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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 NTRS2019

Assessment of a One-Dimensional Finite Element Charring Ablation Material Response Model for Phenolic-Impregnated Carbon Ablator

In this study, mathematical formulations to model charring ablation problems were numerically implemented using finite element analysis (FEA) with ABAQUS, which account for the material decomposition and progressive surface removal in the heat conduction and the surface energy balance equations. FEA was performed for a one-dimensional model to predict the temperature and ablation histories of a phenolic-impregnated carbon ablator sample (i.e., a common heat shield material for hypersonic vehicles and spacecraft) subjected to oxy-acetylene torch flame (i.e., 0.8 SLPM acetylene gas to 2.7 SLPM oxygen gas). The recovery enthalpy and convective heat transfer coefficient for the ablation model were calculated based on gas compositions and two assumed surface conditions (i.e., equilibrium and frozen). Simulations using the calculated recovery enthalpy and convective heat transfer coefficient resulted in a recession rate of 6.38 times (equilibrium) and 14.08 times (frozen) higher than the experimental data, despite fair agreement of the surface temperature. In addition, the effect of the heat transfer coefficient was investigated through a steady-state ablation analysis. The results of the analysis indicate that there is not one single value for the heat transfer coefficient that would allow the prediction to match both measured recession rate and surface temperature. Possible reasons for such an inconsistency are provided and discussed.

Thermal Protection System↗
NASA NTRS2021

Validation of SMAP L2 passive-only soil moisture products using upscaled in situ measurements collected in Twente, the Netherlands

The Twente region in the east of the Netherlands has a network with twenty soil monitoring stations that has been utilized for validation of the Soil Moisture Active/Passive (SMAP) passive-only soil moisture products. Over the period from April 2015 until December 2018, seven stations covered by the SMAP reference pixels 15 have fairly complete data records. Spatially distributed soil moisture simulations with the Dutch national hydrological model have been utilized for the development of upscaling functions to translate the spatial mean of point measurements to the domain of the SMAP reference pixels. The native and upscaled spatial soil moisture means computed using the in-situ measurements have been adopted as references to assess the performance of the SMAP i) Single Channel Algorithm at Horizontal Polarization (SCA-H), ii) Single Channel Algorithm at Vertical Polarization (SCA-V), and iii) Dual Channel Algorithm (DCA) soil moisture estimates. In the case of the Twente network it was found that the SCA-V soil moisture retrieved SMAP observations collected in the afternoon had the best agreement with the native spatial mean leading to an unbiased Root Mean Squared Error (uRMSE) of 0.059 m3 m-3, whereas for the upscaled in-situ references primarily larger biases were found. These error levels are larger than the mission’s target accuracy of 0.04 m3 m-3, which can be attributed to large over- and underestimation errors (>0.08 m3 m-3) in particular at the end of dry spells and during freezing, respectively. The strong vertical dielectric gradients associated with rapid soil freezing and wetting causes the disparity in soil depth characterized by SMAP and in situ that leads to the large mismatches. Once filtered for frozen conditions and antecedent rainfall the uRMSE improves to 0.043 m3 m-3.

Rogier van der Velde↗
NASA NTRSDate not supplied

NASA GeneLab: Open Science for Life in Space

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 350 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, animal and microbial experiments, with a growing number of these having been produced by the GeneLab Sequencing Lab. The GLDS contains rich metadata about each experiment and has integrated radiation dosimetry data from experiments flown on the Space Shuttle, International Space Station, and Free Flying spacecrafts. With the increasing amount and complexity of omics data being generated, GeneLab utilizes community-defined, common models for metadata and terminology so that omics data and results are discoverable and reliably reproducible. GeneLab uses the ISA-Tab specification and semantic model for organizing and representing omics metadata. In addition to metadata standards, data files must be open-source file or common exchange formats to ensure accessibility and usability by all users. To ease data ingestion and transfer, the web-based submission tool allows PIs a user-friendly user interface to curate, organize, and publish their space relevant omics data. In the more recent years, data curation and submission portal has incorporated the FAIR principles making data findable, accessible, interoperable, and reusable. To increase reusability of data, GeneLab has 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 200 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. To train the next generation of scientists, NASA offers training programs such as GeneLab 4 High School (GL4HS) and GeneLab 4 Universities. NLM Curation at a Scale Workshop 2022 | NASA GeneLab (GL4U) to teach students bioinformatics and computational biology methods to analyze omics data. 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.

GeneLab↗
NASA NTRS1993

One Millimeter Horizontal Precision Over a 41 Km Baseline Using P-Codeless Data

High accuracy geodetic solutions at the few ppb level have become fairly standard with data from a global network of P-code Global Positioning System (GPS) receivers. Used as a measurement tool, it has numerous scientific applications including the monitoring of crustal motion, volcanic and post-seismic uplift, co- and post-seismic displacements in fault zones and media delay calibration and monitoring.

Global Positioning System↗
NASA NTRSDate not supplied

ICARTT File Format Enhancements: Supporting FAIRness of Airborne and Field Campaign Data

The ICARTT (International Consortium for Atmospheric Research on Transport and Transformation) standards were developed to fulfill data management needs for the ICARTT campaign in 2004. The ICARTT file format is text-based and composed of a header with important data description information and the data section. The ICARTT format, built on the NASA Ames and GTE data formats, was created to facilitate data exchange and promote collaborations among the science teams for achieving the ICARTT campaign goals. Due to the success of the ICARTT campaign, the ICARTT file format was exposed to a broad range of airborne researchers and was adopted for use in many other field campaigns sponsored by NASA and other partner agencies. The ICARTT format standards became a NASA standard in 2010 and was amended in January 2017 providing many enhancements, including the requirement for variable standard names. Primarily designed for airborne field studies, ICARTT has been further utilized for ground-based studies. The ICARTT format can host metadata that is critical for proper use of the data, especially for in-situ measurements. However, the information that needs to be included is often in free text, meaning the information are human readable, but not machine interpretable. Furthermore, the amount and type of information provided can vary substantially between principal investigators and campaigns. To support interoperability and FAIR principles, further enhancements to the ICARTT standards are recommended. Possible recommendations include standardizing timestamps for easier data comparisons and analysis; potential use of controlled and consistent vocabulary for variable short name and certain common metadata elements; and providing guidance on variable measurement units and how they are reported.

Megan Buzanowicz↗
NASA NTRSDate not supplied

FAIR-ness Assessment of NASA’s Earth Observation System Data and Information System (EOSDIS)

This presentation addresses the challenge of evaluating a multi-disciplinary institutional network of data repositories in operation since 1994 against the relatively recent criteria that constitute FAIR (Findable, Accessible, Interoperable, Reusable) data. NASA’s Earth Observation System Data and Information System (EOSDIS), with its 12 discipline-based Distributed Active Archive Centers (DAACs), preceded the definition and popularization of FAIR by over two decades. An assessment is very useful to describe how well the FAIR principles are met and to identify any improvements needed. In 2020, A “self-assessment” of EOSDIS and DAACs was performed by the ESDIS Project staff and the DAACs from the points of view of human actionability and machine actionability. More recently, a draft of a Science Mission Directorate (SMP) Program Directive (SPD-41a) has been released by NASA Headquarters for comment, where it is recommended that all SMD-funded data should follow the FAIR principles. This presentation is timely to initiate community discussion within the Information Quality Cluster (IQC) of the Earth Science Information Partners (ESIP) and help strategize and develop implementation guidelines for EOSDIS and DAACs to conform to FAIR principles.

Remote sensing↗
NASA NTRS1978

Direct measurements of the atmospheric conduction current

A method of measuring the atmospheric conduction current above the ground has been employed to obtain data for 12 weeks during the first half of 1974. The instrument consists of a split aluminum sphere suspended by insulated wires to a wooden frame. The measuring electronics and the transmitter are enclosed within the spherical structure. The interaction of the instrument with its atmospheric electrical environment is analyzed, and it is shown that in steady state conditions, predictable differences in the instrumentally measured currents and the atmospheric conduction current will be less than 5% and in the nonsteady state situations the difference is less than 20%. Diurnal variations, a probable winter-summer variation, sunrise, and fog effects were observed for the data obtained during fair-weather conditions. Disturbed weather data are interpreted for the effects of low clouds on the atmospheric current. The charge concentrations within overcast clouds sufficient to produce the observed reversed atmospheric currents are estimated to be small in relation to values in thunderclouds.

Burke, H. K.↗
NASA NTRS1984

The dust around R Coronae Borealis type stars

Measurements taken by the International Ultraviolet Explorer spacecraft of the stars RY Sgr and R CrB have been analyzed using Mie theory. The extinction data, which show a 2400-2500 A peak, are consistent with a distribution of 5-60 nm glassy or amorphous carbon particles obscuring the stellar flux. The data are also fairly consistent with a cloud ejection model. Since the extinction data lack the commonly observed peak at 2170 A, it is proposed that this difference is due to the conditions present when the dust condenses. Interstellar carbon grains appear to originate in normal carbon stars which are carbon and hydrogen rich. In contrast, the grains around R CrB type stars seem to condense from a carbon-rich and hydrogen-poor vapor.

Hecht, J. H.↗
DOE OSTI2025

A Grassroots Network and Community Roadmap for Interconnected Autonomous Science Laboratories for Accelerated Discovery

Scientific discovery is being revolutionized by AI and autonomous systems, yet current autonomous laboratories remain isolated islands unable to collaborate across institutions. We present the Autonomous Interconnected Science Lab Ecosystem (AISLE), a grassroots network transforming fragmented capabilities into a unified system that shorten the path from ideation to innovation to impact and accelerates discovery from decades to months. AISLE addresses five critical dimensions: (1) cross-institutional equipment orchestration, (2) intelligent data management with FAIR compliance, (3) AI-agent driven orchestration grounded in scientific principles, (4) interoperable agent communication interfaces, and (5) AI/ML-integrated scientific education. By connecting autonomous agents across institutional boundaries, autonomous science can unlock research spaces inaccessible to traditional approaches while democratizing cutting-edge technologies. This paradigm shift toward collaborative autonomous science promises breakthroughs in sustainable energy, materials development, and public health.

Ferreira da Silva, Rafael [Oak Ridge National Labo↗
DOE OSTI2025

Applying the FAIR Principles to computational workflows

Recent trends within computational and data sciences show an increasing recognition and adoption of computational workflows as tools for productivity and reproducibility that also democratize access to platforms and processing know-how. As digital objects to be shared, discovered, and reused, computational workflows benefit from the FAIR principles, which stand for Findable, Accessible, Interoperable, and Reusable. The Workflows Community Initiative’s FAIR Workflows Working Group (WCI-FW), a global and open community of researchers and developers working with computational workflows across disciplines and domains, has systematically addressed the application of both FAIR data and software principles to computational workflows. We present recommendations with commentary that reflects our discussions and justifies our choices and adaptations. These are offered to workflow users and authors, workflow management system developers, and providers of workflow services as guidelines for adoption and fodder for discussion. The FAIR recommendations for workflows that we propose in this paper will maximize their value as research assets and facilitate their adoption by the wider community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A data integration framework of additive manufacturing based on FAIR principles

Abstract Laser-powder bed fusion (L-PBF) is a popular additive manufacturing (AM) process with rich data sets coming from both in situ and ex situ sources. Data derived from multiple measurement modalities in an AM process capture unique features but often have different encoding methods; the challenge of data registration is not directly intuitive. In this work, we address the challenge of data registration between multiple modalities. Large data spaces must be organized in a machine-compatible method to maximize scientific output. FAIR (findable, accessible, interoperable, and reusable) principles are required to overcome challenges associated with data at various scales. FAIRified data enables a standardized format allowing for opportunities to generate automated extraction methods and scalability. We establish a framework that captures and integrates data from a L-PBF study such as radiography and high-speed camera video, linking these data sets cohesively allowing for future exploration. Graphical abstract

36 MATERIALS SCIENCE↗
DOE OSTI2024

Challenges of open data in aquatic sciences: issues faced by data users and data providers

Free use and redistribution of data (i.e., Open Data) increases the reproducibility, transparency, and pace of aquatic sciences research. However, barriers to both data users and data providers may limit the adoption of Open Data practices. Here, we describe common Open Data challenges faced by data users and data providers within the aquatic sciences community (i.e., oceanography, limnology, hydrology, and others). These challenges were synthesized from literature, authors’ experiences, and a broad survey of 174 data users and data providers across academia, government agencies, industry, and other sectors. Through this work, we identified seven main challenges: 1) metadata shortcomings, 2) variable data quality and reusability, 3) open data inaccessibility, 4) lack of standardization, 5) authorship and acknowledgement issues 6) lack of funding, and 7) unequal barriers around the globe. Our key recommendation is to improve resources to advance Open Data practices. This includes dedicated funds for capacity building, hiring and maintaining of skilled personnel, and robust digital infrastructures for preparation, storage, and long-term maintenance of Open Data. Further, to incentivize data sharing we reinforce the need for standardized best practices to handle data acknowledgement and citations for both data users and data providers. We also highlight and discuss regional disparities in resources and research practices within a global perspective.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1975

An interactive graphics program to retrieve, display, compare, manipulate, curve fit, difference and cross plot wind tunnel data

The Aerodynamic Data Analysis and Integration System (ADAIS), developed as a highly interactive computer graphics program capable of manipulating large quantities of data such that addressable elements of a data base can be called up for graphic display, compared, curve fit, stored, retrieved, differenced, etc., was described. The general nature of the system is evidenced by the fact that limited usage has already occurred with data bases consisting of thermodynamic, basic loads, and flight dynamics data. Productivity using ADAIS of five times that for conventional manual methods of wind tunnel data analysis is routinely achieved. In wind tunnel data analysis, data from one or more runs of a particular test may be called up and displayed along with data from one or more runs of a different test. Curves may be faired through the data points by any of four methods, including cubic spline and least squares polynomial fit up to seventh order.

Elliott, R. D.↗
NASA NTRS2012

Model to Test Electric Field Comparisons in a Composite Fairing Cavity

Evaluating the impact of radio frequency transmission in vehicle fairings is important to sensitive spacecraft. This study shows cumulative distribution function (CDF) comparisons of composite . a fairing electromagnetic field data obtained by computational electromagnetic 3D full wave modeling and laboratory testing. This work is an extension of the bare aluminum fairing perfect electric conductor (PEC) model. Test and model data correlation is shown.

Trout, Dawn↗
NASA NTRS2013

Model to Test Electric Field Comparisons in a Composite Fairing Cavity

Evaluating the impact of radio frequency transmission in vehicle fairings is important to sensitive spacecraft. This study shows cumulative distribution function (CDF) comparisons of composite a fairing electromagnetic field data obtained by computational electromagnetic 3D full wave modeling and laboratory testing. This work is an extension of the bare aluminum fairing perfect electric conductor (PEC) model. Test and model data correlation is shown.

Trout, Dawn H.↗
NASA NTRSDate not supplied

Big-data Efficient and Automated Science Transfer (BEAST): An Open-Source Software Architecture for Arc Jet Data Management, Modeling, and Automation

Big-data Efficient and Automated Science Transfer (BEAST) was conceived to address the existing ground testing data management of the NASA Ames arc jet facilities (e.g., manually entered Excel files and USB drive data transfers). These data management practices were seen as a choke point for future thermal protection system (TPS) development as they limit statistical tracking, resolution of diagnostics, coordination between video/time series, data throughput, and data processing speed/efficiency. Consequently, BEAST was developed to provide a new data infrastructure with streamlined data collection, processing, transfer, and analysis. This new framework also seeks to implement the FAIR principles of data stewardship: Findable, Accessible, Interoperable, and Reusable. The BEAST framework is based on a combination of the Python Django web framework and the Python data stack to provide a monolithic, open-source platform for data management, automation, and machine learning. This architecture was chosen for maintainability and scalability for a small, in-house development team. This paper will describe the application framework, deployment, and discuss the benefits and future plans for the system.

Data management↗