bed model is utilized to investigate the possible morphological changes in the downstream of Naga-Hammadi Barrage from possible higher flood releases. Monte Carlo simulation analyses (MCS) is applied to the deterministic results of the 2D model to account for and assess the uncertainty of sediment parameters and formulations in addition to sacristy of field measurements. Results showed that the predicted volume of erosion yielded the highest uncertainty and variation from deterministic run, while navigation velocity yielded the least uncertainty. Furthermore, the error budget method is used to rank various sediment parameters for their contribution in the total prediction uncertainty. It is found that the suspended sediment contributed to output uncertainty more than other sediment parameters followed by bed load with 10% less order of magnitude. PMID:25685476

Predicting morphological changes DS New Naga-Hammadi Barrage for extreme Nile flood flows: A Monte Carlo analysis.

PubMed

Sattar, Ahmed M A; Raslan, Yasser M

2014-01-01

While construction of the Aswan High Dam (AHD) has stopped concurrent flooding events, River Nile is still subject to low intensity flood waves resulting from controlled release of water from the dam reservoir. Analysis of flow released from New Naga-Hammadi Barrage, which is located at 3460 km downstream AHD indicated an increase in magnitude of flood released from the barrage in the past 10 years. A 2D numerical mobile bed model is utilized to investigate the possible morphological changes in the downstream of Naga-Hammadi Barrage from possible higher flood releases. Monte Carlo simulation analyses (MCS) is applied to the deterministic results of the 2D model to account for and assess the uncertainty of sediment parameters and formulations in addition to sacristy of field measurements. Results showed that the predicted volume of erosion yielded the highest uncertainty and variation from deterministic run, while navigation velocity yielded the least uncertainty. Furthermore, the error budget method is used to rank various sediment parameters for their contribution in the total prediction uncertainty. It is found that the suspended sediment contributed to output uncertainty more than other sediment parameters followed by bed load with 10% less order of magnitude.

Modelling uncertainty in incompressible flow simulation using Galerkin based generalized ANOVA

NASA Astrophysics Data System (ADS)

Chakraborty, Souvik; Chowdhury, Rajib

2016-11-01

This paper presents a new algorithm, referred to here as Galerkin based generalized analysis of variance decomposition (GG-ANOVA) for modelling input uncertainties and its propagation in incompressible fluid flow. The proposed approach utilizes ANOVA to represent the unknown stochastic response. Further, the unknown component functions of ANOVA are represented using the generalized polynomial chaos expansion (PCE). The resulting functional form obtained by coupling the ANOVA and PCE is substituted into the stochastic Navier-Stokes equation (NSE) and Galerkin projection is employed to decompose it into a set of coupled deterministic 'Navier-Stokes alike' equations. Temporal discretization of the set of coupled deterministic equations is performed by employing Adams-Bashforth scheme for convective term and Crank-Nicolson scheme for diffusion term. Spatial discretization is performed by employing finite difference scheme. Implementation of the proposed approach has been illustrated by two examples. In the first example, a stochastic ordinary differential equation has been considered. This example illustrates the performance of proposed approach with change in nature of random variable. Furthermore, convergence characteristics of GG-ANOVA has also been demonstrated. The second example investigates flow through a micro channel. Two case studies, namely the stochastic Kelvin-Helmholtz instability and stochastic vortex dipole, have been investigated. For all the problems results obtained using GG-ANOVA are in excellent agreement with benchmark solutions.

Free energy of RNA-counterion interactions in a tight-binding model computed by a discrete space mapping

NASA Astrophysics Data System (ADS)

Henke, Paul S.; Mak, Chi H.

2014-08-01

The thermodynamic stability of a folded RNA is intricately tied to the counterions and the free energy of this interaction must be accounted for in any realistic RNA simulations. Extending a tight-binding model published previously, in this paper we investigate the fundamental structure of charges arising from the interaction between small functional RNA molecules and divalent ions such as Mg2+ that are especially conducive to stabilizing folded conformations. The characteristic nature of these charges is utilized to construct a discretely connected energy landscape that is then traversed via a novel application of a deterministic graph search technique. This search method can be incorporated into larger simulations of small RNA molecules and provides a fast and accurate way to calculate the free energy arising from the interactions between an RNA and divalent counterions. The utility of this algorithm is demonstrated within a fully atomistic Monte Carlo simulation of the P4-P6 domain of the Tetrahymena group I intron, in which it is shown that the counterion-mediated free energy conclusively directs folding into a compact structure.

Operations analysis (study 2.1): Program manual and users guide for the LOVES computer code

NASA Technical Reports Server (NTRS)

Wray, S. T., Jr.

1975-01-01

Information is provided necessary to use the LOVES Computer Program in its existing state, or to modify the program to include studies not properly handled by the basic model. The Users Guide defines the basic elements assembled together to form the model for servicing satellites in orbit. As the program is a simulation, the method of attack is to disassemble the problem into a sequence of events, each occurring instantaneously and each creating one or more other events in the future. The main driving force of the simulation is the deterministic launch schedule of satellites and the subsequent failure of the various modules which make up the satellites. The LOVES Computer Program uses a random number generator to simulate the failure of module elements and therefore operates over a long span of time typically 10 to 15 years. The sequence of events is varied by making several runs in succession with different random numbers resulting in a Monte Carlo technique to determine statistical parameters of minimum value, average value, and maximum value.

Free energy of RNA-counterion interactions in a tight-binding model computed by a discrete space mapping.

PubMed

Henke, Paul S; Mak, Chi H

2014-08-14

The thermodynamic stability of a folded RNA is intricately tied to the counterions and the free energy of this interaction must be accounted for in any realistic RNA simulations. Extending a tight-binding model published previously, in this paper we investigate the fundamental structure of charges arising from the interaction between small functional RNA molecules and divalent ions such as Mg(2+) that are especially conducive to stabilizing folded conformations. The characteristic nature of these charges is utilized to construct a discretely connected energy landscape that is then traversed via a novel application of a deterministic graph search technique. This search method can be incorporated into larger simulations of small RNA molecules and provides a fast and accurate way to calculate the free energy arising from the interactions between an RNA and divalent counterions. The utility of this algorithm is demonstrated within a fully atomistic Monte Carlo simulation of the P4-P6 domain of the Tetrahymena group I intron, in which it is shown that the counterion-mediated free energy conclusively directs folding into a compact structure.

Simulation of 2D rarefied gas flows based on the numerical solution of the Boltzmann equation

NASA Astrophysics Data System (ADS)

Poleshkin, Sergey O.; Malkov, Ewgenij A.; Kudryavtsev, Alexey N.; Shershnev, Anton A.; Bondar, Yevgeniy A.; Kohanchik, A. A.

2017-10-01

There are various methods for calculating rarefied gas flows, in particular, statistical methods and deterministic methods based on the finite-difference solutions of the Boltzmann nonlinear kinetic equation and on the solutions of model kinetic equations. There is no universal method; each has its disadvantages in terms of efficiency or accuracy. The choice of the method depends on the problem to be solved and on parameters of calculated flows. Qualitative theoretical arguments help to determine the range of parameters of effectively solved problems for each method; however, it is advisable to perform comparative tests of calculations of the classical problems performed by different methods and with different parameters to have quantitative confirmation of this reasoning. The paper provides the results of the calculations performed by the authors with the help of the Direct Simulation Monte Carlo method and finite-difference methods of solving the Boltzmann equation and model kinetic equations. Based on this comparison, conclusions are made on selecting a particular method for flow simulations in various ranges of flow parameters.

Modeling Defects, Shape Evolution, and Programmed Auto-origami in Liquid Crystal Elastomers

NASA Astrophysics Data System (ADS)

Konya, Andrew; Gimenez-Pinto, Vianney; Selinger, Robin

2016-06-01

Liquid crystal elastomers represent a novel class of programmable shape-transforming materials whose shape change trajectory is encoded in the material’s nematic director field. Using three-dimensional nonlinear finite element elastodynamics simulation, we model a variety of different actuation geometries and device designs: thin films containing topological defects, patterns that induce formation of folds and twists, and a bas-relief structure. The inclusion of finite bending energy in the simulation model reveals features of actuation trajectory that may be absent when bending energy is neglected. We examine geometries with a director pattern uniform through the film thickness encoding multiple regions of positive Gaussian curvature. Simulations indicate that heating such a system uniformly produces a disordered state with curved regions emerging randomly in both directions due to the film’s up/down symmetry. By contrast, applying a thermal gradient by heating the material first on one side breaks up/down symmetry and results in a deterministic trajectory producing a more ordered final shape. We demonstrate that a folding zone design containing cut-out areas accommodates transverse displacements without warping or buckling; and demonstrate that bas-relief and more complex bent/twisted structures can be assembled by combining simple design motifs.