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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Validating ENDF/B-VIII.1beta3 with LLNL Pulsed-Sphere Experiments and ICSBEP Benchmarks [Slides]

LPS experiments are not benchmarks but can provide valuable input. 22/1/24 LPS experiments are not benchmarks but can provide valuable input. LPS (run by Denise) help validate nuclear data up to 15 MeV. They test scattering and fission data. They have incomplete uncertainties from 2-8%. I take note when: (1) I see C/E differences above the peak and before late times => 20%. (2) Simulated results between libraries change by => 20% without a good explanation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Scattering Calorimeter FY24 Deliverable Report

A simulation-based method has been developed to prototype new detector designs for nuclear data measurements utilizing neutron scattering. This method uses representative physics inputs for signal and background generation, full detector resolution smearing benchmarked by experimental data, and a neutron beam timing simulation to produce analyzable output like a physical measurement. A test case has been studied using a hybrid time-of-flight calorimeter detector for scattering cross-section measurements on 239 Pu with 1-5 MeV incident monoenergetic neutrons. Data analysis methods have been developed to perform event-level particle reconstruction and reaction channel discrimination. This analysis has been used to estimate the capability of the test detector to perform simultaneous scattering and fission cross section measurements, as well as its ability to provide neutron spectra and particle angular information.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Investigation of Anomalous Thread Wear in EDS Vessels

Potential causes of anomalous thread wear observed on EDS system fasteners were investigated using the V25 two-piece clamped vessel as a test bed. Thread wear and metal particulate were analyzed across operational conditions. Despite intensive testing, galling wear was not triggered, reducing uncertainty about design tolerances, material selection, and environmental factors. Minimum service life benchmarks were established for two-piece clamp fasteners under normal EDS conditions.

42 ENGINEERING↗
DOE OSTI2020

AlphaBuilding - Synthetic Buildings Operation Dataset

This is a synthetic building operation dataset which includes HVAC, lighting, miscellaneous electric loads (MELs) system operating conditions, occupant counts, environmental parameters, end-use and whole-building energy consumptions at 10-minute intervals. The data is created with 1395 annual simulations using the U.S. DOE detailed medium-sized reference office building, and 30 years' historical weather data in three typical climates including Miami, San Francisco, and Chicago. Three energy efficiency levels of the building and systems are considered. Assumptions regarding occupant movements, occupants' diverse temperature preferences, lighting, and MELs are adopted to reflect realistic building operations. A semantic building metadata schema - BRICK, is used to store the building metadata. The dataset is saved in a 1.2 TB of compressed HDF5 file. This dataset can be used in various applications, including building energy and load shape benchmarking, energy model calibration, evaluation of occupant and weather variability and their influences on building performance, algorithm development and testing for thermal and energy load prediction, model predictive control, policy development for reinforcement learning based building controls.

AlphaBuilding↗
DOE OSTI2025

Cluster Dynamics Modeling Needs for the Advanced Materials and Manufacturing Technologies Program

This milestone report aims to identify and assess the cluster dynamics (CD) modeling requirements within the Department of Energy's Office of Nuclear Energy (DOE-NE) Advanced Materials and Manufacturing Technologies (AMMT) program and to communicate these needs to the DOE-NE Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. The goal is to ensure NEAMS is well-informed about the CD modeling requirements to support AMMT's mission of accelerating the development, qualification, demonstration, and deployment of advanced structural materials and manufacturing for nuclear energy applications. CD modeling is an essential tool for predicting the degradation of structural materials under irradiation, which is a key component of AMMT's accelerated qualification process. The AMMT program focuses on both additively manufactured and wrought structural alloys, such as laser powder-bed fusion 316H austenitic stainless steel, alloy 709, Haynes 244, and alloy 617. These materials require a generalized CD modeling framework to facilitate rapid model development and computational simulation. A flexible, generalized CD software, similar to the Multiphysics Object-Oriented Simulation Environment (MOOSE) finite element framework, would enable modeling of various cluster types, including defect clusters, defect-solute clusters, and multicomponent clusters, incorporating thermodynamics and kinetics parameters. Radiation effects, microstructural feature evolution, and multi-dimensional modeling are critical considerations for the CD model. The usability of the CD code should allow for easy modification and coupling with MOOSE-based simulations. Additionally, the software should adhere to Nuclear Quality Assurance-1 standards, include a testing suite for verification and validation, and be version-controlled within a national laboratory-managed Git repository. Benchmark problems are needed to assess code predictions and performance.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Flow reversal benchmark of a one-sided heated narrow rectangular channel with CATHARE and RELAP5

Flow reversal in narrow coolant channels can be a crucial phenomenon for the safety of research reactors with a downward nominal flow direction. During a loss of forced flow accident, the downward flow stagnates briefly before transitioning into an upward natural circulation flow. The fuel may be damaged if dryout occurs and threshold fuel and/or cladding temperatures are exceeded. A comprehensive study is provided for flow reversal in narrow rectangular channels by examining experimental data and conducting software model analyses. The literature on flow reversal was reviewed, and selected experimental datasets were used to benchmark against CATHARE and RELAP5 models and also compare the code calculations with each other. The experimental data comes from flow reversal tests conducted with a narrow rectangular channel with one-sided heating. The results were compared with experimental data for successful flow reversal tests and predicted dryout power for dryout conditions. Also, the study examined the effects of the pump coastdown period, inlet liquid temperature, system pressure, and localized pressure drops. The experimental results showed that shorter coastdown periods, reduced pressure drops, and lower coolant inlet temperatures increased the dryout power. However, the system pressure did not noticeably affect the results. The simulation results showed that both CATHARE and RELAP5 agreed with experimental data, capturing the trends of the experimental results. Slight differences between each code calculation, as well as the predicted and measured dryout powers, were attributed to experimental uncertainties and the modeling of physical phenomena such as wall nucleation, interfacial heat transfer, drag coefficients, and critical heat flux. Overall, this study provides an understanding of flow reversal and the prediction capabilities of thermal-hydraulics software models. In conclusion, a future study of the flow reversal benchmark of a narrow rectangular channel with two-sided heating may provide additional valuable insights.

CATHARE↗
DOE OSTI2022

High-performance and high-fidelity Monte Carlo solutions to the BEAVRS benchmark

The BEAVRS (Benchmark for Evaluation and Validation of Reactor Simulation) benchmark is solved by PRAGMA, the GPU-based continuous energy Monte Carlo code. The resulting solutions are comprised of the detailed simulation results of two cycles, each of which consists of the zero power physics test (ZPPT) and the core depletion calculations. The ZPPT consists of characteristic parameters, such as critical boron concentration (CBC), control rod bank worth, isothermal temperature coefficients, and assembly-wise detector signal, which are compared with measured data provided by the benchmark administration. The core depletion calculations were performed for both the hot full power and the load follow modes, and the comparison was made with the measured or deduced CBCs and assembly-wise detector signals. In the load follow calculations, the operating power history was approximately applied to simulate the real operation as closely as possible. PRAGMA performed the various calculations with a tremendous number of histories ranging up to hundreds of millions per cycle, exploiting GPUs' massively parallel performance. The load follow run time was shorter than 16 hours on a single rack of computing nodes mounded with 24 gaming GPUs with a remarkable agreement with the measurements for most comparisons. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Using Likwid and Byfl to Benchmark Hardware Performance

This paper outlines a benchmarking study conducted during my internship at LANL, focusing on CPU (Computer Processing Unit) and program performance assessment. The primary goal was to gather memory access data using three methods across five polybench kernels The data gathered would then be used to compare and contrast to one another and calculate operational intensity for performance comparisons. Benchmarking tools like Byfl and Likwid were employed, with Byfl offering hardware-independent data through LLVM compiler communication and Likwid directly interacting with computer hardware. The study considered various benchmarking factors, including optimization levels, Big O notation ((n)), CPU diversity and specific kernel equations. Big O notation was utilized to simplify code complexity, with detailed breakdwons of operations and memory components for each polybench application. Specific O(n) equations enabled nuanced kernel compariosns, facilitating the identification of performance variations. CPU efficiency assessments were conducted using Likwid tests on two CPUs. The central focus on code optimization aimed at achieving higher speeds and reduced memory usage through streamlined code. Future work propsoes creating a roofline model, synthesizing benchmarking data into a comprehensive data graph to assist in optimizing code and improving hardware performance. The potential impact on the laboratory or national mission was underscored, emphasizing the importance of optimizing applications and hardware to conserve resources and accelerate program execution. The specific relevance to LANL’s operations in math-intensive fields such as Nuclear Fission, Space Exploration, and Nanotechnology highlights the necessity of efficient benchmarking for resource conservation and proram speed. Overall, this study contributes to the understanding of CPU and program performance, providing insights for future optimization efforts in a laboratory setting

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Evaluation of Graph Analytics Frameworks Using the GAP Benchmark Suite

The analysis of connected data is an increasingly important application in high-performance computing. Such analyses can reveal fraudulent patterns in financial transactions, optimize telecommunications networks, predict information flow in social networks, etc. However, the landscape of graph analytics is highly diverse. Graph algorithms stress processor architectures differently, and no one graph can represent all topologies. Consequently, no single approach or framework is expected to be optimal for all graph analytics problems. To help make sense of this diverse landscape, we evaluated four approaches to graph analytics: GraphBLAS, Galois, BGL17, GraphIt; and compare them against hand-tuned implementations that take advantage of hardware features on our test platform. Graph- BLAS formulates graph analytics as sparse linear algebra. Galois provides syntactic constructs for data parallelism over irregular data structures. BGL17 is a generic C++ template library for implementing graph algorithms. GraphIt provides a domain- specific language to describe and optimize graph algorithms. We use the GAP Benchmark Suite to establish baseline performance and guide the side-by-side evaluation of each framework. GAP consists of 30 tests: six graph analytics algorithms (breadth- first search, single-source shortest path, PageRank, betweenness centrality, connected components, and triangle counting) run on five graphs, each with different topological characteristics (e.g., high diameter, skewed degree distribution, high average degree). High-performance reference implementations are included for each benchmark algorithm. Because a graph can be loaded into memory a number of ways (e.g., flat file on disk, compressed sparse format, data frames, retrieved from SQL or NoSQL databases), our evaluation focused on computational performance rather than I/O. Our results show the relative strengths of each framework.

Graph algorithms, Benchmarking, shared-memory prog↗
DOE OSTI2022

Comparison of Irradiated and Unirradiated Graphite Oxidation Performance

This work examines the oxidation behavior of NBG-25 graphite using irradiated specimens and (not irradiated) companion specimens from the Advanced Graphite Creep (AGC) Experiment. Irradiated and companion specimens were quartered, split into four small samples, to enable four oxidation test runs for each (0.5 inch diameter by 0.25 inch tall) piggyback button obtained from the AGC Experiment inventory. These split samples were oxidized in air in a thermogravimetric analyzer (TGA) and benchmarked against observations with separately sourced same-grade specimens of three geometries oxidized either in the TGA or in a vertical furnace built and operated to satisfy the specifications of ASTM D7542. The study considers both irradiation damage and relief of damage by thermal annealing. A range of isothermal oxidation temperatures were tested for specimens exposed to a similar irradiation environment, nominally 6.5 dpa at 650°C. To assess the dose dependency of observed oxidation behavior, specimens with a range of irradiation exposures (up to 6.8 dpa) were tested at the single oxidation temperature of 650°C. In the conventional analysis (rate determined over the 5-10% mass loss range) any annealing effects appear to be negligible, while irradiation to ~6.5 dpa may double or triple the subsequent oxidation rate. However, examination of rate with extent of reaction (over incremental ranges from initial onset up to 5% mass loss) illustrates that (at least for ~6.5 dpa) irradiation initially inhibits oxidation. Slower rates of oxidation than companion split samples are clearly indicated across all oxidation temperatures tested up to ~0.5% mass loss. Over the range of 0.5-1% mass loss, regardless of normalization strategy, there is no statistically meaningful difference in oxidation rate. However, beyond 0.5% mass loss oxidation of the irradiated sample becomes progressively faster than the companion sample (up to the 10% mass loss level observed).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Concavity-based local erosion and sphere-size-based local dilation applied to lithium-ion battery electrode microstructures for particle identification

Performance metrics of lithium-ion batteries can be extracted from the analysis of electrode microstructures nanoscale imaging. The characterization workflow can involve a challenging particle identification, or instance segmentation, step. In this work, we propose a new identification method based on an original transformation: a sphere-size-based local dilation followed by a concavity-based local erosion, that is local morphology closing. The new transformation is much more efficient than the global morphology closing, with correct identification achieved with only 1.7 % dilation volume and 2.6 % erosion volume on a test geometry, compared to 39.2 % and more than 50 %, respectively, with its global counterpart. The new method has been then benchmarked versus other identification algorithms (watershed and pseudo coulomb repulsive field) on a real electrode microstructure with equal or better segmentation achieved.

25 ENERGY STORAGE↗
DOE OSTI2023

Estimate long-term impact on battery degradation by considering electric vehicle real-world end-use factors

Many estimates of battery capacity degradation are based on accelerated lab tests that involve charge-discharge cycles or rely on data or electrochemical modeling. These methods are reasonable for technology benchmarking but rarely consider real-world end-use factors. To address this issue, this study develops the Battery Run-down under Electric Vehicle Operation (BREVO) model. It links the driver's travel pattern to physics-based battery degradation and powertrain energy consumption models. The model simulates the impacts of charging behavior, charging rate, driving patterns, and multiple energy management modules on battery capacity degradation. It finds that, over a 10-year timespan, firstly, for a random driver situated in the New England area, daily direct-current fast charging (60 kW) could lead to up to 22% less battery capacity when compared to daily Level-1 charging (1.8 kW). Second, the battery thermal management system can delay battery degradation by approximately 0.5% in the New England area. Third, warmer ambient temperatures enhance BEV battery usage. The model indicates that the battery capacity in the Los Angeles area is 6% higher than that in the New England area. The BREVO model provides crucial information for consumers and BEV manufacturers on range anxiety, BEV battery design, and decision support of battery warranty.

25 ENERGY STORAGE↗
DOE OSTI2022

Using artificial intelligence to detect human errors in nuclear power plants: A case in operation and maintenance

Human error (HE) is an important concern in safety-critical systems such as nuclear power plants (NPPs). HE has played a role in many accidents and outage incidents in NPPs. Despite the increased automation in NPPs, HE remains unavoidable. Hence, the need for HE detection is as important as HE prevention efforts. In NPPs, HE is rather rare. Hence, anomaly detection, a widely used machine learning technique for detecting rare anomalous instances, can be repurposed to detect potential HE. In this study, we develop an unsupervised anomaly detection technique based on generative adversarial networks (GANs) to detect anomalies in manually collected surveillance data in NPPs. More specifically, our GAN is trained to detect mismatches between automatically recorded sensor data and manually collected surveillance data, and hence, identify anomalous instances that can be attributed to HE. We test our GAN on both a real-world dataset and an external dataset obtained from a testbed, and we benchmark our results against state-of-the-art unsupervised anomaly detection algorithms, including one-class support vector machine and isolation forest. Our results show that the proposed GAN provides improved anomaly detection performance. Our study is promising for the future development of artificial intelligence based HE detection systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Probing atomic physics at ultrahigh pressure using laser-driven implosions

Abstract Spectroscopic measurements of dense plasmas at billions of atmospheres provide tests to our fundamental understanding of how matter behaves at extreme conditions. Developing reliable atomic physics models at these conditions, benchmarked by experimental data, is crucial to an improved understanding of radiation transport in both stars and inertial fusion targets. However, detailed spectroscopic measurements at these conditions are rare, and traditional collisional-radiative equilibrium models, based on isolated-atom calculations and ad hoc continuum lowering models, have proved questionable at and beyond solid density. Here we report time-integrated and time-resolved x-ray spectroscopy measurements at several billion atmospheres using laser-driven implosions of Cu-doped targets. We use the imploding shell and its hot core at stagnation to probe the spectral changes of Cu-doped witness layer. These measurements indicate the necessity and viability of modeling dense plasmas with self-consistent methods like density-functional theory, which impact the accuracy of radiation transport simulations used to describe stellar evolution and the design of inertial fusion targets.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

Toward the performance assessment of advanced nuclear waste forms: temperature dependence of lanthanide borosilicate glass dissolution

Lanthanide borosilicate (LaBS) glasses are among the most promising waste forms for the immobilization of high-level radioactive waste generated from advanced nuclear fuel cycles. However, the temperature dependence of their dissolution kinetics remains poorly understood and constrained, limiting the integration of these materials into established performance assessment models. Here, we investigate the dissolution behavior of the legacy AmCm2-19 LaBS glass and the benchmark alkali aluminoborosilicate ISG-1 in deionized water between 50 °C and 250 °C using ASTM C1285 (Product Consistency Test-B) protocols. For AmCm2-19 LaBS glass, normalized elemental release rates for boron and silicon increase with temperature before plateauing near 150 °C, consistent with solubility-limited behavior. From data obtained at 50 °C and 100 °C, Arrhenius analysis yields activation energies of E a (B) = 24.8 ± 0.3 kJ mol⁻¹ and E a (Si) = 14.4 ± 0.2 kJ mol⁻¹, similar or slightly lower than those previously reported for two other compositions of LaBS glasses. No secondary phases or alteration layers were detected by SEM-EDX or pXRD. These results establish one of the first temperature-dependent kinetic datasets for LaBS glass dissolution, providing quantitative parameters to inform mechanistic corrosion models and predictive simulations of glass degradation in geological disposal environments.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Towards utility-scale electronic structure with sample-based quantum bootstrap embedding

One of the main applications for which quantum computers are hoped to find utility is in simulating ground state energies and other observables of molecular chemical systems. The recently proposed sample-based diagonalization method is a readily implementable method for this task on current-day hardware using short circuit depths and has been demonstrated on as many as 85 qubits in recent studies. In this work, we combine the recently proposed quantum bootstrap embedding (QBE) method with sampled-based diagonalization (QBE-SQD) and present the first benchmarking study of the QBE method on real quantum hardware, ibm_pittsburgh, a Heron r3 processor with 156 qubits. Our test system is a hydrogen ring with 8 hydrogen atoms in the cc-pVDZ basis. We show that for this system, QBE-SQD using an active space of (8e, 19o) per fragment with a 43 qubit footprint produces a ground state energy accuracy which exceeds that of an SQD calculation with an (8e, 30o) active space with a 67 qubit footprint when using a comparable number of Slater determinants. This demonstrates that the use of quantum bootstrap embedding techniques is a promising path towards extending the capabilities of state-of-the-art quantum eigensolvers on near-term devices.

Bierman, Joel [North Carolina State University, Ra↗
DOE OSTI2024

High-Fidelity Multiphysics Modeling of a Heat Pipe Microreactor Using BlueCrab

Researchers who are actively developing nuclear microreactors are planning to employ innovative designs and features using traditional commercial modeling tools that may be inadequate for their design and licensing activities. The codes developed under the U.S. Department of Energy Office of Nuclear Energy Advanced Modeling and Simulation (NEAMS) program provide flexibility in terms of geometry modeling and multiphysics coupling and are particularly well suited for modeling novel microreactor concepts. To test the maturity of these codes, this paper introduces a conceptual heat pipe microreactor (HP-MR) designed to gather various technologies of interest to microreactor developers such as control drums, heat pipes, and hydride moderators. Here, the objective of this effort is to demonstrate NEAMS tools capability to perform high-fidelity multiphysics simulations, using coupled neutronics (via the Griffin code), heat conduction (via the BISON code), heat pipe modeling (via the Sockeye code), and hydrogen redistribution in hydride metal moderator (via the SWIFT code). Codes are coupled in-memory through the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, which permits flexible multiphysics data transfer schemes. The analysis confirmed two key aspects of the HP-MR concept: (1) its ability to follow the power load requested from the heat pipe and (2) its ability to avoid heat pipe cascading failure unless designed with high power close to operating failure limits of its heat pipes. The developed computational model was distributed publicly on the Virtual Test Bed for training purposes to accelerate adoption by industry and to provide a high-fidelity multiphysics solution for benchmarking against other tools. Additional multiphysics analyses including other transients and coupled physics were identified as necessary future work, together with a focus on validating multiphysics behavior against experiments.

Microreactor↗
DOE OSTI2023

The HPC Container Experience on the Summit Supercomputer

Containers are seeing widespread use in the world of High Performance Computing, with many HPC Centers either providing their own containerization solution or adopting existing ones like Singularity and Apptainer. The demand for containerization options come from users who want to take advantage of the portability and reproducibility containers can provide, as well as being able to build and use applications that are only distributed in container form or are otherwise unsuited to natively run in an HPC environment. The users served by the Oak Ridge Leadership Computing Facility are no exception. We go over the past and current containerization offerings at the Oak Ridge Leadership Computing Facility, mainly focusing on the Summit supercomputer. We arrive at using a combination of Podman and Singularity to allow users to build and run containers directly on Summit, without requiring external resources or hardware for any step of the process. We look at a couple of projects running on Summit that greatly benefited from being able to use containers on Summit. And we compare benchmarks running natively and in containers on Summit at different scales, observing minimal performance difference and consistent behavior across all tests.

Abraham, Subil↗