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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

Real-time digital processing of SAR data

Current SAR data processing techniques for spaceborne SAR systems utilize commercial equipment to carry the computation load. For a data set such as SEASAT, the resulting throughput rate is two to three orders of magnitude slower than real time. Such rates impede the progress of scientific investigations and preclude the potential for any practical operational SAR mission. It is suggested that a set of special-purpose hardware could be designed for efficient SAR data processing so that real time rates are both practical and affordable.

Bickwell, T. J.↗
NASA NTRS1993

SPARTAN high resolution solar studies

This report summarizes the work performed on Contract NAS5-29739, a sub-orbital research program directed toward the study of the geometry of and physical conditions in matter found in the upper layers of the solar atmosphere. The report describes a new sounding rocket payload developed under the contract, presents a guide to the contents of semiannual reports submitted during the contract, discusses the results of the first flight of the payload and the progress on scientific analysis. A bibliography of papers and publications is included.

Bruner, Marilyn E.↗
NASA NTRS1987

Progress toward a cosmic dust collection facility on space station

Scientific and programmatic progress toward the development of a cosmic dust collection facility (CDCF) for the proposed space station is documented. Topics addressed include: trajectory sensor concepts; trajectory accuracy and orbital evolution; CDCF pointing direction; development of capture devices; analytical techniques; programmatic progress; flight opportunities; and facility development.

Mackinnon, Ian D. R.↗
DOE OSTI2025

Enabling Command-and-Control in Advanced In Situ Workflows

Scientific discovery is progressing towards autonomous science with the combination of scientific instruments, high-performance computing, and artificial intelligence in complex workflows. This evolution introduces new requirements for managing scientific workflows, including feedback loops, near real-time constraints, and the ability to dynamically control workflow execution. In situ workflows that analyze and visualize data as it is generated are well-suited to satisfy stringent time constraints and their iterative nature offers greater opportunities for command-and-control. However, only a few of the many workflow management systems available have been specifically designed to manage in situ workflows and often lack support for automated feedback loops that allow analysis and visualization components to interact with the main scientific data producer. To address this need, we present in this paper how to add command-and-control capabilities to a workflow management system. We identify the functional design requirements of such a command-and-control system, detail its architecture, interface, and core mechanisms, and illustrate how advanced in situ workflows can leverage command-and-control in three use cases: graceful termination with checkpoint, dynamic and adaptive data reduction, and event-triggered analysis.

Mehta, Kshitij [ORNL] (ORCID:0000000297149981)↗
DOE OSTI2023

Pathways to Discovery in Astronomy and Astrophysics for the 2020s

We live in an extraordinary period of discovery in astronomy and astrophysics. Six Nobel Prizes have been awarded over the past decade alone for discoveries based on astronomical data (dark energy, gravitational waves, neutrino oscillations, the discovery of exoplanets, cosmology, and supermassive black holes). Many of the ambitious scientific visions of the 2010 New Worlds, New Horizons (Astro2010)1 decadal survey are being fulfilled, but momentum has only grown. We stand on the threshold of new endeavors that will transform not only our understanding of the universe and the processes and physical paradigms that govern it but also humanity’s place in it. This report of the Committee for a Decadal Survey on Astronomy and Astrophysics 2020 (Astro2020) proposes a broad, integrated plan for space- and ground-based astronomy and astrophysics for the decade 2023–2032.2 It also lays the foundations for further advances in the following decade. This is the seventh in a sequence of decadal survey studies in this field from the National Academies of Sciences, Engineering, and Medicine. This survey examines the program of record, providing advice on the major projects from prior surveys that are yet to be completed. It also lays out priorities for future investments driven by scientific opportunities. The recommendations in this report advance foundational activities that support the people who drive innovation and discovery, and that promote the technologies and tools needed to carry out the science. The report also recommends sustaining activities on a broad range of cost and time scales, as well as activities that enable future visionary projects by maturing them scientifically and technically. Last, the recommendations set in motion the construction of frontier facilities that will change the view and understanding of the cosmos. The survey is bounded by plausible budget scenarios based on briefings from the sponsoring agencies— the Department of Energy (DOE), the National Aeronautics and Space Administration (NASA), and the National Science Foundation (NSF). Within these bounds, the survey aims high, reflecting this time of great scientific promise and progress, with opportunities to pursue some of the most compelling scientific quests of our times.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2000

Progress in The Semantic Analysis of Scientific Code

This paper concerns a procedure that analyzes aspects of the meaning or semantics of scientific and engineering code. This procedure involves taking a user's existing code, adding semantic declarations for some primitive variables, and parsing this annotated code using multiple, independent expert parsers. These semantic parsers encode domain knowledge and recognize formulae in different disciplines including physics, numerical methods, mathematics, and geometry. The parsers will automatically recognize and document some static, semantic concepts and help locate some program semantic errors. These techniques may apply to a wider range of scientific codes. If so, the techniques could reduce the time, risk, and effort required to develop and modify scientific codes.

Stewart, Mark↗
NASA NTRS1995

A review of the US Global Change Research Program and NASA's Mission to Planet Earth/Earth Observing System

This report reflects the results of a ten-day workshop convened at the Scripps Institution of Oceanography July 19-28, 1995. The workshop was convened as the first phase of a two part review of the U.S. Global Change Research Program (USGCRP). The workshop was organized to provide a review of the scientific foundations and progress to date in the USGCRP and an assessment of the implications of new scientific insights for future USGCRP and Mission to Planet Earth/Earth Observing System (MTPE/EOS) activities; a review of the role of NASA's MTPE/EOS program in the USGCRP observational strategy; a review of the EOS Data and Information System (EOSDIS) as a component of USGCRP data management activities; and an assessment of whether recent developments in the following areas lead to a need to readjust MTPE/EOS plans. Specific consideration was given to: proposed convergence of U.S. environmental satellite systems and programs, evolving international plans for Earth observation systems, advances in technology, and potential expansion of the role of the private sector. The present report summarizes the findings and recommendations developed by the Committee on Global Change Research on the basis of the presentations, background materials, working group deliberations, and plenary discussions of the workshop. In addition, the appendices include summaries prepared by the six working groups convened in the course of the workshop.

Moore, Berrien, III↗
NASA NTRS2004

Autonomous Vegetation Cover Scene Classification of EO-1 Hyperion Hyperspectral Data

The Autonomous Sciencecraft Experiment (ASE) is a JPL-led, New Millennium Program mission containing new technology in the form of software to be flown on the Earth Observer-1 (EO-1) satellite in early 2004. This new technology will facilitate an artificially intelligent machine with autonomous science-driven capabilities. Among the ASE flight software is a set of onboard science algorithms designed for autonomous data processing, primarily based on change detection from observation to observation. Using the output from these algorithms, ASE has the ability to autonomously modify the EO-1 observation plan, retargeting itself for a more in-depth observation of a scientific event in progress. Furthermore, intelligent and selective information down-linking will maximize return of the most valuable scientific data. Among the algorithms developed for use on ASE is a Lava-Vegetation (L-V) detection algorithm. This algorithm can effectively identify the initial location and extent of lava and vegetation coverage based on spectral shape. Comparison of several different observations, all classified via this algorithm, can make change detection possible.

Lee, R. J.↗
DOE OSTI2025

NewLife Nuclear - An Environmentally and Economically Minded Solution for Fusion Energy Waste Handling

Energy demand is rising as a result of innovative and increasingly more energy intensive processes coming to fruition, particularly through the recent interest in the development of AI data centers as well as manufacturing with the push towards increasing domestic manufacturing interest. Fusion energy can provide virtually limitless energy to support this increase in energy demand. Fusion energy concepts, largely classified as magnetic fusion energy (MFE) and inertial fusion energy (IFE) are being pursued, each having unique challenges to overcome before the successful deployment of electricity to the grid. Achieving fusion ignition on the National Ignition Facility, first in December 2022, and eight times since, has demonstrated the scientific viability of the IFE approach. Meanwhile, MFE test stands continue to improve confinement times, making meaningful strides in progressing towards experimental scientific viability. In each of these approaches, an emphasis is placed on generating more power out of the system than what is required to power the system. An under-researched area applicable to both IFE and MFE is handling activated waste coming out of fusion energy systems, both in the course of normal daily operations, as well as in intermittent periods as structural materials may need to be replaced. In the context of an IFE plant system, commonly discussed plant designs suggest targets are ignited within a chamber at a rate of up to one million targets per day. Between each shot, the chamber housing the ignition event will clear a portion of the chamber – resulting in a mixture of vaporized target gas, target debris, and other materials being expelled from the chamber [source]. Additionally, IFE system concepts typically discuss the modularization of plant designs, which are expected to be replaced periodically as the components degrade over time. This would result in the irradiated chamber structure materials, likely metals and alloys, needing to be removed and safely stored. In MFE plant systems, while targets are not ignited at a repetition rate with the frequent chamber clearing as is expected in IFE plant systems, it is anticipated that portions of the confinement area interfacing with the hot plasma will need to be replaced periodically. In each system, without additional investment and research into alternative processing and recycling methods, the result is storing irradiated materials, and other elements in a safe containment area until they are no longer activated. – resulting in significant waste both economic and environmental.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Mixed-precision numerics in scientific applications: survey and perspectives

The explosive demand for artificial intelligence (AI) workloads has led to a significant increase in silicon area dedicated to lower-precision computations on recent high-performance computing hardware designs. However, mixed-precision capabilities, which can achieve performance improvements of up to 8x compared to double-precision in extreme compute-intensive workloads, remain largely untapped in most scientific applications. A growing number of efforts have shown that mixed-precision algorithmic innovations can deliver superior performance without sacrificing accuracy. These developments should prompt computational scientists to seriously consider whether their scientific modeling and simulation applications could benefit from the acceleration offered by new hardware and mixed-precision algorithms. In this survey, we (1) review progress across diverse scientific domains—fluid dynamics, weather and climate, quantum chemistry, and computational genomics—that have begun adopting mixed-precision strategies; (2) examine state-of-the-art algorithmic techniques such as iterative refinement, splitting and emulation schemes, and adaptive precision solvers; (3) assess their implications for accuracy, performance, and resource utilization; and (4) survey the emerging software ecosystem that enables mixed-precision methods at scale. We conclude with perspectives and recommendations on cross-cutting opportunities, domain-specific challenges, and the role of co-design between application scientists, numerical analysts, and computer scientists. Collectively, this survey underscores that mixed-precision numerics can reshape computational science by aligning algorithms with the evolving landscape of hardware capabilities.

Graphics processing units↗
NASA NTRS1987

The Large Deployable Reflector (LDR) - Plans and progress

The program history, scientific aims, design, and projected performance of the LDR, a 20-m-primary two-stage four-mirror orbiting sub-mm/FIR astronomical observatory under NASA development, are reviewed. It is shown that the LDR would provide capabilities complementary to those of IRAS, the Kuiper Airborne Observatory, the IRTF, the Hubble Space Telescope, and the planned Space IR Telescope Facility for observations of small-scale background anisotropies, high-redshift galaxies, and objects at temperatures of a few times 10 K or lower. The current design concept is illustrated with extensive drawings, diagrams, and tables of instrument parameters. Particular attention is given to the graphite-epoxy facing and Al-honeycomb core of the primary structure, the focal-plane instruments, and outstanding technological problems.

Swanson, Paul N.↗
DOE OSTI2020

Cigeo Design Options, Risks and Opportunities Management: A Continuous Project Management Goal - 20015

Andra is currently completing the detailed engineering phase of the stepwise design development of Cigeo, the French HLW and IL-LLW Deep Geological Repository. If licensed in the coming years, Cigeo's progressive construction, industrial operation and closure will last over more than a century. Such a duration, associated with the requirements in terms of reversibility, lead to the choice of an incremental design that allows, for next operator generations, to integrate improvements derived from scientific and technical progress as well as from experience feedback from its operation. This incremental design approach, associated with a permanent risks and opportunities management process, will continue after licensing of Cigeo as a tool for the governance of the reversibility. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Reliable and Efficient Machine Learning (Final Technical Report)

Modern scientific experiments generate massive amounts of data at a pace much faster than humans can manually analyze. While machine learning has revolutionized commercial data analysis (such as recommending movies or recognizing faces), applying these tools to complex scientific discovery is challenging because scientific answers must be precise, interpretable, and adhere to physical laws. The research under this project aims to develop new mathematical tools and computer algorithms specifically designed for scientific applications. Major progress has been made in automatically cleaning and deconstructing messy experimental data, analyzing the visual information of physical phenomena, determining the underlying physical variables, and providing rig orous mathematical analysis of interesting algorithms and concepts widely used in machine learning. This project addressed the critical gap between our ability to generate massive scientific data and our ability to extract interpretable information from it. We established mathematical foundations for Scientific Machine Learning (SciML) aimed at effective data analytics and automated discovery. Our work focused on three core objectives: (1) developing reliable feature extraction methods for dynamic high-dimensional data, (2) establishing mathematical foundations for discovering dynamics via neural networks, and (3) creating rigorous optimization techniques for these models. Key outcomes come from two fronts. On the practical side, they include the development of algorithms that significantly enhance the extraction of signals from field data, as well as the capability to handle situations that exhibit smooth variations or physical stretching due to temperature changes. They also include the creation of an automated framework for discovering fundamental state variables from raw experimental data, demonstrating the ability to identify intrinsic physical dimensions without prior knowledge of the governing laws. On the theoretical front, the research results in theoretical advances in Optimal Transport, a widely used notion in SciML, specifically regarding functions with fixed-size nodal sets, provide sharp bounds relevant to uncertainty quantification. Meanwhile, the outcomes also include the establishment of convergence theories for nonlocal gradient descent methods, enabling robust optimization with noisy data in high-dimensional settings commonly encountered in scientific modeling. The project also helps creating opportunities to train the next generation of researchers, equipping them with the necessary technical skills for today’s workplace and preparing them for future advances.

97 MATHEMATICS AND COMPUTING↗