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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Decision Support System

Forest-based value chains involve decisions that begin at the landscape level and extend through processing, product manufacturing, and end-use markets. However, these decisions are often made independently across sectors, with limited visibility into how upstream resource conditions, incentives, and land management choices influence downstream production systems. In forested regions of the United States, wildfire risk, fragmented ownership, and uncertain markets for low-value residues complicate efforts to align extraction, processing, and utilization decisions. Without tools that link these stages, stakeholders may overlook opportunities to improve resource utilization or inadvertently shift impacts elsewhere in the value chain. This repository introduces a decision support system (DSS) that applies a system-impact-analysis approach to forest biomass residues and co-products. The framework integrates forest inventory data, geospatial resource assessments, and economic modeling to evaluate how biomass extraction decisions influence downstream product pathways. By linking regional feedstock avail- ability with market incentives and processing options—such as fuels, wood products, or soil amendments like biochar—the tool allows decision-makers to compare value chain outcomes across multiple utilization strategies.

Davis, Maggie [Oak Ridge National Laboratory (ORNL↗
DOE OSTI2018

Utah FORGE: Drilling Data for Student Competition

Diagnostic drilling data (Pason log files) from Well 58-32 (previously labeled MU-ESW1), which was drilled near Milford Utah during Phase 2B of the FORGE Project to confirm geothermal reservoir characteristics met requirements for the final FORGE site. This dataset includes both raw, unprocessed drilling data recorded at 1Hz and a processed version downscaled to ~0.3m (1 ft) intervals. Diagnostic drilling data were collected to obtain useful information regarding the nature of the subsurface materials and performance of the drilling rig and drilling bits, including rate of penetration (ROP), weight on bit, temperature, pump pressure, etc.

15 GEOTHERMAL ENERGY↗
DOE OSTI2016

The 2016 Edition of the IRPhEP Handbook

The International Reactor Physics Experiment Evaluation Project (IRPhEP) was established to preserve integral reactor physics experimental data, including separate or special effects data for nuclear energy and technology applications. Over the past several decades, numerous experiments have been performed worldwide to support reactor operations, measurements, design, and safety. These experiments represent a large investment of infrastructure, expertise, and cost, and are valuable resources of data for present and future research. These valuable assets provide the basis for recording, development, and validation of methods. If the experimental data are lost, the high cost to repeat many of these measurements may be prohibitive. The purpose of the IRPhEP is to provide an extensively peer-reviewed set of reactor physics-related integral data that can be used by reactor designers and safety analysts to validate the analytical tools used to design next-generation reactors and establish the safety basis for operation of those reactors. Contributors from around the world collaborate in the evaluation and review of selected benchmark experiments for inclusion in the International Handbook of Evaluated Reactor Physics Benchmark Experiments (IRPhEP Handbook). Further information regarding the IRPhEP, including highlighted reactor physics benchmark evaluations, can be found in the November and December 2014 editions of Nuclear Science and Engineering. Several new evaluations have been prepared for inclusion in the September 2016 edition of the IRPhEP Handbook. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Path Forward: Materials Data Modernization for ASME Codes and Standards in the Artificial Intelligence Era

Development of the ASME Materials Properties Database was initiated in the early 2010s to support the ASME Codes and Standards. As information technologies advance at an accelerated pace with the artificial intelligence era on the horizon, the ASME Materials Properties Database must be further modernized from a database to a knowledgebase to ride the wave of digital information revolution and effectively support the ASME Codes and Standards in the new era. This paper is intended to provide an overview of the ASME Materials Properties Database and discuss a roadmap for its future development to facilitate understanding of and participation from different sectors of the Codes and Standards community. Further, it first reviews the basic concepts of data, information, knowledge, database, and database system as well as the pros and cons in different types of data management and then discusses the path forward for a desired evolution of the database into a self-explanatory and machine-readable knowledgebase that is consistent with human cognitive processes for the Codes and Standards development and, furthermore, provides resources for data processing and analysis to reach an eventual goal of streamlining the Codes and Standards development from the initial inquiry, throughout data submission, analysis, …, to Codes and Standards rule establishment for final publication.

36 MATERIALS SCIENCE↗
NASA NTRS2005

In-situ Resources In Space

This tutorial is a primer on the motivational and materials science basis for utilizing space resources to lower the cost and increase the safety and reliability of human systems beyond Earth's orbit. Past research in materials processing in orbit will be briefly reviewed to emphasize the challenges and advantages inherent in processing materials in space. Data on resource availability from human Lunar and robotic/sensor missions beyond the Moon will be overviewed for resource relevance to human exploration and development of space. Specific scenarios such as propellant production on the Moon and Mars, and lunar photovoltaic power production from in-situ materials will be discussed in relation to exploration and commercialization of space. A conclusion will cover some of the visionary proposals for the use of space resources to extend human society and prosperity beyond Earth.

Curreri, Peter A.↗
DOE OSTI2023

Path Forward: Materials Data Modernization for ASME Codes and Standards in the Artificial Intelligence Era

Development of the ASME Materials Properties Database was initiated in the early 2010s to support the ASME Codes and Standards. As information technologies advance at an accelerated pace with the artificial intelligence era on the horizon, the ASME Materials Properties Database must be further modernized from a database to a knowledgebase to ride the wave of digital information revolution and effectively support the ASME Codes and Standards in the new era.This paper is intended to provide an overview of the ASME Materials Properties Database and discuss a roadmap for its future development to facilitate understanding of and participation from different sectors of the Codes and Standards community. It first reviews the basic concepts of data, information, knowledge, database, and database system; as well as the pros and cons in different types of data management, and then discusses the path forward for a desired evolution of the database into a self-explanatory and machine-readable knowledgebase that is consistent with human cognitive processes for the Codes and Standards development and furthermore provides resources for data processing and analysis to reach an eventual goal of streamlining the Codes and Standards development from the initial inquiry, throughout data submission, analysis, …, to Codes and Standards rule establishment for final publication.

Ren, Weiju↗
DOE OSTI2017

GRACC: New generation of the OSG accounting

Throughout the last decade the Open Science Grid (OSG) has been fielding requests from user communities, resource owners, and funding agencies to provide information about utilization of OSG resources. Requested data include traditional accounting - core-hours utilized - as well as users certicate Distinguished Name, their affiliations, and eld of science. The OSG accounting service, Gratia, developed in 2006, is able to provide this information and much more. However, with the rapid expansion and transformation of the OSG resources and access to them, we are faced with several challenges in adapting and maintaining the current accounting service. The newest changes include, but are not limited to, acceptance of users from numerous university campuses, whose jobs are flocking to OSG resources, expansion into new types of resources (public and private clouds, allocation-based HPC resources, and GPU farms), migration to pilot-based systems, and migration to multicore environments. In order to have a scalable, sustainable and expandable accounting service for the next few years, we are embarking on the development of the next-generation OSG accounting service, GRACC, that will be based on open-source technology and will be compatible with the existing system. It will consist of swappable, independent components, such as Logstash, Elasticsearch, Grafana, and RabbitMQ, that communicate through a data exchange. GRACC will continue to interface EGI and XSEDE accounting services and provide information in accordance with existing agreements. Lastly, we will present the current architecture and working prototype.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1994

NASA historical data book. Volume 4: NASA resources 1969-1978

This is Volume 4, NASA Resources 1969-1978, of a series providing a 20-year statistical summary of NASA programs. This series is an important component of NASA published historical reference works, used by NASA personnel, managers, external researchers, and other government agencies. This volume combines statistical data of the component facilities with the data of the parent installation.

Gawdiak, Ihor Y.↗
NASA NTRS1973

Application of ERTS-1 Data to Aid in Solving Water Resources Management Problems in the State of California

Investigations within the State of California have emphasized the direct utilization of the supplemental data source afforded by the Earth Resources Technology Satellite-1 (ERTS-1) imagery. A user-oriented study has been underway for several years to identify and develop application methodology for remotely sensed information available from several platform levels, including low and high flights by NASA's U2 aircraft, and other manned and unmanned devices. The University of California has pursued both basic and applied research in remote sensing, through ongoing studies supported by NASA, and in cooperation with state agencies responsible for water resources and related fields. This report reviews the applications of ERTS-1 and supplementary imagery sources applicable to water resources planning, operations, and management.

Burgy, R. H.↗
NASA NTRS2007

Use of Remote Sensing for Decision Support in Africa

Over the past 30 years, the scientific community has learned a great deal about the Earth as an integrated system. Much of this research has been enabled by the development of remote sensing technologies and their operation from space. Decision makers in many nations have begun to make use of remote sensing data for resource management, policy making, and sustainable development planning. This paper makes an attempt to provide a survey of the current state of the requirements and use of remote sensing for sustainable development in Africa. This activity has shown that there are not many climate data ready decision support tools already functioning in Africa. There are, however, endusers with known requirements who could benefit from remote sensing data.

Policelli, Frederick S.↗
DOE OSTI2021

How AI Predicts the Untrained and Unseen

Focus Area: Model predictability improvements (Primary); Data optimization (secondary); Data complexity insights (secondary). The Scientific Challenge: If we believe that a future under extreme conditions will look very differently from today, we can likely agree that ML/AI models trained on past and present datasets will not be adequate to make reliable predictions into the future. This is true for water cycling, as well as biogeochemistry and other Earth system components and behaviors. Additionally, ML/AI models are inherently non-physical. Despite the flourishing success of ML/AI in many applications, such as computer vision, natural language process, and gaming, even the most sophisticated AI models don’t understand the very basic physical laws. Therefore, a natural question is: Can we trust ML/AI based predictions of Earth system behaviors that are fundamentally driven by physical laws? So, are physics models with meticulous process representation a better choice? Not exactly. Physical models, when firmly rooted in first principles, work great at predicting behaviors of systems with a well-defined set of boundary conditions and variables. However, as a complex system, the number of parameters and the degree of complexity and dynamics in processes, coupling, and scale dependent emergent behaviors make the Earth system behaviors very challenging to predict with physical models composed of deterministic laws. In addition, due to the lack of fundamental understandings, physical models often implement empirical correlations derived from observations with biases from locality of data generation. Because correlation is not necessarily causation or comply with first principles, scaling of model predictions beyond locality is often invalid. Beyond the limitation of models, physics or AI, our knowledge of the Earth system is limited by the lack of observational technologies and resources. Insufficient data density, dimensionality and diversity only offer a sliced (or projected) view of the Earth system, e.g. Plato’s Cave analogy, limiting our capability to better understand and represent fundamental processes in models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Tidal Energy Resource Characterization, Bottom Lander Measurements, Cook Inlet, AK, 2021

These datasets are from tidal resource characterization measurements collected on the Terrasond High Energy Oceanographic Mooring (THEOM) from 1 July 2021 to 30 August 2021 (60 days) in Cook Inlet, Alaska. The lander was deployed at 60.7207031 N, 151.4294998 W in ~50 m of water. The dataset contains raw and processed data from the following two instruments: 1. A Nortek Signature 500 kHz acoustic Doppler current profiler (ADCP). Data were recorded in 4 Hz in the beam coordinate system from all 5 beams. Processed data has been averaged into 5 minutes bins and converted to the East-North-Up (ENU) coordinate system. 2. A Nortek Vector acoustic Doppler velocimeter (ADV). Data were recorded at 8 Hz in the beam coordinate system. Processed data has been averaged into 5 minutes bins and converted to the Streamwise - Cross-stream - Vertical (Principal) coordinate system. Turbulence statistics were calculated from 5-minute bins, with an FFT length equal to the bin length, and saved in the processed dataset. Data was read and analyzed using the DOLfYN (version 1.0.2) python package and saved in MATLAB (.mat) and netCDF (.nc) file formats. Files containing analyzed data (".b1") were standardized using the TSDAT (version 0.4.2) python package. NetCDF files can be opened using DOLfYN (e.g., `dat = dolfyn.load(''*.nc")`) or the xarray python package (e.g. `dat = xarray.open_dataset("*.nc"). All distances are in meters (e.g., depth, range, etc), and all velocities in m/s. See the DOLfYN documentation linked in the submission, and/or the Nortek documentation for additional details.

16 TIDAL AND WAVE POWER↗
NASA NTRS1975

Utilizing Skylab data in on-going resources management programs in the state of Ohio

The author has identified the following significant results. The use of Skylab imagery for total area woodland surveys was found to be more accurate and cheaper than conventional surveys using aerial photo-plot techniques. Machine-aided (primarily density slicing) analyses of Skylab 190A and 190B color and infrared color photography demonstrated the feasibility of using such data for differentiating major timber classes including pines, hardwoods, mixed, cut, and brushland providing such analyses are made at scales of 1:24,000 and larger. Manual and machine-assisted image analysis indicated that spectral and spatial capabilities of Skylab EREP photography are adequate to distinguish most parameters of current, coal surface mining concern associated with: (1) active mining, (2) orphan lands, (3) reclaimed lands, and (4) active reclamation. Excellent results were achieved when comparing Skylab and aerial photographic interpretations of detailed surface mining features. Skylab photographs when combined with other data bases (e.g., census, agricultural land productivity, and transportation networks), provide a comprehensive, meaningful, and integrated view of major elements involved in the urbanization/encroachment process.

Baldridge, P. E.↗
DOE OSTI2021

Demonstration of the On-the-Fly Shielding Analysis Method: Spent Fuel and Waste Disposition

This report documents work performed supporting the US Department of Energy (DOE) Office of Nuclear Energy (NE) Spent Fuel and Waste Disposition (SFWD) Integrated Waste Management activities under work breakdown structure element 1.08.02.04.01, “Data and Tools Development, Validation, and Maintenance.” In particular, this report fulfills milestone M3SF-21OR020401016, “Implement on-the-fly dose analysis methodology in UNF-ST&DARDS” within work package SF-21OR02040101, “Commercial SNF Characterization - ORNL.” The Used Nuclear Fuel - Storage, Transportation & Disposal Analysis Resource and Data System (UNFST& DARDS) enables automated dose rate calculations for spent nuclear fuel (SNF) transportation packages and storage casks using a Monte Carlo radiation transport code. The explicit method uses a detailed model of the SNF system and its contents. Therefore, a dose rate calculation is required for each as-loaded transportation package or storage cask because the SNF assemblies within a canister typically have unique irradiation characteristics. An alternate method, referred to as the “on-the-fly” shielding analysis method, has been proposed that requires only a set of Monte Carlo dose rate calculations for each transportation packaging/storage cask design. The results of the Monte Carlo dose rate calculations are independent of the SNF assembly irradiation and decay characteristics. The dose rate values may then be combined with the radiation source strength of the SNF assemblies associated with a particular transportation packaging/storage cask design to determine actual dose rates. This report presents on-the-fly dose rate calculations for a representative SNF storage cask and verification of the on-the-fly dose rate calculation results by comparison with reference dose rate calculations using the explicit Monte Carlo dose rate calculation. The on-the-fly shielding analysis method was implemented in UNF-ST&DARDS. A Python program was developed to process the MAVRIC dose rate results obtained by source particle type, energy group, and fuel geometry region. A Python processor created binary files, which were saved as a special UNF-ST&DARDS library for on-the- fly shielding analyses. UNF-ST&DARDS uses the precalculated on-the-fly binary libraries generated by the Python data processor and directly executes the Python code for on-the-fly dose analysis. This Python code unzips the pre-generated binary files mentioned above, reads the data, and combines them with user-specified sources for dose and uncertainty calculations. The Python programs were verified using Excel calculations and by comparison with the values obtained with the MAVRIC post-processing utilities applied to the 3dmap files. This method can currently be used to determine dose rates for as-loaded HI-STORM FW storage casks. The UNF-ST&DARDS analysis wizard for on-the-fly shielding analysis is described in this report.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Multi-fidelity modeling to predict the rheological properties of a suspension of fibers using neural networks and Gaussian processes

Unveiling the rheological properties of fiber suspensions is of paramount interest to many industrial applications. There are multiple factors, such as fiber aspect ratio and volume fraction, that play a significant role in altering the rheological behavior of suspensions. Three-dimensional (3D) numerical simulations of coupled differential equations of the suspension of fibers are computationally expensive and time-consuming. Machine learning algorithms can be trained on the available data and make predictions for the cases where no numerical data are available. However, some widely used machine learning surrogates, such as neural networks, require a relatively large training dataset to produce accurate predictions. Multi-fidelity models, which combine high-fidelity data from numerical simulations and less expensive lower fidelity data from resources such as simplified constitutive equations, can pave the way for more accurate predictions. Here, we focus on neural networks and the Gaussian processes with two levels of fidelity, i.e., high and low fidelity networks, to predict the steady-state rheological properties, and compare them to the single-fidelity network. High-fidelity data are obtained from direct numerical simulations based on an immersed boundary method to couple the fluid and solid motion. The low-fidelity data are produced by using constitutive equations. Multiple neural networks and the Gaussian process structures are used for the hyperparameter tuning purpose. Results indicate that with the best choice of hyperparameters, both the multi-fidelity Gaussian processes and neural networks are capable of making predictions with a high level of accuracy with neural networks demonstrating marginally better performance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

The 2018 Edition of the IRPhEP Handbook

The International Reactor Physics Experiment Evaluation Project (IRPhEP) was established to preserve and evaluate integral reactor physics experiment data, including separate or special effects measurements for nuclear energy and technology applications. Numerous international experiments have been performed over the past several decades to support reactor operations, safety, measurements, and design. Those experiments represent a significant investment of infrastructure, cost, and expertise that, in many instances, cannot be as easily reproduced. They represent valuable resources of data for present and future research, and provide the basis for recording, development, and validation of methods. Contributors worldwide collaborate to evaluate and review selected benchmark experiments for inclusion in the International Handbook of Evaluated Reactor Physics Benchmark Experiments (IRPhEP Handbook). Several new evaluations, and revisions to existing benchmarks have been prepared for inclusion in the 2018 edition of the IRPhEP Handbook. Additionally, an IRPhEP Uncertainty Guide was discussed to provide further guidance regarding the evaluation of reactor physics measurements besides criticality. Recent changes to the IRPhEP Handbook include five minor revisions, the revision of a previous benchmark evaluation to include new data, publication of a new draft benchmark evaluation, final approval for the formal publication of two formerly draft benchmark evaluations, and the inclusion of ten new benchmark evaluations. The IRPhEP Handbook now contains a total of 159 experimental measurement series from 54 unique reactor facilities. International participants are actively engaged in ongoing evaluation activities to prepare content for future editions of the IRPhEP Handbook. There are numerous experiment and reactor facilities worldwide from which benchmark data can be evaluated. Those interested in serving as evaluators and/or reviewers for the IRPhEP are encouraged to participate.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

The 2019 Edition of the IRPhEP Handbook

The International Reactor Physics Experiment Evaluation Project (IRPhEP) [1] was established to preserve and evaluate integral reactor physics experiment data, including separate or special effects measurements for nuclear energy and technology applications. Numerous international experiments have been performed over the past several decades to support reactor operations, safety, measurements, and design. Those experiments represent a significant investment of infrastructure, cost, and expertise that, in many instances, cannot be as easily reproduced. They represent valuable resources of data for present and future research, and provide the basis for recording, development, and validation of methods [2]. The primary focus of the IRPhEP is to provide an extensively peer-reviewed set of reactor physics integral data suitable for reactor designers and safety analysts to not just validate the analytical tools necessary to design next-generation nuclear reactors, but to establish the safety basis for their operation. Contributors worldwide collaborate to evaluate and review selected benchmark experiments for inclusion in the International Handbook of Evaluated Reactor Physics Benchmark Experiments (IRPhEP Handbook) [3]. More detailed discussion of the IRPhEP, including highlighted reactor physics benchmark evaluations, can be found in the November and December 2014 editions of Nuclear Science and Engineering [1]. Several new evaluations, and revisions to existing benchmarks have been prepared for inclusion in the 2019 edition of the IRPhEP Handbook (see Table I).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Techno-Economic Assessment of Data Center Load Demand Powered by Small Modular Reactors and Distributed Energy Resources

The rapid increase in data center energy demand, driven by AI and large-scale data processing, poses significant challenges to global energy infrastructure. Data centers require substantial and reliable energy for continuous operations and high-performance computing. Current electrical grids face issues such as transmission bottlenecks and aging infrastructure, making it difficult to meet these demands. Integrating inverter-based-resources (IBRs) like solar and wind presents both opportunities and challenges due to their intermittent nature. Small Modular Reactors (SMRs) offer a promising solution with their enhanced safety, modularity, reliability, and scalability, providing consistent base load power ideal for data center operations. This study presents a comprehensive techno-economic assessment of powering data center load demand using a combination of SMRs and IBRs with grid-connected and islanded mode. This study utilized Idaho National Laboratory’s (INL) HPC data center hourly load profiles and Xendee microgrid optimization platform to conduct the analysis. In this configuration, SMRs serves as the primary base load power source, consistently providing a steady supply of electricity necessary to meet the minimum load demand of the data center with support from the IBRs. Key performance indicators such as Levelized Cost of Electricity (LCOE), Net Present Value (NPV) has been calculated to assess the economic feasibility. The findings from this research will underscore the strategic benefits of integrating SMR plant with DERs – particularly for critical infrastructure load such as data centers.

14 - SOLAR ENERGY↗