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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Comparison of Corcos-Based and Experimentally-Derived Coherence Factors for Buffet Forcing Function Estimation

In this paper, high-spatial-resolution unsteady Pressure Sensitive Paint (uPSP) data are utilized to compare two methods for panel Buffet Forcing Function (BFF) estimation for the Space Launch System (SLS). Such methods are based on discrete pressure measurements within a panel but employ coherence factors to account for partially-correlated fluctuating pressures across the whole panel. In one method, coherence factors are derived based on the Corcos model, whereas the second method utilizes experimentally-derived coherence factors. To simulate discrete measurements using uPSP data, suitable subsets of the data are extracted. When full uPSP resolution is retained, uPSP data provide a benchmark to assess discrete-measurement-based methods. The analysis focuses on the peak SLS buffet environment located downstream of the Forward Attachment Hardware (FAH) between the core stage and solid rocket boosters. Trends of Corcos-based and experimentally-derived coherence factors are in reasonable agreement with the benchmark. However, at certain frequencies, experimentally-derived coherence factors are sensitive to the separation distance between pressure measurements utilized to compute coherence lengths. Such sensitivity originates from deviation of the experimentally-based coherence function from an exponential decay assumption. On the other hand, the present implementation of the Corcos model fails to capture certain nonturbulent boundary layer related environments, such as a subharmonic of FAH vortex-shedding. For all methods presented in this paper, at near transonic conditions, increased pressure coherence and spatial nonuniformity lead to BFF overestimation and sensitivity to the pressure measurement location within the panel.

transonic buffet↗
DOE OSTI2026

The Role of Nuclear Data Sensitivities in Prompt α-Eigenvalue Predictions of Delayed Critical Benchmarks

Alpha (α) eigenvalues, which describe the logarithmic time derivative of the neutron population in a multiplying system, are integral to time-dependent behavior and diagnostic applications. However, uncertainties in the evaluated nuclear data can significantly impact the accuracy of transport simulations for such quantities. This work explores the use of machine learning models to predict two key outputs, α-eigenvalues and keff bias, using input features derived from α-eigenvalue sensitivities to nuclear data. The criticality safety benchmark models used in this study come from the International Handbook of Evaluated Criticality Safety Benchmark Experiments. Three models, random forest, XGBoost, and NGBoost, are trained on both energy-resolved and energy-summed α sensitivities. For the α-eigenvalue bias prediction, NGBoost achieved the highest R 2 (0.9476) using energy-resolved features, while XGBoost performed best using summed sensitivities. In contrast, when predicting the keff bias, all the models showed moderate predictive capability (best R 2 ≈ 0.72), as the mapping from the static α-sensitivities to the static keff bias was less direct. SHAP (SHapley Additive exPlanations) analysis was used to interpret the model predictions. Across both prediction tasks, the features associated with neutron capture [H-1 (n, γ)], uranium scattering reactions (such as 235 U elastic/inelastic), and actinide capture/fission reactions (such as 239 Pu and 234 U) were consistently identified as the most impactful. This highlights the key role of specific nuclear reactions and energy ranges in shaping both time-dependent and steady-state criticality behavior. These results demonstrated that α-sensitivities, despite being computed for time-dependent metrics, can provide valuable insights for predicting both α-eigenvalues and the keff bias. Moreover, machine learning models offer a promising pathway for uncovering important nuclear data dependencies and guiding future data evaluation efforts.

Nuclear data↗
NASA NTRS2006

Sensor to User - NASA/EOS Data for Coastal Zone Management Applications Developed from Integrated Analyses: Verification, Validation and Benchmark Report

The NASA Applied Sciences Program seeks to transfer NASA data, models, and knowledge into the hands of end-users by forming links with partner agencies and associated decision support tools (DSTs). Through the NASA REASoN (Research, Education and Applications Solutions Network) Cooperative Agreement, the Oceanography Division of the Naval Research Laboratory (NRLSSC) is developing new products through the integration of data from NASA Earth-Sun System assets with coastal ocean forecast models and other available data to enhance coastal management in the Gulf of Mexico. The recipient federal agency for this research effort is the National Oceanic and Atmospheric Administration (NOAA). The contents of this report detail the effort to further the goals of the NASA Applied Sciences Program by demonstrating the use of NASA satellite products combined with data-assimilating ocean models to provide near real-time information to maritime users and coastal managers of the Gulf of Mexico. This effort provides new and improved capabilities for monitoring, assessing, and predicting the coastal environment. Coastal managers can exploit these capabilities through enhanced DSTs at federal, state and local agencies. The project addresses three major issues facing coastal managers: 1) Harmful Algal Blooms (HABs); 2) hypoxia; and 3) freshwater fluxes to the coastal ocean. A suite of ocean products capable of describing Ocean Weather is assembled on a daily basis as the foundation for this semi-operational multiyear effort. This continuous realtime capability brings decision makers a new ability to monitor both normal and anomalous coastal ocean conditions with a steady flow of satellite and ocean model conditions. Furthermore, as the baseline data sets are used more extensively and the customer list increased, customer feedback is obtained and additional customized products are developed and provided to decision makers. Continual customer feedback and response with new improved products are required between the researcher and customer. This document details the methods by which these coastal ocean products are produced including the data flow, distribution, and verification. Product applications and the degree to which these products are used successfully within NOAA and coordinated with the Mississippi Department of Marine Resources (MDMR) is benchmarked.

Hall, Callie↗
NASA NTRS2012

NASA IMAGESEER: NASA IMAGEs for Science, Education, Experimentation and Research

A number of web-accessible databases, including medical, military or other image data, offer universities and other users the ability to teach or research new Image Processing techniques on relevant and well-documented data. However, NASA images have traditionally been difficult for researchers to find, are often only available in hard-to-use formats, and do not always provide sufficient context and background for a non-NASA Scientist user to understand their content. The new IMAGESEER (IMAGEs for Science, Education, Experimentation and Research) database seeks to address these issues. Through a graphically-rich web site for browsing and downloading all of the selected datasets, benchmarks, and tutorials, IMAGESEER provides a widely accessible database of NASA-centric, easy to read, image data for teaching or validating new Image Processing algorithms. As such, IMAGESEER fosters collaboration between NASA and research organizations while simultaneously encouraging development of new and enhanced Image Processing algorithms. The first prototype includes a representative sampling of NASA multispectral and hyperspectral images from several Earth Science instruments, along with a few small tutorials. Image processing techniques are currently represented with cloud detection, image registration, and map cover/classification. For each technique, corresponding data are selected from four different geographic regions, i.e., mountains, urban, water coastal, and agriculture areas. Satellite images have been collected from several instruments - Landsat-5 and -7 Thematic Mappers, Earth Observing-1 (EO-1) Advanced Land Imager (ALI) and Hyperion, and the Moderate Resolution Imaging Spectroradiometer (MODIS). After geo-registration, these images are available in simple common formats such as GeoTIFF and raw formats, along with associated benchmark data.

Le Moigne, Jacqueline↗
NASA NTRS2017

Benchmarking the Collocation Stand-Alone Library and Toolkit (CSALT)

This paper describes the processes and results of Verification and Validation (VV) efforts for the Collocation Stand Alone Library and Toolkit (CSALT). We describe the test program and environments, the tools used for independent test data, and comparison results. The VV effort employs classical problems with known analytic solutions, solutions from other available software tools, and comparisons to benchmarking data available in the public literature. Presenting all test results are beyond the scope of a single paper. Here we present high-level test results for a broad range of problems, and detailed comparisons for selected problems.

trajectory optimization↗
NASA NTRS2017

Benchmarking the Collocation Stand-Alone Library and Toolkit (CSALT)

This paper describes the processes and results of Verification and Validation (V&V) efforts for the Collocation Stand Alone Library and Toolkit (CSALT). We describe the test program and environments, the tools used for independent test data, and comparison results. The V&V effort employs classical problems with known analytic solutions, solutions from other available software tools, and comparisons to benchmarking data available in the public literature. Presenting all test results are beyond the scope of a single paper. Here we present high-level test results for a broad range of problems, and detailed comparisons for selected problems.

optimal control↗
NASA NTRS2010

Experimental Creep Life Assessment for the Advanced Stirling Convertor Heater Head

The United States Department of Energy is planning to develop the Advanced Stirling Radioisotope Generator (ASRG) for the National Aeronautics and Space Administration (NASA) for potential use on future space missions. The ASRG provides substantial efficiency and specific power improvements over radioisotope power systems of heritage designs. The ASRG would use General Purpose Heat Source modules as energy sources and the free-piston Advanced Stirling Convertor (ASC) to convert heat into electrical energy. Lockheed Martin Corporation of Valley Forge, Pennsylvania, is integrating the ASRG systems, and Sunpower, Inc., of Athens, Ohio, is designing and building the ASC. NASA Glenn Research Center of Cleveland, Ohio, manages the Sunpower contract and provides technology development in several areas for the ASC. One area is reliability assessment for the ASC heater head, a critical pressure vessel within which heat is converted into mechanical oscillation of a displacer piston. For high system efficiency, the ASC heater head operates at very high temperature (850 C) and therefore is fabricated from an advanced heat-resistant nickel-based superalloy Microcast MarM-247. Since use of MarM-247 in a thin-walled pressure vessel is atypical, much effort is required to assure that the system will operate reliably for its design life of 17 years. One life-limiting structural response for this application is creep; creep deformation is the accumulation of time-dependent inelastic strain under sustained loading over time. If allowed to progress, the deformation eventually results in creep rupture. Since creep material properties are not available in the open literature, a detailed creep life assessment of the ASC heater head effort is underway. This paper presents an overview of that creep life assessment approach, including the reliability-based creep criteria developed from coupon testing, and the associated heater head deterministic and probabilistic analyses. The approach also includes direct benchmark experimental creep assessment. This element provides high-fidelity creep testing of prototypical heater head test articles to investigate the relevant material issues and multiaxial stress state. Benchmark testing provides required data to evaluate the complex life assessment methodology and to validate that analysis. Results from current benchmark heater head tests and newly developed experimental methods are presented. In the concluding remarks, the test results are shown to compare favorably with the creep strain predictions and are the first experimental evidence for a robust ASC heater head creep life.

Krause, David L.↗
NASA NTRS2018

Towards the Prediction of the Mars 2020 Heatshield Material Response

Introduction: NASA’s next mission to Mars, the Mars 2020, will use the same heatshield of the Mars Science Laboratory (MSL) for thermal protection during entry, descent and landing. The heatshield is a tiled system made of Phenolic Impregnated Carbon Ablators (PICA) blocks [1]. PICA is a lightweight carbon fiber/polymeric resin material that offers excellent performances for protecting probes during planetary entry. The Mars Entry Descent and Landing Instrument (MEDLI) suite on MSL offers unique in-flight validation data for models of atmospheric entry and material response. MEDLI recorded, among others, time-resolved in-depth temperature data of PICA using thermocouple sensors assembled in the MEDLI Integrated Sensor Plugs (MISP). The objective of this work is to compare the thermal response of the MSL heatshield to the MISP flight data. In preparation to Mars 2020 post-flight analysis, the predictive material response capability is benchmarked against MEDLI flight data.

heatshield↗
NASA NTRS1984

The performance of differential VLBI delay during interplanetary cruise

Project Voyager radio metric data are used to evaluate the orbit determination abilities of several data strategies during spacecraft interplanetary cruise. Benchmark performance is established with an operational data strategy of conventional coherent doppler, coherent range, and explicitly differenced range data from two intercontinental baselines to ameliorate the low declination singularity of the doppler data. Employing a Voyager operations trajectory as a reference, the performance of the operational data strategy is compared to the performances of data strategies using differential VLBI delay data (spacecraft delay minus quasar delay) in combinations with the aforementioned conventional data types. The comparison of strategy performances indicates that high accuracy cruise orbit determination can be achieved with a data strategy employing differential VLBI delay data, where the quantity of coherent radio metric data has been greatly reduced.

Moultrie, B.↗
NASA NTRS1984

The Performance of Differential VLBI Delay During Interplanetary Cruise

Project Voyager radio metric data are used to evaluate the orbit determination utilities of several data strategies during spacecraft interplanetary cruise. Benchmark performance is established with an operational data strategy of conventional coherent Doppler, coherent range, and explicitly differenced range data from two intercontinental baselines to ameliorate the low declination singularity of the Doppler data. Employing a Voyager operations trajectory as a reference, the performance of the operational data strategy is compared to the performances of data strategies using differential VLBI delay data (spacecraft delay minus quasar delay) in combination with the aforementioned conventional data types. The comparison of strategy performances indicates that high accuracy cruise orbit determination can be achieved with a data strategy employing differential VLBI delay data, where the quantity of coherent radio metric data was reduced by over 95% with a concurrent 90% reduction in the DSN time allocated to radio metric data acquisition.

Moultrie, B.↗
NASA NTRS2015

Between a Map and a Data Rod

A Digital Divide has long stood between how NASA and other satellite-derived data are typically archived (time-step arrays or maps) and how hydrology and other point-time series oriented communities prefer to access those data. In essence, the desired method of data access is orthogonal to the way the data are archived. Our approach to bridging the Divide is part of a larger NASA-supported data rods project to enhance access to and use of NASA and other data by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) Hydrologic Information System (HIS) and the larger hydrology community. Our main objective was to determine a way to reorganize data that is optimal for these communities. Two related objectives were to optimally reorganize data in a way that (1) is operational and fits in and leverages the existing Goddard Earth Sciences Data and Information Services Center (GES DISC) operational environment and (2) addresses the scaling up of data sets available as time series from those archived at the GES DISC to potentially include those from other Earth Observing System Data and Information System (EOSDIS) data archives. Through several prototype efforts and lessons learned, we arrived at a non-database solution that satisfied our objectivesconstraints. We describe, in this presentation, how we implemented the operational production of pre-generated data rods and, considering the tradeoffs between length of time series (or number of time steps), resources needed, and performance, how we implemented the operational production of on-the-fly (virtual) data rods. For the virtual data rods, we leveraged a number of existing resources, including the NASA Giovanni Cache and NetCDF Operators (NCO) and used data cubes processed in parallel. Our current benchmark performance for virtual generation of data rods is about a years worth of time series for hourly data (9,000 time steps) in 90 seconds. Our approach is a specific implementation of the general optimal strategy of reorganizing data to match the desired means of access. Results from our project have already significantly extended NASA data to the large and important hydrology user community that has been, heretofore, mostly unable to easily access and use NASA data.

machine learning↗
NASA NTRS2011

A Critical Examination of Spatial Biases Between MODIS and MISR Aerosol Products - Application for Potential AERONET Deployment

AErosol RObotic NETwork (AERONET) data are the primary benchmark for evaluating satellite-retrieved aerosol properties. However, despite its extensive coverage, the representativeness of the AERONET data is rarely discussed. Indeed, many studies have shown that satellite retrieval biases have a significant degree of spatial correlation that may be problematic for higher-level processes or inverse-emissions-modeling studies. To consider these issues and evaluate relative performance in regions of few surface observations, cross-comparisons between the Aerosol Optical Depth (AOD) products of operational MODIS Collection 5.1 Dark Target (DT) and operational MODIS Collection 5.1 Deep Blue (DB) with MISR version 22 were conducted. Through such comparisons, we can observe coherent spatial features of the AOD bias while side-stepping the full analysis required for determining when or where either retrieval is more correct. We identify regions where MODIS to MISR AOD ratios were found to be above 1.4 and below 0.7. Regions where lower boundary condition uncertainty is likely to be a dominant factor include portions of Western North America, the Andes mountains, Saharan Africa, the Arabian Peninsula, and Central Asia. Similarly, microphysical biases may be an issue in South America, and specific parts of Southern Africa, India Asia, East Asia, and Indonesia. These results help identify high-priority locations for possible future deployments of both in situ and ground based remote sensing measurements. The Supplement includes a km1 file.

Shi, Y.↗
NASA NTRS2017

Benchmark Shock Tube Experiments for Radiative Heating Relevant to Earth Re-Entry

Detailed spectrally and spatially resolved radiance has been measured in the Electric Arc Shock Tube (EAST) facility for conditions relevant to high speed entry into a variety of atmospheres, including Earth, Venus, Titan, Mars and the Outer Planets. The tests that measured radiation relevant for Earth re-entry are the focus of this work and are taken from campaigns 47, 50, 52 and 57. These tests covered conditions from 8 km/s to 15.5 km/s at initial pressures ranging from 0.05 Torr to 1 Torr, of which shots at 0.1 and 0.2 Torr are analyzed in this paper. These conditions cover a range of points of interest for potential fight missions, including return from Low Earth Orbit, the Moon and Mars. The large volume of testing available from EAST is useful for statistical analysis of radiation data, but is problematic for identifying representative experiments for performing detailed analysis. Therefore, the intent of this paper is to select a subset of benchmark test data that can be considered for further detailed study. These benchmark shots are intended to provide more accessible data sets for future code validation studies and facility-to-facility comparisons. The shots that have been selected as benchmark data are the ones in closest agreement to a line of best fit through all of the EAST results, whilst also showing the best experimental characteristics, such as test time and convergence to equilibrium. The EAST data are presented in different formats for analysis. These data include the spectral radiance at equilibrium, the spatial dependence of radiance over defined wavelength ranges and the mean non-equilibrium spectral radiance (so-called 'spectral non-equilibrium metric'). All the information needed to simulate each experimental trace, including free-stream conditions, shock time of arrival (i.e. x-t) relation, and the spectral and spatial resolution functions, are provided.

Shock Tube↗
DOE OSTI2025

DOE FAIR Surrogate Benchmarks Supporting AI and Simulation Research (SBI Surrogate Benchmark Initiative) (Final Report)

Computational Science is being revolutionized by integrating AI and simulation and, in particular, by deep learning surrogate models that can replace all or part of traditional large‐scale HPC computations. Such surrogates can achieve remarkable performance improvements, as much as several orders of magnitude, and save both compute time and energy. The Surrogate Benchmark Initiative (SBI) project creates a community repository and FAIR (Findable, Accessible, Interoperable, and Reusable) data ecosystem for HPC application surrogate benchmarks. The SBI team comes from Argonne National Laboratory (ANL), Indiana University (IU), Rutgers University, the University of Tennessee, Knoxville (UTK), and the University of Virginia(UVA). SBI repositories include data, code, and all relevant collateral artifacts, that the science and engineering community needs to use and reuse these data sets and surrogates. SBI repositories generate active research from both participants in SBI and the broader AI and domain science communities. This project develops surrogates that use several different neural nets to learn and quickly infer the results of simulations and data systems and capture them as surrogate benchmarks with a rich set of metadata, covering. Data; Model; Metrics specification; Machine specification; Science, Speed, Power Results, We research FAIR metadata for these benchmarks. We develop application surrogate examples as benchmarks across many fields (ANL, UTK, IU, UVA). We also study non Surrogate benchmarks that have many common features and similar issues regarding FAIRness. We work with MLCommons (UVA, UTK), which is a major machine learning benchmarking activity where we get metadata ontologies, software, and benchmarks, benchmarks have datasets, models, and metadata, and they need a technical framework developed by UTK and Rutgers and deployed by UVA. We study features of Surrogates, including performance, training set size, and uncertainty quantification (Rutgers, UVA and IU).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

FAIR Surrogate Benchmarks Supporting AI and Simulation Research (Final Report)

Computational Science is being revolutionized by integrating AI and simulation and, in particular, by deep learning surrogate models that can replace all or part of traditional large‐scale HPC computations. Such surrogates can achieve remarkable performance improvements, as much as several orders of magnitude, and save both compute time and energy. The Surrogate Benchmark Initiative (SBI) project creates a community repository and FAIR (Findable, Accessible, Interoperable, and Reusable) data ecosystem for HPC application surrogate benchmarks. The SBI team comes from Argonne National Laboratory (ANL), Indiana University (IU), Rutgers University, the University of Tennessee, Knoxville (UTK), and the University of Virginia (UVA). SBI repositories include data, code, and all relevant collateral artifacts that the science and engineering community need to use and reuse these data sets and surrogates. SBI repositories generate active research from both the participants in SBI and the broad community of AI and domain scientists. This project develops surrogates that use several different neural nets to learn and quickly infer the results of simulations and data systems and captures them as surrogate benchmarks with a rich set of metadata covering: Data; Model; Metrics specification; Machine specification; and Science, Speed, and Power Results. We research FAIR metadata for these benchmarks. We develop application surrogate examples as benchmarks across many fields (ANL, UTK, IU, UVA). We also study non-Surrogate benchmarks that have many common features and similar issues as regards FAIRness. We work with MLCommons (UVA, UTK), which is a major machine learning benchmarking activity where we get metadata ontologies, software, and benchmarks, Benchmarks have datasets, models, and metadata and they need a technical framework developed by UTK and Rutgers and deployed by UVA. We study features of Surrogates including performance, training set size, and uncertainty quantification (Rutgers, UVA and IU).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Cooling Matters: Benchmarking Large Language Models and Vision-Language Models on Liquid-Cooled Versus Air-Cooled H100 GPU Systems

The unprecedented growth in artificial intelligence (AI) workloads, recently dominated by large language models (LLMs) and vision-language models (VLMs), has intensified power and cooling demands in data centers. This study benchmarks LLMs and VLMs on two HGX nodes, each with 8× NVIDIA H100 graphics processing units (GPUs), using liquid and air cooling. Leveraging GPU Burn, Weights & Biases, and IPMItool, we collect detailed thermal, power, and computation data. Results show that the liquid-cooled systems maintain GPU temperatures between 41-50$^\circ$C, while the air-cooled counterparts fluctuate between 54-72$^\circ$C under load. This thermal stability of liquid-cooled systems yields 17% higher performance (54 TFLOPs/ GPU vs. 46 TFLOPs/GPU), performance-per-watt, reduced energy overhead, and greater system efficiency than the air-cooled counterparts. These findings underscore the energy and sustainability benefits of liquid cooling, offering a compelling path forward for hyperscale data centers seeking to optimize AI infrastructure. https://github.com/iscaas/Cooling-Matters.

Latif, Imran↗
DOE OSTI2024

Nuclear Data Sensitivity/Uncertainty Studies of Tantalum-Reflected Systems

Tantalum (Ta) is a refractory metal with high melting point and high corrosion resistance. This makes it useful as a mold material for plutonium casting operations. Unfortunately, there are few criticality benchmarks with high nuclear data sensitivities. Given that, it is desirable to design new experiments to provide additional validation data for nuclear criticality safety, which is the objective of the Thales project. Multiple application models were used to represent process conditions (either normal conditions or credible upset conditions) both at Los Alamos National Laboratory (LANL) and Savannah River Site (SRS). A total of 40 application models were obtained. Given this, it was useful to down-select the number of needed application models. This work documents the down-selection based on several nuclear data sensitivity/uncertainty metrics. In addition to showing the result of the specific down-selection used for the Thales project, this work discusses potential ways to represent a large number of application models with a smaller number of proposed experiment designs.

42 ENGINEERING↗