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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
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.↗
DOE OSTI2019

Modeling and Simulation of Microstructure Evolution and Deformation in an Irradiated Environment

The ability to predict the behavior of structural components in a nuclear power plant is critical to the nuclear industry. Structural metals in the primary loop of nuclear power plants must endure challenges such as irradiation and mechanical and thermal loading, and these structural metal components must continue to function in potential transient and accident conditions throughout the operational lifetime of the power plant. This extreme operational environment changes the metal microstructure by creating additional defects. The physical interactions of dislocations with these defects govern how the metal will respond to future conditions. Therefore predicting the mechanical response of these metals requires a set of physically based and reliable models of dislocation and defect interactions. These microstructure elements include glide mobile and immobile dislocations, geometrically necessary dislocations, twinning dislocations, irradiation defects, and thermal aging defects. We present here a continuum dislocation dynamics crystal plasticity framework to capture the interaction mechanisms of these dislocations and defects, verified with a combination of benchmark problems and comparisons with experimental data for two different types of structural metals: alpha iron and nickel-based alloys. In our simulations of $\alpha$ iron we highlight the advantages of applying a Monte Carlo stochastic model of cross slip dislocation motion and show the importance of capturing the 3D nature of glide dislocation and self-interstitial atom loop radiation defect interactions. We demonstrate coupling of glide dislocations with geometrically necessary dislocations to capture the influence of lattice bending, including the sensitivity of the geometrically necessary dislocations to changes in the grain boundary angle. We further examine the interaction of glide dislocations with the twin dislocations and thermally aged defects which have been observed in a nickel-based alloy with additional models. Finally we assess the reliability of this crystal plasticity framework by comparing two dislocation glide velocity models across the range of normal operation temperatures. In successfully applying our crystal plasticity framework to multiple metals, we provide further evidence of the reliability of our approach. The results of this mechanism-based continuum dislocation dynamics crystal plasticity framework can be used to inform engineering scale models throughout the nuclear industry.

36 MATERIALS SCIENCE↗
DOE OSTI2017

An assessment of flexibility reserves in stochastic modeling at multiple timescales

Due to recent technological achievements, stochastic optimization, which inherently captures the uncertainty of intermittent resources, is being used to capture the variability and uncertainty of wind and solar resources. However, due to persistent computational limitations, it is not practical to consider all possible variable generation scenarios. As a result, a reduced number of most likely scenarios is usually considered. While this helps reduce the computational burden, it also leaves the system operator vulnerable to some risk. In order to address this issue, this paper aims at providing insight into using an explicit reserve requirement in a stochastic modeling framework in order to provide system operators with greater confidence in stochastic dispatch solutions. This is accomplished by simulating a modified version of the IEEE 118 bus system in a fully stochastic, multi-timescale framework with flexibility reserve requirements. Results show that utilizing a stochastic flexibility reserve requirement within the stochastic modeling framework offers the most reliability benefit.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2012

THE X-FACTOR IN GALAXIES. II. THE MOLECULAR-HYDROGEN-STAR-FORMATION RELATION

There is ample observational evidence that the star formation rate (SFR) surface density, {Sigma}{sub SFR}, is closely correlated with the surface density of molecular hydrogen, {Sigma}{sub H{sub 2}}. This empirical relation holds both for galaxy-wide averages and for individual {approx}>kpc sized patches of the interstellar medium, but appears to degrade substantially at a sub-kpc scale. Identifying the physical mechanisms that determine the scale-dependent properties of the observed {Sigma}{sub H{sub 2}}-{Sigma}{sub SFR} relation using a set of cosmological, galaxy formation simulations with a peak resolution of {approx}100 pc. These simulations include a chemical network for molecular hydrogen, a model for the CO emission, and a simple, stochastic prescription for star formation that operates on {approx}100 pc scales. Specifically, star formation is modeled as a Poisson process in which the average SFR is directly proportional to the present mass of H{sub 2}. The predictions of our numerical model are in good agreement with the observed Kennicutt-Schmidt and {Sigma}{sub H{sub 2}}-{Sigma}{sub SFR} relations. We show that observations based on CO emission are ill suited to reliably measure the slope of the latter relation at low ({approx}< 20 M {sub Sun} pc{sup -2}) H{sub 2} surface densities on sub-kpc scales. Our models also predict that the inferred {Sigma}{sub H{sub 2}}-{Sigma}{sub SFR} relation steepens at high H{sub 2} surface densities as a result of the surface density dependence of the CO/H{sub 2} conversion factor. Finally, we show that on sub-kpc scales most of the scatter of the relation is a consequence of discreteness effects of the star formation process. In contrast, variations of the CO/H{sub 2} conversion factor are responsible for most of the scatter measured on super-kpc scales.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Accelerated Sensitivity Analysis in High-Dimensional Stochastic Reaction Networks

Existing sensitivity analysis approaches are not able to handle efficiently stochastic reaction networks with a large number of parameters and species, which are typical in the modeling and simulation of complex biochemical phenomena. In this paper, a two-step strategy for parametric sensitivity analysis for such systems is proposed, exploiting advantages and synergies between two recently proposed sensitivity analysis methodologies for stochastic dynamics. The first method performs sensitivity analysis of the stochastic dynamics by means of the Fisher Information Matrix on the underlying distribution of the trajectories; the second method is a reduced-variance, finite-difference, gradient-type sensitivity approach relying on stochastic coupling techniques for variance reduction. Here in this paper we demonstrate that these two methods can be combined and deployed together by means of a new sensitivity bound which incorporates the variance of the quantity of interest as well as the Fisher Information Matrix estimated from the first method. The first step of the proposed strategy labels sensitivities using the bound and screens out the insensitive parameters in a controlled manner. In the second step of the proposed strategy, a finite-difference method is applied only for the sensitivity estimation of the (potentially) sensitive parameters that have not been screened out in the first step. Results on an epidermal growth factor network with fifty parameters and on a protein homeostasis with eighty parameters demonstrate that the proposed strategy is able to quickly discover and discard the insensitive parameters and in the remaining potentially sensitive parameters it accurately estimates the sensitivities. The new sensitivity strategy can be several times faster than current state-of-the-art approaches that test all parameters, especially in “sloppy” systems. In particular, the computational acceleration is quantified by the ratio between the total number of parameters over the number of the sensitive parameters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Patchy snapshots of nuclear chain reactions

Stochastic fluctuations of the neutron population within a nuclear reactor are typically prevented by operating the core at a sufficiently high power. This regime, where the evolution of the neutron density is essentially deterministic, is key for automatic protection and safety systems to safely detect unwanted power excursions during an accident, and to rapidly initiate a reactor trip procedure in case it is needed. Recent works, supported by numerical simulations, have however reported that, for large reactors, the branching nature of the fission reactions might induce strongly non-Poissonian patterns in the neutron spatial distribution, and that stochastic fluctuations might still persist at reactor powers close to operating conditions (startup phase). An international program conducted by LANL, IRSN and CEA was therefore setup to experimentally detect and characterize such fluctuations and correlations. An experiment took place in 2017 at the Reactor Critical Facility (RCF) of the Rensselaer Polytechnic Institute (USA). In this paper we will report the main findings of this experimental program, supported by stochastic models and by the development of a dedicated high-fidelity Monte Carlo simulation code. We will in particular describe and explain the strong patchiness in neutron power distributions measured at the RCF, as well as a peculiar ‘blinking’ behavior of the core, and discuss the consequences of these findings on nuclear safety.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2014

A stepped leader model for lightning including charge distribution in branched channels

The stepped leader process in negative cloud-to-ground lightning plays a vital role in lightning protection analysis. As lightning discharge usually presents significant branched or tortuous channels, the charge distribution along the branched channels and the stochastic feature of stepped leader propagation were investigated in this paper. The charge density along the leader channel and the charge in the leader tip for each lightning branch were approximated by introducing branch correlation coefficients. In combination with geometric characteristics of natural lightning discharge, a stochastic stepped leader propagation model was presented based on the fractal theory. By comparing simulation results with the statistics of natural lightning discharges, it was found that the fractal dimension of lightning trajectory in simulation was in the range of that observed in nature and the calculation results of electric field at ground level were in good agreement with the measurements of a negative flash, which shows the validity of this proposed model. Furthermore, a new equation to estimate the lightning striking distance to flat ground was suggested based on the present model. The striking distance obtained by this new equation is smaller than the value estimated by previous equations, which indicates that the traditional equations may somewhat overestimate the attractive effect of the ground.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2012

Experiences using DAKOTA stochastic expansion methods in computational simulations

Uncertainty quantification (UQ) methods bring rigorous statistical connections to the analysis of computational and experiment data, and provide a basis for probabilistically assessing margins associated with safety and reliability. The DAKOTA toolkit developed at Sandia National Laboratories implements a number of UQ methods, which are being increasingly adopted by modeling and simulation teams to facilitate these analyses. This report disseminates results as to the performance of DAKOTA's stochastic expansion methods for UQ on a representative application. Our results provide a number of insights that may be of interest to future users of these methods, including the behavior of the methods in estimating responses at varying probability levels, and the expansion levels for the methodologies that may be needed to achieve convergence.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

A hierarchical stochastic modeling approach for representing point bar geometries and petrophysical property variations

The flow of fluids in point bars is affected by the existence of heterogeneities like shale drapes that are found on the surfaces of inclined heterolithic stratifications. In fact, these shale drapes can act as fluid flow baffles; therefore, developing a framework for modeling point bars and their associated heterogeneities is vital. In this study, a stochastic process-based modeling approach is presented for capturing the main point bar heterogeneities: accretion surfaces (i.e., the aerial heterogeneity) and inclined heterolithic stratifications (i.e., the vertical heterogeneity). The former was modeled using a sine-generation function and the latter, with a sigmoidal function after which they were combined into a 3D point bar model. To ensure proper modeling of petrophysical properties, we developed a more representative gridding scheme which generates curvilinear grids representative of the point bar geometry. This grid was then transformed into a rectilinear grid to allow for geostatistical simulation after which all petrophysical properties were mapped back into the original curvilinear grid. An essential element of this modeling approach is the stochastic representation of shale drapes at the interface between successive accretion surfaces. The workflow was tested using a real field dataset for the Cranfield field, Mississippi. The constructed model was then subjected to a flow simulation study mimicking a CO 2 storage scenario. Various sensitivities were simulated to evaluate the effect of heterogeneities on CO 2 flow within the point-bar. Results demonstrate the importance of representing point-bar related heterogeneity and the spatial distribution of shale drapes on CO 2 plume migration and storage.

58 GEOSCIENCES↗
DOE OSTI2012

THE METALLICITY EVOLUTION OF INTERACTING GALAXIES

Nuclear inflows of metal-poor interstellar gas triggered by galaxy interactions can account for the systematically lower central oxygen abundances observed in local interacting galaxies. Here, we investigate the metallicity evolution of a large set of simulations of colliding galaxies. Our models include cooling, star formation, feedback, and a new stochastic method for tracking the mass recycled back to the interstellar medium from stellar winds and supernovae. We study the influence of merger-induced inflows, enrichment, gas consumption, and galactic winds in determining the nuclear metallicity. The central metallicity is primarily a competition between the inflow of low-metallicity gas and enrichment from star formation. An average depression in the nuclear metallicity of {approx}0.07 is found for gas-poor disk-disk interactions. Gas-rich disk-disk interactions, on the other hand, typically have an enhancement in the central metallicity that is positively correlated with the gas content. The simulations fare reasonably well when compared to the observed mass-metallicity and separation-metallicity relationships, but further study is warranted.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Detailed soot modeling of mixing controlled compression ignition engines

Abstract Most soot simulation is done using relatively simple models that only provide basic information regarding general soot mass due to the high computational cost of more complex models. In this work, a stochastic model capable of detailed soot description has been implemented in a hybrid fashion such that a relatively fast conventional combustion simulation followed by many independent highly parallelizable soot simulations can be used. The model is used to characterize the origin and advection of soot for a diesel engine with a triple injection scheme.

Strickland, Tyler↗
DOE OSTI2021

Potential vorticity transport in weakly and strongly magnetized plasmas

Tangled magnetic fields, often coexisting with an ordered mean field, have a major impact on turbulence and momentum transport in many plasmas, including those found in the solar tachocline and magnetic confinement devices. We present a novel mean field theory of potential vorticity mixing in β-plane magnetohydrodynamic (MHD) and drift wave turbulence. Our results show that mean square stochastic fields strongly reduce Reynolds stress coherence. This decoherence of potential vorticity flux due to stochastic field scattering leads to suppression of momentum transport and zonal flow formation. A simple calculation suggests that the breaking of the shear-eddy tilting feedback loop by stochastic fields is the key underlying physics mechanism. Furthermore, a dimensionless parameter that quantifies the increment in power threshold is identified and used to assess the impact of stochastic field on the L-H transition. We discuss a model of stochastic fields as a resisto-elastic network.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Phase transitions in Schloegl's second model for autocatalysis on a Bethe lattice

Schloegl's second model (also known as the quadratic contact process) on a lattice involves spontaneous particle annihilation at rate p and autocatalytic particle creation at empty sites with n ≥ 2 occupied neighbors. The particle creation rate for exactly n occupied neighbors is selected here as n(n - 1)/[z(z - 1)] for lattice coordination number z. We analyze this model on a Bethe lattice. Precise behavior for stochastic models on regular periodic infinite lattices is usually surmised from kinetic Monte Carlo simulation on a finite lattice with periodic boundary conditions. However, the persistence of boundary effects for a Bethe lattice complicates this process, e.g., by inducing spatially heterogenous states. This motivates the exploration of various boundary conditions and unconventional simulation ensembles on the Bethe lattice to predict behavior for infinite size. Here, we focus on z = 3, and predict a discontinuous transition to the vacuum state on the infinite lattice when p exceeds a threshold value of around 0.053.

97 MATHEMATICS AND COMPUTING↗