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Results for “Stochastic Modeling and Simulation”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Nucleation-mediated reshaping of facetted metallic nanocrystals: Breakdown of the classical free energy picture

Shape stability is key to avoiding degradation of performance for metallic nanocrystals synthesized with facetted non-equilibrium shapes to optimize properties for catalysis, plasmonics, and so on. Reshaping of facetted nanocrystals is controlled by the surface diffusion-mediated nucleation and growth of new outer layers of atoms. Here, Kinetic Monte Carlo (KMC) simulation of a realistic stochastic atomistic-level model is applied to precisely track the reshaping of Pd octahedra and nanocubes. Unexpectedly, separate constrained equilibrium Monte Carlo analysis of the free energy profile during reshaping reveals a fundamental failure of the classical nucleation theory (CNT) prediction for the reshaping barrier and rate. Why? Nucleation barriers can be relatively low for these processes, so the system is not locally equilibrated before crossing the barrier, as assumed in CNT. This claim is supported by an analysis of a first-passage problem for reshaping within a master equation framework for the model that reasonably captures the behavior in KMC simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Occupancy Simulator Solver v1.0

Occupant schedules at room level are crucial information to building energy modeling which quantifies occupant-related energy efficiency technologies and controls for new building design as well as existing building retrofits to reduce energy use and GHG emissions in buildings. The Occupancy Simulator Solver is an executable software component that simulates the stochastic behavior of occupant presence and movement in a building using the Markov-chain model. Occupancy Simulator Solver is the calculation engine of the web App Occupancy Simulator, which takes high level input on occupants, spaces and events, then calls the Solver to simulate occupant movement and generate occupant schedules for each space. The generated schedules capture diversity and stochastic nature of occupant activities (e.g., in office, meetings, breaks, lunch). These schedules are stored in CSV files and can be downloaded from the App and used for building performance simulation.

Hong, Tianzhen↗
DOE OSTI2019

Impact of climate change on European winter and summer flood losses

Climate change is expected to alter European floods and associated economic losses in various ways. Here we investigate the impact of precipitation change on European average winter and summer financial losses due to flooding under a 1.5 °C warming scenario (reflecting a projected climate in the year 2115 according to RCP2.6)and for a counterfactual current-climate scenario where the climate has evolved without anthropogenic influence (reflecting a climate corresponding to pre-industrial conditions). Climate scenarios were generated with the Community Atmospheric Model (CAM)version 5. For each scenario, we derive a set of weights that when applied to the current climate's precipitation results in a climatology that approximates that of the scenario. We apply the weights to annual losses from a well-calibrated (to the current climate)flood loss model that spans 50,000 years and re-compute the average annual loss to assess the impact of precipitation changes induced by anthropogenic climate change. The method relies on a large stochastic set of physically based flood model simulations and allows quick assessment of potential loss changes due to change in precipitation based on two statistics, namely total precipitation, and total precipitation of very wet days (defined here as the total precipitation of days above the 95th percentile of daily precipitation). We compute the statistics with the raw CAM precipitation and bias-corrected precipitation. Our results show that for both raw and bias-corrected statistics i)average flood loss in Europe generally tend to increase in winter and decrease in summer for the future scenario, and consistent with that change we also show that ii)average flood losses have increased (decreased)for winter (summer)from pre-industrial conditions to the current day. Finally, the magnitude of the change varies among scenarios and statistics chosen.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Development of Ensemble Neural Network Convection Parametrizations for Climate Models Using ARM Data

The novel neural network (NN) approach has been formulated and used for development of a NN ensemble stochastic convection parametrization for climate models. This fast parametrization is built based on data from Cloud Resolving Model (CRM) simulations initialized with and forced by TOGA-COARE data. The SAM (System for Atmospheric Modeling), developed by D. Randall, M. Khairoutdinov, and their collaborators, has been used for CRM simulations. The observational data are also used for validation of model simulations. The SAM-simulated data have been averaged and projected onto the GCM space of atmospheric states to implicitly define a stochastic convection parametrization. This parametrization is emulated using an ensemble of NNs. An ensemble of NNs with different NN parameters has been trained and tested. The inherent uncertainty of the stochastic convection parametrization derived in such a way is estimated. Due to these inherent uncertainties, NN ensemble is used to constitute a stochastic NN convection parametrization. The developed NN convection parametrization have been validated in a diagnostic CAM (CAM-NN) run vs. the control CAM run. Actually, CAM inputs have been used, at every time step of the control/original CAM integration, for parallel calculations of the NN convection parametrization (CAM-NN) to produce its outputs as a diagnostic byproduct. Total precipitation (P) and cloudiness (CLD) time series, diurnal cycles, and P and CLD distributions for the large Tropical Pacific Ocean for the parallel CAM-NN and CAM runs show similarity and consistency with the NCEP reanalysis. The P and CLD distributions for the tropical area for the parallel runs have been analyzed first for the TOGA-COARE boreal winter season (November 1992 through February 1993) and then for the winter seasons of the follow-up parallel decadal simulations. The obtained results are encouraging and practically meaningful. They show the validity of the NN approach. This constitutes an important practical conclusion of the study: the obtained results on NN ensembles as a stochastic physics parametrization show a realistic possibility of development of NN convection parametrization for climate (and NWP) models based on learning cloud physics from CRM/SAM simulated data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Testing Dynamic Earthquake Rupture Models Generated With Stochastic Stress Drop

In this study we investigate the performance of rupture dynamics modeling using heterogenous stress for a M6.5 crustal earthquake on a strike slip vertical fault. In our simulations we used heterogenous stress drop models with correlated stochastic perturbations following the work of Pitarka et al. (2009). The stress model with multiscale variability is designed to accommodate high-stress drop asperity areas and small-scale spatial stress variations. The stress drop and size of asperities are estimated following the IM2011 (Irikura and Miyake, 2011) recipe. In our spontaneous rupture modeling we use a linear slip weakening friction law, with depth depend strength excess, dynamic stress drop and slip weakening distance Dc. Our strategy in preparing the model parameters with vertical and lateral variability is in agreement with observations of earthquake rupture that have confirmed the existence of a shallow weak rupture zone consisting of ductile crustal material, located in the upper 4 km of the crust, and brittle seismogenic zone, underlying the weak zone. The primary objective of our study is to develop a procedure for preparing dynamic rupture models with multiscale variability, and validate spontaneous rupture modeling of strike-slip faults by comparing the resulting rupture kinematics and near-fault ground motion with empirical models for this type of rupture.

58 GEOSCIENCES↗
DOE OSTI2025

Bottom-Up Simulation, Reconstruction, and Quantification of Macromolecule Sequences from Experimental Polymerizations

Motivated by the canonical sequence–structure–function paradigm, tools to characterize chemical patterning in natural biomacromolecules, from proteins to nucleic acids, have grown exponentially in recent years. However, analogous strategies for synthetic macromolecules remain in nascent stages, complicated by sequence polydispersity and analytical limitations. To address this, we have developed a comprehensive and open-source Python package, PRISM (polymer rate insights and sequence modeling), an end-to-end workflow that provides a path from experimental kinetics measurements to quantitative and qualitative metrics for describing chemical patterning in stochastic polymers. First, a numerical integration strategy was constructed to simulate and fit experimental data from reversible addition–fragmentation chain transfer (RAFT) polymerization kinetics, enabling the facile estimation of relevant reactivity ratios. These ratios were then used in a mechanism-specific stochastic kinetic simulation strategy to simulate sequence ensembles corresponding to model systems spanning experimental copolymers, classes of statistical polymers (e.g., alternating, block, and gradient), and multiblock copolymers. Lastly, inspired by sequence homology metrics from bioinformatics, we introduce visualization strategies and quantitative metrics to facilitate comparisons of different sequence ensembles. As the sequence–structure–function paradigm becomes increasingly central in de novo design of synthetic macromolecules, this toolkit provides a first step toward accurate and representative sequence description and featurization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Informing Geologic CO 2 Storage Site Management Decisions under Uncertainty: Demonstration of NRAP's Integrated Assessment Model (NRAP-IAM-CS) Application

Managing risks at geologic CO 2 storage sites requires making decisions based on the quantitative predictions of long-term performance that not only take into account various subsurface hydrologic, geochemical processes and interactions but also the related uncertainties. The US DOE (United States Department of Energy) funded National Risk Assessment Partnership (NRAP) has developed a quantitative approach and a model to predict the long-term behavior of GCS sites. The approach uses an integrated assessment model (NRAP-IAM-CS) that couples reduced order models (ROMs) for various GCS components (such as storage reservoir, wells, seals, and groundwater aquifer) in a stochastic modeling framework that allows for rapid simulations of entire system behavior over thousands of years while probing uncertainties. We demonstrate application of NRAP-IAM-CS using two examples that focus on CO 2 leakage and resulting impacts. The examples are used to demonstrate how different uncertain parameters affect overall performance objectives.

03 NATURAL GAS↗
NASA NTRS1997

Implicit Kalman filtering

For an implicitly defined discrete system, a new algorithm for Kalman filtering is developed and an efficient numerical implementation scheme is proposed. Unlike the traditional explicit approach, the implicit filter can be readily applied to ill-conditioned systems and allows for generalization to descriptor systems. The implementation of the implicit filter depends on the solution of the congruence matrix equation (A1)(Px)(AT1) = Py. We develop a general iterative method for the solution of this equation, and prove necessary and sufficient conditions for convergence. It is shown that when the system matrices of an implicit system are sparse, the implicit Kalman filter requires significantly less computer time and storage to implement as compared to the traditional explicit Kalman filter. Simulation results are presented to illustrate and substantiate the theoretical developments.

Non-NASA Center↗
NASA NTRSDate not supplied

NESC GN&C TDT Workshop on 2D Image Motion Optical Transfer Functions, Pointing Performance Analysis, and Requirements

What You Will Learn: The focus is on payload imaging performance due to pointing motion. Some historical background on pointing performance analysis is given. The Optical Transfer Function (OTF) and Modulation Transfer Function (MTF) are defined. The imaging performance due to pointing motion is measured by image motion optical transfer functions (IM OTF). IM OTFs are defined for displacement, smear, and jitter motions, which are all rigorously defined. Deterministic and Statistical IM OTFs are briefly derived and graphically illustrated and compared. The IM OTFs are parameterized by pointing error metrics(PEM), which are means and covariances of displacement, smear, and jitter. Emphasis is on procedures and algorithms to evaluate the image motion optical transfer functions and pointing error metrics. Three procedures are covered, which depend on whether the pointing error data is from time-domain simulation, frequency-domain analysis, or stochastic modeling. A method to evaluate the relative contribution of disturbance sources and to identify the most significant contributors is presented. The presentation includes pertinent discussion of flexible structures and control-structure interaction. No single book can adequately cover this subject, so a book is not required for the course. A list of selected articles, reports, documents, and books is provided for reference and further study. Mathis kept to the minimum necessary to convey principles; lengthy derivations are left to the reference material. Graphics are used to illustrate concepts. As with any such learning endeavor, the knowledge gained will be retained and strengthened through actual practice.c©2019–2021 Mark E. Pittelkau— 5

NASA Engineering and Safety Center (NESC)↗
DOE OSTI2017

Diffusion of two-dimensional epitaxial clusters on metal (100) surfaces: Facile versus nucleation-mediated behavior and their merging for larger sizes

For diffusion of two-dimensional homoepitaxial clusters of N atoms on metal(100) surfaces mediated by edge atom hopping, macroscale continuum theory suggests that the diffusion coefficient scales like DN ~ N -β with β = 3/2. However, we find quite different and diverse behavior in multiple size regimes. These include: (i) facile diffusion for small sizes N < 9; (ii) slow nucleation-mediated diffusion with small β < 1 for “perfect” sizes N = N p = L 2 or L(L+1), for L = 3, 4,… having unique ground state shapes, for moderate sizes 9 ≤ N ≤ O(10 2 ); the same also applies for N = N p +3, N p + 4,… (iii) facile diffusion but with large β > 2 for N = Np + 1 and N p + 2 also for moderate sizes 9 ≤ N ≤ O(10 2 ); (iv) merging of the above distinct branches and subsequent anomalous scaling with 1 ≲ β < 3/2, reflecting the quasi-facetted structure of clusters, for larger N = O(10 2 ) to N = O(10 3 ); and (v) classic scaling with β = 3/2 for very large N = O(103) and above. The specified size ranges apply for typical model parameters. We focus on the moderate size regime where show that diffusivity cycles quasi-periodically from the slowest branch for N p + 3 (not Np) to the fastest branch for Np + 1. Behavior is quantified by Kinetic Monte Carlo simulation of an appropriate stochastic lattice-gas model. However, precise analysis must account for a strong enhancement of diffusivity for short time increments due to back-correlation in the cluster motion. Further understanding of this enhancement, of anomalous size scaling behavior, and of the merging of various branches, is facilitated by combinatorial analysis of the number of the ground state and low-lying excited state cluster configurations, and also of kink populations.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Numerical analysis of randomly forced glycolitic oscillations

Randomly forced glycolytic oscillations in Higgins model are studied both numerically and analytically. Numerical analysis is based on the direct simulation of the solutions of stochastic system. Non-uniformity of the stochastic bundle along the deterministic cycle is shown. For the analytical investigation of the randomly forced Higgins model, the stochastic sensitivity function technique and confidence domains method are applied. Results of the influence of additive noise on the cycle of this model are given.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Learning stochastic dynamics with statistics-informed neural network

We introduce a machine-learning framework named statistics-informed neural network (SINN) for learning stochastic dynamics from data. This new architecture was theoretically inspired by a universal approximation theorem for stochastic systems, which we introduce in this paper, and the projection-operator formalism for stochastic modeling. Here, we devise mechanisms for training the neural network model to reproduce the correct statistical behavior of a target stochastic process. Numerical simulation results demonstrate that a well-trained SINN can reliably approximate both Markovian and non-Markovian stochastic dynamics. We demonstrate the applicability of SINN to coarse-graining problems and the modeling of transition dynamics. Furthermore, we show that the obtained reduced-order model can be trained on temporally coarse-grained data and hence is well suited for rare-event simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Determining Reduced Order Models for Optimal Stochastic Reduced Order Models

The use of parameterized reduced order models(PROMs) within the stochastic reduced order model (SROM) framework is a logical progression for both methods. In this report, five different parameterized reduced order models are selected and critiqued against the other models along with truth model for the example of the Brake-Reuss beam. The models are: a Taylor series using finite difference, a proper orthogonal decomposition of the the output, a Craig-Bampton representation of the model, a method that uses Hyper-Dual numbers to determine the sensitivities, and a Meta-Model method that uses the Hyper-Dual results and constructs a polynomial curve to better represent the output data. The methods are compared against a parameter sweep and a distribution propagation where the first four statistical moments are used as a comparison. Each method produces very accurate results with the Craig-Bampton reduction having the least accurate results. The models are also compared based on time requirements for the evaluation of each model where the Meta- Model requires the least amount of time for computation by a significant amount. Each of the five models provided accurate results in a reasonable time frame. The determination of which model to use is dependent on the availability of the high-fidelity model and how many evaluations can be performed. Analysis of the output distribution is examined by using a large Monte-Carlo simulation along with a reduced simulation using Latin Hypercube and the stochastic reduced order model sampling technique. Both techniques produced accurate results. The stochastic reduced order modeling technique produced less error when compared to an exhaustive sampling for the majority of methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Stochastic growth logistic model with aftereffect for batch fermentation process

In this paper, the stochastic growth logistic model with aftereffect for the cell growth of C. acetobutylicum P262 and Luedeking-Piret equations for solvent production in batch fermentation system is introduced. The parameters values of the mathematical models are estimated via Levenberg-Marquardt optimization method of non-linear least squares. We apply Milstein scheme for solving the stochastic models numerically. The effciency of mathematical models is measured by comparing the simulated result and the experimental data of the microbial growth and solvent production in batch system. Low values of Root Mean-Square Error (RMSE) of stochastic models with aftereffect indicate good fits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2013

Reservoir Modeling by Data Integration via Intermediate Spaces and Artificial Intelligence Tools in MPS Simulation Frameworks

Conditioning stochastic simulations are very important in many geostatistical applications that call for the introduction of nonlinear and multiple-point data in reservoir modeling. Here, a new methodology is proposed for the incorporation of different data types into multiple-point statistics (MPS) simulation frameworks. Unlike the previous techniques that call for an approximate forward model (filter) for integration of secondary data into geologically constructed models, the proposed approach develops an intermediate space where all the primary and secondary data are easily mapped onto. Definition of the intermediate space, as may be achieved via application of artificial intelligence tools like neural networks and fuzzy inference systems, eliminates the need for using filters as in previous techniques. The applicability of the proposed approach in conditioning MPS simulations to static and geologic data is verified by modeling a real example of discrete fracture networks using conventional well-log data. The training patterns are well reproduced in the realizations, while the model is also consistent with the map of secondary data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2014

Simulations of DSB Yields and Radiation-induced Chromosomal Aberrations in Human Cells Based on the Stochastic Track Structure Induced by HZE Particles

The formation of double‐strand breaks (DSBs) and chromosomal aberrations (CAs) is of great importance in radiation research and, specifically, in space applications. We are presenting a new particle track and DNA damage model, in which the particle stochastic track structure is combined with the random walk (RW) structure of chromosomes in a cell nucleus. The motivation for this effort stems from the fact that the model with the RW chromosomes, NASARTI (NASA radiation track image) previously relied on amorphous track structure, while the stochastic track structure model RITRACKS (Relativistic Ion Tracks) was focused on more microscopic targets than the entire genome. We have combined chromosomes simulated by RWs with stochastic track structure, which uses nanoscopic dose calculations performed with the Monte‐Carlo simulation by RITRACKS in a voxelized space. The new simulations produce the number of DSBs as function of dose and particle fluence for high‐energy particles, including iron, carbon and protons, using voxels of 20 nm dimension. The combined model also calculates yields of radiation‐induced CAs and unrejoined chromosome breaks in normal and repair deficient cells. The joined computational model is calibrated using the relative frequencies and distributions of chromosomal aberrations reported in the literature. The model considers fractionated deposition of energy to approximate dose rates of the space flight environment. The joined model also predicts of the yields and sizes of translocations, dicentrics, rings, and more complex‐type aberrations formed in the G0/G1 cell cycle phase during the first cell division after irradiation. We found that the main advantage of the joined model is our ability to simulate small doses: 0.05‐0.5 Gy. At such low doses, the stochastic track structure proved to be indispensable, as the action of individual delta‐rays becomes more important.

Ponomarev, Artem↗