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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

EMASS (tm): An expandable solution for NASA space data storage needs

The data acquisition, distribution, processing, and archiving requirements of NASA and other U.S. Government data centers present significant data management challenges that must be met in the 1990's. The Earth Observing System (EOS) project alone is expected to generate daily data volumes greater than 2 Terabytes (2(10)(exp 12) Bytes). As the scientific community makes use of this data their work product will result in larger, increasingly complex data sets to be further exploited and managed. The challenge for data storage systems is to satisfy the initial data management requirements with cost effective solutions that provide for planned growth. This paper describes the expandable architecture of the E-Systems Modular Automated Storage System (EMASS (TM)), a mass storage system which is designed to support NASA's data capture, storage, distribution, and management requirements into the 21st century.

Peterson, Anthony L.↗
NASA NTRS2011

Policies and Procedures for Accessing Archived NASA Data via the Web

The National Space Science Data Center (NSSDC) was established by NASA to provide for the preservation and dissemination of scientific data from NASA missions. This white paper will address the NSSDC policies that govern data preservation and dissemination and the various methods of accessing NSSDC-archived data via the web.

James, Nathan↗
NASA NTRSDate not supplied

Biological Data for Deep Space Mission Support

Increased biomedical risks and challenges associated with deep space missions (cis-Lunar, Mars transit, Mars surface) require new knowledge discovery and development of novel ecosystem and biomedical support capabilities. This paradigm shift supporting distant and long-duration missions requires biological data to be findable, accessible, interoperable, reusable (FAIR), and maximally open-access (i.e., there is a data governance continuum from closed to mediated to embargoed to open). The NASA “Open Science Data Repositories” (OSDR) aims to meet scientific, technical, and operational spaceflight needs, and offers the ability to upload, download, search, share, analyze, and visualize data across physiological, behavioral, ‘omics, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive (ALSDA), and NASA Biological Institutional Scientific Collection (NBISC). In the past year, ALSDA has undergone a transformation in its data collection, curation, and architecture methods. Standardizing non-genomic (phenotypic) datasets was, and will continue to be, a challenge because of their diverse nature (e.g., molecular, cellular, tissue, whole organism behavior; micro-computed tomography, intraocular pressure, fluorescence microscopy, western blot, ultrasonography; tabular, images, video). This year ALSDA, alongside GeneLab, introduced the Biological Data Management Environment (BDME) with the purpose to accept submission of data from space relevant experiments including spaceflight, radiation, simulated gravity, gravitropism, isolation and confinement, hostile closed environments and/or distance from Earth. In addition to bringing together omics, phenotypic, physiological, bioimaging, and behavioral data into one repository. By integrating with GeneLab a multi-project submission portal aims to reduce the burden on PIs submitting data and enabling the discovery of both omics and phenotypic data. The purpose of ALSDA is to collect, curate, and make all non-human space-relevant biological data maximally findable, accessible, interoperable, and reusable (FAIR). These scope of ALSDA data collected and submitted by PIs include study design metadata, subject metadata, assay metadata (parameters), raw and processed assay data, assay imagery/video, and subject-experienced mission data telemetry (radiation, temperature, humidity, acoustics, vibrations, etc.). In 2021, a community of researchers rallied to form the ALSDA Analysis Working Group (AWG) and provided scientific consensus on dataset sample and assay metadata. The community and excitement around the ALSDA/OSDR system has already led to several data reuse studies, demonstrating value using machine learning (ML), knowledge graphs, and meta-analysis approaches.

space biology↗
NASA NTRSDate not supplied

Biological Data for Deep Space Mission Support

Increased biomedical risks and challenges associated with deep space missions (cis-Lunar, Mars transit, Mars surface) require new knowledge discovery and development of novel ecosystem and biomedical support capabilities. This paradigm shift supporting distant and long-duration missions requires biological data to be findable, accessible, interoperable, reusable (FAIR), and maximally open-access (i.e., there is a data governance continuum from closed to mediated to embargoed to open). The NASA “Open Science Data Repositories” (OSDR) aims to meet scientific, technical, and operational spaceflight needs, and offers the ability to upload, download, search, share, analyze, and visualize data across physiological, behavioral, ‘omics, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive (ALSDA), and NASA Biological Institutional Scientific Collection (NBISC). In the past year, ALSDA has undergone a transformation in its data collection, curation, and architecture methods. Standardizing non-genomic (phenotypic) datasets was, and will continue to be, a challenge because of their diverse nature (e.g., molecular, cellular, tissue, whole organism, behavior; micro-computed tomography, intraocular pressure, fluorescence microscopy, western blot, ultrasonography; tabular, images, video). This year ALSDA, alongside GeneLab, introduced the Biological Data Management Environment (BDME) with the purpose to accept submission of data from space relevant experiments including spaceflight, radiation, simulated gravity, gravitropism, isolation and confinement, hostile closed environments and/or distance from Earth. In addition to bringing together omics, phenotypic, physiological, bioimaging, and behavioral data into one repository. By integrating with GeneLab a multi-project submission portal aims to reduce the burden on PIs submitting data and enabling the discovery of both omics and phenotypic data. The purpose of ALSDA is to collect, curate, and make all non-human space-relevant biological data maximally findable, accessible, interoperable, and reusable (FAIR). These scope of ALSDA data collected and submitted by PIs include study design metadata, subject metadata, assay metadata (parameters), raw and processed assay data, assay imagery/video, and subject-experienced mission data telemetry (radiation, temperature, humidity, acoustics, vibrations, etc.). In 2021, a community of researchers rallied to form the ALSDA Analysis Working Group (AWG) and provided scientific consensus on dataset sample and assay metadata. The community and excitement around the ALSDA/OSDR system has already led to several data reuse studies, demonstrating value using machine learning (ML), knowledge graphs, and meta-analysis approaches.

space biology↗
NASA NTRS2011

Atomic Data for Neutron-capture Elements I. Photoionization and Recombination Properties of Low-charge Selenium Ions

We present multi-configuration Breit-Pauli AUTOSTRUCTURE calculations of distorted-wave photoionization (PI) cross sections. and total and partial final-state resolved radiative recombination (RR) and dielectronic recombination (DR) rate coefficients for the first six ions of the trans-iron element Se. These calculations were motivated by the recent detection of Se emission lines in a large number of planetary nebulae. Se is a potentially useful tracer of neutron-capture nucleosynthesis. but accurate determinations of its abundance in photoionized nebulae have been hindered by the lack of atomic data governing its ionization balance. Our calculations were carried out in intermediate coupling with semi re1ativistic radial wavefunctions. PI and recombination data were determined for levels within the ground configuration of each ion, and experimental PI cross-section measurements were used to benchmark our results. For DR, we allowed (Delta)n = 0 core excitations, which are important at photoionized plasma temperatures. We find that DR is the dominant recombination process for each of these Se ions at temperatures representative of photoionized nebulae (approx.10(exp 4) K). In order to estimate the uncertainties of these data, we compared results from three different configuration-interaction expansions for each ion, and also tested the sensitivity of the results to the radial scaling factors in the structure calculations. We find that the internal uncertainties are typically 30-50% for the direct PI cross sections and approx.10% for the computed RR rate coefficients, while those for low-temperature DR can be considerably larger (from 15-30% up to two orders of magnitude) due to the unknown energies of near-threshold autoionization resonances. These data are available at the CDS, and fitting coefficients to the total RR and DR rate coefficients are presented. The results are suitable for incorporation into photoionization codes used to numerically simulate astrophysical nebulae, and will enable robust determinations of nebular Se abundances.

Sterling, N. C.↗
NASA NTRSDate not supplied

Opening Historical Airborne Data to Present Day Researchers

For more than 50 years, NASA has flown airborne sensors to carry out research, validate satellite sensors, and test new instrument capabilities. Data collected prior to 2000 are typically analog and difficult to locate and use. The Airborne Data Management Group (ADMG) facilitates rescue of these valuable data to ensure easier discovery, access, and use. But opening historical data comes at a cost of both time and money. Careful decisions are required in assessing the return on investment. - Is there interest in the science community? - Are there government data requirements? - What is the temporal / spatial value of the data? - Can data be transformed to a digital format? - What is cost of transformation? - What time period is needed for rescue? Converting the data to today’s digital storage standards increases value and provides data access. The addition of metadata makes the data easier to search for.

Deborah Smith↗
DOE OSTI2026

Universality of Shallow Global Quenches in Critical Spin Chains

Measuring universal data in the strongly correlated regime of quantum critical points remains a fundamental objective for quantum simulators. In foundational work, Calabrese and Cardy demonstrated how these data govern the dynamics of certain global quenches to 1+1-dimensional conformal field theories. While the quasiparticle picture they introduce has been widely successful in both theory and experiment, their seminal prediction that the critical exponents are simply encoded in the relaxation rates of local observables is challenging to investigate experimentally. In this Letter, we examine the critical quench dynamics of local observables from two types of readily accessible initial conditions: ground states and finite-temperature ensembles. Here, we identify universal scaling collapses and scaling functions, utilizing a combination of conformal perturbation theory and tensor network numerics. For the finite-temperature quenches, we determine a regime in which the conformal field theory results are recovered, thereby allowing universal quantum critical data to be extracted from realistic quenches.

Quantum many-body systems↗
DOE OSTI2026

Robotic automation of maintenance work in nuclear power plants a cross-sector survey and roadmap

Nuclear power plants face increasing cost pressures, workforce constraints (aging workforce and skilled labor shortages), and safety requirements that are accelerating interest in robotic systems for inspection and maintenance. We conducted semi-structured interviews with personnel from seven U.S. nuclear utilities and compared deployment models, operational use cases, and integration practices with those reported by participants in the oil, gas, and petrochemical sector. In nuclear plants, robotic use remains concentrated in inspection—particularly indoor unmanned aerial vehicles and submersible remotely operated vehicles—with limited application to physical maintenance tasks. Reported near-term value includes reduced radiological and industrial risk, reduced outage labor, and improved data for planning and condition assessment. Key barriers include integration and data-interoperability constraints, operator qualification requirements, cybersecurity review burden, and difficulty demonstrating reliability in plant-representative environments. Cross-sector benchmarking highlights organizational and deployment practices that may help nuclear plants scale from pilots to routine use. We propose a deployment-oriented roadmap emphasizing modular payload strategies, representative qualification pathways and testing environments, and improved data governance to support safe and economically justified expansion of robotics in operating nuclear power plants.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Considerations for Introducing Artificial Intelligence into Nuclear Power Plants

Advanced computational tools and techniques such as artificial intelligence and machine learning (AI/ML) can transform the nuclear power industry. This is necessary given that the economic viability of the existing fleet is in jeopardy and its labor-centric approach to operations and maintenance. Currently, AI/ML research is being undertaken for reactor system design and analysis including fault and accident prognosis, nuclear risk analysis such as plant safety and security evaluation, and plant operations and maintenance including predictive maintenance. Applications include both existing and advanced reactor technologies with the aim of improving operational and business efficiencies. Most every aspect of the organization can benefit, from instrumentation and control, to work planning, to human-machine interactions and business management. AI/ML in nuclear can simplify complex problems and produce more effective decision-making. Nonetheless, careful consideration must be given to the implementation of an AI/ML initiative. The aims of this research are to 1) review barriers to AI/ML adoption within the nuclear power industry, and 2) suggest potential solutions. These barriers are organized along five distinct categories (Figure 1) that are interconnected. The first are historical barriers that track the industry’s development over the decades including worldwide nuclear events that shaped public perceptions. The resulting federal scrutiny and intense safety culture that emerged are discussed. Technical barriers to AI/ML adoption are considerable, and include data privacy concerns, data governance, and the current lack of AI/ML expert knowledge at the plants. The main business case barrier remains cost, but an absence of an industry-wide vision and wide-scale adoption also produces reluctance. Stakeholder readiness is reviewed with special attention given to regulatory readiness. The 5-year strategic plan for AI readiness recently published by the U.S. Nuclear Regulatory Commission is highlighted. Last, adoption barriers at the user level are addressed including the importance of user experience and explainable AI. The AI adoption barriers described here are inter-related and ideally should be addressed in a holistic fashion.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Bridging Control and Deployment: A Cross-Layer Analysis of Scalable Building Cluster Control

Building cluster control has emerged as a promising approach for enabling flexible and coordinated operation of distributed building systems, yet its transition from pilot demonstrations to routine grid-interactive operation remains limited. This paper argues that this gap cannot be explained by control algorithms alone. Instead, it arises from interacting barriers in communication infrastructure, data and semantic interoperability, uncertainty management, stakeholder participation, market design, and policy support. Accordingly, the paper reviews both technical and non-technical barriers to building cluster control. Technical challenges include heterogeneous devices and protocols, communication latency and reliability, distributed decision-making, and uncertainty propagation across aggregated loads. Non-technical barriers include user participation, stakeholder coordination, incentive allocation, and data governance. Existing solution approaches are synthesized, including semantic interoperability frameworks, edge and hierarchical communication architectures, distributed and transactive control strategies, uncertainty-aware optimization, policy mechanisms, and market reforms. Based on this analysis, two research directions are identified: testing infrastructures that can evaluate control performance under realistic multi-building conditions, and abstraction methods that allow building clusters to interact with other energy sectors through standardized flexibility representations. Overall, the paper provides a structured review of how building cluster control can move from isolated demonstrations toward reproducible, market-compatible, and grid-relevant implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1993

Rounded Approximate Step Functions For Interpolation

Rounded approximate step functions of form x(Sup m)/(x(Sup n) + 1) and 1/(x(Sup n) + 1) useful in interpolating between local steep slopes or abrupt changes in tabulated data varying more smoothly elsewhere. Used instead of polynomial curve fits. Interpolation formulas based on these functions implemented quickly and easily on computers. Used in real-time control computations to interpolate between tabulated data governing control responses.

Nunes, Arthur C., Jr.↗
NASA NTRS2002

U.S. Aerospace and Aviation Industry: A State-By-State Analysis

President George W. Bush and the Congress created the Commission on the Future of the United States Aerospace Industry to evaluate the current and future health of the industry as well as the challenges that lie ahead for the U.S. workforce and the economy. To accomplish our mission, we commissioned a study on the economic impact of the aerospace industry nationally and on a state-by-state basis, using the best available U.S. government data. This study sought to define the core of the aerospace industry. The resulting data represents that very core those jobs specifically tied to commercial and civilian aerospace. U.S. Aerospace and Aviation: A State-by-State Analysis examines the civilian and commercial aerospace and aviation industry by employment, wages, payroll, and establishments. The report found that the U.S. civilian and commercial aerospace and aviation industry employed over 2 million workers in 2001.

STATES (UNITED STATES)↗
NASA NTRS2013

High-Resolution Satellite Data Open for Government Research

U.S. satellite commercial imagery (CI) with resolution less than 1 meter is a common geospatial reference used by the public through Web applications, mobile devices, and the news media. However, CI use in the scientific community has not kept pace, even though those who are performing U.S. government research have access to these data at no cost.Previously, studies using multiple CI acquisitions from IKONOS-2, Quickbird-2, GeoEye-1, WorldView-1, and WorldView-2 would have been cost prohibitive. Now, with near-global submeter coverage and online distribution, opportunities abound for future scientific studies. This archive is already quite extensive (examples are shown in Figure 1) and is being used in many novel applications.

Data↗
NASA NTRSDate not supplied

Empowering Open Science with the Science Discovery Engine

: This presentation will describe the work to date in building the SDE as well as what the team has learned about the SMD ecosystem, information curation, and data governance. A short demonstration of the SDE will be presented, and an overview of near- and long-term goals for future development will be shared. Community feedback will be welcomed about the interface, content, and other features to help inform actions to maximize the SDE’s performance and usability. Whether users aim to discover Earth-like atmospheres on planets outside of our solar system or better understand the impacts of solar energy on our own planet, the Science Discovery Engine provides a means for scientists and all curious individuals to find content to further their understanding of science across all time and space scales.

Emily Foshee↗
NASA NTRS1996

The cornerstone of data warehousing for government applications

The purpose of this paper is to discuss data warehousing storage issues and the impact of EMC open storage technology for meeting the myriad of challenges government organizations face when building Decision Support/Data Warehouse system.

Kenbeek, Doug↗
NASA NTRS1992

National Media Laboratory

A review of the National Media Laboratory (NML) is presented. The mission of the NML is to support current government user data storage needs and assist them in getting the most efficient 'commercial' solutions in the future. The motivation for a National Media Laboratory is as follows: recording systems are the major government image and data exploitation bottleneck; government data recording performance and storage requirements lead commercial practice by 3-5 years; the supporting commercial recorder industry is large but principally focused on video not data formats; lack of standards; and lack of transfer of commercial knowledge base to program offices and operational sites.

Clark, Robert↗