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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 217 records · Page 12

Isotopic Analysis using FRAM [Slides]

FRAM is an isotopic analysis code nominally designed for plutonium and uranium. It stands for Fixed-energy Response-function Analysis with Multiple efficiencies. It has self-calibration using several gamma-ray peaks and can analyze gamma ray data from 30keV to >1MeV of HPGe, CdTe, or LaBr3 detector. The key difference between the MGA family of codes (from LLNL) and FRAM is the use of user-editable analysis parameters in the latter.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Hourly Electricity Demand Profiles for Each County in the Contiguous United States

This dataset provides estimated hourly electricity demand for each county in the contiguous United States from 2016-2023. The demand profiles represent the sum of two components: (1) Weighted averages of reported hourly demand profiles for North American Electric Reliability Corporation balancing authority (BA) regions and subregions, scaled to match annual estimates of county-level retail sales and direct use of electricity and weighted by the estimated percentage of county load served by each BA region or subregion. (2) Weighted averages of modeled hourly, county- and sector-level distributed photovoltaic (DPV) capacity factor profiles, scaled to match annual estimates of on-site consumption of DPV-generated electricity for each county and weighted by the percentage of consumption attributable to each sector Annual county-level retail sales are estimated by aggregating utility-reported sales to the state level and allocating the results to counties according to each county's share of state population. Annual county-level direct use is calculated by aggregating power plant-reported direct use values. Annual county-level on-site consumption of DPV-generated electricity is estimated by aggregating utility-reported net metering data to determine the amount of DPV-generated electricity sold back to the grid for each state, subtracting those values from modeled state-level DPV generation estimates, and allocating the results to counties according to each county's share of statewide modeled DPV generation. The open-source Python code used to develop this dataset is available at "Historical Load Data Repository" link below.

14 SOLAR ENERGY↗

Adapting CLUTCH methodology to multigroup TSUNAMI-3D for eigenvalue sensitivity calculations

The sensitivity of the eigenvalue to uncertainties in nuclear data and its evaluation are important for nuclear criticality safety. TSUNAMI-3D sequences within the SCALE code system offer several options to the user community for calculating eigenvalue sensitivity coefficients with multigroup (MG) and continuous energy (CE) 3D transport capabilities. TSUNAMI-3D sequences implement the adjoint-based perturbation theory with MG KENO code, the Contributon Linked eigenvalue sensitivity/Uncertainty estimation via Track length importance CHaracterization (CLUTCH) method with CE KENO code, and the Iterated Fission Probability (IFP) method with CE KENO and Shift codes. Each method has benefits and limitations depending on the problem that is run. The work presented here aims to adapt the CLUTCH method, which enables the Contributon method's mesh-free, memory-efficient approach for calculating adjoint-weighted tallies for sensitivity calculations, to the MG TSUNAMI-3D sequence. This application would eliminate the explicit adjoint KENO calculation, as well as the memory-consuming mesh flux moment tallies required by the conventional MG TSUNAMI-3D. Smaller memory footprints in the CLUTCH methodology and relatively shorter runtimes in MG KENO transport can make MG TSUNAMI-3D a viable method for some complex problems. Moreover, this adaptation allows MG sensitivity calculations with Shift, ORNL's next-generation high-performance Monte Carlo transport code, which currently does not offer any sensitivity capabilities with MG particle transport simulations. Initial implementation of the new MG TSUNAMI-3D sequence and its preliminary results with a selected critical benchmark experiment in the Verified, Archived Library of Inputs and Data (VALID) are presented in this study.

KENO↗

Machine Learning-Driven Optimization of Building Enclosures for Moisture Durability and Thermal Performance

The design of moisture-durable building enclosures with low embodied carbon often involves an iterative process of selecting the materials for the specific exposure conditions to meet the performance requirements. While hygrothermal simulations are commonly used to evaluate moisture durability, they often require advanced expertise for proper implementation. Machine learning (ML) provides a promising alternative by streamlining the design process and minimizing the reliance on complex simulations. This study presents a machine learning-based approach for predicting moisture durability in residential wall assemblies. The ML model was trained to estimate the mold index and maximum moisture content of various layers under typical exposure conditions. The model achieved a high predictive accuracy, with a coefficient of determination (R²) exceeding 0.90 when compared to traditional hygrothermal simulations on materials that were not part of training the ML model. Building on these results, the ML model was developed into a practical tool for optimizing wall assembly designs. This tool allows users to automatically optimize material selections based on energy, moisture, and carbon performance criteria. By incorporating multi-objective optimization, the tool identifies configurations that minimize embodied carbon while maintaining moisture safety and code-compliant thermal performance. Additionally, it provides insights into how material choices influence assembly durability, energy efficiency, and carbon reduction. The tool will be implemented in the Building Science Advisor (BSA) to enhance its performance and provide more granularity on the results. This research highlights the potential for ML-driven tools to simplify the design of high-performance building enclosures, offering architects and engineers a faster, more efficient way to balance critical performance factors.

Salonvaara, Mikael [ORNL] (ORCID:0000000318991554)↗

Automated Hybrid Variance Reduction on Advanced Architectures in the Shift Monte Carlo Code

Monte Carlo transport methods are the most accurate schemes for solving problems with complex energy and spatial features, but they come with a high computational cost. Although hybrid methods have enabled the use of Monte Carlo transport for a large class of problems, they still require significant computing resources. Modern multicore CPUs with large numbers of compute cores and graphical processing units (GPUs) provide opportunities to optimize the memory and run-time costs of hybrid Monte Carlo methods. This paper documents the development and analysis of three Monte Carlo transport algorithms that support hybrid transport using the consistent adjoint-driven importance sampling (CADIS) and forward-weighted CADIS methods in the Shift Monte Carlo code: history-based transport using static and dynamic threading on multicore CPUs and event-based transport enabling weight window tracking on GPUs. The results are shown for two challenging hybrid problems on the Frontier supercomputer at the Oak Ridge Leadership Computing Facility. The results show that all three methods yield good performance and enable solutions of difficult fixed-source transport problems in less than 2 min on 20 nodes of Frontier. Dynamic threading was observed to give up to 20% better scaling behavior than static threading. Moreover, the AMD Instinct 250X GPU was found to give 9 to 11 times greater throughput per graphics compute die than the best CPU performance. In conclusion, additional opportunities for optimization of hybrid transport on GPUs are discussed.

Denovo↗

User’s Manual for RESRAD-RDD&IND Code Version 2: Vol. 1—Methodology and Models Used in RESRAD-RDD&IND Code

RESRAD-RDD&IND is part of the RESRAD family of codes that Argonne National Laboratory developed for the U.S. Department of Energy (DOE). An earlier version, RESRAD-RDD published in 2009, dealt only with radiological dispersal device (RDD) incidents (DOE 2009). This new version, RESRAD-RDD&IND, is designed to evaluate both RDD incidents and improvised nuclear device (IND) incidents. This report is Volume 1 of the RESRAD-RDD&IND User’s Manual that documents the methodology, models, and radionuclide-specific data used in RESRAD-RDD&IND code Version 2.0. Volume 2 of the RESRAD-RDD&IND User’s Manual is called the User’s Guide for RESRAD-RDD&IND Code . It describes how to use RESRAD-RDD&IND code Version 2.0 and includes screen shots and parameter information.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Advanced Manufactured Home Case Study: A Collaboration for High-Efficiency, Affordable Housing

The introduction of Zero Energy Ready Home Manufactured Homes (ZERH MH) qualification for manufactured homes has generated significant attention and interest in the industry to employ advanced manufacturing techniques. VEIC engaged with Titan Homes to create an advanced manufactured home design that employs ZERH standards, other client-driven above-code standards, and manufacturing processes improvements. This case study identifies specific energy-efficient and quality improvement measures that were implemented to achieve certification and take advantage of incentives for home manufacturers.

14 SOLAR ENERGY↗

SAM Enhancements for Air-Ingress Event Modeling in HTGRs

The SAM code is under development and supported by DOE-NE’s Nuclear Energy Advanced Modeling and Simulation program as a modern system-level modeling and simulation tool for advanced non-light water reactor safety analyses. These advanced reactor concepts incorporate novel and improved approaches to achieve safety and economic feasibility. This report summarizes recent efforts and progress in addressing the code capability gaps in SAM for the modeling of the air-ingress phenomenon in High-Temperature Gas-Cooled Reactors (HTGRs). The capability enhancements implemented to the code include: a multi-component flow model to capture the air-helium mixture during air ingress, a 0-D graphite oxidation model to capture the reaction of graphite with oxygen in the air, and an isentropic critical flow model to more accurately predict the de-pressurization of the reactor system due to a small break in the primary loop. Verification cases and demonstrations are provided to showcase these capabilities in SAM. Existing gaps in the code’s capability are also identified.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Direct comparison of gyrokinetic and fluid scrape-off layer simulations

Typically, fluid simulations are used for tokamak divertor design. However, fluid models are only valid if the scrape-off layer (SOL) is highly collisional. This assumption is valid in many present-day experiments but is questionable in the upstream SOL of some high-power scenarios envisioned for burning plasmas and fusion pilot plants. This paper reports on comparisons between fluid and kinetic simulations of the SOL for upstream parameters and geometry representative of the Spherical Tokamak for Energy Production fusion pilot plant. The SOLPS-ITER (fluid) and Gkeyll (gyrokinetic) codes are operated in a two-dimensional axisymmetric mode, which replaces turbulence with ad-hoc diffusivities. In kinetic simulations, we observe that the ions in the upstream SOL experience significant mirror trapping. This substantially increases the upstream temperature and has important implications for impurity dynamics. We show that the mirror force, which is excluded in SOLPS’s fluid equations, enhances the electrostatic potential drop along the field line in the SOL. We also show that the assumption of equal main ion and impurity temperatures, which is made in commonly used fluid codes, is invalid for the regimes explored here. The combination of these effects results in superior confinement of impurities to the divertor region in kinetic simulations, consistent with our earlier predictions [Kotschenreuther et al., in 29th IAEA 29 Fusion Energy Conference (IAEA, London, UK, 2023)]. This effect can be dramatic, reducing the midplane impurity density by orders of magnitude. These results indicate that in lower collisionality SOL’s the tolerable downstream impurity densities may be higher than would be predicted by fluid simulations, allowing for higher radiated power while avoiding unacceptable core contamination. Our results highlight the importance of kinetic simulations for divertor design and optimization for fusion pilot plants.

Computational fluid dynamics↗

Thermal-Mechanic Modeling of Fusion Components Using The MOOSE Framework

With fusion energy rapidly developing, there is a great need for an open source tool that can rapidly model different fusion tokamak designs. The open-source codes Multiphysics Object Oriented Simulation Environment (MOOSE), Tritium Migration Analysis Program Version 8 (TMAP8) , and Fusion ENergy Integrated Multiphysi-X (FENIX) are being used to demonstrate thermal-mechanic modeling of fusion components. In this study, an analysis is done on the fusion nuclear science facility (FNSF) blanket, and ITER toroidal magnet and diverter mono block. This study will lay the groundwork for fusion component analysis in the MOOSE framework and FENIX program.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Measuring Impact: Evaluating Thermal Zoning Simplification on Energy Efficiency Measures Analysis

Building Energy Modeling (BEM) is a versatile tool for designing, retrofitting, ensuring code compliance, obtaining certifications, qualifying for incentives, and enabling real-time building control. However, capturing all the details of building geometry for thermal zoning can be time-consuming, costly, and sometimes computationally challenging. As a result, modelers have been applying zoning simplification based on factors such as space functions and internal loads, as well as relying on their experience and judgment while adhering to zoning rules outlined in industry standards. Despite the prevalence of this common practice, a notable gap exists in the literature regarding studies quantifying the influence of simplified thermal zoning on the evaluation of Energy Efficiency Measures (EEMs). Recognizing this gap, this paper seeks to contribute to the field by enhancing the understanding of how the simplification of thermal zoning influences the evaluation of EEMs against a baseline design. The study utilized a medium office prototype model with a detailed floor plan featuring over 20 zones per floor covering diverse functional spaces with varying internal loads and occupancy schedules. A standard thermal zoning strategy outlined in ASHRAE Standard 90.1 Appendix G was employed as the simplified zoning method. This strategy condenses the zoning into a core zone and four perimeter zones per floor. It was compared with the detailed zoning approach, which involves one zone per space. Common Energy EEMs, such as enhanced envelope, high-efficiency appliances and equipment, and HVAC controls, were individually implemented and evaluated. The results indicate that the performance comparison between the two zoning methods varies depending on the type of measures considered. Basic measures, such as adding wall insulation, demonstrate similar energy impacts, while advanced HVAC control measures, such as static pressure reset, exhibit a more substantial difference that cannot be overlooked.

Xie, Jiarong↗

PNNL-Sequim Campus Radionuclide Air Emissions Report for Calendar Year 2023

The U.S. Department of Energy Office of Science (DOE-SC) Pacific Northwest Site Office has oversight and stewardship duties associated with the Pacific Northwest National Laboratory Sequim campus. Some research projects have the potential to emit low levels of radioactive materials. This report is prepared to document compliance with the Code of Federal Regulation, Title 40, Protection of the Environment, Part 61, National Emission Standards for Hazardous Air Pollutants, Subpart H, “National Emission Standards for Emissions of Radionuclides Other than Radon from Department of Energy Facilities” and Washington Administrative Code Chapter 246-247, "Radiation Protection–Air Emissions." Compliance is indicated by comparing the estimated effective dose equivalent (EDE) to the maximally exposed individual (MEI) member of the public with the 10 millirem per year (mrem/yr) U.S. Environmental Protection Agency (EPA) standard. The PNNL-Sequim campus has only fugitive emissions sources.

40 CFR 61 Subpart H↗

Toward a high-fidelity tritium transport modeling for retention and permeation experiments

We report Tritium Migration Analysis Program version 8 (TMAP8), the latest version of TMAP, was developed within the framework of the Multiphysics Object-Oriented Simulation Environment (MOOSE). Created at Idaho National Laboratory (INL), MOOSE is an open-source, dimension-agnostic, fully coupled, and fully implicit multiphysics platform featuring massively parallel computation capabilities. Using TMAP8, tritium transport in a divertor monoblock was analyzed to elucidate the effects of pulsed operation (up to fifty 1,600 s plasma discharge and cool-down cycles) on the tritium in-vessel inventory source term and ex-vessel release term (i.e., tritium retention and permeation) for safety analysis. With its built-in Message Passing Interface capability, TMAP8 can, in under 2 h, simulate tritium transport in three different layered materials (i.e., tungsten, copper, and copper-chromium-zirconium alloy) in 2D geometry, using a single device/computer with 10 cores. The MOOSE-based TMAP8 code can leverage other MOOSE tools developed under the Nuclear Energy Advanced Modeling and Simulation program to perform tritium and thermal transport in complex geometries and multiphysics environments. And via its massively parallel computation, MOOSE will enable the fusion pilot plant designers to conduct high-fidelity multiphysics modeling for the design of the divertor and blanket systems as well as for the safety analysis.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Characterization of Fuel-to-Coolant Heat Transfer During Reactivity-Initiated Accidents Using Tightly Coupled Thermal Hydraulics and Fuel Thermomechanics

The reactivity-initiated accident (RIA) is a complex scenario with several tightly interacting physical phenomena. Accurately predicting fuel behavior during these transients is difficult due to limitations in the modeling of fuel-to-coolant heat transfer. Common approaches to simulate RIAs involve standalone calculations using either a fuel performance code or a thermal-hydraulic code. The complex interdependencies of thermal-hydraulic and fuel mechanical behavior suggest that a tight coupling between these codes may provide more accurate predictions of fuel-to-coolant heat transfer and cladding mechanical response. Here, RELAP5-3D and BISON are coupled in this paper to simulate RIAs, and a sensitivity analysis is performed to rank key thermal properties and two-phase heat transfer parameters relevant for fuel-to-coolant heat transfer and cladding failure mechanisms in UO 2 –Zircaloy-4 systems. Gas gap conductance, film boiling heat transfer uncertainty, pulse width, fuel-specific heat capacity, and cladding-specific heat capacity were identified as important parameters. Variations in figures of merit resulting from changes to pulse width and the material thermal properties indicate that time-dependent heat transfer rates are significant for safety-relevant mechanical parameters due to the time dependence of cladding ductility and pellet-cladding mechanical interaction loading. The results suggest that the thermal-hydraulic factors have a nonnegligible influence on the thermomechanical solution and vice versa. Tight coupling of both sets of physics is recommended to improve prediction of fuel behavior during RIAs. Highlights include the following: 1. The RELAP5-3D thermal-hydraulic code and the BISON fuel performance code are tightly coupled for simulation of RIA transients with energy depositions at the Zircaloy-4 cladding failure threshold. 2. Departure from nucleate boiling occurred for all simulated cases. Due to the ductility of fresh fuel, substantial ballooning occurred in most cases. 3. Gas gap conductance, fuel-specific heat capacity, cladding-specific heat capacity, transient pulse width, and film boiling heat transfer were the dominant thermal factors impacting the safety figures of merit at energy depositions.

Critical Heat Flux (CHF)↗

HydraGNN v4.0

The new version of HydraGNN v4.0 provides additional core capabilities, such as: Inclusion of multi-body atomistic cluster expansion MACE, polarizable atom interaction neural network PAINN, and equivariant principal neighborhood aggregation (PNAEq) among the message passing layers supported -Inclusion of graph transformers to directly model long-range interactions between nodes that are distant in the graph topology Integration of graph transformers with message passing layers by combining the graph embedding generated by the two mechanisms, which allows for an improved expressivity of the HydraGNN architecture Improved re-implementation of multi-task learning (MTL) to allow its use for stabilized training across imbalanced, multi-source, multi-fidelity data Introduction of multi-task parallelism, a newly proposed type of model parallelism specifically for MTL architectures, which allows to dispatch different output decoding heads to different GPU devices Integration of multi-task parallelism with pre-existing distributed data parallelism to enable a 2D parallelization for distributed training Improved portability of the distributed training across Intel GPUs, which has been testes on ALCF exascale supercomputer Aurora Inclusion of 2-level fine-grained energy profilers portable across NVIDIA, AMD, and Intel GPUs to monitor the power and energy consumption associated with different functions executed by the HydraGNN code during data pre-load and training Restructuring of previous examples and inclusion of new sets of examples to illustrate the download, preprocess, and training of HydraGNN models on new large-scale open-source datasets for atomistic materials modeling (e.g., Alexandria, Transition1x, OMat24, OMol25)

Lupo Pasini, Massimiliano [Oak Ridge National Labo↗

2024 Electric Vehicles at Scale Semiannual Stakeholder Meeting

The U.S. Department of Energy (DOE) Electric Vehicles at Scale Lab Consortium (EVs@Scale Lab Consortium) is accelerating research to support the establishment of a secure and scalable national network of charging infrastructure. This network will be critical to support tens of millions of light-, medium-, and heavy-duty EVs on American roads by 2030. The EVs@Scale Lab Consortium brings together national laboratories and key stakeholders to conduct infrastructure research and development (R&D) that advances innovations in, and sets unified standards for, high-power and wireless charging. The effort will also develop technologies to integrate vehicle charging with the power grid, and develop cybersecurity measures to protect drivers, vehicles, equipment, and the grid. The first hybrid EVs@Scale Lab Consortium Semiannual Stakeholder Meeting was held at ANL on September 27-28, 2023, to identify research, development, and deployment needs to accelerate technology development for electric vehicles at scale and explore opportunities for collaboration across government, academia, and industry.

advanced charging and grid interface technologies↗

Suppressed Cosmic-Ray Energy Densities in Molecular Clouds from Streaming Instability-regulated Transport

Abstract Cosmic rays (CRs) are the primary driver of ionization in star-forming molecular clouds (MCs). Despite their potential impacts on gas dynamics and chemistry, no simulations of star cluster formation following the creation of individual stars have included explicit cosmic-ray transport (CRT) to date. We conduct the first numerical simulations following the collapse of a 2000 M ⊙ MC and the subsequent star formation including CRT using the STAR FORmation in Gaseous Environments framework implemented in the GIZMO code. We show that when CRT is streaming-dominated, the CR energy in the cloud is strongly attenuated due to energy losses from the streaming instability. Consequently, in a Milky Way–like environment the median CR ionization rate in the cloud is low ( ζ ≲ 2 × 10 −19 s −1 ) during the main star-forming epoch of the calculation and the impact of CRs on the star formation in the cloud is limited. However, in high-CR environments, the CR distribution in the cloud is elevated ( ζ ≲ 6 × 10 −18 ), and the relatively higher CR pressure outside the cloud causes slightly earlier cloud collapse and increases the star formation efficiency by 50% to ∼13%. The initial mass function is similar in all cases except with possible variations in a high-CR environment. Further studies are needed to explain the range of ionization rates observed in MCs and explore star formation in extreme CR environments.

Astronomy & Astrophysics↗

Enhancing EnergyPlus capabilities to model dynamic building envelopes using python plugin

Nearly half of the energy consumption in the United States is related to buildings, resulting in an urgent need to develop innovative technologies to improve building energy efficiency. Dynamic building envelopes, comprising switchable insulation and thermal energy storage materials, have been proposed recently as a promising solution to reduce buildings' heating and cooling loads by thermally coupling the indoor environment with the ambient environment when beneficial while decoupling them when outdoor conditions are not favorable. Although various related technologies are still underway, the whole-building energy modeling tools, like EnergyPlus, do not have the capability to simulate the transient and dynamic nature of dynamic envelope materials and components to accurately predict their impact on building energy use. The objective of this study is to formulate a method in EnergyPlus simulation engine to model multilayer envelopes, comprising dynamic building materials with variable thermophysical properties, and discuss the changes made to the program using a Python plugin. Furthermore, the thermal performance of the dynamic envelopes using the proposed method is compared and verified with the results from a well-established commercial code, COMSOL Multiphysics. A parametric assessment is also conducted to evaluate the energy efficiency benefits of dynamic envelopes in a single-family residential building, demonstrating total annual energy savings up to 11.6 %, when a dynamic envelope operates alone, and up to 18.2 % when it is combined with a thin layer of phase change material as a thermal storage medium. Finally, a United States wide energy efficiency assessment is presented to showcase the geographical spread of the energy savings. The method designed and implemented in this study provides the researchers with the ability to implement their dynamic insulation methods in EnergyPlus and evaluate the whole building energy impact.

25 ENERGY STORAGE↗