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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Second release of the CoRe database of binary neutron star merger waveforms

Abstract We present the second data release of gravitational waveforms from binary neutron star (BNS) merger simulations performed by the Computational Relativity ( CoRe ) collaboration. The current database consists of 254 different BNS configurations and a total of 590 individual numerical-relativity simulations using various grid resolutions. The released waveform data contain the strain and the Weyl curvature multipoles up to ℓ = m = 4 . They span a significant portion of the mass, mass-ratio, spin and eccentricity parameter space and include targeted configurations to the events GW170817 and GW190425. CoRe simulations are performed with 18 different equations of state, seven of which are finite temperature models, and three of which account for non-hadronic degrees of freedom. About half of the released data are computed with high-order hydrodynamics schemes for tens of orbits to merger; the other half is computed with advanced microphysics. We showcase a standard waveform error analysis and discuss the accuracy of the database in terms of faithfulness. We present ready-to-use fitting formulas for equation of state-insensitive relations at merger (e.g. merger frequency), luminosity peak, and post-merger spectrum.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2002

Sources of Variability in Chlorophyll Analysis by Fluorometry and High-Performance Liquid Chromatography in a SIMBIOS Inter-Calibration Exercise

The purpose of this technical report is to provide current documentation of the Sensor Intercomparison and Merger for Biological and Interdisciplinary Oceanic Studies (SIMBIOS) Project activities, NASA Research Announcement (NRA) research status, satellite data processing, data product validation, and field calibration. This documentation is necessary to ensure that critical information is related to the scientific community and NASA management. This critical information includes the technical difficulties and challenges of validating and combining ocean color data from an array of independent satellite systems to form consistent and accurate global bio-optical time series products. This technical report is not meant as a substitute for scientific literature. Instead, it will provide a ready and responsive vehicle for the multitude of technical reports issued by an operational project. This particular document focus on the variability in chlorophyll pigment measurements resulting from differences in methodologies and laboratories conducting the pigment analysis.

VanHeukelem, Laurie↗
NASA NTRS1972

Analyses and tests confirm design of proprotor aircraft.

Discussion of the design features of a 300-knot tilt-proprotor VTOL aircraft having proprotors 25 ft in diameter, an empty weight of 7390 pounds, a maximum VTOL gross weight of 12,400 pounds, and a design dive speed of 300 knots. Proprotor stability analysis covering aeroelastic stability characteristics and flight mode stability characteristics is included. An evaluation of the proprotor design is made on the basis of aerodynamic and aeroelastic model tests and full-scale performance tests. The test results and other obtained research data suggest the readiness of this aircraft design for construction and flight tests.

Edenborough, H. K.↗
NASA NTRS1981

AgRISTARS: Foreign commodity production forecasting. The 1980 US/Canada wheat and barley exploratory experiment

The crop identification procedures used performed were for spring small grains and are conducive to automation. The performance of the machine processing techniques shows a significant improvement over previously evaluated technology; however, the crop calendars require additional development and refinements prior to integration into automated area estimation technology. The integrated technology is capable of producing accurate and consistent spring small grains proportion estimates. Barley proportion estimation technology was not satisfactorily evaluated because LANDSAT sample segment data was not available for high density barley of primary importance in foreign regions and the low density segments examined were not judged to give indicative or unequvocal results. Generally, the spring small grains technology is ready for evaluation in a pilot experiment focusing on sensitivity analysis to a variety of agricultural and meteorological conditions representative of the global environment.

Payne, R. W.↗
NASA NTRS1972

Operational Weight Estimations of Commercial Jet Transport Aircraft

In evaluating current or proposed commercial transport airplanes, there has not been available a ready means to determine weights so as to compare airplanes within this particular class. This paper describes the development of and presents such comparative tools. The major design characteristics of current American jet transport airplanes were collected, and these data were correlated by means of regression analysis to develop weight relationships for these airplanes as functions of their operational requirements. The characteristics for 23 airplanes were assembled and examined in terms of the effects of the number of people carried, the cargo load, and the operating range. These airplane characteristics were correlated for the airplanes as one of three subclasses, namely the small, twin-engine jet transport, the conventional three- and four-engine jets, and the new wide-body jets.

Anderson, Joseph L.↗
DOE OSTI2023

Conquering Data Chaos: Research Data Management with Kubernetes

Managing massive volumes of data and effectively making it accessible to researchers poses significant challenges and is a barrier to scientific discovery. In many cases, critical data is locked up in unwieldy file formats or one-off databases and is too large to effectively process on a single machine. This talk explores the role of Kubernetes, an open-source container orchestration platform, in addressing research data management challenges. I will discuss how we are using a set of publicly available open-source and home-grown tools in the National Renewable Energy Lab (NREL) Data, Analysis, and Visualization (DAV) group to help researchers overcome data-related bottlenecks. The talk will begin by providing an overview of the data challenges faced in research data management, including data storage, processing, and analysis. I will highlight Kubernetes' ability to handle large-scale data by leveraging containerization and distributed computing, including distributed storage. Kubernetes allows researchers to encapsulate data processing infrastructure and workflows into portable containers, enabling reproducibility and ease of deployment. Kubernetes can then schedule and manage the resource allocation of these containers to enable efficient utilization of limited computing resources, leading to more efficient data processing and analysis. I will discuss some limitations of traditional, siloed approaches to dealing with data and emphasize the need for solutions which foster collaboration. I will highlight how we are using Kubernetes at NREL to facilitate data sharing and cooperation among research teams. Kubernetes' flexible architecture enables the deployment of shared computing environments, such as Apache Superset, where researchers can seamlessly access and analyze shared datasets. Providing the ability to have one research team easily consume data generated by another, utilizing Kubernetes' as a central data platform, is one of the major wins we've encountered by adopting the platform. Finally, I will showcase real-world use cases from NREL where we have used Kubernetes to solve some persistent data challenges involving large volumes of sensor and monitoring data. I will discuss the challenges we encountered when creating our cluster and making it available as a production-ready resource. I will also discuss the specific suite of tools, including Postgres and Apache Druid for columnar and timeseries data, and Redpanda Kafka for streaming data we have deployed in our infrastructure, and the process that went into the selection of these tools.

collaborative environment↗
NASA NTRS1984

Low lift-to-drag aero-assisted orbit transfer vehicles

The results of systems analysis conducted on low life drag ratio (L/D) aero-assisted orbit transfer vehicle (AOTV's) are presented. The objectives for this class of vehicle and formulate technology development plans and funding levels to bring the required technologies to readiness levels, as well as develop a credible decision data base encompassing the entire range of low L/D concepts for use in future NASA Aeroassist Orbit Transfer Vehicles studies. Each candidate low L/D concept, the aerobrake, the lifting brake, and the aeromaneuvering concept could be made to work with technologies achievable by the early 1990's. All concepts require flexible structure with flexible thermal protection system (TPS) to be successfully integrated into the shuttle orbiter for launch, all required improvements in guidance and control to fly the dispersed atmospheres at high altitude, and all concepts had potential to evolve from ground-based to space-based operations.

Andrews, D. G.↗
NASA NTRS2012

Making the Case for Reusable Booster Systems: The Operations Perspective

Presentation to the Aeronautics Space Engineering Board National Research Council Reusable Booster System: Review and Assessment Committee. Addresses: the criteria and assumptions used in the formulation of current RBS plans; the methodologies used in the current cost estimates for RBS; the modeling methodology used to frame the business case for an RBS capability including: the data used in the analysis, the models' robustness if new data become available, and the impact of unclassified government data that was previously unavailable and which will be supplied by the USAF; the technical maturity of key elements critical to RBS implementation and the ability of current technology development plans to meet technical readiness milestones.

reusable launch vehicle↗
NASA NTRS2018

Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS): Final Report of the ASCENDS Ad Hoc Science Definition Team

Improved remote sensing observations of atmospheric carbon dioxide (CO2) are critically needed to quantify, monitor, and understand the Earth's carbon cycle and its evolution in a changing climate. The processes governing ocean and terrestrial carbon uptake remain poorly understood,especially in dynamic regions with large carbon stocks and strong vulnerability to climate change,for example, the tropical land biosphere, the northern hemisphere high latitudes, and the Southern Ocean. Because the passive spectrometers used by GOSAT (Greenhouse gases Observing SATellite) and OCO-2 (Orbiting Carbon Observatory-2) require sunlit and cloud-free conditions,current observations over these regions remain infrequent and are subject to biases. These short comings limit our ability to understand and predict the processes controlling the carbon cycle on regional to global scales.In contrast, active CO2 remote-sensing techniques allow accurate measurements to be taken day and night, over ocean and land surfaces, in the presence of thin or scattered clouds, and at all times of year. Because of these benefits, the National Research Council recommended the National Aeronautics and Space Administration (NASA) Active Sensing of CO2 Emissions over Nights,Days, and Seasons (ASCENDS) mission in the 2007 report Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond. The ability of ASCENDS to collect low-bias observations in these key regions is expected to address important gaps in our knowledge of the contemporary carbon cycle.The ASCENDS ad hoc Science Definition Team (SDT), comprised of carbon cycle modeling and active remote sensing instrument teams throughout the United States (US), worked to develop the mission's requirements and advance its readiness from 2008 through 2018. Numerous scientific investigations were carried out to identify the benefit and feasibility of active CO2 remote sensing measurements for improving our understanding of CO2 sources and sinks. This report summarizes their findings and recommendations based on mission modeling studies, analysis of ancillary meteorological data products, development and demonstration of candidate technologies, anddesign studies of the ASCENDS mission concept.

Kawa, S. Randolph↗
DOE OSTI2025

DOE Repository Metadata Profile (DRMP): A Metadata Framework for Advancing Interoperability and AI Readiness Across Scientific Repositories

The Department of Energy (DOE) funds a diverse and distributed ecosystem of repositories that steward scientific data, publications, and software across its research programs, user facilities, and national laboratories. While significant progress has been made in standardizing dataset-level metadata, the metadata describing repositories themselves (their identity, governance, access interfaces, policies, and technical capabilities) remains inconsistent and fragmented across DOE-funded systems. This variability limits discoverability, interoperability, automated validation, and AI-driven analysis, all of which are increasingly essential for modern scientific workflows. To address this gap, the DOE Data Curation Working Group (DCWG) developed the DOE Repository Metadata Profile (DRMP). The DRMP is a practical, community-driven framework that defines how repositories can describe themselves in a consistent, machine-actionable, and scalable manner. The DRMP is not a new metadata schema. Instead, it is a mapping profile and structured element set capturing the essential characteristics of DOE repositories. It harmonizes repository-level metadata across six widely adopted community schemas: RE3Data; DCAT-US v3; Schema.org; Dublin Core; DataCite 4.6; and PREMIS 3.0. This harmonization eliminates reinvention and enables interoperability within DOE and across the broader scientific ecosystem. A core objective of the DRMP is to reduce burden on repositories by allowing them to reuse their existing metadata through a Rosetta-style crosswalk rather than redesigning local implementations. The profile introduces a three-level conformance model that supports incremental adoption: • Level 1 – Minimum Viable Record (MVR): foundational identification elements required for workflows, project registration, and basic repository presence. • Level 2 – Interoperable: structured metadata enabling alignment with national and international discovery systems. • Level 3 – AI-Ready: enhanced provenance, policy transparency, fixity, semantic context, and capabilities that support automated reasoning, model training governance, and machine-assisted curation. To support implementation, the DRMP includes JSON Schema definitions, OpenAPI patterns, and MCP templates that allow repositories to publish machine-readable metadata directly within existing platforms. These resources are modular and lightweight, enabling adoption without major architectural change. Adopting the DRMP enables repositories to: • Enhance discoverability and interoperability by aligning identifiers, classifications, and descriptive elements across widely used schema standards. • Support federated discovery and cross-registration across DOE systems, Data.gov, and international catalogs. • Enable AI agents and workflow orchestration systems to interpret repository-level metadata within the American Science Cloud (AmSC) through Model Context Protocol (MCP)-based context publication. • Demonstrate alignment with DOE’s open science, stewardship, and FAIR data priorities. This guidance represents a community-driven step forward. Through voluntary adoption and continued feedback, the DRMP advances a cohesive, machine-actionable description of DOE repositories that supports FAIR data practices, preparing the infrastructure for AI-enabled research, and strengthening the discoverability and reuse of DOE’s scientific outputs.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1994

Potential aerospace applications of high temperature superconductors

The recent discovery of High Temperature Superconductors (HTS) with superconducting transition temperature, T(sub c), above the boiling point of liquid nitrogen has opened the door for using these materials in new and practical applications. These materials have zero resistance to electric current, have the capability of carrying large currents and as such have the potential to be used in high magnetic field applications. One of the space applications that can use superconductors is electromagnetic launch of payloads to low-earth-orbit. An electromagnetic gun-type launcher can be used in small payload systems that are launched at very high velocity, while sled-type magnetically levitated launcher can be used to launch larger payloads at smaller velocities. Both types of launchers are being studied by NASA and the aerospace industry. The use of superconductors will be essential in any of these types of launchers in order to produce the large magnetic fields required to obtain large thrust forces. Low Temperature Superconductor (LTS) technology is mature enough and can be easily integrated in such systems. As for the HTS, many leading companies are currently producing HTS coils and magnets that potentially can be mass-produced for these launchers. It seems that designing and building a small-scale electromagnetic launcher is the next logical step toward seriously considering this method for launching payloads into low-earth-orbit. A second potential application is the use of HTS to build sensitive portable devices for the use in Non Destructive Evaluation (NDE). Superconducting Quantum Interference Devices (SQUID's) are the most sensitive instruments for measuring changes in magnetic flux. By using HTS in SQUID's, one will be able to design a portable unit that uses liquid nitrogen or a cryocooler pump to explore the use of gradiometers or magnetometers to detect deep cracks or corrosion in structures. A third use is the replacement of Infra-Red (IR) sensor leads on Earth Orbit Systems (EOS) with HTS leads. IR detectors on these EOS missions are cooled to a 4.2K to improve their signal to noise ratio. They are connected to data acquisitions systems using manganin wires (low thermal conductors) to reduce the heat load on the cryogen. Replacing these wires with HTS leads will increase the lifetime of these missions by about 50 percent. This is a promising application that is ready for actual implementation on such systems. The analysis also show that an the number of IR detectors increase in larger EOS systems, substantial increase in the lifetime of each mission will be realized by using HTS leads instead of the manganin ones.

Selim, Raouf↗
NASA NTRS2003

Simultaneous X-Ray and Optical Timing Observations of GX 339-4

The goal of this proposal is to perform the first comprehensive study of the correlated x-ray and optical variability of the Galactic accreting black hole candidate GX 339-4 using the x-ray and optical instruments on XMM-Newton. With these observations, we hope to make significant progress in understanding the coupled inflow - outflow system around a persistently accreting stellar mass black hole. We are currently analyzing the data. The data analysis is rather complex as it involves all of the instruments on XMM-Newton, the EPIC-PN, the EPIC-MOS, the RGS, and the OM, and our analysis requires study of correlated fast variability in the EPIC-PN and OM. We expect to have results ready to submit for publication within 3 to 4 months.

Kaaret, Philip↗
NASA NTRS2011

Statistical Analysis of an Infrared Thermography Inspection of Reinforced Carbon-Carbon

Each piece of flight hardware being used on the shuttle must be analyzed and pass NASA requirements before the shuttle is ready for launch. One tool used to detect cracks that lie within flight hardware is Infrared Flash Thermography. This is a non-destructive testing technique which uses an intense flash of light to heat up the surface of a material after which an Infrared camera is used to record the cooling of the material. Since cracks within the material obstruct the natural heat flow through the material, they are visible when viewing the data from the Infrared camera. We used Ecotherm, a software program, to collect data pertaining to the delaminations and analyzed the data using Ecotherm and University of Dayton Log Logistic Probability of Detection (POD) Software. The goal was to reproduce the statistical analysis produced by the University of Dayton software, by using scatter plots, log transforms, and residuals to test the assumption of normality for the residuals.

Comeaux, Kayla↗
NASA NTRSDate not supplied

Space Flown Rodent Liver RNA Sequencing Data for Machine Learning in Space Biology Research

High-throughput nucleic acid sequencing (DNA-seq, RNA-seq) has become widespread in biomedical research due to the growing availability and affordability of these assays. Data analysis has been accelerated in recent years by the adoption of artificial intelligence (AI) and machine learning (ML) techniques by biomedical researchers. In space biology research, RNAseq datasets from space-flown experimental samples are critical for characterizing the gene expression aberrations associated with exposure to spaceflight stressors. However, space biological experiments tend to be very low sample size, so identifying proper AI/ML algorithms for sequencing data analysis is an ongoing challenge since these algorithms typically require large sample size. The NASA Science Mission Directorate (SMD) has started the “Benchmark Initiative for AI/ML”, focused on creating datasets meant for three main applications: 1) scientific benchmarking, which finds the best algorithm for a specific problem; 2) application benchmarking, which measures algorithm performance against a set of parameters; and 3) system benchmarking, which evaluates performance of hardware and software architecture. These scientific benchmarks consist of an AI-ready dataset and a reference implementation on a specific scientific question. In this work, we focused on generating standardized datasets to allow the scientific community to benchmark AI/ML algorithms in the domain of space biology. We present here a standardized, AI-ready, publicly available benchmark dataset for space biology RNA-seq data as a collaboration between the NASA AI4LS (Artificial Intelligence for Life Sciences) working group. and NASA’s SMD. This dataset consists of space-flown and ground control mouse liver found in the NASA GeneLab omics database. However, to amplify the small sample number (n=112 samples) for ML purposes, we employ Gaussian noise and a generative adversarial network to extend this dataset to 6,000 synthetic samples, matching the original gene expression characteristics.

James Casaletto↗
DOE OSTI2025

A Modelica Implementation of an Organic Rankine Cycle

Organic Rankine cycle (ORC) systems generate power from low-grade heat sources, such as geothermal sources and industrial waste heat. A key feature is that a working fluid is selected to match the temperature of the source. With the vast pool of candidate working fluids comes the challenge of developing a large number of robust thermodynamic media models. We implemented a subcritical ORC model in Modelica that uses working fluid data records and interpolation schemes in lieu of thermodynamic medium evaluation for energy recovery estimation. This is a component model that can be integrated into a larger energy system model. It does not require detailed thermodynamic, heat transfer, or machine analysis. Our ORC model fills a gap where working fluids are ready to choose or easy to add, and at the same time can be integrated into an energy system.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2003

Sensory characterization of a ready-to-eat sweetpotato breakfast cereal by descriptive analysis

The sweetpotato [Ipomoea batatas (L.) Lam], an important industry in the United States, has been selected as a candidate crop to be grown on future long-duration space missions by NASA. Raw sweetpotato roots were processed into flour, which was used to formulate ready-to-eat breakfast cereal (RTEBC). Twelve trained panelists evaluated the sensory attributes of the extruded RTEBC using descriptive analysis. The samples were significantly different (P<0.05) for all attributes. Twelve perceived sensory attributes, which could be used to differentiate the appearance, texture, and flavor of sweetpotato RTEBC, were described. The data could be used to optimize the RTEBC and for designing studies to test its consumer acceptance.

NASA Discipline Life Support Systems↗
DOE OSTI2023

Providing Geospatial Intelligence through a Scalable Imagery Pipeline

This chapter describes ORNL’s (Oak Ridge National Laboratory’s) contributions to imagery preprocessing for geospatial intelligence research and development (R&D) in four sections. First, we discuss challenges involved in building an effective imagery preprocessing workflow and the world-class high-performance computing (HPC) resources at ORNL available to process petabytes of imagery data. Second, we highlight how we developed imagery preprocessing tools over three decades while paving the way for our current cutting-edge machine learning and computer vision algorithms that are impacting humanitarian and disaster response efforts. Third, we discuss how PIPE modules work together to turn raw images into analysis-ready datasets. Fourth, we look toward the future and discuss planned advancements to PIPE and computing trends that will affect geospatial intelligence R&D.

Reith, Andrew↗
DOE OSTI2021

Sapsan: Framework for Supernovae Turbulence Modeling with Machine Learning

Sapsan is a framework designed to make Machine Learning (ML) more accessible in the study of turbulence, with a focus on astrophysical applications. Sapsan includes modules to load, filter, subsample, batch, and split the data from hydrodynamic (HD) simulations for training and validation. Next, the framework includes built-in conventional and physically-motivated estimators that have been used for turbulence modeling. This ties into Sapsan’s custom estimator module, aimed at designing a custom ML model layer-by-layer, which is the core benefit of using the framework. To share your custom model, every new project created via Sapsan comes with pre-filled, ready-for-release Docker files. Furthermore, training and evaluation modules come with Sapsan as well. The latter, among other features, includes the construction of power spectra and comparison to established analytical turbulence closure models, such as a gradient model. Thus, Sapsan attempts to minimize the hard work required for data preparation and analysis, leaving one to focus on the ML model design itself.

79 ASTRONOMY AND ASTROPHYSICS↗