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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

The 0E2 benchmarks for PWR UO 2 decay heat: an analysis from the NEA WPNCS

This paper presents the work performed in the subgroup 16 of the Working Party for Nuclear Criticality Safety (WPNCS) of the OECD Nuclear Energy Agency. The main goal was to define two decay heat benchmarks for Spent Nuclear Fuel (one pincell and one assembly), perform calculations and compare and analyze the results in light of existing calorimetric measurements. The selected case is the PWR UO2 assembly 0E2, irradiated at the Ringhals-3 reactor and measured at the Clab facility in Sweden. In total, 21 institutes worldwide participated to the exercise, leading to 55 calculated results (named C). It was found that the measured decay heat values (E) can be satisfactorily reproduced with two-dimensional assembly calculations, leading to an average C/E value of 0.99, with an uncertainty (or one standard deviation) of ±0.01.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

A non-intrusive framework using acoustic signals and deep learning for boiling diagnostics in visual-limited environments

Accurate monitoring of boiling heat transfer is critical for safeguarding high-power systems operating in environments where conventional optical diagnostics are hindered by radiation fields or restricted visual accessibility. This study presents a non-intrusive framework that integrates hydroacoustic sensing with deep learning to infer near-wall boiling characteristics and enable predictive thermal assessment without visual access. In a prototypical subcooled flow-boiling facility representative of the Isotope Production Facility (IPF) at Los Alamos, hydrophones capture boiling-induced acoustic emissions that are transformed into background-removed Short-Time Fourier Transform (STFT) spectrograms. A convolutional neural network (CNN) then regresses heat flux, wall superheat, and key bubble parameters directly from these spectrograms. The CNN achieved predictive accuracy under nominal conditions and demonstrated robustness and generalization under acoustic noise for Signal-to-Noise Ratios (SNRs) down to approximately 0 dB. When integrated into an ANSYS CFX wall-boiling model, the acoustically inferred parameters reproduced boiling curve and critical heat flux (CHF) values consistent with image-based benchmarks. Furthermore, the model retained reliable performance under moderate variations in bulk temperature, flow rate, and hydrophone placement, confirming its generalizability across practical boundary conditions. These results demonstrate the feasibility of hydroacoustic-based deep learning as a viable path toward real-time, radiation-tolerant boiling diagnostics and predictive thermal safety assessment in inaccessible systems such as the IPF.

42 ENGINEERING↗
DOE OSTI2024

TEX-HEU: Integral Experiment Execution with Polyethylene at Very Low Temperatures

The low-temperature variant of the TEX HEU (called Low-Temperature TEX or sometimes LT TEX) campaign is a highly anticipated and necessary experimental series by the greater nuclear science community. Fundamentally, the need for low-temperature integral experiments is required to perform validation of cross sections below room temperature. There has been substantial international interest in low-temperature benchmarks to validate below room temperature cross sections, namely talks given at the 2019 International Conference on Nuclear Criticality (ICNC): UK (Watson, 2019), France (Milin, 2019), and UK (Gan & Wilson, 2019). Additionally, NCSP funded thermal scattering laws (TSLs) were produced by North Carolina State University and require low-temperature benchmarks to validate them. Validation of low-temperature cross sections is also necessary for criticality safety applications. One particularly important application is to ensure that during transportation, fissile materials must remain subcritical under normal ambient conditions which is defined as temperatures down to -40°C/°F by the United States 10 CRF 71 as well as a regulation put forward by the International Atomic Energy Agency (IAEA).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

LC-Opt: Benchmarking Reinforcement Learning and Agentic AI for End-to-End Liquid Cooling Optimization in Data Centers

Liquid cooling is critical for thermal management in high-density data centers with the rising AI workloads. However, machine learning-based controllers are essential to unlock greater energy efficiency and reliability, promoting sustainability. We present LC-Opt, a Sustainable Liquid Cooling (LC) benchmark environment, for reinforcement learning (RL) control strategies in energy-efficient liquid cooling of high-performance computing (HPC) systems. Built on the baseline of a high-fidelity digital twin of Oak Ridge National Lab's Frontier Supercomputer cooling system, LC-Opt provides detailed Modelica-based end-to-end models spanning site-level cooling towers to data center cabinets and server blade groups. RL agents optimize critical thermal controls like liquid supply temperature, flow rate, and granular valve actuation at the IT cabinet level, as well as cooling tower (CT) setpoints through a Gymnasium interface, with dynamic changes in workloads. This environment creates a multi-objective real-time optimization challenge balancing local thermal regulation and global energy efficiency, and also supports additional components like a heat recovery unit (HRU). We benchmark centralized and decentralized multi-agent RL approaches, demonstrate policy distillation into decision and regression trees for interpretable control, and explore LLM-based methods that explain control actions in natural language through an agentic mesh architecture designed to foster user trust and simplify system management. LC-Opt democratizes access to detailed, customizable liquid cooling models, enabling the ML community, operators, and vendors to develop sustainable data center liquid cooling control solutions.

Naug, Avisek [Hewlett Packard Enterprise]↗
DOE OSTI2026

Forest aboveground biomass estimation through integration of sentinel-2 and PALSAR-2 time series: assessing models trained on GEDI and field inventory benchmarks

Accurate and spatially explicit forest Aboveground Biomass (AGB) mapping through remote sensing is critical for quantifying terrestrial carbon stocks and informing effective forest management strategies. However, AGB estimation in dense forests with complex terrain remains challenging due to satellite sensor signal saturation problem (saturation issue occurs in high biomass forests), structural complexity, and limited ground truth for calibration. This study presents a novel framework that integrates multi-temporal Sentinel-2 optical imagery, ALOS PALSAR-2 Synthetic Aperture Radar (SAR) data, and topographic variables with explainable Machine Learning to map AGB across mountainous forests within subtropical and temperate oceanic climate zones of Mexico. We evaluate the effects of temporal granularity and sensor synergy by comparing multiple temporal inputs and sensor configurations (Sentinel-2, PALSAR-2, and their fusion), and assess model performance using two reference datasets: NASA GEDI LiDAR-derived biomass and Mexico’s National Forest and Soil Inventory (INFyS). Our results showed that models trained on INFyS consistently outperformed those trained on GEDI, highlighting limitations in GEDI’s reliability in biomass estimates within this study region. Furthermore, the integration of Sentinel-2 and PALSAR-2 provided improved predictions compared to single-sensor models, particularly when combined with temporally explicit yearly statistics. The best-performing model, which was trained on INFyS data, and considered both Sentinel-2 and PALSAR-2 yearly statistics, as well as topographic variables, achieved an R2 of 0.64, RMSE of 51.10 Mg/ha, and relative RMSE (rRMSE) of 58.69%. Explainable ML analysis identified Sentinel-2 spectral indices and topographic features as key predictors, while PALSAR-2 metrics provided complementary information, partially mitigating saturation effects in high-biomass areas. Specifically, integrating both sensors substantially improved AGB estimation in high biomass forest (≥200 Mg/ha), yielding 98% gains over optical-only model, with resulting estimates exceeding GEDI L4B by 29% and ESA-CCI-BIOMASS by 174%. Terrain-stratified analysis indicated close agreement with GEDI in low-slope areas, with increasing divergence as slope steepness increased, while estimates remained consistently higher than ESA-CCI-BIOMASS across all slope classes. The proposed approach advances multi-sensor fusion and temporal feature engineering for AGB mapping using open-access satellite datasets, providing a scalable and reproducible framework for annual biomass monitoring in topographically complex mountainous forests. The resulting 25 m resolution biomass product has the potential to provide spatially detailed information for forest monitoring and may support applications in carbon accounting and forest management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Benchmarking the Performance of Neuromorphic and Spiking Neural Network Simulators

Software simulators play a critical role in the development of new algorithms and system architectures in any field of engineering. Neuromorphic computing, which has shown potential in building brain-inspired energy-efficient hardware, suffers a slow-down in the development cycle due to a lack of flexible and easy-to-use simulators of either neuromorphic hardware itself or of spiking neural networks (SNNs), the type of neural network computation executed on most neuromorphic systems. While there are several openly available neuromorphic or SNN simulation packages developed by a variety of research groups, they have mostly targeted computational neuroscience simulations, and only a few have targeted small-scale machine learning tasks with SNNs. Evaluations or comparisons of these simulators have often targeted computational neuroscience-style workloads. In this work, we seek to evaluate the performance of several publicly available SNN simulators with respect to non-computational neuroscience workloads, in terms of speed, flexibility, and scalability. We evaluate the performance of the NEST, Brian2, Brian2GeNN, BindsNET and Nengo packages under a common front-end neuromorphic framework. Our evaluation tasks include a variety of different network architectures and workload types to mimic the computation common in different algorithms, including feed-forward network inference, genetic algorithms, and reservoir computing. We also study the scalability of each of these simulators when running on different computing hardware, from single core CPU workstations to multi-node supercomputers. Our results show that the BindsNET simulator has the best speed and scalability for most of the SNN workloads (sparse, dense, and layered SNN architectures) on a single core CPU. However, when comparing the simulators leveraging the GPU capabilities, Brian2GeNN outperforms the others for these workloads in terms of scalability. NEST performs the best for small sparse networks and is also the most flexible simulator in terms of reconfiguration capability NEST shows a speedup of at least 2x compared to the other packages when running evolutionary algorithms for SNNs. The multi-node and multi-thread capabilities of NEST show at least 2x speedup compared to the rest of the simulators (single core CPU or GPU based simulators) for large and sparse networks. We conclude our work by providing a set of recommendations on the suitability of employing these simulators for different tasks and scales of operations. We also present the characteristics for a future generic ideal SNN simulator for different neuromorphic computing workloads.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A High-Fidelity Cyber-Physical Testbed-Based Benchmarking Dataset For Testing Operational Technology Specific Intrusion Detection Systems

Quality datasets serve a critical purpose in cyber security research. Data is needed to understand system behavior and develop security controls to protect critical systems. However, for critical infrastructure operational environments there is a lack of available datasets to study because of the high cost and specialized capabilities necessary to generate them. This paper documents the development of a dataset of high fidelity hardware in the loop laboratory simulated models of electric and natural gas distribution systems with real cyber attack test cases. A deep dive discussion for the experimental setup and controls for generating the data is provided along with observations from using the data in evaluating intrusion detection approaches.

Ashok, Aditya↗
DOE OSTI2023

Completion of Critical Experiments with Molybdenum Sleeves at Sandia

Sandia National Laboratories (SNL) and the Institut de Radioprotection et de Sûreté Nucléaire (IRSN) have collaborated on the design and execution of a set of critical experiments that explore the effects of molybdenum in water moderated fuel-rod arrays. The molybdenum is included as sleeves (tubes) on some of the fuel rods in the arrays. The fuel used in the experiments is known at Sandia as the Seven Percent Critical Experiment (7uPCX) fuel. This fuel has been used is several published benchmark evaluations in including LEU-COMP-THERM-78 and LEU-COMP THERM-080.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

An uncertainty quantification method relevant to material test reactors

Within material test reactor calculations, energy dependent flux and reaction rate uncertainties are typically not quantified when performing as-run analyses to determine the neutron field experienced by the experiment. When high fidelity Monte-Carlo codes are used in such analyses, straight forward methods to calculate output uncertainties are not available, instead expert opinion is used to postulate computational uncertainties. New methods to propagate uncertainties through these high fidelity simulations are available when sufficient computational power is available. A tool is developed here for sampling any part of an MCNP input from random distributions to determine output uncertainties based on those inputs. Another tool is developed to sample nuclear data cross-section in ACE format using multi-group nuclear data covariances. The Total Monte-Carlo Method and Gesellschaft für Anlagen-und Reaktorsicherheit method (GRS) are implemented and compared to one another as well as MCNP sensitivity and uncertainty calculations. The methods were applied to the Godiva critical sphere k-eigenvalue, the UAM pin-cell benchmark energy dependent flux and reaction rates, and the Advanced Test Reactor energy dependent flux within an experimental location. Furthermore, the two methods agree well, with GRS allowing for an order of magnitude speedup for reaction rate uncertainty calculations and several orders of magnitude for eigenvalue uncertainty calculations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Validation of SAS4A/SASSYS-1 for predicting steady-state single-phase natural circulation

The validation of system analysis codes for nuclear reactor systems is required for the development and application of these computational tools. Designed as a comprehensive system analysis code for advanced nuclear reactors, SAS4A/SASSYS-1 requires validation of its physics model for capturing single-phase natural circulation behavior. To support the validation of SAS4A/SASSYS-1, high-precision experiments are performed capturing steady-state single-phase natural circulation on a scaled facility with comprehensive instrumentation. In this work, dedicated tests are performed quantifying the critical modeling parameters, and a single-phase natural circulation benchmark dataset is obtained with well-documented uncertainty and comprehensive facility description. The validation is then performed against the dataset examining the capability of SAS4A/SASSYS-1 in simulating steady-state single-phase natural circulation. The experimental facility is modeled in the candidate code. Solution verification is performed using Richardson-extrapolation-based estimators which quantify and restrict numerical errors from discretization. Input uncertainty provided by the benchmark dataset is forward propagated through the candidate code, quantifying the output uncertainty in a Monte Carlo approach. The composition of the output uncertainty is also quantified through a variance-based sensitivity analysis. With the uncertainty quantified for each individual condition, a detailed comparison between the simulation results and experimental data is performed covering the whole dataset. The results show consistent agreement for all primary parameters. The current validation activity provides a valuable benchmark dataset for the validation of system analysis codes in capturing single-phase natural circulation and demonstrates satisfactory prediction capability of SAS4A/SASSYS1 for steady-state single-phase natural circulation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Barrier Function-based Reinforcement Learning for Emergency Control of Power Systems

Under voltage load shedding has been considered as a standard and effective measure to recover the voltage stability of the electric power grid under emergency and severe conditions. However, this scheme usually trips a massive amount of load which can be unnecessary and harmful to customers. Recently, deep reinforcement learning (RL) has been regarded and adopted as a promising approach that can significantly reduce the amount of load shedding. However, like most existing machine learning (ML)-based control techniques, RL control usually cannot guarantee the safety of the systems under control. In this paper, we introduce a novel safe RL method for load shedding of power systems, that can enhance the safe voltage recovery of the electric power grid after experiencing faults. Unlike standard RL method, the safe RL method integrates a Barrier function into the reward function. Consequently, the optimal control policy can render the power system to avoid the safety bounds. This method is general and can be applied to other safety-critical control problems. Numerical simulations on the 39-bus IEEE benchmark is performed to demonstrate the effectiveness of the proposed safe RL emergency control, as well as its adaptive capability to faults not seen in the training.

Vu, Thanh Long↗
DOE OSTI2022

LLNL FY21 NCSP Overview [Slides]

This presentation details the impacts of the COVID-19 pandemic on LLNL in fiscal year 2021, as well as budget information and major accomplishments for the lab in IE, analytical methods, nuclear data, training and education, and IPD.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2003

Particle Engulfment and Pushing By Solidifying Interfaces - Recent Theoretical and Experimental Developments

The objective of the work on Particle Engulfment and Pushing by Solidifying Interfaces (PEP) include: 1) to obtain fundamental understanding of the physics of particle pushing and engulfment, 2) to develop mathematical models to describe the phenomenon, and 3) to perform critical experiments in the microgravity environment of space to provide benchmark data for model validation. Successful completion of this project will yield vital information relevant to a diverse area of terrestrial applications. With PEP being a long term research effort, this report will focus on advances in the theoretical treatment of the solid/liquid interface interaction with an approaching particle, experimental validation of some aspects of the developed models, and the experimental design aspects of future experiments to be performed on board the International Space Station.

Stefanescu, D. M.↗
DOE OSTI2025

Integral Experiment Validation of Hafnium with TEX-HEU and TEX-Hf [Slides]

Lead by Lawrence Livermore National Laboratory under the U.S. Department of Energy's Nuclear Criticality Safety Program. The goal of TEX is to provide integral benchmark experiments than span the entire neutron energy spectrum and incorporate high-priority materials. TEX includes two test bed configurations providing a baseline for comparison to better understand the contribution of additional materials

Highly Enriched Uranium↗
DOE OSTI2021

Integral Measurement of the 237 Np($n$, γ) 238 Np Cross Section with Fast Neutrons

Accurate knowledge of the 237 Np($n$; γ) 238 Np cross section at fast neutron energies is important for applied nuclear science. The presently available experimental data has large disagreements in the fast neutron region. A measurement of the 237 Np($n$; γ) 238 Np cross section was performed using a well characterized fast neutron source and the results was compared with previous measurements and current nuclear data evaluations. This will provide an integral measurement that can be used as a benchmark for current evaluations. Multiple samples of 237 Np were irradiated in the Godiva-IV critical assembly. Following the irradiation, the samples placed in a γ-ray counting setup and the γ-rays emitted from the decay of 238 Np were measured over a time period of approximately 7 days. Multiple γ-ray decay branches of 238 Np were observed. The observed activity of 238 Np was used to calculate the amount of 238 Np produced during the irradiation via the 237 Np($n$; γ) 238 Np and an integral cross section of 359(10) mb was measured. The 238 Np half-life has been measured with a result of 50.34(7) hours. An integral measurement of the 237 Np($n$; γ) 238 Np cross section has been measured. This result has been compared to the calculated integral cross section using the ENDF/BVIII.0 and JENDL-4.0 evaluated cross sections and found to be in good agreement with ENDF/BVIII.0 value of 367 mb. The JENDL-4.0 evaluation yields a value of 312 mb, which is significantly lower than the measured cross section.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1973

Experimental detailed power distribution in a fast spectrum thermionic reactor fuel element at the core/BeO reflector interface region

A zero-power critical assembly was designed, constructed, and operated for the prupose of conducting a series of benchmark experiments dealing with the physics characteristics of a UN-fueled, Li-7-cooled, Mo-reflected, drum-controlled compact fast reactor for use with a space-power conversion system. The critical assembly was modified to simulate a fast spectrum advanced thermionics reactor by: (1) using BeO as a reflector in place of some of the existing molybdenum, (2) substituting Nb-1Zr tubing for some of the existing Ta tubing, and (3) inserting four full-scale mockups of thermionic type fuel elements near the core and BeO reflector boundary. These mockups were surrounded with a buffer zone having the equivalent thermionic core composition. In addition to measuring the critical mass of this thermionic configuration, a detailed power distribution in one of the thermionic element stages in the mixed spectrum region was measured. A power peak to average ratio of two was observed for this fuel stage at the midplane of the core and adjacent to the reflector. Also, the power on the outer surface adjacent to the BeO was slightly more than a factor of two larger than the power on the inside surface of a 5.08 cm (2.0 in.) high annular fuel segment with a 2.52 cm (0.993 in. ) o.d. and a 1.86 cm (0.731 in.) i.d.

Klann, P. G.↗