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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Algorithms and analyses for stochastic optimization for turbofan noise reduction using parallel reduced-order modeling

Simulation-based optimization of acoustic liner design in a turbofan engine nacelle for noise reduction purposes can dramatically reduce the cost and time needed for experimental designs. Because uncertainties are inevitable in the design process, a stochastic optimization algorithm is posed based on the conditional value-at-risk measure so that an ideal acoustic liner impedance is determined that is robust in the presence of uncertainties. A parallel reduced-order modeling framework is developed that dramatically improves the computational efficiency of the stochastic optimization solver for a realistic nacelle geometry. The reduced stochastic optimization solver takes less than 500 s to execute. Additionally, well-posedness and finite element error analyses of the state system and optimization problem are provided.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Reshaping of Truncated Pd Nanocubes: Energetic and Kinetic Analysis Integrating Transmission Electron Microscopy with Atomistic-Level and Coarse-Grained Modeling

Stability against reshaping of metallic fcc nanocrystals synthesized with tailored far-from-equilibrium shapes is key to maintaining optimal properties for applications such as catalysis. Yet Arrhenius analysis of experimental reshaping kinetics, and appropriate theory and simulation, is lacking. As such, we use TEM to monitor the reshaping of Pd nanocubes of ~25 nm side length between 410 °C (over ~4.5 h) and 440 °C (over ~0.25 h), extracting a high effective energy barrier of E eff ≈ 4.6 eV. We also provide an analytic determination of the energy variation along the optimal pathway for reshaping that involves transfer of atoms across the nanocube surface from edges or corners to form new layers on side {100} facets. The effective barrier from this analysis is shown to increase strongly with the degree of truncation of edges and corners in the synthesized nanocube. Theory matches experiment for the appropriate degree of truncation. In addition, we perform simulations of a stochastic atomistic-level model incorporating a realistic description of diffusive hopping for undercoordinated surface atoms, thereby providing a visualization of the initial reshaping process.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Stochastic parametric skeletal dosimetry model for humans: General approach and application to active marrow exposure from bone-seeking beta-particle emitters

The objective of this study is to develop a skeleton model for assessing active marrow dose from bone-seeking beta-emitting radionuclides. This article explains the modeling methodology which accounts for individual variability of the macro- and microstructure of bone tissue. Bone sites with active hematopoiesis are assessed by dividing them into small segments described by simple geometric shapes. Spongiosa, which fills the segments, is modeled as an isotropic three-dimensional grid (framework) of rod-like trabeculae that “run through” the bone marrow. Randomized multiple framework deformations are simulated by changing the positions of the grid nodes and the thickness of the rods. Model grid parameters are selected in accordance with the parameters of spongiosa microstructures taken from the published papers. Stochastic modeling of radiation transport in heterogeneous media simulating the distribution of bone tissue and marrow in each of the segments is performed by Monte Carlo methods. Model output for the human femur at different ages is provided as an example. The uncertainty of dosimetric characteristics associated with individual variability of bone structure was evaluated. An advantage of this methodology for the calculation of doses absorbed in the marrow from bone-seeking radionuclides is that it does not require additional studies of autopsy material. The biokinetic model results will be used in the future to calculate individual doses to members of a cohort exposed to 89,90 Sr from liquid radioactive waste discharged to the Techa River by the Mayak Production Association in 1949–1956. Further study of these unique cohorts provides an opportunity to gain more in-depth knowledge about the effects of chronic radiation on the hematopoietic system. In addition, the proposed model can be used to assess the doses to active marrow under any other scenarios of 90 Sr and 89 Sr intake to humans.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Interpreting experimental measurements of helium bubbles using stochastic cluster dynamics models of heterogeneous nucleation and growth in irradiated ferritic alloys

Among a number of other advantageous features, ferritic/martensitic steels are being considered as fusion reactor structural materials due to their low intrinsic swelling under irradiation. However, under high-energy neutron irradiation, He produced through (n, α) reactions stabilizes vacancy clusters, which then act as seeds for bubble formation and growth, negating the intrinsic swelling resistance of these alloys. Standard models of irradiation damage accumulation and microstructural evolution consider homogeneous nucleation as the basis for bubble formation and growth. However, this generally does not explain the large bubble densities and sizes observed experimentally under a number of different conditions. Here, we propose a more realistic physical model of bubble nucleation, formation, and growth designed to capture recent experimental measurements of He-bubble formation and evolution during co-implantation of He and Fe ions in model ferritic alloys. We find that experimental results are explained only when the following three features are considered simultaneously: (i) heterogeneous nucleation of He-vacancy bubbles at defect sinks (e.g., dislocations, grain boundaries, and second-phase precipitates), (ii) nucleation and growth of bubbles via the ‘trap mutation’ mechanism (i.e., spontaneous production of Frenkel pairs due to absorption of He atoms), and (iii) transition from a growth-limited, He-stabilized bubble regime to a ‘runaway’ void-type growth scenario. The model is implemented into a stochastic cluster dynamics framework that takes advantage of cluster size grouping methods to accelerate the simulations, allowing us to reach 10 dpa of simulated irradiated dose, and to capture cluster sizes in excess of 20 nm. Further, a careful extrapolation exercise conducted assuming classical nucleation theory leads to excellent agreement with the experimental measurements at 50 dpa of irradiation.

36 MATERIALS SCIENCE↗
NASA NTRS2009

Development of a GCR Event-based Risk Model

A goal at NASA is to develop event-based systems biology models of space radiation risks that will replace the current dose-based empirical models. Complex and varied biochemical signaling processes transmit the initial DNA and oxidative damage from space radiation into cellular and tissue responses. Mis-repaired damage or aberrant signals can lead to genomic instability, persistent oxidative stress or inflammation, which are causative of cancer and CNS risks. Protective signaling through adaptive responses or cell repopulation is also possible. We are developing a computational simulation approach to galactic cosmic ray (GCR) effects that is based on biological events rather than average quantities such as dose, fluence, or dose equivalent. The goal of the GCR Event-based Risk Model (GERMcode) is to provide a simulation tool to describe and integrate physical and biological events into stochastic models of space radiation risks. We used the quantum multiple scattering model of heavy ion fragmentation (QMSFRG) and well known energy loss processes to develop a stochastic Monte-Carlo based model of GCR transport in spacecraft shielding and tissue. We validated the accuracy of the model by comparing to physical data from the NASA Space Radiation Laboratory (NSRL). Our simulation approach allows us to time-tag each GCR proton or heavy ion interaction in tissue including correlated secondary ions often of high multiplicity. Conventional space radiation risk assessment employs average quantities, and assumes linearity and additivity of responses over the complete range of GCR charge and energies. To investigate possible deviations from these assumptions, we studied several biological response pathway models of varying induction and relaxation times including the ATM, TGF -Smad, and WNT signaling pathways. We then considered small volumes of interacting cells and the time-dependent biophysical events that the GCR would produce within these tissue volumes to estimate how GCR event rates mapped to biological signaling induction and relaxation times. We considered several hypotheses related to signaling and cancer risk, and then performed simulations for conditions where aberrant or adaptive signaling would occur on long-duration space mission. Our results do not support the conventional assumptions of dose, linearity and additivity. A discussion on how event-based systems biology models, which focus on biological signaling as the mechanism to propagate damage or adaptation, can be further developed for cancer and CNS space radiation risk projections is given.

Cucinotta, Francis A.↗
NASA NTRS2015

A Climate Generator for Agricultural Planning in Southeastern South America

A method is described for the generation of climate scenarios in a form suitable for driving agricultural models. The scenarios are tailored to the region in southeastern South America bounded by 25-40 deg S, 45-65 deg W, denoted here as SESA. SESA has been characterized by increasing summer precipitation, particularly during the late 20th century, which, in the context of favorable market conditions, has enabled increases in agricultural production. Since about year 2000, however, the upward tendency appears to have slowed or possibly stopped, raising questions about future climate inputs to regional agricultural yields. The method is not predictive in the deterministic sense, but rather attempts to characterize uncertainty in near-term future climate, taking into account both forced trends and unforced, natural climate fluctuations. It differs from typical downscaling methods in that GCM information is utilized only at the regional scale, subregional variability being modeled based on the observational record. Output, generated on the monthly time scale, is disaggregated to daily values with a weather generator and used to drive soybean yields in the crop model DSSAT-CSM, for which preliminary results are discussed. The simulations produced permit assessment of the interplay between long-range trends and near-term climate variability in terms of agricultural production.

climate simulation↗
DOE OSTI2018

Utilization of Renewable Energy to Meet New National Challenges in Energy and Climate Change

The project aims to design a microgrid system to promote utilization of renewable energy resources such as wind and solar to address the national challenges in energy and climate change. Different optimization techniques and simulation software are used to study the performance of the renewable energy system under study. A series of research works performed under the grant Department of Energy (DOE) is presented. This grant opportunity affords Howard faculty, students, graduates, undergraduates, K-12, postdocs and visiting scholars to benefit state of the art research work. The research work has led to improve or advance understanding of new hardware technologies, software development and engineering optimization methods necessary and sufficient for handling probabilistic models and real-time computation and functions necessary for development of microgrid system. Consistent with State of Project Objective Howard University has partitioned the task into the following integrated activities: 1. Stochastic Model for RER and Load • Development of modeling Renewable Energy Resources (RER) and load which is used to perform distribution power flow study which leads to publication in refereed journals and conferences. The work was also published at the IEEE conference. 2. Stochastic optimization for voltage/Var • The development of voltage VAr optimization based on a review of existing knowledge in optimization led to the use of stochastic program and evolution of programming optimization method for V/VAr optimization. Papers were presented at the North America Power Systems Conference and the IEEE PES general meeting. 3. Modeling RER and Storage • Extending the concept of optimization method an RER with storage, such as the development of microgrid V/VAr and storage is performed. Several papers were published at the North America Power Systems Conference and the IEEE PES general meeting. 4. Power Game • Development of power game experiment using Labvolt to allow for hands on understanding of design and development of microgrid functions is performed. Publication were done by students at the end of their summer program. 5. Designing Microgrid Testbed • Example microgrid test bed is developed. In addition, function of the test bed are developed. The papers were presented at the North America Power Systems Conference and the IEEE general meeting. 6. Outreach Program • From the outreach program, topics from the project have been included in the revision of courses at Howard University, new book called Energy Processing and Smartgrid has being developed. • Hosted masters students from University of Denver to complete their projects with us. • Hosted high school students for early exposure for careers in STEM • Representations made in IEEE conferences to share the lessons learned in the use of micro grid to expose students to STEM education and research.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

3D Representative Volume Element Reconstruction of Fiber Composites via Orientation Tensor and Substructure Features

To provide a seamless integration of manufacturing processing simulation and fiber microstructure modeling, two new stochastic 3D microstructure reconstruction methods are proposed for two types of random fiber composites: random short fiber composites, and Sheet Molding Compounds (SMC) chopped fiber composites. A Random Sequential Adsorption (RSA) algorithm is first developed to embed statistical orientation information into 3D RVE reconstruction of random short fiber composites. For the SMC composites, an optimized Voronoi diagram based approach is developed for capturing the substructure features of SMC chopped fiber composites. The proposed methods are distinguished from other reconstruction works by providing a way of integrating statistical information (fiber orientation tensor) obtained from material processing simulation, as well as capturing the multiscale substructures of the SMC composites.

Li, Yi↗
DOE OSTI2020

A Machine Learning Assisted Development of a Model for the Populations of Convective and Stratiform Clouds

Abstract Traditional parameterizations of the interaction between convection and the environment have relied on an assumption that the slowly varying large‐scale environment is in statistical equilibrium with a large number of small and short‐lived convective clouds. They fail to capture nonequilibrium transitions such as the diurnal cycle and the formation of mesoscale convective systems as well as observed precipitation statistics and extremes. Informed by analysis of radar observations, cloud‐permitting model simulation, theory, and machine learning, this work presents a new stochastic cloud population dynamics model for characterizing the interactions between convective and stratiform clouds, with the goal of informing the representation of these interactions in global climate models. Fifteen wet seasons of precipitating cloud observations by a C‐band radar at Darwin, Australia are fed into a machine learning algorithm to obtain transition functions that close a set of coupled equations relating large‐scale forcing, mass flux, the convective cell size distribution, and the stratiform area. Under realistic large‐scale forcing, the derived transition functions show that, on the one hand, interactions with stratiform clouds act to dampen the variability in the size and number of convective cells and therefore in the convective mass flux. On the other, for a given convective area fraction, a larger number of smaller cells is more favorable for the growth of stratiform area than a smaller number of larger cells. The combination of these two factors gives rise to solutions with a few convective cells embedded in a large stratiform area, reminiscent of mesoscale convective systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Tonal Emergence: An agent-based model of tonal coordination

Humans have a remarkable capacity for coordination. Our ability to interact and act jointly in groups is crucial to our success as a species. Joint Action (JA) research has often concerned itself with simplistic behaviors in highly constrained laboratory tasks. But there has been a growing interest in understanding complex coordination in more open-ended contexts. In this regard, collective music improvisation has emerged as a fascinating model domain for studying basic JA mechanisms in an unconstrained and highly sophisticated setting. A number of empirical studies have begun to elucidate coordination mechanisms underlying joint musical improvisation, but these empirical findings have yet to be cached out in a working computational model. The present work fills this gap by presenting TonalEmergence, an idealized agent-based model of improvised musical coordination. TonalEmergence models the coordination of notes played by improvisers to generate harmony (i.e., tonality), by simulating agents that stochastically generate notes biased towards maximizing harmonic consonance given their partner’s previous notes. Here, the model replicates an interesting empirical result from a previous study of professional jazz pianists: feedback loops of mutual adaptation between interacting agents support the production of consonant harmony. The model is further explored to show how complex tonal dynamics, such as the production and dissolution of stable tonal centers, are supported by agents that are characterized by (i) a tendency to strive toward consonance, (ii) stochasticity, and (iii) a limited memory for previously played notes. TonalEmergence thus provides a grounded computational model to simulate and probe the coordination mechanisms underpinning one of the more remarkable feats of human cognition: collective music improvisation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Development of a Physics-Based Combustion Model for Engine Knock Prediction

The objective of this project is to improve the prediction of engine knock by developing a new combustion modeling framework. Engine knock is a limiting factor to constrain the increase of fuel efficiency for spark ignition (SI) engines in most passenger cars. Efforts to increase fuel efficiency, increasing the compression ratio or downsizing, lead to the increase in the tendency of the knock occurrence. The knock is an undesired ignition of the end-gas, unburned fuel/air mixture ahead of the spark-ignited premixed flame, resulting in rapid in-cylinder pressure rises and engine damages. The combustion modeling framework developed in this project can consider turbulence-chemistry interactions during end-gas ignition, while using a reasonably detailed chemical mechanism developed for ignition and combustion reactions under engine relevant conditions, and the subtle characteristics of spark-ignited flame propagation. It is developed in the context of large eddy simulation (LES), which can capture stochastic in-cylinder processes. The developed model is incorporated into a commercial software for engine simulation, CONVERGE CFD, as a user defined function, and validated. Engine knock and knock-free experiments as well as direct numerical simulation (DNS) of end-gas ignition in homogeneous turbulence are performed to help model development and provide data sets for model validation. With further validation, the developed model is expected to advance the predictive capability for engine knock simulations and thus contribute to improving the fuel efficiency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Development of a Physics-Based Combustion Model for Engine Knock Prediction

The objective of this project is to improve the prediction of engine knock by developing a new combustion modeling framework. Engine knock is a limiting factor to constrain the increase of fuel efficiency for spark ignition (SI) engines in most passenger cars. Efforts to increase fuel efficiency, increasing the compression ratio or downsizing, lead to the increase in the tendency of the knock occurrence. The knock is an undesired ignition of the end-gas, unburned fuel/air mixture ahead of the spark-ignited premixed flame, resulting in rapid in-cylinder pressure rises and engine damages. The combustion modeling framework developed in this project can consider turbulence-chemistry interactions during end-gas ignition, while using a reasonably detailed chemical mechanism developed for ignition and combustion reactions under engine relevant conditions, and the subtle characteristics of spark-ignited flame propagation. It is developed in the context of large eddy simulation (LES), which can capture stochastic in-cylinder processes. The developed model is incorporated into a commercial software for engine simulation, CONVERGE CFD, as a user defined function, and validated. Engine knock and knock-free experiments as well as direct numerical simulation (DNS) of end-gas ignition in homogeneous turbulence are performed to help model development and provide data sets for model validation. With further validation, the developed model is expected to advance the predictive capability for engine knock simulations and thus contribute to improving the fuel efficiency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Enhanced mechanical property evaluation using innovative data analytics capability

This report focuses on efforts to improve Multiphysics Object-Oriented Simulation Environ-ment (MOOSE) for mechanical property evaluation using data analytics. These efforts involveimprovements to the stochastic tools module (STM) for stochastic simulations of MOOSE mul-tiphysics model and the development of inverse optimization capabilities. The report gives anoverview of the STM and describes recent updates to its core capabilities and theory on itsreduced-order model (ROM) schemes. Examples are also provided showing the impact of theseupdates and exhibits the usefulness of ROMs. An overview of the gradient based inverse opti-mziation algorithms are given along with examples of their application to source identification.Inverse optimization will provide a new methodology in STM for fitting model parameters toexperimental data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

A Gompertzian model with random effects to cervical cancer growth

In this paper, a Gompertzian model with random effects is introduced to describe the cervical cancer growth. The parameters values of the mathematical model are estimated via maximum likehood estimation. We apply 4-stage Runge-Kutta (SRK4) for solving the stochastic model numerically. The efficiency of mathematical model is measured by comparing the simulated result and the clinical data of the cervical cancer growth. Low values of root mean-square error (RMSE) of Gompertzian model with random effect indicate good fits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1996

Probabilistic Fatigue Life Analysis of High Density Electronics Packaging

The fatigue of thin film metal interconnections in high density electronics packaging subjected to thermal cycling has been evaluated using a probabilistic fracture mechanics methodology. This probabilistic methodology includes characterization of thin film stress using an experimentally calibrated finite element model and simulation of flaw growth in the thin films using a stochastic crack growth model.

thermal cycling fatigue of thin film metal interco↗
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↗