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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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NASA NTRS2003

Accelerating Large Data Analysis By Exploiting Regularities

We present techniques for discovering and exploiting regularity in large curvilinear data sets. The data can be based on a single mesh or a mesh composed of multiple submeshes (also known as zones). Multi-zone data are typical to Computational Fluid Dynamics (CFD) simulations. Regularities include axis-aligned rectilinear and cylindrical meshes as well as cases where one zone is equivalent to a rigid-body transformation of another. Our algorithms can also discover rigid-body motion of meshes in time-series data. Next, we describe a data model where we can utilize the results from the discovery process in order to accelerate large data visualizations. Where possible, we replace general curvilinear zones with rectilinear or cylindrical zones. In rigid-body motion cases we replace a time-series of meshes with a transformed mesh object where a reference mesh is dynamically transformed based on a given time value in order to satisfy geometry requests, on demand. The data model enables us to make these substitutions and dynamic transformations transparently with respect to the visualization algorithms. We present results with large data sets where we combine our mesh replacement and transformation techniques with out-of-core paging in order to achieve significant speed-ups in analysis.

Moran, Patrick J.↗
NASA NTRS2017

Giovanni - The Bridge Between Data and Science

This article describes new features in the Geospatial Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni), a user-friendly online tool that enables visualization, analysis, and assessment of NASA Earth science data sets without downloading data and software. Since the satellite era began, data collected from Earth-observing satellites have been widely used in research and applications; however, using satellite-based data sets can still be a challenge to many. To facilitate data access and evaluation, as well as scientific exploration and discovery, the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) has developed Giovanni for a wide range of users around the world. This article describes the latest capabilities of Giovanni with examples, and discusses future plans for this innovative system.

GPM↗
NASA NTRS1999

Global Modeling of Mineral Dust and its Optical Thickness

Global distribution of dust in the atmosphere has been simulated using the NASA Goddard chemical transport model (GEOS-CTM) to help retrieve the aerosol optical thickness from TOMS absorbing aerosol index. The model contains a dust module which accounts for sources and removal processes. The transport is driven by the assimilated meteorological fields generated by the NASA Goddard Earth Observing System Data Assimilation System (GEOS DAS). One of the key parameters, in the retrieval algorithm of optical thickness from Total Ozone Mapping Spectrometer (TOMS) data, is the vertical profile of aerosols. During the period 10- 19 September 1994, Lidar on-space Technology Experiment (LITE) was flown on space shuttle Discovery. The 53 hours of data collected cover the lower atmosphere from the earth surface to 20 kilometers altitude and from 57 N to 57 S with a high resolution of about 15 meters. The model results are compared with LITE data over the source regions of dust (Africa, Middle East, Asia, Australia) and in the remote troposphere (Atlantic and Pacific). The simulated horizontal distribution is compared with TOMS absorbing aerosol index. Finally the calculated optical thickness will be assessed with ground based sun-photometers (AERONET).

Ginoux, Paul A.↗
NASA NTRS1989

Reexamination of data from the asteroid/meteoroid detector

The discovery of the existence of cosmoids, a class of meteoroid in near hyperbolic orbits, was made in a reevaluation of the Sisyphus Experiment on Pioneer 10 and 11. This experiment measured the spontaneous jetting of cosmoids and showed that the dispersion and increase in brightness occurs in microseconds and lasts only briefly. Cosmoid jetting caused multiple telescope thresholds to be exceeded simultaneously which explains the earlier inabilty to compute trajectories from the measured times in the fields of view. A new calculation correlated the Sisyphus individual event measurements with the zodiacal light. That the meteoroid population is dominated by cosmoids is demonstrated. Reported telescopic small comets, measured by a similar optical technique, appear consistent with the Sisyphus results. Characteristic jetting times measured by Sisyphus also show that the volatile cosmoids could not survive in short period orbits.

Soberman, Robert K.↗
NASA NTRS2012

A Possible Organic Contribution to the Low Temperature CO2 Release Seen in Mars Phoenix Thermal and Evolved Gas Analyzer Data

Two of the most important discoveries of the Phoenix Mars Lander were the discovery of approx.0.6% perchlorate [1] and 3-5% carbonate [2] in the soils at the landing site in the martian northern plains. The Thermal and Evolved Gas Analyzer (TEGA) instrument was one of the tools that made this discovery. After soil samples were delivered to TEGA and transferred into small ovens, the samples could be heated up to approx.1000 C and the gases that evolved during heating were monitored by a mass spectrometer. A CO2 signal was detected at high temperature (approx.750 C) that has been attributed to calcium carbonate decomposition. In addition to this CO2 release, a lower temperature signal was seen. This lower temperature CO2 release was postulated to be one of three things: 1) desorption of CO2, 2) decomposition of a different carbonate mineral, or 3) CO2 released due to organic combustion. Cannon et al. [3] present another novel hypothesis involving the interaction of decomposition products of a perchlorate salt and calcium carbonate.

Archer, P. D. Jr.↗
NASA NTRS2012

Organic Combustion in the Presence of Ca-Carbonate and Mg-Perchlorate: A Possible Source for the Low Temperature CO2 Release Seen in Mars Phoenix Thermal and Evolved Gas Analyzer Data

Two of the most important discoveries of the Phoenix Lander were the detection of approx.0.6% perchlorate [1] and 3-5% carbonate [2] in landing site soils. The Thermal and Evolved Gas Analyzer (TEGA) instrument on the Phoenix lander could heat samples up to approx.1000 C and monitor evolved gases with a mass spectrometer. TEGA detected a low (approx.350 C) and high (approx.750 C) temperature CO2 release. The high temp release was attributed to the thermal decomposition of Ca-carbonate (calcite). The low temperature CO2 release could be due to desorption of CO2, decomposition of a different carbonate mineral, or the combustion of organic material. A new hypothesis has also been proposed that the low temperature CO2 release could be due to the early breakdown of calcite in the presence of the decomposition products of certain perchlorate salts [3]. We have investigated whether or not this new hypothesis is also compatible with organic combustion. Magnesium perchlorate is stable as Mg(ClO4)2-6H2O on the martian surface [4]. During thermal decomposition, this perchlorate salt releases H2O, Cl2, and O2 gases. The Cl2 can react with water to form HCl which then reacts with calcite, releasing CO2 below the standard thermal decomposition temperature of calcite. However, when using concentrations of perchlorate and calcite similar to what was detected by Phoenix, the ratio of high:low temperature CO2 evolved is much larger in the lab, indicating that although this process might contribute to the low temp CO2 release, it cannot account for all of it. While H2O and Cl2 cause calcite decomposition, the O2 evolved during perchlorate decomposition can lead to the combustion of any reduced carbon present in the sample [5]. We investigate the possible contribution of organic molecules to the low temperature CO2 release seen on Mars.

Archer, Douglas↗
NASA NTRS2017

Big lunar data visualization and analysis

NASA's earth and planetary spacecraft return large amounts of remote sensing data, such as imagery and raw science measurements, in support of remarkable research. Not only does the data lead to new scientific discoveries about our planet and the solar system, it provides a wealth of information to educate, inspire, and engage the public at large. To leverage this rich data for mission planning, scientific research, public outreach and education, it is essential to make it accessible and understandable, analyzable, all while appealing to their interests. This presentation will highlight web-based capabilities that showcase NASA's large volume of lunar data collected from past and current Moon missions. It is particularly relevant as the new Administration has more plans for the Moon. We will illustrate big data visualization and analysis in easy-touse and interactive mediums for diverse use.

Malhotra, Shan↗
NASA NTRSDate not supplied

WEBINAR, May 6: New Discoveries Using GeneLab

The NASA GeneLab project capitalizes on multi-omic technologies to maximize the return on spaceflight experiments. To do this, GeneLab maintains a publicly accessible database (GLDS) that houses spaceflight and spaceflight relevant multi-omics data and collaborates with NASA principal investigators and projects to generate additional omics data. GeneLab houses more than 220 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, animal and microbial experiments, with a growing number of these having been produced by the GeneLab sample processing lab. The GLDS contains rich metadata about each experiment and has recently integrated radiation dosimetry data from experiments flown on the Space Shuttle. GeneLab has also recently implemented an effort to present processed data in the GLDS in addition to the raw omics data. The processed data will enable interpretation of the data by a larger group of students, scientists and the general public. Standard pipelines for the transformation of raw data into visualizations were developed by four GeneLab Analysis Working Groups (animals, plants, microbes, multi-omics) comprised of over 120 scientists from NASA, industry, and academia. To explore the data, the GLDS provides users various tools for data analysis, collaborative workspace for file storage and sharing, and a visualization portal. The analysis platform built using the Galaxy toolshed provides access to a broad variety of users including those with limited bioinformatics experience and students to learn how to analyze spaceflight omics data. The visualization portal takes GeneLab one step closer to data democratization by removing all bioinformatics requisites to interpret transcriptomics data hosted in the repository. Discoveries made using GeneLab have begun and will continue to deepen our understanding of biology, advance the field of genomics, and help to discover cures for diseases, create better diagnostic tools, and ultimately allow astronauts to better withstand the rigors of long-duration spaceflight.

Sylvain V. Costes↗
DOE OSTI2026

SEED: Semantic Energy Exploration and Discovery

The Bioenergy Knowledge Discovery Framework (KDF) hosts a vast repository of specialized data, yet traditional keyword-based search methods often struggle to provide direct answers, requiring significant domain expertise and manual effort to filter through raw documents. To overcome these barriers, this software introduces a semantic search engine that enables both specialists and non-specialists to query the KDF using natural language. By shifting from rigid keyword matching to intent-based retrieval, the tool automatically identifies and ranks the most relevant sources within the database. The system functions by processing natural language queries to extract the most pertinent information, delivering an AI-generated plain-language summary alongside exact supporting quotes from retrieved documents. This integrated approach provides users with immediate, evidence-based answers while eliminating the need for exhaustive manual review. By surfacing direct insights and contextual evidence, the software enhances the usability of existing KDF resources and democratizes access to complex bioenergy data. Ultimately, this semantic search solution accelerates the discovery process and supports faster, more informed decision-making across the bioenergy sector.

Pan, Meiyu (Melrose) [Oak Ridge National Laborator↗
NASA NTRS2003

Mercury Conditions for the MESSENGER Mission Simulated in High- Solar-Radiation Vacuum Tests

The MESSENGER (Mercury Surface, Space Environment, Geochemistry, and Ranging) spacecraft, planned for launch in March 2004, will perform two flybys of Mercury before entering a year-long orbit of the planet in September 2009. The mission will provide opportunities for detailed characterization of the surface, interior, atmosphere, and magnetosphere of the closest planet to the Sun. The NASA Glenn Research Center and the MESSENGER spacecraft integrator, the Johns Hopkins University Applied Physics Laboratory, have partnered under a Space Act Agreement to characterize a variety of critical components and materials under simulated conditions expected near Mercury. Glenn's Vacuum Facility 6, which is equipped with a solar simulator, can simulate the vacuum and high solar radiation anticipated in Mercury orbit. The MESSENGER test hardware includes a variety of materials and components that are being characterized during the Tank 6 vacuum tests, where the hardware will be exposed to up to 11 suns insolation, simulating conditions expected in Mercury orbit. In 2002, ten solar vacuum tests were conducted, including beginning of life, end of life, backside exposure, and solar panel thermal shock cycling tests. Components tested include candidate solar array panels, sensors, thermal shielding materials, and communication devices. As an example, for the solar panel thermal shock cycling test, two candidate solar array panels were suspended on a lift mechanism that lowered the panels into a liquid-nitrogen-cooled box. After reaching -140 C, the panels were then lifted out of the box and exposed to the equivalent of 6 suns (8.1 kilowatts per square meters). After five cold soak/heating cycles were completed successfully, there was no apparent degradation in panel performance. An anticipated 100-hr thermal shield life test is planned for autumn, followed by solar panel flight qualification tests in winter. Glenn's ongoing support to the MESSENGER program has been instrumental in identifying design solutions and validating thermal performance models under a very aggressive development schedule. The test data have assisted Johns Hopkins engineers in selecting a flight solar array vendor and a thermal shield design. MESSENGER is one in a series of missions in NASA's Discovery Program. Infrared thermography provides data on the thermal gradients in the MESSENGER components during high solar insolation vacuum testing.

Wong, Wayne A.↗
DOE OSTI2026

Intelligent experiments through real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and future EIC detectors (Phase-I)

With an ever increasing demand for high precision data from modern detectors for discovery science and precision measurements, all major high energy nuclear and particle experiments, current and future, are facing the challenge on how to deal with the large volume of raw data generated from sophisticated state-of-the-art detectors in high rate collisions. These goals need to be balanced with available hardware and cost limits on DAQ (Data AcQuisition system) bandwidth and offline computing resources to capture, store and process the signal events. Two prototypical examples are the upcoming sPHENIX experiment, the DOE next generation heavy ion physics experiment at the Relativistic Heavy Ion Collider at BNL, and the future EIC experiments that are planned to be online circa 2030.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Weak Form Scientific Machine Learning: Test Function Construction for System Identification

Weak form Scientific Machine Learning (WSciML) is a recently developed framework for data-driven modeling and scientific discovery. It leverages the weak form of equation error residuals to provide enhanced noise robustness in system identification via convolving model equations with test functions, reformulating the problem to avoid direct differentiation of data. The performance, however, relies on wisely choosing a set of compactly supported test functions. In this work, we mathematically motivate a novel data-driven method for constructing Single-scale-Local reference functions for creating the set of test functions. Our approach numerically approximates the integration error introduced by the quadrature and identifies the support size for which the error is minimal, without requiring access to the model parameter values. Through numerical experiments across various models, noise levels, and temporal resolutions, we demonstrate that the selected supports consistently align with regions of minimal parameter estimation error. We also compare the proposed method against the strategy for constructing Multi-scale-Global (and orthogonal) test functions introduced in our prior work, demonstrating the improved computational efficiency.

FOS: Computer and information sciences↗
NASA NTRS1986

Fault and Error Latency Under Real Workload: an Experimental Study

A practical methodology for the study of fault and error latency is demonstrated under a real workload. This is the first study that measures and quantifies the latency under real workload and fills a major gap in the current understanding of workload-failure relationships. The methodology is based on low level data gathered on a VAX 11/780 during the normal workload conditions of the installation. Fault occurrence is simulated on the data, and the error generation and discovery process is reconstructed to determine latency. The analysis proceeds to combine the low level activity data with high level machine performance data to yield a better understanding of the phenomena. A strong relationship exists between latency and workload and that relationship is quantified. The sampling and reconstruction techniques used are also validated. Error latency in the memory where the operating system resides was studied using data on the physical memory access. Fault latency in the paged section of memory was determined using data from physical memory scans. Error latency in the microcontrol store was studied using data on the microcode access and usage.

Chillarege, Ram↗
NASA NTRS2014

Use of Semantic Technology to Create Curated Data Albums

One of the continuing challenges in any Earth science investigation is the discovery and access of useful science content from the increasingly large volumes of Earth science data and related information available online. Current Earth science data systems are designed with the assumption that researchers access data primarily by instrument or geophysical parameter. Those who know exactly the data sets they need can obtain the specific files using these systems. However, in cases where researchers are interested in studying an event of research interest, they must manually assemble a variety of relevant data sets by searching the different distributed data systems. Consequently, there is a need to design and build specialized search and discovery tools in Earth science that can filter through large volumes of distributed online data and information and only aggregate the relevant resources needed to support climatology and case studies. This paper presents a specialized search and discovery tool that automatically creates curated Data Albums. The tool was designed to enable key elements of the search process such as dynamic interaction and sense-making. The tool supports dynamic interaction via different modes of interactivity and visual presentation of information. The compilation of information and data into a Data Album is analogous to a shoebox within the sense-making framework. This tool automates most of the tedious information/data gathering tasks for researchers. Data curation by the tool is achieved via an ontology-based, relevancy ranking algorithm that filters out non-relevant information and data. The curation enables better search results as compared to the simple keyword searches provided by existing data systems in Earth science.

Ramachandran, Rahul↗
NASA NTRS2011

PSR J0007+7303 in the CTA1 SNR: New Gamma-ray Results from Two Years of Fermi-LAT Observations

One of the main results of the Fermi Gamma-Ray Space Telescope is the discovery of gamma-ray selected pulsars. The high magnetic field pulsar, PSR J0007+7303 in CTA1, was the first ever to be discovered through its gamma-ray pulsations. Based on analysis of 2 years of LAT survey data, we report on the discovery of I-ray emission in the off-pulse phase interval at the approx. 6sigma level. The flux from this emission in the energy range E > or =::: 100 MeV is F(sub 100) = (1.73+/-0.40) x 10(exp -8) photons/sq cm/s and is best fitted by a power law with a photon index of Gamma = 2.54+/-0.14. The pulsed gamma-ray flux in the same energy range is F(sub 100) = (3.95+/-0.07) x 10(exp -7) photons/sq cm/s and is best fitted by an exponentially-cutoff power-law spectrum with a photon index of Gamma = 1.41+/-0.23 and a cutoff energy E(sub c) = 4.04+/-0.20 GeV. We find no flux variability neither at the 2009 May glitch nor in the long term behavior. We model the gamma-ray light curve with two high-altitude emission models, the outer gap and slot gap, and find that the model that best fits the data depends strongly on the assumed origin of the off-pulse emission. Both models favor a large angle between the magnetic axis and observer line of sight, consistent with the nondetection of radio emission being a geometrical effect. Finally we discuss how the LAT results bear on the understanding of the cooling of this neutron star.

Abdo, A. A.↗
DOE OSTI2026

Expanding Access to Science Participation: A FAIR Framework for Petascale Data Visualization and Analytics

The massive data generated by scientists daily serve as both a major catalyst for new discoveries and innovations, as well as a significant roadblock that restricts access to the data. Here, our paper introduces a new approach to removing Big Data barriers and democratizing access to petascale data for the broader scientific community. Our novel data fabric abstraction layer allows user-friendly querying of scientific information while hiding the complexities of dealing with file systems or cloud services. We enable FAIR (Findable, Accessible, Interoperable, and Reusable) access to datasets such as NASA’s petascale climate datasets. Our paper presents an approach to managing, visualizing, and analyzing petabytes of data within a browser on equipment ranging from the top NASA supercomputer to commodity hardware like a laptop. Our novel data fabric abstraction utilizes state-of-the art progressive compression algorithms and machine-learning insights to power scalable visualization dashboards for petascale data. The result provides users with the ability to identify extreme events or trends dynamically, expanding access to scientific data and further enabling discoveries. We validate our approach by improving the ability of climate scientists to visually explore their data via three fully interactive dashboards. We further validate our approach by deploying the dashboards and simplified training materials in the classroom at a minority-serving institution. These dashboards, released in simplified form to the general public, contribute significantly to a broader push to democratize the access and use of climate data.

Computer science↗