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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Description of FY25 Theory and Simulation Performance Target: Development of an integrated modeling framework for fusion reactor design and assessment

The urgency to deliver fusion power is growing now more than ever, with increasing pressure for both public programs and private companies to meet milestones timelines and overcome significant remaining technical challenges to ensure growth of a nascent fusion industry in time to meet rapidly growing clean energy demands. With incredible advancements in computation and years of investment in fusion model development and validation, integrated modeling is poised to fill a key role in accelerating the timeline to a fusion pilot plant (FPP). Future fusion pilot plants will operate in regimes far beyond current experience, and device design will rely on physics-based prediction and extrapolation. Many concepts will also rely on simulation to assess safety (shielding, tritium management, materials activation and lifetimes), economics and scalability before the decision to build. Importantly, integrated simulation can be used to reveal and solve the complexities of system integration that may otherwise not be apparent in physical components or models developed in isolation. New experimental test facilities that produce relevant conditions to validate and resolve key technical challenges for various subsystems (materials, blankets, fuel cycle, etc.) have been repeatedly called for by the fusion community but are not yet realized. Integrated modeling has an important role in identifying realistic load conditions (thermal, electromagnetic, plasma, neutron and photon loads, etc.) and defining the components and experiments for these test facilities in order to ensure meaningful validation that sufficiently reduces modeling uncertainties and technical risk for the full integrated reactor. The Fusion REactor Design and Assessment (FREDA) SciDAC project is building a component-based integrated modeling framework & data structure to enable self-consistent, multi-fidelity, iterative optimization workflows for the fusion reactor design process. FREDA aims to shorten the time to viable designs by providing a set of flexible workflows to support the various stages of the design process using an integrated model hierarchy, ranging from the simple analytic descriptions to the highest fidelity, theory-based plasma and engineering modeling developed by the fusion and fission communities. These tools are expected to be needed for timely support of FPP design in the milestone program and in the FIRE collaboratives. The plasma simulation backbone of FREDA is IPS-FASTRAN with newly developed coupled Core-Edge Pedestal-SOL (CESOL) workflows, which is being extended to the far-SOL region up to the plasma facing components. FREDA incorporates the FERMI engineering modeling suite and will enable self-consistent evaluation of the thermal shields, limiters, blanket, magnets, and other surrounding structures with predictions of temperatures, erosion, dpa, activation, tritium generation and transport, creep, corrosion, material degradation, etc. Parametric generation of 3D CAD enables rapid iteration of component geometry in response to plasma and loading specifications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

STARTR: An Open-Source MARVEL model for the NRIC Virtual Test Bed [Poster]

The National Reactor Innovation Center (NRIC) seeks to improve the understanding of microreactor physics in industry and academia through the development of a Microreactor Applications Research Validation and Evaluation (MARVEL) reactor-based model, published on the Virtual Test Bed (VTB). To achieve this goal, the Sodium-cooled Thermal-spectrum Advanced Research Test Reactor (STARTR) model was built using publicly available MARVEL specifications where possible and approximations where applicable, and was optimized for fast runtimes for researchers to receive rapid simulation feedback. STARTR will fill a gap between stakeholder interest and available models, as the first Sodium-cooled Thermal Reactor (STR) hosted on the VTB with baseline performance sanctioned by INL. This project involved the definition of all materials used in the reactor, geometry and all reactor subcomponents, and assertion of tallies and simulation settings within OpenMC 0.13.3. This poster details a small subset of the overall reactor physics testing: the two-dimensional power peaking factors and the flux energy spectrum, as well as plots of the created geometry. Future work includes code-to-code verification between the OpenMC-based model and a separately designed MCNP 6.2-based model.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2024

Lambda-PFLOTRAN 1.0: a workflow for incorporating organic matter chemistry informed by ultra high resolution mass spectrometry into biogeochemical modeling

Abstract. Organic matter (OM) composition plays a central role in microbial respiration of dissolved organic matter and subsequent biogeochemical reactions. Here, a direct connection of organic matter chemistry and thermodynamics to reactive transport simulators has been achieved through the newly developed Lambda-PFLOTRAN workflow tool that succinctly incorporates carbon chemistry data generated from Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) into reaction networks to simulate organic matter degradation and the resulting biogeochemistry. Lambda-PFLOTRAN is a Python-based workflow, executed through a Jupyter notebook interface, that digests raw FTICR-MS data, develops a representative reaction network based on substrate-explicit thermodynamic modeling (also termed lambda modeling due to its key thermodynamic parameter λ used therein), and completes a biogeochemical simulation with the open source, reactive flow and transport code PFLOTRAN. The workflow consists of the following five steps: configuration, thermodynamic (lambda) analysis, sensitivity analysis, parameter estimation, and simulation output and visualization. Two test cases are provided to demonstrate the functionality of the Lambda-PFLOTRAN workflow. The first test case uses laboratory incubation data of temporal oxygen depletion to fit lambda parameters (i.e., maximum utilization rate and microbial carrying capacity). A slightly more complex second test case fits multiple lambda formulation and soil organic matter release parameters to temporal greenhouse gas generation measured during a soil incubation. Overall, the Lambda-PFLOTRAN workflow facilitates upscaling by using molecular-scale characterization to inform biogeochemical processes occurring at larger scales.

58 GEOSCIENCES↗
DOE OSTI2025

Multi-scale, Multi-disciplinary, and Multi-agent Explainable AI with Koopman-Undergirded Learning, Prediction, and Analysis (M3EA KULPA) (Project Closeout Report)

The goal of this project was to develop and use domain-aware machine learning formulations, based on the Koopman Operator (KO), for modelling multi-scale, multi-disciplinary (e.g., multi-physics), and/or multi-agent systems. The project developed these formulations for the following cases: • Systems with dynamics at two separate time scales, • Systems with a bi-level hierarchical control structure, • Systems with bi-level hierarchical control and dynamics at two separate time scales (the lower level controls operating at the faster time scale), and • Systems with n separate but interacting agents/disciplines (with/without control, respectively); the controls for each agent could include bi-level hierarchical control and dynamics at two separate time scales as described above. The project then defined a set of dynamical systems consisting of different nonlinear oscillators that could be used to test these different formulations and then subsequently learned the KO models for those systems. With the KO models, we were able to do the following: • Quantify system stability, including both long-term and transient behavior, • Quantify the effects of feedbacks between the different time scales and agents/disciplines in terms of those feedbacks’ effects on system stability, • Replace a standard Proportional-Integral (PI) control in the hierarchical control structure with a KO-based Linear-Quadratic Regular (LQR), a form of optimal control, • Calculate optimal supervisory control policies a) with and without time scale separated dynamics at the lower level control levels and b) with both PI and KO-based LQR lower level control policies, and • Calculate dynamic Nash equilibria for multi-agent systems where each agent makes its own control decisions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Techno-Economic Simulation Results Using dGeo for EGS-Based District Heating in the Northeastern United States

This dataset presents the results of techno-economic simulations performed using the Distributed Geothermal Market Demand Model (dGeo) to evaluate the feasibility of Enhanced Geothermal Systems (EGS)-based district heating in the Northeastern United States. Developed by the National Renewable Energy Laboratory (NREL), dGeo is a geospatially resolved, bottom-up modeling framework designed to explore the deployment potential of geothermal distributed energy resources. The dataset, created as part of the Cornell EGS Ground-Truthing Project, provides census tract-level data that includes inputs and outputs such as thermal demand, road length, energy prices, geothermal system sizing, annual energy contributions from geothermal and natural gas peaking boilers, system capital costs (CAPEX), operation and maintenance costs (OPEX), and the levelized cost of heat (LCOH). Key simulation parameters include geothermal gradients, measured well depths, production temperatures, and district heating piping lengths based on S1400 neighborhood road lengths. The simulations assume a target bottom hole temperature of 80C and the development of new district heating networks in each census tract.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Collaborative Research: Enabling multi-scale studies of magnetic reconnection with interpretable data-driven models

The development of accurate reduced descriptions and improved closures for magnetic reconnection is an important and a long‐standing challenge in plasma physics. The four‐fluid approach, and associated closures, that were investigated have the potential to improve the accuracy of plasma fluid models, capturing physical effects which would otherwise require a kinetic description. If successful, this approach could have an important impact for the modeling of laboratory and space plasmas. The major goals of this project were to develop new machine learning (ML) tools based on sparse and symbolic regression techniques, and to extract interpretable and generalizable reduced models (e.g., in the form of partial differential equations - PDEs) from data generated by first principles plasma simulations. Preserving interpretability of such data‐driven models is key to addressing the long‐standing theoretical and numerical challenges. Prior proof‐of‐principle studies have demonstrated the enormous potential of this approach, by recovering the well‐established hierarchy of plasma equations (from Vlasov to MHD) from data produced by particle‐in‐cell (PIC) simulations. Our goal in this project was to extend and apply these new tools to construct better kinetic closures for magnetic reconnection; to derive better models of particle injection and acceleration by this fundamental plasma process; and to use this understanding to accelerate the development of multi‐scale plasma algorithms. While our immediate focus was on the problem of magnetic reconnection, the tools that were will developed are general and applicable to other areas of plasma physics, and more broadly to many‐body phenomena. We anticipate that the development of these multi‐scale models will have a significant impact across different areas of plasma science, from fusion to space and astrophysical plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Community Resilience Through Rapid Restoration Leveraging Distributed Energy Resources (DERs) and Low-Cost Sensors

Equitable and automated bottoms-up power restoration following an extreme event will be demonstrated at a site in Puerto Rico. To do so, the team will develop enhanced grid situational awareness techniques integrating behind-the-meter (BTM) distributed energy resources (DER) discovery, impedance sweeping based outage boundary detection, and feasible restoration path identification algorithms. Resilience metric will be developed and incorporated along with situational awareness information in a distributed Model Predictive Control (MPC)-based restoration optimization algorithm to control and mobilize grid assets. These algorithms will be validated through power hardware-in-the-loop experiments and ultimately, a site demonstration to show that outage recovery time and total recovered load could be improved by >20% over the baseline.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

A Method for Producing Hierarchical and Statistically Calibrated Predictions of Nuclear Material Properties from Existing Models

Computer vision-based analysis of micrographs of nuclear materials is an emerging technique for property prediction, synthetic route identification, and other material analysis tasks. These analysis tasks play a pivotal role in many material characterization applications such as signature development for treaty verification, process optimization, etc. The backbone in many of the recent computer vision-based techniques is a deep learning model, which takes a fixed-size set of pixels and provides a class prediction for that set of pixels. For example, previous work developed a deep convolutional neural network (CNN) to predict the synthetic route from a 256 px x 256 px patch taken from a larger image of uranium ore concentrates. In this work, we present several methods for first calibrating these models in a manner that they can provide accurate probabilities of their predictions’ veracity, and several methods of combining these probabilities. Overall, the combination of these two steps into a pipeline allows for full-image and even full-sample (where a sample has many images) predictions with associated confidence values. Finally, we show that one can also use the patch predictions and confidence to produce a visualization to map predicted constituents through the image. Results and examples for predicting and mapping uranium ore concentrates’ synthetic process from imagery will be presented.

artificial intelligence↗
DOE OSTI2026

Electric-vehicle battery second-life and recycling pathways: How economics depend on chemistry, processing, and application

We assess the economics of repurposing and recycling electric vehicle (EV) batteries by estimating the maximum acquisition price repurposers and recyclers could pay for used EV packs across cathode chemistries, first-life conditions, second-life applications, and recycling processes. We develop a novel open-source process-based cost model of a UL-1974-certified repurposing facility and leverage battery degradation models to estimate the maximum acquisition price repurposers could pay for used EV batteries while producing second-life battery energy storage systems with life-adjusted costs equivalent to new systems. We compare these maximum price estimates to maximum prices for recyclers based on cost and revenue estimates from the EverBatt model. We find that repurposing is more economical than recycling for lithium iron phosphate (LFP) batteries, due to their relatively long life and low value materials; recycling is generally more economical than repurposing for lithium nickel cobalt aluminum oxide (NCA) batteries, due to their shorter life and higher value materials; and the economics for lithium nickel manganese cobalt oxide (NMC) batteries depend more heavily on first life retirement conditions and second life application intensity. These results suggest an overall strategy: reuse LFP, recycle NCA, and sort NMC into recycling or repurposing pathways based on state of health and second-life application.

25 ENERGY STORAGE↗
DOE OSTI2025

Machine learning models for volumetric swelling in uranium nitride

Machine learning methods are applied to predict the volumetric swelling rate of the nuclear fuel uranium nitride (UN) over various temperatures, irradiation conditions, and power densities. Both kernel-based methods and symbolic regression models for UN swelling are developed and compared with multiple experimental datasets. We find that the UN pellet geometry and dimensions must be taken into account to accurately model swelling behavior. Strong agreement is observed between the developed machine learning models and the data. The predictive error generated by the machine learning models improves on empirical models taken from the literature. Sensitivity analysis is performed to determine which properties such as temperature, burnup, and power density, are most important in the swelling process. We find that machine learning can be used to quickly develop accurate swelling models for nuclear materials. In conclusion, the presented results illustrate the potential of machine learning to determine volumetric swelling in UN.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Rapid characterization of MSW and RDF feedstocks for waste-to-energy process using LIBS and ML techniques

The heterogeneity in the composition of municipal solid wastes (MSW) poses significant challenges in the production of biofuel and bioproducts. This research aims to enhance the accuracy and efficiency of waste analysis and characterization by introducing a fast characterization approach for MSW-derived refuse-derived fuels (RDF) by combining Laser-Induced Breakdown Spectroscopy (LIBS) with advanced machine learning (ML) techniques. The approach combines data pre-processing of LIBS spectra of RDF, and the development of ML models trained on domain and theory-based spectral features for predicting process parameters. These models are adept at predicting key process parameters like High Heating Value (HHV), carbon content, and volatile matter. This approach can achieve an average RRMSE of 2.13% and R 2 of 0.98 or higher for all considered parameters on testing data. This work demonstrates significant potential for improving waste sorting, processing efficiency, and environmental compliance over traditional labor- and time-intensive laboratory waste analysis and characterization.

09 BIOMASS FUELS↗
DOE OSTI2024

Beyond the Charge Transfer Mechanism for 2D Materials-Assisted Surface Enhanced Raman Scattering

Two-dimensional (2D) materials have been extensively implemented as surface-enhanced Raman scattering (SERS) substrates, enabling trace-molecule detection for broad applications. However, the accurate understanding of the mechanism remains elusive because most theoretical explanations are still phenomenological or qualitative based on simplified models and rough assumptions. To advance the development of 2D material-assisted SERS, it is vital to attain a comprehensive understanding of the enhancement mechanism and a quantitative assessment of the enhancement performance. Here, the microscopic chemical mechanism of 2D material-assisted SERS is quantitatively investigated. The frequency-dependent Raman scattering cross sections suggest that the 2D materials’ SERS performance is strongly dependent on the excitation wavelengths and the molecule types. By analysis of the microscopic Raman scattering processes, the comprehensive contributions of SERS can be revealed. Beyond the widely postulated charge transfer mechanisms, the quantitative results conclusively demonstrate that the resonant transitions within 2D materials alone are also capable of enhancing the molecular Raman scattering through the diffusive scattering of phonons. Furthermore, all of these scattering routines will interfere with each other and determine the final SERS performance. Our results not only provide a complete picture of the SERS mechanisms but also demonstrate a systematic and quantitative approach to theoretically understand, predict, and promote the 2D materials SERS toward analytical applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Dynamic Behavior of Oval-Twisted Helical Tube Heat Exchanger: Numerical Study with RELAP5-3D

Convective heat transfer characteristics and theoretical thermal stress behaviors are numerically calculated using RELAP5-3D for the helical-coiled once-through steam generator (H-OTSG) and the novel heat exchanger design known as the oval-twisted helically coiled heat exchanger (OTHCHX) under (1) fluctuating wall temperature conditions, (2) square-wave pulsating flow conditions, and (3) the combined effects of fluctuating wall temperature and square-wave pulsating flow conditions. Heat transfer coefficient models for the H-OTSG and OTHCHX were developed based on existing data and implemented into RELAP5-3D, successfully capturing the N⁢uavg behavior within 8% to 10% of the reported data. Under fluctuating wall temperature conditions, the OTHCHX displayed higher N⁢u avg behavior than the H-OTSG. As 𝑓 increased, the $𝜎^{𝑚⁢𝑎⁢𝑥}_{𝑡⁢ℎ}$ decreased. The $𝜎^{𝑚⁢𝑎⁢𝑥}_{𝑡⁢ℎ}$ was higher for the OTHCHX than for the H-OTSG under fluctuating wall temperature conditions. Under pulsating flow conditions, the H-OTSG and OTHCHX displayed much higher 𝑁⁢𝑢 𝑎𝑣𝑔 than under constant flow conditions. The H-OTSG displayed a higher $𝜎^{𝑚⁢𝑎⁢𝑥}_{𝑡⁢ℎ}$ over the OTHCHX. Under combined fluctuating wall temperature and pulsating flow conditions, the augmented heat transfer behavior from the pulsating flow was counteracted by the wall temperature fluctuations, producing slightly higher 𝑁⁢𝑢 𝑎𝑣𝑔 over constant wall temperature, constant flow conditions, but much lower than only constant pulsating flow under constant wall temperature conditions. The effects of simultaneous wall temperature fluctuations and square-wave pulsating flow caused higher $𝜎^{𝑚⁢𝑎⁢𝑥}_{𝑡⁢ℎ}$ than that of only wall temperature fluctuations or pulsating flow. As the Reynolds number (Re) increased, $𝜎^{𝑚⁢𝑎⁢𝑥}_{𝑡⁢ℎ}$ increased. However, when 𝑓=𝑓$_{\dot{m}}$, the $𝜎^{𝑚⁢𝑎⁢𝑥}_{𝑡⁢ℎ}$ showed decreasing values as Re increased. In conclusion, the results indicate that thermal-fluid resonance can help mitigate thermal stresses.

Thermal stress↗
DOE OSTI2024

Integrating chromosome conformation and DNA repair in a computational framework to assess cell radiosensitivity

Objective. The arrangement of chromosomes in the cell nucleus has implications for cell radiosensitivity. The development of new tools to utilize Hi-C chromosome conformation data in nanoscale radiation track structure simulations allows for in silico investigation of this phenomenon. We have developed a framework employing Hi-C-based cell nucleus models in Monte Carlo radiation simulations, in conjunction with mechanistic models of DNA repair, to predict not only the initial radiation-induced DNA damage, but also the repair outcomes resulting from this damage, allowing us to investigate the role chromosome conformation plays in the biological outcome of radiation exposure. Approach. In this study, we used this framework to generate cell nucleus models based on Hi-C data from fibroblast and lymphoblastoid cells and explore the effects of cell type-specific chromosome structure on radiation response. The models were used to simulate external beam irradiation including DNA damage and subsequent DNA repair. The kinetics of the simulated DNA repair were compared with previous results. Main results. We found that the fibroblast models resulted in a higher rate of inter-chromosome misrepair than the lymphoblastoid model, despite having similar amounts of initial DNA damage and total misrepairs for each irradiation scenario. Significance. This framework represents a step forward in radiobiological modeling and simulation allowing for more realistic investigation of radiosensitivity in different types of cells.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Power Profile Monitoring and Tracking Evolution of System-Wide HPC Workloads

The power & energy demands of HPC machines have grown significantly. Modern exascale HPC systems require tens of megawatts of combined power for computing resources and cooling facilities at full capacity. The current energy trend is not sustainable for future HPC systems, and there is a need to work toward the energy efficiency aspect of HPC performance. Energy awareness of the HPC applications at the job level is essential for running an efficient HPC system. This work aims to develop a pipeline to provide a production-level system-wide overview of the HPC workloads' power profile while handling evolving workloads exhibiting new power trends. We developed an open-set classification model for HPC jobs based on the properties of power profiles to continuously provide a system-wide holistic view of recently completed jobs. The pipeline helps continuously monitor the job-level power usage pattern of HPC and enables us to capture the new trends in applications' power behavior. We employed a comprehensive set of techniques to generate job-level data, custom-designed feature extraction methods to extract critical features from jobs' power profiles, clustering techniques powered by generative modeling, and open-set classification for identifying job profiles into known classes or an unknown set. With extensive evaluations, we demonstrate the effectiveness of each component in our pipeline. We provide an analysis of the resulting clusters that characterize the power profile landscape of the Summit supercomputer from more than 60K jobs executed in a year. The open-set classification classifies the known data sets into known classes with high accuracy and identifies unknown data noints with over 85% accuracy.

Karimi, Ahmad Maroof↗
DOE OSTI2024

Bayesian calibration of stochastic agent based model via random forest

SAND2024-11403O The Bayesian Calibration of Stochastic Agent-based Model via Random Forest is a code that was developed in concurrence with an article by the same name that was written for a journal. The code reproduces simulation results and plots from the article. 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.

SciDAC↗
DOE OSTI2025

pnnl/battery-degradation-prognosis

This is a tool for long term prognosis of redox flow battery parameter degradation which uses Transformer-based deep learning models for time series forecasting which was developed as part of the ROVI project.

Saldanha, Emily [Pacific Northwest National Labora↗
DOE OSTI2024

Improving Additive Manufactured Component Performance through Multi-Scale Microstructure Simulation and Process Optimization

The purpose of this project was to utilize computational tools to understand the relationships between processing, microstructure, and properties for additively manufactured (AM) aluminum alloys for automotive applications, and to provide an engineering solution for helping to optimize process conditions. The project leverages ORNL developments in computational modeling, including AM process modeling, phase-field based microstructure evolution predictions, and data analytics techniques for mapping process conditions to material outcomes. The project utilized an Al-Cu-Mn-Zr alloy as a model material for studying formation of defects and microstructural features in response to variations in process conditions. Based on both pre-existing experimental data and simulation results, statistical process maps were constructed to identify regions of process space with minimal defect formation and advantageous microstructures and properties. The software tools used for this purpose were successful disseminated to GM, who were able to successful compile the relevant HPC codes within their own computing ecosystem and perform initial calculations to reproduce ORNL results.

36 MATERIALS SCIENCE↗