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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

A stochastic extension of the explicit algebraic subgrid-scale models

The explicit algebraic subgrid-scale (SGS) stress model (EASM) of Marstorp et al. [“Explicit algebraic subgrid stress models with application to rotating channel flow,” J. Fluid Mech. 639, 403–432 (2009)] and explicit algebraic SGS scalar flux model (EASFM) of Rasam et al. [“An explicit algebraic model for the subgrid-scale passive scalar flux,” J. Fluid Mech. 721, 541–577 (2013)] are extended with stochastic terms based on the Langevin equation formalism for the subgrid-scales by Marstorp et al. [“A stochastic subgrid model with application to turbulent flow and scalar mixing,” Phys. Fluids 19, 035107 (2007)]. The EASM and EASFM are nonlinear mixed and tensor eddy-diffusivity models, which improve large eddy simulation (LES) predictions of the mean flow, Reynolds stresses, and scalar fluxes of wall-bounded flows compared to isotropic eddy-viscosity and eddy-diffusivity SGS models, especially at coarse resolutions. The purpose of the stochastic extension of the explicit algebraic SGS models is to further improve the characteristics of the kinetic energy and scalar variance SGS dissipation, which are key quantities that govern the small-scale mixing and dispersion dynamics. LES of turbulent channel flow with passive scalar transport shows that the stochastic terms enhance SGS dissipation statistics such as length scale, variance, and probability density functions and introduce a significant amount of backscatter of energy from the subgrid to the resolved scales without causing numerical stability problems. The improvements in the SGS dissipation predictions in turn enhances the predicted resolved statistics such as the mean scalar, scalar fluxes, Reynolds stresses, and correlation lengths. Moreover, the nonalignment between the SGS stress and resolved strain-rate tensors predicted by the EASM with stochastic extension is in much closer agreement with direct numerical simulation data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

SDYN-GANs: Adversarial learning methods for multistep generative models for general order stochastic dynamics

We introduce adversarial learning methods for data-driven generative modeling of dynamics of nth-order stochastic systems. Our approach builds on Generative Adversarial Networks (GANs) with generative model classes based on stable m-step stochastic numerical integrators. From observations of trajectory samples, we introduce methods for learning long-time predictors and stable representations of the dynamics. Our approaches use discriminators based on Maximum Mean Discrepancy (MMD), training protocols using both conditional and marginal distributions, and methods for learning dynamic responses over different time-scales. We show how our approaches can be used for modeling physical systems to learn force-laws, damping coefficients, and noise-related parameters. Our adversarial learning approaches provide methods for obtaining stable generative models for dynamic tasks including long-time prediction and developing simulations for stochastic systems.

• Artificial intelligence (AI) / machine learning ↗
DOE OSTI2016

Effective computation of stochastic protein kinetic equation by reducing stiffness via variable transformation

The stochastic protein kinetic equations can be stiff for certain parameters, which makes their numerical simulation rely on very small time step sizes, resulting in large computational cost and accumulated round-off errors. For such situation, we provide a method of reducing stiffness of the stochastic protein kinetic equation by means of a kind of variable transformation. Theoretical and numerical analysis show effectiveness of this method. Its generalization to a more general class of stochastic differential equation models is also discussed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

The Multi-Plume Eddy-Diffusivity/Mass-Flux (EDMF) Unified Parameterization: Stratocumulus and the Transition to Cumulus Boundary Layers

The primary objective of this project is to mitigate inaccuracies and biases in subtropical boundary layer clouds, particularly in oceanic upwelling regions. Our approach began with developing a novel unified boundary layer and convection parameterization, utilizing the multi-plume Eddy-Diffusivity/Mass-Flux (EDMF) framework. We evaluated this advanced stochastic multi-plume EDMF parameterization in an independent single-column model (SCM), benchmarking it against Large-Eddy Simulation (LES) reference data and satellite observations. This version of the EDMF is designed to seamlessly represent a continuum of convective processes, from dry and cloudy boundary layer convection to deep moist convection. We progressed by integrating the stochastic multi-plume EDMF scheme into the Simple Cloud-Resolving E3SM (Energy Exascale Earth System Model) Atmosphere model (SCREAM), coupling it with the SHOC (Simplified Higher-Order Closure) turbulence and convection scheme. This combined approach was then evaluated against LES reference data for both maritime and continental shallow convection within the E3SM SCM.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Application of stochastic weighted algorithms to a multidimensional silica particle model

Highlights: •Stochastic weighted algorithms (SWAs) are developed for a detailed silica model. •An implementation of SWAs with the transition kernel is presented. •The SWAs’ solutions converge to the direct simulation algorithm’s (DSA) solution. •The efficiency of SWAs is evaluated for this multidimensional particle model. •It is shown that SWAs can be used for coagulation problems in industrial systems. -- Abstract: This paper presents a detailed study of the numerical behaviour of stochastic weighted algorithms (SWAs) using the transition regime coagulation kernel and a multidimensional silica particle model. The implementation in the SWAs of the transition regime coagulation kernel and associated majorant rates is described. The silica particle model of Shekar et al. [S. Shekar, A.J. Smith, W.J. Menz, M. Sander, M. Kraft, A multidimensional population balance model to describe the aerosol synthesis of silica nanoparticles, Journal of Aerosol Science 44 (2012) 83–98] was used in conjunction with this coagulation kernel to study the convergence properties of SWAs with a multidimensional particle model. High precision solutions were calculated with two SWAs and also with the established direct simulation algorithm. These solutions, which were generated using large number of computational particles, showed close agreement. It was thus demonstrated that SWAs can be successfully used with complex coagulation kernels and high dimensional particle models to simulate real-world systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Stochastic parametric skeletal dosimetry model for humans: Pediatric and adult computational skeleton phantoms for internal bone marrow dosimetry

Currently, computational phantoms that simulate skeletal tissues are used in active red bone marrow (AM) internal dosimetry. Up-to-date reference computational phantoms recommended by the ICRP are based on the analysis of CT-images of cadavers. Such phantoms have significant disadvantages. One disadvantage is that the assessment of uncertainty due to the population variability of skeleton dimensions and microstructure results from the limited availability of autopsy material. Another disadvantage is the simplified modelling of cortical layer and bone microarchitecture. A method of stochastic parametric skeletal dosimetry modelling of the bone structures – SPSD modelling – has been developed as an alternative to the ICRP reference phantoms. In the framework of this approach, skeletal phantom parameters are evaluated based on extensively reviewed results of published measurements of real bones. The SPSD approach allows for the assessment of both population-average values and their variability. SPSD-phantoms of the skeleton are modelled in voxel representation. They consist of smaller phantoms of the bone sites – segments – described by simple geometric shapes with uniform microarchitecture parameters. Such segmentation makes it possible to account for non-homogeneous skeletal microarchitecture and to model the bone structure with the required voxel resolution to elaborate suitable skeletal phantoms. The current study presents the parameters of the SPSD skeletal phantoms for the following age-groups: newborn, 1-year-old, 5-year-old, 10-year-old, 15-year-old (male and female), and adult (male and female). This skeletal phantom can be used for dosimetry as an alternative to available reference phantoms for bone-seeking radionuclides. The above-mentioned age- and sex-specific skeletal phantoms are comprised of 289 unique segments. The characteristics of the SPSD phantoms do not contradict published data and are in good agreement with the measurement results of real bones.

Science & Technology - Other Topics↗
DOE OSTI2023

A Generalization of Threshold-Based and Probability-Based Models of Information Diffusion

Diffusion of information through complex networks is of interest in studies such as propagation prediction and influence maximization, both of which have applications in viral marketing and rumor controlling. There are a variety of information diffusion models, all of which simulate the adoption and spread of information over time. However, there is a lack of understanding of whether, despite their conceptual differences, these models represent the same underlying generative structures. For instance, if two different models utilize different conceptual mechanisms, but generate the same results, does the choice of model matter? A classification of diffusion of information models is developed based on the neighbor knowledge of the model infection requirement and the stochasticity of the model. This classification allows for the identification of models that fall into each respective category. The study involves the analysis of the following agent-based models on directed scale-free networks: (1) a linear absolute threshold model (LATM), (2) a linear fractional threshold model (LTFM), (3) the independent cascade model (ICM), (4) Bass-Rand-Rust model (BRRM) (5) a stochastic linear absolute threshold model (SLATM) (6) a stochastic fractional threshold model (SLFTM), and (7) Dodds–Watts model (DWM). Through the execution of simulations and analysis of the experimental results, the distinctive properties of each model are identified. Our analysis reveals that similarity in conceptual design does not imply similarity in behavior concerning speed, final state of nodes and edges, and sensitivity to parameters. Therefore, we highlight the importance of considering the unique behavioral characteristics of each model when selecting a suitable information diffusion model for a particular application.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Quantification of errors in large-eddy simulations of a spatially evolving mixing layer using polynomial chaos

A stochastic approach based on generalized polynomial chaos (gPC) is used to quantify the error in large-eddy simulation (LES) of a spatially evolving mixing layer flow and its sensitivity to different simulation parameters, viz., the grid stretching in the streamwise and lateral directions and the subgrid-scale (SGS) Smagorinsky model constant (C{sub S}). The error is evaluated with respect to the results of a highly resolved LES and for different quantities of interest, namely, the mean streamwise velocity, the momentum thickness, and the shear stress. A typical feature of the considered spatially evolving flow is the progressive transition from a laminar regime, highly dependent on the inlet conditions, to a fully developed turbulent one. Therefore, the computational domain is divided in two different zones (inlet dependent and fully turbulent) and the gPC error analysis is carried out for these two zones separately. An optimization of the parameters is also carried out for both these zones. For all the considered quantities, the results point out that the error is mainly governed by the value of the C{sub S} constant. At the end of the inlet-dependent zone, a strong coupling between the normal stretching ratio and the C{sub S} value is observed. The error sensitivity to the parameter values is significantly larger in the inlet-dependent upstream region; however, low-error values can be obtained in this region for all the considered physical quantities by an ad hoc tuning of the parameters. Conversely, in the turbulent regime the error is globally lower and less sensitive to the parameter variations, but it is more difficult to find a set of parameter values leading to optimal results for all the analyzed physical quantities. A similar analysis is also carried out for the dynamic Smagorinsky model, by varying the grid stretching ratios. Comparing the databases generated with the different subgrid-scale models, it is possible to observe that the error cost function computed for the streamwise velocity and for the momentum thickness is not significantly sensitive to the used SGS closure. Conversely, the prediction of the shear stress is much more accurate when using a dynamic subgrid-scale model and the variance of the error is lower in magnitude.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Modeling stratified flames with and without shear using multiple mapping conditioning

A stochastic sparse particle approach is coupled with an artificial thickening flame (ATF) model for large eddy simulations (LES) to predict a series of turbulent premixed-stratified flames with and without shear and stratification. The thickened reaction progress variable serves as reference variable for the multiple mapping conditioning (MMC) mixing model which emulates turbulent mixing of the stochastic particles. The key feature of MMC is to enforce localness in this reference space when particle pairs are mixed and prevents unphysical mixing of burnt and unburnt fluid across the flame front. We apply MMC-ATF to three flames of a series of turbulent stratified flames and validate the method by comparison with experimental data. The new measurements feature increased accuracy in comparison to previously published data of the same flames due to a better signal-to-noise ratio and a setup which is less prone to beam steering. All flame locations are well predicted by the LES-ATF approach and an analysis of the MMC particle statistics demonstrates that MMC preserves the flamelet-like behaviour in regions where the experiments show low scatter around the flamelet solution. Predicted (local) deviations from the flamelet-solution are comparable to deviations observed in the measurements and variations in the flame structure due to differences in stratification and shear are reasonably well captured by the method.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Battery and backup generator sizing for a resilient microgrid under stochastic extreme events

Owing to the recent power outages caused by extreme events, installing battery energy storage and backup generators is important to improve resiliency for a grid-tied microgrid. In the design stage, the event occurrence time and duration, which are highly uncertain and cannot be effectively predicted, may affect the needed battery and backup generator capacity but are usually assumed to be pre-determined in utility planning tools. This study investigates the optimal battery and backup generator sizing problem considering the stochastic event occurrence time and duration for the grid-tied microgrid under islanded operation. The reliability requirement is quantified by the mean value of the critical customer interruption time in each stochastic islanding time window (ITW), whose length is the duration and the centre is the occurrence time. The stochastic ITW constraint is then transformed to a probability-weighted expression to derive an equivalent Mixed Integer Linear Programming model. Numerical simulations on a realistic grid-tied PV-based microgrid demonstrate that the total cost is reduced by 11.5% considering the stochastic ITW, compared with the deterministic ITW under the same reliability requirement.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Ab Initio Thermodynamics and Kinetics for Coalescence of Two-Dimensional Nanoislands and Nanopits on Metal (100) Surfaces

Postdeposition coalescence or sintering of pairs of lowstrain two-dimensional nanoislands and nanopits on unreconstructed metal (100) surfaces is typically mediated by diffusion along step edges, and is highly sensitive to the associated kinetics. Thus, for selected systems, we provide an ab initio density functional theory (DFT) level description of both system thermodynamics and kinetics. Specifically, we assess lateral pair and trio interactions both conventionally with adatoms at 4-fold hollow adsorption sites, and unconventionally with one adatom at the bridge-site transition state for hopping. Rather than use standard cluster expansion algorithms, these interactions are determined subject to the constraint that key step-edge properties are recovered exactly. Together, both classes of interactions determine barriers for edge diffusion processes for any local step configuration, including diffusion along close-packed $\langle$110$\rangle$ edges, kink rounding, meandering processes at kinked $\langle$100$\rangle$ steps, and extraction processes at pit corners. Our formalism applies for homoepitaxial systems, and also for several lattice-matched heteroepitaxial systems. The barriers provide input for stochastic models of nanocluster evolution, which are analyzed by kinetic Monte Carlo simulation. Such modeling with DFT energetics from the PBEsol functional recovers extensive experimental observations of both the time scale and the island-size dependence for sintering of Ag islands on Ag(100). In conclusion, description of pit sintering on Ag(100) is more delicate, being sensitive to specific unconventional trio interactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

The range of variation of the mass of the most massive star in stellar clusters derived from 35 million Monte Carlo simulations

A growing fraction of simple stellar population models, in an aim to create more realistic simulations capable of including stochastic variation in their outputs, begin their simulations with a distribution of discrete stars following a power-law function of masses. Careful attention is needed to create a correctly sampled initial mass function (IMF), and here we provide a solid mathematical method, called MASSCLEAN IMF Sampling, for doing so. We use our method to perform 10 million MASSCLEAN Monte Carlo stellar cluster simulations to determine the most massive star in a mass distribution as a function of the total mass of the cluster. We find that a maximum mass range is predicted, not a single maximum mass. This range is (1) dependent on the total mass of the cluster and (2) independent of an upper stellar mass limit, M{sub limit} , for unsaturated clusters and emerges naturally from our IMF sampling method. We then turn our analysis around, starting with our new database of 25 million simulated clusters, to constrain the highest mass star from the observed integrated colors of a sample of 40 low-mass Large Magellanic Cloud stellar clusters of known age and mass. Finally, we present an analytical description of the maximum mass range of the most massive star as a function of the cluster's total mass and present a new M{sub max} -M{sub cluster} relation.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1990

Probabilistic low cycle fatigue failure analysis with application to liquid propellant rocket engines

A probabilistic Low Cycle Fatigue (LCF) failure analysis of a candidate turbine disk for use in a turbopump of a rocket engine of the Space Shuttle Main Engine class is described. A state-of-the-art LCF failure prediction method was used in a Monte Carlo simulation to generate a distribution of failure lives. A stochastic Stress/Life (S/N) model was used for materials characterization. The LCF failure model expresses fatigue life as a function of stochastic parameters including environmental parameters, loads, material properties, structural parameters, and model specification errors. The rationale for the particular characterization of each stochastic input parameter is described. The results and interpretation of the failure analysis are given.

Newlin, L.↗
DOE OSTI2013

Dynamic Event Tree Analysis Through RAVEN

Conventional Event-Tree (ET) based methodologies are extensively used as tools to perform reliability and safety assessment of complex and critical engineering systems. One of the disadvantages of these methods is that timing/sequencing of events and system dynamics is not explicitly accounted for in the analysis. In order to overcome these limitations several techniques, also know as Dynamic Probabilistic Risk Assessment (D-PRA), have been developed. Monte-Carlo (MC) and Dynamic Event Tree (DET) are two of the most widely used D-PRA methodologies to perform safety assessment of Nuclear Power Plants (NPP). In the past two years, the Idaho National Laboratory (INL) has developed its own tool to perform Dynamic PRA: RAVEN (Reactor Analysis and Virtual control ENvironment). RAVEN has been designed in a high modular and pluggable way in order to enable easy integration of different programming languages (i.e., C++, Python) and coupling with other application including the ones based on the MOOSE framework, developed by INL as well. RAVEN performs two main tasks: 1) control logic driver for the new Thermo-Hydraulic code RELAP-7 and 2) post-processing tool. In the first task, RAVEN acts as a deterministic controller in which the set of control logic laws (user defined) monitors the RELAP-7 simulation and controls the activation of specific systems. Moreover, RAVEN also models stochastic events, such as components failures, and performs uncertainty quantification. Such stochastic modeling is employed by using both MC and DET algorithms. In the second task, RAVEN processes the large amount of data generated by RELAP-7 using data-mining based algorithms. This paper focuses on the first task and shows how it is possible to perform the analysis of dynamic stochastic systems using the newly developed RAVEN DET capability. As an example, the Dynamic PRA analysis, using Dynamic Event Tree, of a simplified pressurized water reactor for a Station Black-Out scenario is presented.

42 ENGINEERING↗
DOE OSTI2013

Uncertainty Analysis of Runoff Simulations and Parameter Identifiability in the Community Land Model – Evidence from MOPEX Basins

With the emergence of earth system models as important tools for understanding and predicting climate change and implications to mitigation and adaptation, it has become increasingly important to assess the fidelity of the land component within earth system models to capture realistic hydrological processes and their response to the changing climate and quantify the associated uncertainties. This study investigates the sensitivity of runoff simulations to major hydrologic parameters in version 4 of the Community Land Model (CLM4) by integrating CLM4 with a stochastic exploratory sensitivity analysis framework at 20 selected watersheds from the Model Parameter Estimation Experiment (MOPEX) spanning a wide range of climate and site conditions. We found that for runoff simulations, the most significant parameters are those related to the subsurface runoff parameterizations. Soil texture related parameters and surface runoff parameters are of secondary significance. Moreover, climate and soil conditions play important roles in the parameter sensitivity. In general, site conditions within water-limited hydrologic regimes and with finer soil texture result in stronger sensitivity of output variables, such as runoff and its surface and subsurface components, to the input parameters in CLM4. This study demonstrated the feasibility of parameter inversion for CLM4 using streamflow observations to improve runoff simulations. By ranking the significance of the input parameters, we showed that the parameter set dimensionality could be reduced for CLM4 parameter calibration under different hydrologic and climatic regimes so that the inverse problem is less ill posed.

analysis↗
DOE OSTI2024

Model data for Flood Frequency Analysis using Stochastic Storm Transposition and an Integrated Surface-Subsurface Hydrological Model

This archived provides scripts and input files used for the implementation of a novel approach to conduct process-based Flood Frequency Analysis using a Stochastic Storm Transposition (SST) and an Integrated Surface-Subsurface Hydrological Model (ISSHM). As a proof-of-concept, this study uses the ISSHM, Advanced Terrestrial Simulator (Amanzi-ATS) model, and the SST model, RainyDay, to conduct flood frequency analysis by simulating the flood response to 5,000 annual synthetic storm events in a ~2000 km2 Southeast Texas watershed.The Watershed Workflow package is implemented in Python3. The Jupyter notebooks can be executed through multiple open-source tools, for example, Anaconda Jupyter Lab, VS Studio Code, etc. Other data files include TXT, CSV, DAT, SBATCH, SHP, TIF, NetCDF, and HDF5 files, which can be read through Python scripts. The input files for the ATS model and RainyDay model have .XML and .SST extensions, respectively, and can be edited in any commonly used text editors.This archive contains:* Scripts and data files essential for generating the ATS model input. It uses the Watershed Workflow package to produce both mesh and ATS input files. * Jupyter notebooks designated for the ATS model evaluation, covering both long-term simulations and 40 rainfall-runoff events.* Input files required to simulate SST storm events using RainyDay.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Advanced probabilistic risk analysis using RAVEN and RELAP-7

RAVEN, under the support of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program [1], is advancing its capability to perform statistical analyses of stochastic dynamic systems. This is aligned with its mission to provide the tools needed by the Risk Informed Safety Margin Characterization (RISMC) path-lead [2] under the Department Of Energy (DOE) Light Water Reactor Sustainability program [3]. In particular this task is focused on the synergetic development with the RELAP-7 [4] code to advance the state of the art on the safety analysis of nuclear power plants (NPP). The investigation of the probabilistic evolution of accident scenarios for a complex system such as a nuclear power plant is not a trivial challenge. The complexity of the system to be modeled leads to demanding computational requirements even to simulate one of the many possible evolutions of an accident scenario (tens of CPU/hour). At the same time, the probabilistic analysis requires thousands of runs to investigate outcomes characterized by low probability and severe consequence (tail problem). The milestone reported in June of 2013 [5] described the capability of RAVEN to implement complex control logic and provide an adequate support for the exploration of the probabilistic space using a Monte Carlo sampling strategy. Unfortunately the Monte Carlo approach is ineffective with a problem of this complexity. In the following year of development, the RAVEN code has been extended with more sophisticated sampling strategies (grids, Latin Hypercube, and adaptive sampling). This milestone report illustrates the effectiveness of those methodologies in performing the assessment of the probability of core damage following the onset of a Station Black Out (SBO) situation in a boiling water reactor (BWR). The first part of the report provides an overview of the available probabilistic analysis capabilities, ranging from the different types of distributions available, possible sampling strategies, and post processing analysis capabilities. The first part of the report provides an extensive description of two major developments introduced this year: adaptive sampling for limit surface sampling and multi variate distributions. The document concludes with a description of the demo case (BWR-SBO) and a discussion of the results obtained.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Risk-Based Comparison of Carbon Capture Technologies

In this paper, we describe an integrated probabilistic risk assessment methodological framework and a decision-support tool suite for implementing systematic comparisons of competing carbon capture technologies. Culminating from a collaborative effort among national laboratories under the Carbon Capture Simulation Initiative (CCSI), the risk assessment framework and the decision-support tool suite encapsulate three interconnected probabilistic modeling and simulation components. The technology readiness level (TRL) assessment component identifies specific scientific and engineering targets required by each readiness level and applies probabilistic estimation techniques to calculate the likelihood of graded as well as nonlinear advancement in technology maturity. The technical risk assessment component focuses on identifying and quantifying risk contributors, especially stochastic distributions for significant risk contributors, performing scenario-based risk analysis, and integrating with carbon capture process model simulations and optimization. The financial risk component estimates the long-term return on investment based on energy retail pricing, production cost, operating and power replacement cost, plan construction and retrofit expenses, and potential tax relief, expressed probabilistically as the net present value distributions over various forecast horizons.

Engel, David W.↗