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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Tracer Particles for Core-collapse Supernova Nucleosynthesis: The Advantages of Moving Backward

Abstract After decades, the theoretical study of core-collapse supernova explosions is moving from parameterized, spherically symmetric models to increasingly realistic multidimensional simulations. However, obtaining nucleosynthesis yields based on such multidimensional core-collapse supernova simulations is not straightforward. Frequently, tracer particles are employed. Tracer particles may be tracked in situ during the simulation, but often they are reconstructed in a post-processing step based on the information saved during the hydrodynamic simulation. Reconstruction can be done in a number of ways, and here we compare the approaches of backward and forward integration of the equations of motion to the results based on inline particle trajectories. We find that both methods agree reasonably well with the inline results for isotopes for which a large number of particles contribute. However, for rarer isotopes that are produced only by a small number of particle trajectories, deviations can be large. For our setup, we find that backward integration leads to better agreement with the inline particles by more accurately reproducing the conditions following freeze-out from nuclear statistical equilibrium, because the establishment of nuclear statistical equilibrium erases the need for detailed trajectories at earlier times. Based on our results, if inline tracers are unavailable, we recommend backward reconstruction to the point when nuclear statistical equilibrium was last applied, with an interval between simulation snapshots of at most 1 ms for nucleosynthesis post-processing.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Fornax: A Flexible Code for Multiphysics Astrophysical Simulations

This paper describes the design and implementation of our new multigroup, multidimensional radiation hydrodynamics code Fornax and provides a suite of code tests to validate its application in a wide range of physical regimes. Instead of focusing exclusively on tests of neutrino radiation hydrodynamics relevant to the core-collapse supernova problem for which Fornax is primarily intended, we present here classical and rigorous demonstrations of code performance relevant to a broad range of multidimensional hydrodynamic and multigroup radiation hydrodynamic problems. Our code solves the comoving-frame radiation moment equations using the M1 closure, utilizes conservative high-order reconstruction, employs semi-explicit matter and radiation transport via a high-order time stepping scheme, and is suitable for application to a wide range of astrophysical problems. To this end, we first describe the philosophy, algorithms, and methodologies of Fornax and then perform numerous stringent code tests that collectively and vigorously exercise the code, demonstrate the excellent numerical fidelity with which it captures the many physical effects of radiation hydrodynamics, and show excellent strong scaling well above 100,000 MPI tasks.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Physics Opportunities in the Far-forward Region at the Future Electron–Ion Collider

The Electron–Ion Collider provides the opportunity to drastically advance our understanding of QCD and the multidimensional structure of both protons and nuclei. An essential component of the EIC physics program is the identification and characterization of exclusive, diffractive, and tagged events using detectors integrated with the outgoing hadron beamline, the so-called “far-forward” detectors. The ePIC experiment includes a suite of far-forward detectors designed to deliver the necessary geometric coverage and resolution required to achieve the exclusive physics program envisioned at the EIC. Additionally, to the multidimensional imaging program at the EIC, topics such as spectator tagging in e + d and e + 3 He reactions to access structure functions and searches for gluon saturation in e + A collisions are also enabled by this experimental apparatus. In these proceedings, the ePIC far-forward detectors will be briefly introduced, and a few selected physics topics focused on tagged deep-inelastic scattering will be discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Electrochemical force microscopy

A system and method for electrochemical force microscopy are provided. The system and method are based on a multidimensional detection scheme that is sensitive to forces experienced by a biased electrode in a solution. The multidimensional approach allows separation of fast processes, such as double layer charging, and charge relaxation, and slow processes, such as diffusion and faradaic reactions, as well as capturing the bias dependence of the response. The time-resolved and bias measurements can also allow probing both linear (small bias range) and non-linear (large bias range) electrochemical regimes and potentially the de-convolution of charge dynamics and diffusion processes from steric effects and electrochemical reactivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

MultiDimChemImaging v1.30

The purpose of this software is to correlate and visualize multidimensional chemical images where the different modalities were collected with different resolution and/or offset. List of features: - change dimension of data stored in XYZ (XYcoordinate, value) or Comma Separated Values (CSV) formats via bipolar or bicubic interpolation - determine and apply offset between different modalities - create a multimodal image file from multiple single modalities in Tab Separate Values (TSV) or TissueViewTM (industry standard for chemical image visualization) formats - visualize and save (whole or partial) composite chemical images made of multiple (up to 3 at once) modalities Specific application: correlated multidimensional chemical imaging by AFM-based nano-thermal (nano-TA) analysis coupled to spectroscopy and mass spectrometry for spatially resolved surface analysis. In case of a phase separated polymer film, the modalities were: 1. topography image 203. IR images collected at two different wavelengths 4-5. mass spectral chemical images of two different m/z values.

Kertesz, Vilmos↗
DOE OSTI2019

A Metaheuristic Optimization Tool for High Flux Isotope Reactor Low-Enriched Uranium Core Design

A metaheuristic optimization tool was developed for design optimization studies of a High Flux Isotope Reactor low-enriched uranium core. Low-enriched uranium core design presents a multidimensional, multi-objective optimization problem that requires a systematic approach to avoid arbitrary design decisions. This tool searches this multidimensional design space to generate an optimum design, considering a weighted design performance metric that includes several parameters. The search methods are discussed herein, and the tool is applied to uranium silicide fuel designs.

Variansyah, I.↗
DOE OSTI2023

Release on the Virtual Test Bed of an MSRE thermal hydraulics model

The nuclear industry is taking leaps in innovations with companies seeking a sustainable energy future through advanced nuclear reactors. The \gls{DOE}’s \gls{neams} program seeks to substantiate and bolster the deployment of advanced reactors through flexible multifidelity, multiphysics simulations of advanced nuclear reactors. Applications like SAM for one-dimensional systems thermal-hydraulics, and Pronghorn for multidimensional coarse mesh thermal-hydraulics, are geared to support innovations in industry by facilitating design, optimization, and licensing of advanced nuclear reactors. Coupling systems thermal-hydraulics and computational fluid dynamics codes can be difficult as the pressure coupling converges slowly; however, it is important to obtain the desired accuracy in each part of the primary loop. The authors of this model created an \gls{odc}~\cite{Mau23} approach to coupling SAM and Pronghorn. Leveraging this coupling technique, a \gls{msre} model was developed and released to the \gls{neams}/\gls{nric} \gls{vtb}. The \gls{msre} was chosen to be modeled because of the wealth of experimental data available and because of the strong physics coupling between the core and primary circuit \cite{doi:10.13182/NT8-2-118}. This document contextualizes the history of the \gls{msre}, describes the thermal hydraulics models used, and detail the implementation of multidimensional thermal-hydraulics and system codes based on the \gls{odc} method~\cite{Penn} for the \gls{msre} model. Finally, this document presents how other modelers could apply the SAM-Pronghorn \gls{odc} for other advanced reactor models. Current item is the set of slides for ANS Winter 23. The release of the model and the ANS summary have already been approved

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Release on the Virtual Test Bed of an MSRE thermal hydraulics model

The nuclear industry is taking leaps in innovations with companies seeking a sustainable energy future through advanced nuclear reactors. The \gls{DOE}’s \gls{neams} program seeks to substantiate and bolster the deployment of advanced reactors through flexible multifidelity, multiphysics simulations of advanced nuclear reactors. Applications like SAM for one-dimensional systems thermal-hydraulics, and Pronghorn for multidimensional coarse mesh thermal-hydraulics, are geared to support innovations in industry by facilitating design, optimization, and licensing of advanced nuclear reactors. Coupling systems thermal-hydraulics and computational fluid dynamics codes can be difficult as the pressure coupling converges slowly; however, it is important to obtain the desired accuracy in each part of the primary loop. The authors of this model created an \gls{odc}~\cite{Mau23} approach to coupling SAM and Pronghorn. Leveraging this coupling technique, a \gls{msre} model was developed and released to the \gls{neams}/\gls{nric} \gls{vtb}. The \gls{msre} was chosen to be modeled because of the wealth of experimental data available and because of the strong physics coupling between the core and primary circuit \cite{doi:10.13182/NT8-2-118}. This document contextualizes the history of the \gls{msre}, describes the thermal hydraulics models used, and detail the implementation of multidimensional thermal-hydraulics and system codes based on the \gls{odc} method~\cite{Penn} for the \gls{msre} model. Finally, this document presents how other modelers could apply the SAM-Pronghorn \gls{odc} for other advanced reactor models.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Release on the Virtual Test Bed of a Molten Salt Reactor Experiment SAM-Pronghorn Coupled Model using the Domain Overlapping Approach

The nuclear industry is taking leaps in innovations with companies seeking a sustainable energy future through advanced nuclear reactors. The Department of Energy (DOE)’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program seeks to substantiate and bolster the deployment of advanced reactors through flexible multifidelity, multiphysics simulations of advanced nuclear reactors. Applications like SAM for one-dimensional systems thermalhydraulics, and Pronghorn for multidimensional coarse mesh thermal-hydraulics, are geared to support innovations in industry by facilitating design, optimization, and licensing of advanced nuclear reactors. Coupling systems thermal-hydraulics and computational fluid dynamics codes can be difficult as the pressure coupling converges slowly; however, it is important to obtain the desired accuracy in each part of the primary loop. The authors of this model created an Overlapping-Domain Coupling (ODC) approach to coupling SAM and Pronghorn. Leveraging this coupling technique, a Molten Salt Reactor Experiment (MSRE) model was developed and released to the NEAMS/National Reactor Innovation Center (NRIC) Virtual Test Bed (VTB). The MSRE was chosen to be modeled because of the wealth of experimental data available and because of the strong physics coupling between the core and primary circuit. This paper contextualizes the history of the MSRE, describes the thermal hydraulics models used, and detail the implementation of multidimensional thermal-hydraulics and system codes based on the ODC method for the MSRE model. Finally, this paper presents how other modelers could apply the SAM Pronghorn ODC for other advanced reactor models.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Reduce-Order Modeling of Multigroup Neutron Cross Sections for High-Temperature Gas-cooled Reactors

Deterministic neutronics calculations rely on multigroup neutron cross section libraries, which usually consists of a database of tabulated values, used to calculate the cross sections through multivariate linear interpolation. However, interpolation of the multidimensional cross section data becomes memory inefficient and time consuming as the number of tabulations increases, significantly slowing down the neutronics calculation, especially in the case of micro cross section libraries where every isotope (on the order of hundreds) has its own set of specific reactions and cross sections. To address this challenge, this work constructs efficient and robust reduced-order models (ROMs) of the multi-group cross sections to support the Griffin simulation of high-temperature gas-cooled reactors (HTGRs). The first part of the study investigates the linearity of the multi-group cross section data across isotopes, reaction types and energy groups on pre-generated datasets for the purpose of dimensionality reduction. Secondly, a down-selection of ROM techniques is presented on representative classical machine learning (ML) techniques, including variants of linear regression, kernel-based methods, tree-based algorithms, and artificial neural networks. The selection criteria jointly consider the memory efficiency, predictive accuracy, prediction speed, and scalability in comparison to the multidimensional interpolation. Among all the ML techniques, deep neural networks (DNNs) have proven to be the best selection with sufficient accuracy, high robustness, good memory efficiency, great scalability, and superior flexibility. DNNs for have been trained for all isotopes in this work and systematic Griffin testing is ongoing at this moment to ensure the feasibility of this ROM technique for cross section predictions.

42 - ENGINEERING↗
DOE OSTI2024

Reduced-Order Modeling of Multigroup Neutron Cross Sections for High-Temperature Gas-cooled Reactors

Abstract – Deterministic neutronics calculations rely on multigroup neutron cross section libraries, which consist of databases of tabulated values, used to calculate the neutron cross sections through multivariate linear interpolation. However, interpolation of the multidimensional cross section data becomes memory inefficient and time consuming as the number of tabulations increases, significantly slowing down the neutronics calculation, especially in the case of microscopic cross section libraries where every isotope (on the order of hundreds) has its own set of specific reactions and cross sections. In order to address this challenge, this work constructs efficient and robust reduced-order models (ROMs) of the multi-group cross sections to support the Griffin simulation of high-temperature gas-cooled reactors (HTGRs). The first part of the study investigates the linearity of the multigroup cross section data across isotopes, reaction types, and energy groups on pre-generated datasets for the purpose of dimensionality reduction. Secondly, a down-selection of ROM techniques is presented on representative classical machine learning (ML) techniques, including variants of linear regression, kernel-based methods, tree-based algorithms, and artificial neural networks. The selection criteria jointly consider the memory efficiency, predictive accuracy, prediction speed, and scalability in comparison to the multidimensional interpolation. Among all the ML techniques, deep neural networks (DNNs) have proven to be the best selection with sufficient accuracy, high robustness, good memory efficiency, great scalability, and superior flexibility. DNNs have been trained for all isotopes in this work and systematic Griffin testing is ongoing to ensure the feasibility of this ROM technique for predicting cross section and reducing memory requirements without a significant sacrifice in computational performance.

42 - ENGINEERING↗
DOE OSTI2024

Advanced Cross Section Library Generation using Reduced Order Models

Deterministic neutronics calculations rely on multigroup neutron cross section libraries, which consist of databases of tabulated values, used to calculate the neutron cross sections through multivariate linear interpolation. However, interpolation of the multidimensional cross section data becomes memory inefficient and time consuming as the number of tabulations increases, significantly slowing down the neutronics calculation, especially in the case of microscopic cross section libraries where every isotope (on the order of hundreds) has its own set of specific reactions and cross sections. In order to address this challenge, this work constructs efficient and robust reduced-order models (ROMs) of the multi-group cross sections to support the Griffin simulation of high-temperature gas-cooled reactors (HTGRs). The first part of the study investigates the linearity of the multigroup cross section data across isotopes, reaction types, and energy groups on pre-generated datasets for the purpose of dimensionality reduction. Secondly, a down-selection of ROM techniques is presented on representative classical machine learning (ML) techniques, including variants of linear regression, kernel-based methods, tree-based algorithms, and artificial neural networks. The selection criteria jointly consider the memory efficiency, predictive accuracy, prediction speed, and scalability in comparison to the multidimensional interpolation. Among all the ML techniques, deep neural networks (DNNs) have proven to be the best selection with sufficient accuracy, high robustness, good memory efficiency, great scalability, and superior flexibility. DNNs have been trained for all isotopes in this work and systematic Griffin testing is ongoing to ensure the feasibility of this ROM technique for predicting cross section and reducing memory requirements without a significant sacrifice in computational performance.

42 - ENGINEERING↗
DOE OSTI2023

Development and performance evaluation of a novel γ-γ coincidence analysis software

Processing of large amounts of data from a γ-γ coincidence system is a practical challenge in trace-level radionuclide measurements. Given the benefits and growing number of multidimensional gamma spectrometers worldwide, this is a major restriction in wide-spread applicability of coincidence techniques. This study demonstrates a novel γ-γ coincidence analysis software developed at Pacific Northwest National Laboratory, USA. The software utilizes advanced search techniques and Python libraries to readily identify coincidence signatures in the large data. Further, using both synthetic and experimental data, the study uncovers various strategies/libraries employed during software’s development. This work aims to provide a reference document in handling large data routinely encountered in trace-level measurements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Coupling Visualization, Simulation, and Deep Learning for Ensemble Steering of Complex Energy Models

We describe a new framework that allows users to explore and steer ensembles of energy systems simulations by coupling multiple energy models and interactive visualization through a dataflow API. Through the visual interface, users can interactively explore complex parameter spaces populated by hundreds, or thousands, of simulation runs and interactively spawn new simulations to 'fill in' regions of interest in the parameter space. The computational and visualization capabilities reside within a general-purpose dataflow architecture for connecting producers of multidimensional timeseries data, such as energy simulations, with consumers of that data, whether they be visualizations, statistical analyses, or datastores. Fast computation and agile dataflow can enhance the engagement with energy simulations, allowing users to populate the parameter space in real time. However, many energy simulations are far too slow to provide an interactive response. To support interactive feedback, we are creating reduced-form simulations developed through machine learning techniques, which provide statistically sound estimates of the results of the full simulations at a fraction of the computational cost. These reduced-form simulations have response times on the order of seconds, suitable for real-time human-in-the-loop design and analysis. The approximation methods apply to a wide range of computational models, including supply-chain models, electric power grid simulations, and building models. Such reduced-form representations do not replace or re-implement existing simulations, but instead supplement them by enabling rapid scenario design and exploration for large ensembles of simulations. The improved understanding, facilitated by the reduced-form models, dataflow API, and visualization tools, allows researchers to better allocate computational resources to capture informative relationships within the system as well as provide a low-cost method for validating and quality-checking large-scale modeling efforts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Power System Decomposition for Practical Implementation of Bulk-Grid Voltage Control Methods

Power system algorithms such as AC optimal power flow and coordinated volt/var control of the bulk power system are computationally intensive and become difficult to solve in operational time frames. The computational time required to run these algorithms increases exponentially as the size of the power system increases. The solution time for multiple subsystems is less than that for solving the entire system simultaneously, and the local nature of the voltage problem lends itself to such decomposition. This paper describes an algorithm that can be used to perform power system decomposition from the point of view of the voltage control problem. Our approach takes advantage of the dominant localized effect of voltage control and is based on clustering buses according to the electrical distances between them. One of the contributions of the paper is to use multidimensional scaling to compute n-dimensional Euclidean coordinates for each bus based on electrical distance to perform algorithms like K-means clustering. A simple coordinated reactive power control of photovoltaic inverters for voltage regulation is used to demonstrate the effectiveness of the proposed decomposition algorithm and its components. The proposed decomposition method is demonstrated on the IEEE 118-bus system.

multidimensional scaling↗
DOE OSTI2023

Development of Advanced Radionuclide Gamma spectrOmeter-2 (ARGO-2) for Low-level Radionuclide Measurements

A low-level multidimensional gamma spectrometer was recently installed at Pacific Northwest National Laboratory, USA. The spectrometer utilizes a dual-detector design and advanced detection techniques, demonstrating superior detection efficiency/sensitivity. This work details integral components of the system, including the dual-detector design, its shielding, and cosmic veto panels for background reduction. Results from system’s operation in singles and coincidence mode are highlighted.

ARGO, Coincidence measurements, Multidimensional g↗
DOE OSTI2016

Modeling the influence of preferential flow on the spatial variability and time-dependence of mineral weathering rates

Inferences of weathering rates from laboratory and field observations suggest significant scale and time-dependence. Preferential flow induced by heterogeneity (manifest as permeability variations or discrete fractures) has been suggested as one potential mechanism causing scale/time-dependence. In this paper, we present a quantitative evaluation of the influence of preferential flow on weathering rates using reactive transport modeling. Simulations were performed in discrete fracture networks (DFNs) and correlated random permeability fields (CRPFs), and compared to simulations in homogeneous permeability fields. The simulations reveal spatial variability in the weathering rate, multidimensional distribution of reactions zones, and the formation of rough weathering interfaces and corestones due to preferential flow. In the homogeneous fields and CRPFs, the domain-averaged weathering rate is initially constant as long as the weathering front is contained within the domain, reflecting equilibrium-controlled behavior. The behavior in the CRPFs was influenced by macrodispersion, with more spread-out weathering profiles, an earlier departure from the initial constant rate and longer persistence of weathering. DFN simulations exhibited a sustained time-dependence resulting from the formation of diffusion-controlled weathering fronts in matrix blocks, which is consistent with the shrinking core mechanism. A significant decrease in the domain-averaged weathering rate is evident despite high remaining mineral volume fractions, but the decline does not follow a math formula dependence, characteristic of diffusion, due to network scale effects and advection-controlled behavior near the inflow boundary. Finally, the DFN simulations also reveal relatively constant horizontally averaged weathering rates over a significant depth range, challenging the very notion of a weathering front.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Exploration of Electrochemical Reactions at Organic–Inorganic Halide Perovskite Interfaces via Machine Learning in In Situ Time-of-Flight Secondary Ion Mass Spectrometry

The instability of hybrid organic–inorganic perovskite (HOIP) devices is one of the significant challenges preventing commercialization. Exploring these phenomena is severely limited by the complexity of the intrinsic electrochemistry of HOIPs, the presence of multiple volatile and mobile ionic species, and the possible role of environmentally induced reactions at surfaces and triple-phase junctions. Here, in situ studies of the electrochemistry of methylammonium lead bromide perovskite with the Au electrode interface are reported via light- and voltage-dependent time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging of lateral perovskite heterostructures. While ToF-SIMS allows for the visualization of the chemical composition along the surface and its evolution with light and electrical bias, the interpretation of the multidimensional data obtained is often limited due to strong correlations between chemical signatures and the need to track multiple peaks at once. Here, a machine learning workflow combining the Hough transform and non-negative matrix factorization and no-negative tensor decomposition is developed to avoid this limitation and extract salient features of associated chemical changes and to separate the light- and voltage-dependent dynamics. Finally, combining these in situ characterizations and the machine learning workflow provides comprehensive information on the chemical nature of moving species, ion accumulation, and interfacial electrochemical reactions in HOIP devices.

36 MATERIALS SCIENCE↗