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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 55 records · Page 3

Qualification of System-Level Advanced Reactor Safety Analysis Software for Lead Systems: Final CRADA Report

SAS4A/SASSYS-1 is a simulation tool used to perform deterministic analysis of anticipated events as well as design basis and beyond design basis accidents for advanced liquid-metal-cooled nuclear reactors. With its origin as SAS1A in the late 1960s, the SAS series of codes has been under continuous use and development for over forty-five years and represents a critical investment in safety analysis capabilities for the U.S. Department of Energy. Although SAS4A/SASSYS-1 was developed to support the analysis of any liquid-metal-cooled nuclear reactor, it has primarily been utilized to design and analyze Sodium Fast Reactors (SFRs). As a result, most of the qualification basis for SAS4A/SASSYS-1 has utilized sodium as a coolant and geometry descriptions that are prototypic of SFR configurations. In this project, which partnered with Westinghouse Electric Company, LLC, the initial foundation for a qualification basis centered on prototypic pool-type lead-cooled systems has been established, where the end goal is to extend support for utilization of SAS4A/SASSYS-1 in Lead Fast Reactor (LFR) licensing or authorization. This project included three fundamental technical tasks: LFR V&V test suite development (Task 1); qualification support for LFRs (Task 2); and LFR modeling capabilities evaluation and improvement (Task 3)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Accelerating detector simulations with Celeritas: Profiling and performance optimizations

Celeritas is a GPU-optimized Monte Carlo (MC) particle transport code designed to meet the growing computational demands of next-generation high energy physics (HEP) experiments. It provides efficient simulation of electromagnetic (EM) physics processes in complex geometries with magnetic fields, detector hit scoring, and seamless integration into Geant4-driven applications to offload EM physics to GPUs. Recent efforts have focused on performance optimizations and expanding profiling capabilities. This paper presents some key advancements, including the integration of the Perfetto system profiling tool for detailed performance analysis and the development of track-sorting methods to improve computational efficiency.

Lund, Amanda [Argonne National Laboratory (ANL)]↗

Improving Trustworthiness of Data-Driven Power Grid Contingency Analysis With Bayesian Residual Graph Neural Networks

The evolving energy landscape requires novel tools to efficiently perform contingency analysis and reliability assessment of power grids, potentially in real-time. The high computational cost of traditional power flow solvers limits their applicability in practice. Machine learning (ML) surrogates such as deep neural networks (NNs) accelerate power flow solvers computations, enabling high-order contingency analysis and real-time decision-making by learning highly nonlinear functions and integrating grid topology via graph architectures. However, (graph) NNs lack predictive power away from training data and do not provide predictive confidence estimates. Here, we present a Bayesian residual graph NN that integrates knowledge from low-fidelity data via residual training and embeds granular quantification of uncertainties, improving trustworthiness critical for high-consequence decision-making. Applying Bayesian concepts to NNs is challenging due to the high-dimensionality of both the parameter space, complicating derivation of a meaningful prior, and the output space in large grid systems, requiring enhanced techniques to assess the predicted high-dimensional uncertainties. Our contributions include: (1) Deriving a prior for fully connected and graph NNs that leverages low-fidelity data to guide mean predictions and appropriately control prior predictive uncertainty. (2) Integrating this prior within an ensembling with anchoring scheme for efficient approximate posterior inference. (3) Deriving enhanced metrics to assess accuracy of both the mean and uncertainty predictions in high dimensions, appropriately accounting for correlations propagated through graph layers. The resulting Bayesian residual graph NN is tested on a contingency analysis task for 14-bus and 118-bus grids.

24 - POWER TRANSMISSION AND DISTRIBUTION↗

HERON

HERON is a modeling toolset and plugin for RAVEN to accelerate stochastic technoeconomic assessment of the economic viability of various grid-energy system configurations, especially with application to electrical grids and integrated energy systems (IES). The frameworks enabling this toolset are INL's RAVEN and its TEAL plugin. The toolset includes automated RAVEN workflow generation tools for performing stochastic analysis and optimization driven by technoeconomic factors. The workflows generated employ RAVEN for parametric or optimizing study of grid component characteristics, and again for stochastic sampling of regulated and deregulated markets, load, and weather histories. The toolset further provides methods for economically-optimal dispatch of system components given the stochastic samples of market, load, and weather histories. The toolset allows assembling of existing and new models through the RAVEN interface to the Modelica process modeling language.

Talbot, PaulW↗

Impact of Porcine Arterivirus, Influenza B, and Their Coinfection on Antiviral Response in the Porcine Lung

Interferon (IFN) cytokines induce an autonomous antiviral state in cells of the infected site to restrict virus spreading and critically regulate overall antiviral response. The antiviral state leads to host protection through expression of hundreds of IFN-stimulated genes that restrict viral infection through multiple mechanisms, for example, directly in viral genome degradation and indirectly through cellular metabolic inhibition. Young pigs were split into four treatment groups: control, porcine reproductive and respiratory syndrome virus (PRRSV, also known as porcine arterivirus) infected, influenza B virus (IBV) infected, and IBV/PRRSV coinfection. Lung tissue was collected at 3, 5, and 7 days post infection (dpi) for control, PRRSV and IBV/PRRSV coinfection, and at 3 and 5 dpi for IBV. Transcriptomic analysis, using usegalaxy.org tools, was performed against the S.scrofa 11.1 reference genome. Differentially expressed gene (DEG) analysis was carried out using DeSeq2 based on the model treatment + dpi + treatment:dpi + E. Downstream analysis examined the interaction of DEG at each dpi for over-enriched gene ontology (G.O.) terms and pathways. Comparisons of the infected groups vs. the controls yielded a total of (n = 1412) DEGs for the PRRSV group and (n = 1578) for the IBV/PRRSV group across all timepoints. The IBV group had (n = 64) total DEGs across 3 and 5 dpi. Expression data were considered statistically significant based on false discovery rate (FDR) ⫹ 0.1. Venn diagram comparisons of the DEGs across dpi showed that groups shared only 16 DEGs at 3 dpi, no DEGs were shared at 5 dpi, and for 7 dpi, only the PRRSV and IBV/PRRSV groups were compared and shared a total of 43 DEGs. Across the comparisons, differential expression was observed in antiviral genes such as IRF1, MX1, and OAS2. The IBV and IBV/PRRSV groups showed higher expression of antiviral genes at earlier dpi than the PRRSV group. Additionally, downregulated genes from the comparisons clustered around Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways effecting lung development and cellular integrity. Early expression of host IFN and antiviral genes may lead to viral RNA degradation, and assembly and transcription inhibition in the IBV infections. In comparison, expression of antiviral genes in the PRRSV group decreased across time. The decrease may explain why PRRSV infections persist, while IBV clears. Moreover, all infected groups showed prolonged upregulation in neutrophil degranulation pathway activity, possibly exacerbating symptomatic lung lesion pathology seen in these respiratory infections.

59 BASIC BIOLOGICAL SCIENCES↗

NREL Data, Modeling, and Tools Empower Marine Energy Analysis

NREL performs R&D and economic analyses to drive and empower the development of wave energy and tidal, ocean, and river current energy technologies. These technologies deliver renewable electricity to the grid and provide energy solutions that support the evolving "blue economy."

HYDRO ENERGY↗

Accelerating detector simulations with Celeritas: profiling and performance optimizations

Celeritas is a GPU-optimized MC particle transport code designed to meet the growing computational demands of next-generation HEP experiments. It provides efficient simulation of EM physics processes in complex geometries with magnetic fields, detector hit scoring, and seamless integration into Geant4-driven applications to offload EM physics to GPUs. Recent efforts have focused on performance optimizations and expanding profiling capabilities. This paper presents some key advancements, including the integration of the Perfetto system profiling tool for detailed performance analysis and the development of track-sorting methods to improve computational efficiency.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Performing Numerical Analysis of Cybersecurity Options Using Dynamic Risk Analysis Tool EMRALD

Cyberattacks can have many different attack paths, durations, and goals. There are also many different mitigation options involving hardware, software, and/or humans. Considering a cyber threat should involve defense-in-depth methods and a quantitative or numerical evaluation of overall effectiveness against dynamic, time-dependent attacks to make cost and risk-informed decisions. Typical cyberattack modeling methods only provide a qualitative evaluation. The main areas of cybersecurity are confidentiality, integrity, and availability. For companies with cyber-physical systems such as advanced nuclear reactors, cyber-related safety is a requirement set by North American Electric Reliability and the U.S. Nuclear Regulatory Commission. They are also concerned about availability or reliability as a business case. As cyber threats are evolving to a business-for-hire structure, more attacks may focus on disrupting business success and reliability, causing financial and economic stability risk. Companies want to know business reliability and recovery from those threats, and that requires modeling physical behavior of the targets. Dynamic-state-based and Markov-based modeling provides a method for better cyber scenario modeling with different tools having issues such as state-base explosion. Dynamic modeling enables time and conditional features not found in other numerical evaluation methods. EMRALD (Event Modeling Risk Assessment using Lined Diagrams) is a dynamic risk analysis modeling and simulation tool and has features that reduce modeling issues. It has been used to model different time-dependent events including plant behavior and operator procedures. As a general modeling tool, EMRALD can also be used to model cyberattack scenarios with varying mitigation options and quantify effectiveness, producing numerical data for risk-informed decisions. This paper uses EMRALD to demonstrate that dynamic numerical risk analysis can be used for cyber threat modeling to provide insights for design decision-making and optimize defense strategies. Keywords: cyber modeling; cyber-physical systems; numerical cyber modeling

97 - MATHEMATICS AND COMPUTING↗

srlife : a software tool for estimating the life of high temperature concentrating solar receivers. Part I – metallic receivers

Here, this paper introduces srlife, a tool for estimating the structural service life of concentrating solar power (CSP) receivers operating at high temperatures. Supporting both metallic and ceramic receiver designs, srlife is available as open-source software at https://github.com/applied-material-modeling/srlife and can be installed via the PyPi package manager (https://pypi.org). Given basic receiver geometry and incident heat flux, the tool performs thermohydraulic and structural analysis and estimates the life of a receiver. Designed for easy integration into a software stack, including solar field and levelized cost analysis, the tool can be utilized for optimizing receiver designs to meet service life and economic targets. This paper is Part I in a two-part series. Part I discusses the analysis process used to estimate the life of metallic receivers, along with a description of the required input data. Additionally, several heuristics applied within srlife can reduce analysis time significantly while maintaining accurate life estimations for metallic receivers when compared to full analyses. Several examples demonstrating the utility of srlife in receiver design are also discussed. Part II focuses on the life estimation of ceramic receivers, using time-dependent reliability analysis and various ceramic failure models implemented in srlife.

Creep-fatigue analysis↗

KBase Silver Case Study: Determining Media Formulation Requirements for Isolation of Microbiome Constituents

KBase has powerful tools for extracting microbial genomes from metagenomes and performing phylogenomic analysis and metabolic modeling. These tools can be used to predict key media ingredients for isolating uncultured members of microbiomes. Essential to this process are high-quality genomes extracted from metagenomic assemblies, and Kbase has a tool for assessing the quality of genomes too. Below we identify growth factors for a myxobacteria ("slime bacteria") yet to be isolated from the rhizosphere of Miscanthus xgiganteus (hybrid of "Silvergrass"), cultivated at the Kellogg Biological Station in Michigan. Data was transferred with Globus from JGI-IMG. This narrative and the "KBase Gold Case Study: Can you find Delftia?" make up the Silver and Gold Narrative Set for teaching metagenomics concepts to students in the BIT 477/577 course at North Carolina State University. This tutorial will guide the user through the process of extracting and annotating high-quality genomes from a metagenomic data, performing phylogenomic analysis, building a metabolic model, and using these to predict nutrient requirments for growth and isolation of corresponding microbes.

59 BASIC BIOLOGICAL SCIENCES↗

BatteryPro: A Python Toolkit for Battery Data Analysis and Machine Learning Predictions

Analyzing battery test data for research & development can be time-consuming since battery tests often run on the order of months to years, generating large volumes of data. BatteryPro is a comprehensive Python package and software designed to facilitate advanced analysis and performance predictions for battery test data. Developed for battery researchers, it supports data types from widely used battery testing instruments, including MACCOR and Biologic cycling systems. The software provides a variety of tools for extracting and plotting key battery parameters such as time, voltage, capacity, current, and pressure. In addition to its extensive data analysis capabilities, BatteryPro features a dedicated machine learning module that employs a Bayesian Gaussian Mixture Model (GMM) to predict battery performance and degradation. Users can generate synthetic capacity fade data, calculate fade metrics, and leverage predictive models to forecast long-term battery behavior. The software's graphical user interface (GUI) enhances usability, allowing researchers to upload, merge, and analyze multiple data files with full customizability. The GUI also supports machine learning predictions, enabling users to fit models and make predictions based on selected data and parameters. BatteryPro is built using QtDesigner, scikit-learn, matplotlib, and pandas, ensuring a high level of customization, flexibility, and accuracy in battery data analysis. This tool aims to empower researchers with the ability to perform detailed battery analysis and make informed predictions, ultimately advancing the field of battery research.

25 - ENERGY STORAGE↗

Parametric and Sensitivity Analysis of a Steam Generator Model Using Python and Machine-Learning Tools

For this study, we used Python and machine-learning tools to perform a comprehensive parametric and sensitivity analysis on a steam generator (SG) model. (The Python model was based on a previously completed MATLAB framework for the Holtec SMR-160 SG.) We investigated the influence of various input parameters (e.g., heat transfer coefficient [HTC], Nusselt number, and heat exchanger effectiveness) on the system’s output. With machine-learning tools such as the Risk Analysis Virtual Environment (RAVEN), which was developed at Idaho National Laboratory, we were then able to perform an automated analysis of the SG inputs’ effect on the HTC. The analysis results give valuable insights into the performance and optimization of SG systems. We found the inlet mass flow rate (MFR) to have the greatest impact on the HTC, followed closely by the inlet temperature, and then pressure. Shifting of the input parameters causes the location of the maximum HTC along the SG length to change incrementally. The cold leg (CL) MFR was also found to impact the HTC magnitude as well as the location of the maximum HTC. At between 0.4–0.9 of the total SG length, the input parameters experience maximum impact on the HTC, leading us to suggest that sensors be efficiently placed on the SG so as to closely and effectively monitor thermal-hydraulic properties during reactor operation. We also found that the sensitivity data calculated manually agrees with the RAVEN – based data, confirming the same range of maximum sensitivity. However, the RAVEN-based analysis showed that cold leg pressure and hot leg temperature have a greater impact on the heat transfer coefficient than the mass flow rate, implying that a manual sensitivity study taking only two samples is not accurate.

20 FOSSIL-FUELED POWER PLANTS↗

Sensitivity Analysis for the Component Design App: Analysis of Success Assured Data

A new tool has been developed to perform variance-based global sensitivity analysis (VBGSA) on data from a set-based concurrent engineering software called Success Assured (SA). The tool is part of a digital component design app, which is currently in production as an Accelerated Digital Engineering Pathfinder at Sandia National Laboratories. When working with complex digital models, it is important to understand relationships between inputs and outputs, i.e., how “sensitive” model outputs are to changes in model inputs. After extensive research and trials of various sensitivity analysis methods, it was determined that estimation of Sobol’ indices for VBGSA with Monte Carlo simulation, paired with simple surrogate models, produces the best results for SA data. This tool increases understanding of SA models and streamlines the creation of SA datasets. This report details the methodology and implementation of this sensitivity analysis tool so others can understand it and implement it.

97 MATHEMATICS AND COMPUTING↗

CSP Systems Analysis 2022-2024 (Final Technical Report)

The CSP Systems Analysis project estimates the current market cost of CSP subsystems and technologies for use by the DOE Program and within NREL analysis tools. The project addresses upgrades to NREL's System Advisor Model (SAM) related to CSP, as well as development of new modules within SAM to expand the types of CSP systems that can be simulated and new tools for evaluating CSP subsystem performance or optimizing CSP system layouts, for example, SolarPILOT and SolTrace. The project evaluates the potential cost and performance of new CSP-relevant technologies and generally includes assessment of CSP technologies in support of the DOE subprogram. A primary goal of the CSP System Analysis project is to provide timely and accurate cost data to the DOE to assess the current state of CSP technologies as well as predict performance and cost for pre-commercial and emerging technologies that may impact the industry. Prior work in this task has produced technology cost reports and roadmaps for DOE and highlighted promising research paths that could reduce overall CSP cost, such as the supercritical CO2 power cycle and molten-salt trough systems. CSP Systems Analysis produces modeling tools to evaluate the performance of CSP systems, subsystems, or components, and journal articles, conference papers, technical reports and databases to disseminate information to the CSP industry. CSP technology costs are estimated and tracked using these tools as well as analysis of literature and industry sources. The CSP modeling tools and NREL analysis results are used by researchers; government, financial, and industry analysts; and other CSP stakeholders. The purpose of this final technical report is to summarize the key accomplishments of the past three years of this project. The project was divided into seven separate tasks, and in what follows, we describe the background, objectives, key accomplishments, and path forward for each task within the project, with one section of this report summarizing each task.

14 SOLAR ENERGY↗

Enhanced dynamic contingency analysis for power systems

The present disclosure describes systems and techniques that enhance effectiveness and efficiency of a contingency analysis tool that is used for studying the magnitude and likelihood of extreme contingencies and potential cascading events across a power system. The described systems and techniques include deploying the contingency analysis tool in a high-performance computing (HPC) environment and incorporating visual situational awareness approaches to allow power system engineers to quickly and efficiently evaluate multiple power system simulation models. Furthermore, the described systems and techniques include the power system contingency-analysis tool calculating and coordinating protection element settings, as well as assessing controls of the power system using small-signal nomograms, allowing power system engineers to more effectively comprehend, evaluate, and analyze causes and effects of cascading events against a topology of a power system.

Samaan, Nader A.↗

WEC as a multiport

SAND2025-00555O WEC as a multiport is a software tool that simplifies the modeling and design of ocean wave energy converters (WECs) using a multi-port network framework in the frequency domain. Users can simulate the dynamic interactions between WECs and ocean waves, optimizing energy extraction and system performance. This tool supports the analysis of complex wave energy systems, aiding in the development of efficient and effective WEC designs. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Coe, Ryan↗

mvBayesPy

SAND2025-11476O The mvBayesPy tool is a Python package that performs multivariate Bayesian analysis on generic data. It includes tools for regression modeling, diagnosis, basis decomposition, sensitivity analysis and visualization. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Tucker, James [Sandia National Lab. (SNL-CA), Live↗

Transfer Factors for the FRMAC Assessment Manual and Turbo FRMAC to Improve Radiological Dose Assessment

The Turbo FRMAC analysis tool is used to perform complex calculations to quickly evaluate radiological consequences and aid in decision making during an emergency response by assessing impacts to the public, workers, and the food supply. Turbo FRMAC calculations are based on methods established by the Federal Radiological Monitoring and Assessment Center (FRMAC). To be able to assess impacts, input data called transfer factors that describe radionuclide uptake by local plants and animals are required. During the code application exercises in late 2016, identifying, finding, and validating nonstandard transfer factors proved to be time consuming and diverted the teams’ activity away from other critical tasks. As a result, a task was undertaken to dramatically expand the list of available transfer factors (food and non-food) and incorporate these factors into Turbo FRMAC. This will ultimately result in improved efficiency of the assessment team to perform calculations during times when the FRMAC is activated and provide more defensible, vetted data from which to calculate results. As a result of these and subsequent exercises, transfer factors were needed for the following items: bell peppers, Christmas tree, deer, flowers, fresh cucumbers, tomatoes, grapefruit, lichens, mushrooms, oranges, snap beans, squash, strawberries, sugarcane for sugar and seed, sweet corn, tea, tobacco, tree bark, and watermelon. To expand the applicability of the information tables, generic transfer data were also provided for common categories and recommendations were made for expanding the list of chemicals based on chemical similarity. The data presented in this report were compiled from recent literature with most of the data encompassing the period from 2000 to 2018. The following radionuclides were targeted during the literature search: elements associated with reactor accidents or nuclear detonations (Sr 89/90, Cs 134/137, Ce 141/144, Ru 103/106, I 129/131/133, Pu 238/239, Am 241, Zr 95, and Nb 95) and elements associated with industrial accidents or dirty bombs (Ir 192 and Co 60). Data from other elements were evaluated if they were identified during the literature search. In addition, reports were evaluated that were recommended by the research consultants. For each plant or animal transfer factor, the goal was to determine the geometric mean, the geometric standard deviation, the minimum, the maximum, and the number of measurements used. If only one measurement was available that was presented as the mean. Concentration ratios with large geometric standard deviations (GSDs) were generally the result of a paucity of measurements or a few disparate measurements. Measurement disparity was observed for data from different soil types. The geometric mean (GM) is a good reference value for planning and responses purposes, but the location-specific concentrations are unlikely to be similar to the model results. Recommendations for further work include: segregating the data to reflect the influence of soil type, developing approaches to incorporate animal data based on aggregated transfer measurements, and including data for foliar deposition on plants.

61 RADIATION PROTECTION AND DOSIMETRY↗