Search NASA⌕ Search

SEARCH · Search NASA

Results for “Model initialization”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

SAM Theory Manual

The System Analysis Module (SAM) is an advanced and modern system analysis tool being developed at Argonne National Laboratory under the U.S. DOE Office of Nuclear Energy’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. SAM development aims for advances in physical modeling, numerical methods, and software engineering to enhance its user experience and usability for reactor transient analyses. To facilitate the code development, SAM utilizes an object-oriented application framework (MOOSE), and its underlying meshing and finite-element library (libMesh) and linear and non-linear solvers (PETSc), to leverage modern advanced software environments and numerical methods. SAM focuses on modeling advanced reactor concepts such as SFRs (sodium fast reactors), LFRs (lead-cooled fast reactors), and FHRs (fluoride-salt-cooled high temperature reactors) or MSRs (molten salt reactors). These advanced concepts are distinguished from light-water reactors in their use of single-phase, low-pressure, high-temperature, and low Prandtl number (sodium and lead) coolants. As a new code development, the initial effort has been focused on modeling and simulation capabilities of heat transfer and single-phase fluid dynamics responses in Sodium-cooled Fast Reactor (SFR) systems. The system-level simulation capabilities of fluid flow and heat transfer in general engineering systems and typical SFRs have been verified and validated. This document provides the theoretical and technical basis of the code to help users understand the underlying physical models (such as governing equations, closure models, and component models), system modeling approaches, numerical discretization and solution methods, and the overall capabilities in SAM. As the code is still under ongoing development, this SAM Theory Manual will be updated periodically to keep it consistent with the state of the development.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Peridynamic Model for Microballistic Perforation of Multilayer Graphene

The peridynamic theory of solid mechanics is applied to the continuum modeling of the impact of small, high-velocity silica spheres on multilayer graphene targets. The model treats the laminate as a brittle elastic membrane. The material model includes separate failure criteria for the initial rupture of the membrane and for propagating cracks. Material variability is incorporated by assigning random variations in elastic properties within Voronoi cells. Here, the computational model is shown to reproduce the primary aspects of the response observed in experiments, including the growth of a family of radial cracks from the point of impact.

42 ENGINEERING↗
DOE OSTI2016

Mean-Field-Game Model for Botnet Defense in Cyber-Security

We initiate the analysis of the response of computer owners to various offers of defence systems against a cyber-hacker (for instance, a botnet attack), as a stochastic game of a large number of interacting agents. We introduce a simple mean-field game that models their behavior. It takes into account both the random process of the propagation of the infection (controlled by the botner herder) and the decision making process of customers. Its stationary version turns out to be exactly solvable (but not at all trivial) under an additional natural assumption that the execution time of the decisions of the customers (say, switch on or out the defence system) is much faster that the infection rates.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Multi-decadal variation of ENSO forecast skill since the late 1800s

Diagnosing El Niño-Southern Oscillation (ENSO) predictability within operational forecast models is hindered by computational expense and the need for initialization with three-dimensional fields generated by global data assimilation. We instead examine multi-year ENSO predictability since the late 1800s using the model-analog technique, which has neither limitation. We first draw global coupled model states from pre-industrial control simulations, from the Coupled Model Intercomparison Project Phase 6, that are chosen to initially match observed monthly sea surface temperature and height anomalies in the Tropics. Their subsequent 36-month model evolution are the hindcasts, whose 20th century ENSO skill is comparable to twice-yearly hindcasts generated by a state-of-the-art European operational forecasting system. Despite the so-called spring predictability barrier, present throughout the record, there is substantial second-year ENSO skill, especially after 1960. Overall, ENSO exhibited notably high values of both amplitude and skill towards the end of the 19th century, and again in recent decades.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Examining the radiation drive asymmetries present in the high foot series of implosion experiments at the National Ignition Facility

This paper details and examines the origins of radiation drive asymmetries present during the initial High Foot implosion experiments. Such asymmetries are expected to reduce the stagnation pressure and the resulting yield of these experiments by several times. Analysis of reemission and dual axis shock timing experiments indicates that a flux asymmetry, with a P2/P0 amplitude that varies from -10 to -5 %, is present during the first shock of the implosion. This first shock asymmetry can be corrected through adjustments to the laser cone fraction. A thin shell model and more detailed radiation hydrodynamic calculations indicate that an additional negative P2/P0 asymmetry during the second or portions of the third shock is required to reach the observed amount of asymmetry in the shape of the ablator at peak implosion velocity. In conjunction with symmetry data from the x-ray self emission produced at stagnation, these models also indicate that after the initially negative P2/P0 flux asymmetry, the capsule experiences a positive P2/P0 flux asymmetry that develops at or before ~2 ns into the peak of the laser power. Here, direct evidence for this inference, using measurements of the x-ray emission produced by the lasers irradiating the hohlraum, is presented. This data indicates that the reduction in the transmitted inner laser cone energy results from impeded propagation through the plasma associated with the ablation of the capsule target. This paper also correlates measurements of the outer cone laser deposition location with variations in the observed x-ray self emission shape from experiments conducted with nominally the same input conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

MODELING THE INFLUENCE OF NOZZLE-GENERATED TURBULENCE ON DIESEL SPRAYS

Predictive engine simulations are key for rapidly exploring and optimizing the design of cleaner burning and more fuel efficient engines. Injection strategies in advanced engine concepts are resulting in the injection and atomization of fuel under a wide range of operating conditions in order to meet stringent emission regulations. However, the physics governing the breakup of an injected liquid fuel jet into droplets under these conditions have not been well studied or experimentally characterized to date. In the sprays literature, three agents have been proposed as the likely mechanisms contributing to primary atomization in diesel sprays, namely the aerodynamic growth of waves on the fuel jet surface, turbulence generated in the injector nozzle, and cavitation. If computational design tools are to be used to guide the use of direct injection strategies for cleaner and more fuel efficient engines, the physics underpinning the role of these primary atomization mechanisms must be better understood to ensure the development of predictive simulations of fuel-air mixing and vaporization within the engine. Thus, the central aim of this thesis is to improve the physical representation of spray breakup physics within today’s engine simulation packages. The work presented in this thesis investigates the role of the proposed physical mechanisms on the primary atomization process in diesel sprays. In order to advance current understanding of spray breakup, the dynamic and geometric factors contributing to cavitation were suppressed so that primary atomization due to aerodynamics and nozzle-generated turbulence could be studied in isolation. In the absence of sufficiently resolved images to visualize the primary atomization process under dieselrelevant conditions, droplet sizing spray measurements are needed to characterize the outcomes of the spray breakup process. Therefore, a new experimental methodology was developed and applied in a high-pressure spray chamber to characterize the average size of droplets formed from the spray breakup process. This experimental data, in conjunction with x-ray measurements from the Advanced Photon Source at Argonne National Laboratory, have been used to assess predictions from existing spray breakup models. Comparison between predicted and measured drop size distributions revealed that a spray atomization model, premised on droplet formation from the growth of aerodynamically induced instabilities, could capture experimentally observed sensitivities and features in the measured droplet size distributions under conventional diesel engine conditions. However, for injection into relatively lower ambient density environments, aerodynamic breakup models could not accurately predict the initial rate of droplet size decrease in the near-nozzle region, suggesting that other mechanisms, such as turbulence generated inside the nozzle, likely augment and enhance the primary breakup process. Evaluation of newly available droplet sizing measurements under low ambient density conditions allowed for the turbulence-induced breakup process to be studied, while minimizing the influence of aerodynamic inertial forces on the spray. Although several turbulence-induced breakup models have been proposed in the literature, the scaling of droplet sizes with the integral length scale, assumed in the majority of turbulent breakup models, was found to be inconsistent with the experimentally observed trends in droplet size along the spray centerline. However, empirical correlations describing droplets formed from eddies within the inertial sub-range of the turbulence spectrum were better able to capture the measured sensitivities in droplet size to changes in ambient and injection conditions. These findings informed recommendations for an improved hybrid spray breakup model, capable of representing both aerodynamic and turbulent breakup mechanisms in the atomization of non-cavitating diesel sprays.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Observable signatures of initial state momentum anisotropies in nuclear collisions

In this work, we show that the correlation between the elliptic momentum anisotropy v 2 and the average transverse momentum [ p T ] at fixed multiplicity in small system nuclear collisions carries information on the origin of the observed momentum anisotropy. A calculation using a hybrid IP-Glasma+ music +UrQMD model that includes contributions from final state response to the initial geometry as well as initial state momentum anisotropies of the color glass condensate predicts a characteristic sign change of the correlator ρ ^ ( v 2 2 , [ p T ] ) as a function of charged particle multiplicity in p + Au and d + Au collisions at s = 200 GeV , and p + Pb collisions at s = 5.02 TeV . This sign change is absent in calculations without initial state momentum anisotropies. The model further predicts a qualitative difference between the centrality dependence of ρ ^ ( v 2 2 , [ p T ] ) in Au + Au collisions at s = 200 GeV and Pb + Pb collisions at s = 5.02 TeV , with only the latter showing a sign change in peripheral events. Predictions for O + O collisions at different collision energy show a similar behavior. Experimental observation of these distinct qualitative features of ρ ^ ( v 2 2 , [ p T ] ) in small and large systems would constitute strong evidence for the presence and importance of initial state momentum anisotropies predicted by the color glass condensate effective theory.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

You Only Look Once v5 and Multi-Template Matching for Small-Crack Defect Detection on Metal Surfaces

This paper compares the performance of Deep Learning (DL) and multi-template matching (MTM) models for detecting small defects. DL models extract distinguishing features of objects but require a large dataset of images. In contrast, alternative computer vision techniques like MTM need a relatively small dataset. The lack of large datasets for small metal-surface defects has inhibited the adoption of automation in small-defect detection in remanufacturing settings. This motivated this preliminary study to compare template-based approaches, like MTM, with feature-based approaches, such as DL models, for small-defect detection on an initial laboratory and remanufacturing industry dataset. This study used You Only Look Once v5 (YOLOv5) as the DL model and compared its performance against the MTM model for small-crack detection. The findings of our preliminary investigation are as follows: (i) YOLOv5 demonstrated higher performance than MTM in detecting small cracks; (ii) an extra-large variant of YOLOv5 outperformed a small-size variant; (iii) the size and object variety of the data are crucial in achieving robust pre-trained weights for use in transfer learning; and (iv) enhanced image resolution contributes to precise object detection.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Developing and testing capabilities for simulating cases with heterogeneous land/water surfaces in a novel atmospheric large eddy simulation code

Large eddy simulations (LES) are the primary computational tool used to simulate high Reynolds number three-dimensional turbulent flows. In the context of earth system sciences, particularly atmospheric science, LES are uniquely able to resolve the scales of atmospheric motion that are key for building process-level understanding of boundary layer turbulence, atmosphere-surface interaction, clouds, and cloud-aerosol-chemistry interaction, and are a core limited-area modeling capability. Increasing demands are being placed on LES code bases as growing high performance computing resources allow LES to address a wider range of scientific problems. In addition, LES are emerging as a source of high-quality machine learning training data. These demands necessitate an agile and extensible code base that allows the model to quickly adapt to emergent needs. However, LES have largely relied on legacy Fortran code bases that lack flexibility. A new, Python-based LES capability called Predicting INteractions of Aerosol and Clouds in Large Eddy Simulation (PINACLES) has been developed as part of the Department of Energy’s Earth System Model Development (ESMD) program area’s Enabling Aerosol-cloud interactions at Global convection-permitting scalES (EAGLES) project. PINACLES was developed from the ground up with a philosophy of maximizing scientific throughput, by attempting to optimize for both model throughput and software extensibility. The initial development of PINACLES delivered a state-of-the-art idealized LES capability solving the non-hydrostatic anelastic equations of motion with doubly periodic boundary conditions and idealized homogenous surface boundary conditions. Here we provide a final report on the outcomes of a fiscal year 2021 Seed Laboratory Directed Research Project that extended PINACLES in two key ways. First, PINACLES was coupled to a state-of-the-art land surface model enabling it to simulate spatially inhomogeneous land-atmosphere interactions that are known to control key atmospheric processes. Second, the dynamical core of PINACLES was modified to permit non-periodic boundary conditions. This model enhancement enables simulation of realistic cases with boundary conditions prescribed from atmospheric reanalysis and enables nested simulations conducted on a hierarchy of computational domains with increasing resolution. Together, these extensions to PINACLES make it a formidable modeling capability and expand its potential application to diverse components of DOE’s atmospheric science portfolio.

42 ENGINEERING↗
DOE OSTI2017

Simulating estimation of California fossil fuel and biosphere carbon dioxide exchanges combining in situ tower and satellite column observations

Here, we report simulation experiments estimating the uncertainties in California regional fossil fuel and biosphere CO 2 exchanges that might be obtained by using an atmospheric inverse modeling system driven by the combination of ground-based observations of radiocarbon and total CO 2 , together with column-mean CO 2 observations from NASA's Orbiting Carbon Observatory (OCO-2). The work includes an initial examination of statistical uncertainties in prior models for CO 2 exchange, in radiocarbon-based fossil fuel CO 2 measurements, in OCO-2 measurements, and in a regional atmospheric transport modeling system. Using these nominal assumptions for measurement and model uncertainties, we find that flask measurements of radiocarbon and total CO 2 at 10 towers can be used to distinguish between different fossil fuel emission data products for major urban regions of California. We then show that the combination of flask and OCO-2 observations yields posterior uncertainties in monthly-mean fossil fuel emissions of ~5–10%, levels likely useful for policy relevant evaluation of bottom-up fossil fuel emission estimates. Similarly, we find that inversions yield uncertainties in monthly biosphere CO 2 exchange of ~6%–12%, depending on season, providing useful information on net carbon uptake in California's forests and agricultural lands. Finally, initial sensitivity analysis suggests that obtaining the above results requires control of systematic biases below approximately 0.5 ppm, placing requirements on accuracy of the atmospheric measurements, background subtraction, and atmospheric transport modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Fission Evaluation Tools and Analytics (FETA)

This living document presents the Python package FETA. FETA computes observables resulting from the fission process. This document provides the definition of these observables as well as the physics models that are implemented to compute them. Some of these models are used to determine the initial conditions of fission fragments, e.g., the excitation energy E* and spin distribution p(J, π) at scission for prompt decay, while others are related to the nuclear structure and decay properties of the fragments, e.g. the ground-state properties, level density and low-lying excitation spectrum, γ-strength functions and electromagnetic transitions, and neutron transmission coefficients. The end goal for FETA is to enable users to substitute every one of these models by their own files providing these quantities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Models Predicting Void Swelling Incubation Dose as a function of Irradiation Conditions

This report documents the development status of a cluster dynamics model to predict microstructural evolution in high-energy particle irradiated ferritic martensitic steels, in which we have recently implemented a model for the biased absorption of self-interstitial defects at small, nanometer-sized cavities that can provide an intrinsic nucleation barrier for the onset of void swelling. Following a brief review of the cluster dynamics modeling approach, this report describes the atomistic modeling results that demonstrate this phenomenon, along with an investigation on the influence of helium content on modifying the intrinsic cavity bias. Subsequently, the report describes the initial results obtained from our cluster dynamics modeling of the incorporation of this cavity bias on the predicted swelling incubation dose as a function of irradiation temperature. Finally, the report concludes with a discussion of the remaining modeling research required to fully validate the model for application to high dose neutron or ion beam irradiation exposure conditions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Solving Stochastic Inverse Problems for Property–Structure Linkages Using Data-Consistent Inversion and Machine Learning

Determining process–structure–property linkages is one of the key objectives in material science, and uncertainty quantification plays a critical role in understanding both process–structure and structure–property linkages. In this work, we seek to learn a distribution of microstructure parameters that are consistent in the sense that the forward propagation of this distribution through a crystal plasticity finite element model matches a target distribution on materials properties. This stochastic inversion formulation infers a distribution of acceptable/consistent microstructures, as opposed to a deterministic solution, which expands the range of feasible designs in a probabilistic manner. Furthermore, to solve this stochastic inverse problem, we employ a recently developed uncertainty quantification framework based on push-forward probability measures, which combines techniques from measure theory and Bayes’ rule to define a unique and numerically stable solution. This approach requires making an initial prediction using an initial guess for the distribution on model inputs and solving a stochastic forward problem. To reduce the computational burden in solving both stochastic forward and stochastic inverse problems, we combine this approach with a machine learning Bayesian regression model based on Gaussian processes and demonstrate the proposed methodology on two representative case studies in structure–property linkages.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Use of multiscale zirconium alloy deformation models in nuclear fuel behavior analysis

Accurate prediction of cladding mechanical behavior is a key aspect of modeling nuclear fuel behavior, especially for conditions of pellet-cladding interaction (PCI), reactivity-initiated accidents (RIA), and loss of coolant accidents (LOCA). Current approaches to fuel performance modeling rely on empirical constitutive models for cladding creep, growth and plastic deformation, which are limited to the materials and conditions for which the models were developed. To improve upon this approach, a microstructurally-based zirconium alloy mechanical deformation analysis capability is being developed within the United States Department of Energy Consortium for Advanced Simulation of Light Water Reactors (CASL). Specifically, the viscoplastic self-consistent (VPSC) polycrystal plasticity modeling approach, developed by Lebensohn and Tomé [1], has been coupled with the BISON engineering scale fuel performance code to represent the mechanistic material processes controlling the deformation behavior of light water reactor (LWR) cladding. A critical component of VPSC is the representation of the crystallographic nature (defect and dislocation movement) and orientation of the grains within the matrix material and the ability to account for the role of texture on deformation. A future goal is for VPSC to obtain information on reaction rate kinetics from atomistic calculations to inform the defect and dislocation behavior models described in VPSC. The multiscale modeling of cladding deformation mechanisms allowed by VPSC far exceed the functionality of typical semi-empirical constitutive models employed in nuclear fuel behavior codes to model irradiation growth and creep, thermal creep, or plasticity. This paper describes the implementation of an interface between VPSC and BISON and provides initial results utilizing the coupled functionality.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

High-to-Low Activities across VVI, AMA and PHI

This milestone report documents work performed in CASL that was broadly focused on the use of validated high-fidelity simulation codes to inform, improve and calibrate low-fidelity codes to facilitate multiphysics coupling, design, performance analysis, and risk assessment. This high-to-low (Hi2Lo) initiative has the following objectives: employ physics encapsulated in high-fidelity codes to improve the functionality of low-fidelity codes, quantify biases in low-fidelity codes, develop and calibrate physical models and closure relations so that low-fidelity codes more accurately incorporate requisite physics, and develop low-fidelity codes that can be effectively coupled to simulate multiphysics behavior. In combination, these efforts are designed to impact both CIPS and DNB. For example, the development and experimental design-based Bayesian calibration of dynamic gas conductance models in CTFFuel will provide the capability to better model crud-induced power shifts. Whereas the present emphasis has focused on the high-fidelity codes STAR-CCM+ and BISON and low-fidelity codes CTF and CTFFuel, future emphasis will be expanded to include MAMBA. During the reporting period, Hi2Lo efforts focused on two broad components: demonstration of the experimental design-based Bayesian calibration framework to calibrate CTF using both STAR-generated synthetic data and experimental data, and the use of STAR to inform and improve the turbulence modeling and grid heat transfer modeling capabilities in CTF. A complementary milestone focused on the implementation and verification of fuel models in CTFFuel, for which we demonstrated initial calibration results. Future Hi2Lo work will focus on the use of the experimental design-based Bayesian calibration framework, demonstrated this year for STAR and CTF, to calibrate parameters in the fuel swelling, fuel irradiation-induced densification, and fuel relocation models in CTFFuel using both experimental and BISON-generated data. A second component will focus on the continued use of STAR to improve grid modeling capabilities in CTF.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Strong Coupling of Hydrodynamics and Reactions in Nuclear Statistical Equilibrium for Modeling Convection in Massive Stars

We build on the simplified spectral deferred corrections (SDC) coupling of hydrodynamics and reactions to handle the case of nuclear statistical equilibrium (NSE) and electron/positron captures/decays in the cores of massive stars. Our approach blends a traditional reaction network on the grid with a tabulated NSE state from a very large, ${\mathcal O }(100)$ nuclei network. We demonstrate how to achieve second-order accuracy in the simplified-SDC framework when coupling NSE to hydrodynamics, with the ability to evolve the star on the hydrodynamics time step. We discuss the application of this method to convection in massive stars leading up to core collapse. We also show how to initialize the initial convective state from a 1D model in a self-consistent fashion. All of these developments are done in the publicly available Castro simulation code and the entire simulation methodology is fully GPU-accelerated.

Explosive nucleosynthesis↗
DOE OSTI2023

Status of PyGriffin Development for Integration into the NEAMS Workbench

The integration of Griffin into Workbench, by way of the PyGriffin code package, was initiated by the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program in FY-2022. PyGriffin was developed as a Python wrapper to Griffin to streamline the complex workflow involving mesh, cross section, and Griffin input generation, code execution, and results postprocessing, by improving user experience with high fidelity neutronics analysis through the Workbench GUI interface. PyGriffin can be used as a standalone application or through the PyARC code suite. FY-2023 saw the continued development of PyGriffin to expand its capabilities including advanced post-processing of simulation results and initial development of the Monte Carlo (MC) cross section generation workflow with the Shift MC code. PyGriffin was also approved for open-source software status in FY-2023, initiating plans to move the code repository into an open location. In addition to PyGriffin development, there were many improvements and developments made in PyARC leading to several software releases (latest version is v2.3.0), but the focus of this report is PyGriffin.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

A Unified Data Infrastructure for Biological and Environmental Research: A Report from the BER Advisory Committee

The Biological and Environmental Research (BER) program within the U.S. Department of Energy (DOE) Office of Science supports large-scale data generation efforts across its two divisions: Biological Systems Science and Earth and Environmental Systems Sciences. These efforts include user facilities in atmospheric radiation measurements, genomics, metabolomics, proteomics, compute, and imaging. In addition, BER supports the development of plant-based fuels; research in biosystems design, environmental microbiomes, and atmospheric systems; energy flux monitoring; climate-based ecosystem experiments; pathogen biopreparedness; and modeling of climate, urban interfaces, and interactions between people and energy resources. For data access, BER supports community data services at its user facilities, along with specialized data initiatives for Earth and environmental science, climate modeling, genomic and microbial analysis, and multisector dynamics modeling.

54 ENVIRONMENTAL SCIENCES↗