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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

The Microwave Radiative Properties of Falling Snow Derived from Nonspherical Ice Particle Models. Part II: Initial Testing Using Radar, Radiometer and In Situ Observations

In this study, two different particle models describing the structure and electromagnetic properties of snow are developed and evaluated for potential use in satellite combined radar-radiometer precipitation estimation algorithms. In the first model, snow particles are assumed to be homogeneous ice-air spheres with single-scattering properties derived from Mie theory. In the second model, snow particles are created by simulating the self-collection of pristine ice crystals into aggregate particles of different sizes, using different numbers and habits of the collected component crystals. Single-scattering properties of the resulting nonspherical snow particles are determined using the discrete dipole approximation. The size-distribution-integrated scattering properties of the spherical and nonspherical snow particles are incorporated into a dual-wavelength radar profiling algorithm that is applied to 14- and 34-GHz observations of stratiform precipitation from the ER-2 aircraft-borne High-Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP) radar. The retrieved ice precipitation profiles are then input to a forward radiative transfer calculation in an attempt to simulate coincident radiance observations from the Conical Scanning Millimeter-Wave Imaging Radiometer (CoSMIR). Much greater consistency between the simulated and observed CoSMIR radiances is obtained using estimated profiles that are based upon the nonspherical crystal/aggregate snow particle model. Despite this greater consistency, there remain some discrepancies between the higher moments of the HIWRAP-retrieved precipitation size distributions and in situ distributions derived from microphysics probe observations obtained from Citation aircraft underflights of the ER-2. These discrepancies can only be eliminated if a subset of lower-density crystal/aggregate snow particles is assumed in the radar algorithm and in the interpretation of the in situ data.

Ice particles; Radiances; Radiative transfer; Airc↗
NASA NTRSDate not supplied

NASA/EOSDIS Persistent Identifier Implementation

This presentation briefly summarizes the NASA Earth Science Data & Information Systems (ESDIS) Project’s approach to persistent identifiers for Earth science data sets and documentation in the Earth Observing System Data & Information System (EOSDIS). It briefly summarizes the ESDIS Project’s policies, and implementation approach, using Digital Object Identifiers.

Persistent Identifiers↗
NASA NTRSDate not supplied

Application of ML/AI for Identifying Earth Science Datasets in Research Publications

NASA Data Active Archive Centers, or DAACs, ingest, store and distribute data acquired from satellites, ground systems as well as modelling data. These data are organized by the datasets, each presenting collection of files usually associated with the certain mission, instrument, processing level, parameter(s), algorithm and/or model. The number of datasets offered by a single DAAC to the public varies. GES DISC, for example, currently offers for public use approximately ~1,300 datasets. While each publicly offered dataset comes with supporting documentation, it is challenging for novice and even experienced scientists to navigate among the datasets that offer similar parameters to find the datasets for their particular research application. Supplying dataset documentation with the scientific paper citations that refer to that dataset provides means for the dataset users to educate themselves with the application research that dataset is being used in. Collecting citations of the papers that use the datasets for their research yield valuable insights into application areas of those datasets, information about usage of the dataset groups for specific applications and those application topics. It also gives insights into the “deep metrics” of the dataset usage, as opposed to the common metrics of the dataset usage such as number of users who downloaded the dataset files and volumes of downloaded data. Association of a certain scientific paper with the dataset(s) presents a challenge because most of the paper authors do not properly cite the datasets, datasets usually have cryptic names and Digital Object Identifiers (DOIs) that are used for dataset identification were assigned to the datasets only few years ago. Simple Google or online library search do not provide even meaningful fraction of the results when performed by the dataset name or DOI, however they provide too many results when the search is done by more broader terms such as mission and instrument names. Attempts to create an AI system capable to identify dataset in the scientific papers have already been made using neural networks classifiers on the basis of the dataset mission, instrument and variable name. This method was applied to NASA SEDAC, which has 41 datasets in total. In GES DISC there can be as many as ~100 datasets per mission/instrument with some of the datasets consisting of multiple variables so there is a need for more differentiating parameters for dataset identification in the paper. The approach we are currently investigating is creating AI classifiers that are based on multiple dataset features, or keywords, extracted from the NASA Earthdata Common Dataset Repository (CMR). The features are weighted based on how precisely they can identify a dataset. The classifier uses preprocessed paper text as input and searches for the CMR datasets whose feature sets are the closest to the feature sets contained in the paper. The challenges of dataset identification include variety of ways the paper authors describe the datasets in their papers and incomplete tagging of the CMR dataset description (DIFs).

Irina Gerasimov↗
DOE OSTI2025

UNR Grant Proposal Contribution

The work of Carrier et. al. (citation needed for Matt’s effect of surface roughness on phase transitions/ETI paper) demonstrates that 1D and 2D resistive magnetohydrodynamic (MHD) simulations can reliably model exploding aluminum rods driven by megaampere currents. Close agreement between simulation results and photonic Doppler velocimetry data from the Mykonos electrothermal instability (METI-II) campaign builds confidence in predictive modelling capabilities for pulsed-power HED experiments. Furthermore, 2D MHD simulations show how machined features and micro-scale surface roughness can seed the electrothermal instability (ETI). Surface roughness was observed to reduce the time of melt by 19%, with ETI growth driving the enhanced heating of the rod surface.

36 MATERIALS SCIENCE↗
NASA NTRS1983

Documentation for the machine-readable version of A Catalogue of Extragalactic Radio Source Identifications (Veron-Cetty and Veron 1983)

Detailed descriptions of the data and reference files of the updated and final version of the machine-readable catalog are given. The computerized catalog has greatly expanded since the original published version (1974), and additional information is given. A separate reference file contains bibliographical citations ordered simultaneously by numerical reference and alphabetically by author.

Warren, W. H., Jr.↗
NASA NTRS1993

NASA STI program database: Journal coverage (1990-1992)

Data are given in tabular form on the extent of recent journal accessions (1990-1992) to the NASA Scientific and Technical Information (STI) Database. Journals are presented by country in two ways: first by an alphabetical listing; and second, by the decreasing number of citations extracted from these journals during this period. An appendix containing a statistical summary is included.

Source record↗
NASA NTRSDate not supplied

A Hybrid Approach to Labeling Datasets in Earth Science Publications

NASA Data Centers provide the public with thousands of datasets that result in published papers, reports, and conference proceedings. Collecting accurate metrics on usage of these datasets is key to connecting different areas of knowledge and evaluating the datasets’ impact. While most of the datasets have Digital Object Identifiers (DOIs) assigned, most publications do not cite them hampering the automated search of these publications. Instead, articles mention attributes like organization, instrument, mission, variable, or a publication describing the dataset. Often only domain experts can deduce the dataset that was used in the publication text. The lack of a citation slows the spread of information and reduces the research’s impact. With thousands of papers produced each year, an automated means of labeling datasets is critical. This paper explores a hybrid approach of heuristics and a Natural Language Processing (NLP) Named Entity Recognition (NER) model to find and label the datasets used within Earth Science papers. Heuristics are used to produce the labelled sentences and any potential dataset candidates that can be derived from a sentence. The heuristic labels the sentences with the names of mission, instrument, re-analysis models, and science keywords taken from the Global Change Master Directory (GCMD) ontology. Additionally, it uses those labels to generate the dataset citation candidates. If the mission, instrument, and variable are sufficient to create the citation for the dataset the citation and the label the domain expert reviews the output without going through the NLP model. If the extracted label is not sufficient to label the dataset on its own, the sentence and its associated dataset labels will be inputted into the NER model. The model outputs the labeled sentence and the potential dataset candidates with their associated probabilities. The domain expert then reviews the NER model’s output and the correct labels are determined. The newly labelled papers can then be used as additional training data. This creates an iterative process for the approach to continuously improve. Because all the possible mentions are gathered by the model, the domain expert can quickly and easily label the papers resulting in large time savings.

Jacob Atkins↗
NASA NTRS2004

Parameterization of the Vertical Variability of Tropical Cirrus Cloud Microphysical and Optical Properties

Cloud Integrating Nephelometers (CIN) were flown on the U. North Dakota Citation aircraft and the NASA WB-57 aircraft for the purpose of measuring in-situ the optical extinction coefficient and the asymmetry parameter (g) at a wavelength of 635 nm of primarily ice particles encountered during the NASA CRYSTAL-FACE study of large cumulus clouds (Cu) and their anvils found in the southern Florida region. The probes performance was largely successful and produced archived data for vertical profiles of extinction, asymmetry parameter, and effective radius (Re), the latter being obtained by combining CIN and CVI (total water; Oregon State U.) measurements. Composites of the CIN and CVI data describing the average microphysical and optical behavior of the Cu and their anvils showed the following: The extinction increases with height as a result of the size of the particles also decreasing with height as shown by the Re measurements; near the top of anvils the size of the primary ice particles is about 10-um radius; and the value of g does not vary significantly with height and has a mean value of about 0.73 consistent with the idea that ambient ice crystals are primarily of complex shape and reflect solar radiation more efficiently than particles of pristine crystal shape. Other observations include: The g measurements were found to be an indicator of the phase of the cloud permitting identification of the clouds with water droplets, rain, and ice; visual ranges as small as several tens of meters were occasionally found in "extinction cores" that coincided with strong updraft cores; and comparison of the cloud probes on the Citation showed significant disagreement.

Gerber, Hermann E.↗
NASA NTRS1996

Blowdown Wind Tunnels: Latest Citations from the Aerospace Database

The bibliography contains citations concerning the design, construction, operation, and performance of blowdown wind tunnels. The use of compressed gas, mechanical piston, or combustion exhaust to provide continuous or short-duration operation from transonic to hypersonic approach velocities is discussed. Also covered are invasive and non-invasive aerothermodynamic instrumentation, data acquisition and reduction techniques, and test reports on aerospace components. Comprehensive coverage of wind tunnel force balancing systems and supersonic wind tunnels are covered in separate bibliographies.

BIBLIOGRAPHIES↗
NASA NTRSDate not supplied

Automated Collection of Scientific Publications Linked to NASA Earth Science Datasets

NASA's Earth Observing System Data and Information System (EOSDIS) began dataset Digital Object Identifier (DOI) registration in 2012. The number of dataset DOIs registered as of January of 2023 exceeds 11,000. As the research community becomes aware of the importance of sharing data through Open Science and optimizing data reuse through Findability, Accessibility, Interoperability, and Reuse (FAIR) data management principles, datasets are increasingly being cited in scientific publications. When datasets are cited explicitly by DOI within published works, automated methods can be developed for collecting these published works from a variety of bibliometric sources. The coverage of the sources varies, so each source can collect citations that are only available within it. Using major citation databases such as Scopus and Web of Science, the Google Scholar search engine, the CrossRef Open Citation Index, and the dataset DOI registry DataCite, we present an automated workflow for dataset citation collection. By harvesting citations automatically, a citation library is created explicitly linking EOSDIS datasets to publications that cite them. Using Zotero, a free and open-source citation manager, we demonstrate how to access and browse this library by the tags indicating bibliometric sources, dataset DOI, and the dataset archive center. We also demonstrate temporary trends in the number of publications harvested from bibliometric sources.

Infometrics↗
NASA NTRS1998

Upper tropospheric water vapor: A field campaign of two Raman lidars, Airborne hygrometers, and Radiosondes

Water vapor in the atmosphere plays an important role in radiative transfer and the process of radiative balance so critical for understanding global change. It is the principal ingredient in cloud formation, one of the most difficult atmospheric processes to model, and the most variable component of the Earth-atmosphere albedo. And as a free molecule, it is the most active infrared absorber and emitter, thus, the most important greenhouse gas. The radiative impact of water vapor is important at all levels of the atmosphere. Even though moisture decreases by several orders-of-magnitude from the Earth's surface to the tropopause, recent research has shown that, from a radiative standpoint, a small percentage change in water vapor at any level is nearly equivalent. Therefore accurate and precise measurements of this important atmospheric constituent are needed at all levels to evaluate the full radiative impact. The need for improved measurements in the upper troposphere is particularly important because of the generally hostile (very dry and cold) conditions encountered. Because of the importance of water vapor to the understanding of radiative transfer, the Department of Energy's Atmospheric Radiation Measurements (ARM) program initiated a series of measurement campaigns at the Cloud And Radiation Testbed (CART) site in Oklahoma, especially focused on atmospheric water vapor. Three water vapor intensive observation period (water vapor IOP) campaigns were planned. Two of the water vapor IOP campaigns have been completed: the first IOP was held during the fall of 1996 with a focus on boundary layer water vapor measurements, and the second was conducted during the fall of 1997 with a focus on both boundary layer moisture e and moisture in the upper troposphere. This paper presents a review of the intercomparisons of water vapor measurements in the upper troposphere aquired during the second water vapor IOP. Data to be presented include water vapor measurements ements from: two Raman Lidars, the NASA Goddard Scanning Raman Lidar (SRL) and the CART Raman Lidar (CARL), a number of Vaisala radiosondes launched during the IOP campaign, and a dew point hygrometer flown on the University of North Dakota Cessna Citation Aircraft.

Melfi, S. Harvey↗
NASA NTRS1975

Research and technology operating plan summary: Fiscal year 1975 research and technology program

Summaries are presented of Research and Technology Operating Plans currently in progress throughout NASA. Citations and abstracts of the operating plans are presented along with a subject index, technical monitor index, and responsible NASA organization index. Research programs presented include those carried out in the Office of Aeronautics and Space Technology, Office of Energy Programs, Office of Applications, Office of Space Sciences, Office of Tracking and Data Acquisition, and the Office of Manned Space Flight.

Source record↗
DOE OSTI2024

MultiSector Dynamics: 2023 Inaugural Workshop Report

Preface The MultiSector Dynamics (MSD) Community of Practice (CoP) hosted an inaugural workshop on October 3-5, 2023 at the University of California, Davis, to bring together members of the MSD community of practice to advance understanding of the co-evolution of human and natural systems, and to build the next generation of tools that bridge sectors, scales, and systems to realize a more resilient and equitable future. The theme of the workshop was "Advancing Complex Adaptive Human-Earth Systems Science in a World of Interconnected Risks". This document outlines the motivation for the workshop, its goals and objectives, the application process, the agenda, overviews of the training sessions offered to the workshop participants and a summary of each breakout session. The MSD workshop report further discusses the feedback from workshop participants and presents some reflections and next steps. The MSD Workshop organizers thank the DOE Office of Science, Earth and Environmental System Modeling, MultiSector Dynamics program area for financial support of its activities through the Integrated Multisector Multiscale Modeling (IM3) project. For more information related to the broader DOE MultiSector Dynamics Program please see https://climatemodeling.science.energy.gov/program-area/multisector-dynamics. D.L.M. and C.M.B. acknowledge support from the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory (ORNL), managed by UT-Battelle, LLC, for the US Department of Energy (DOE). Disclaimer This report was prepared as an account of work sponsored by an agency of the United States Government. Neither theUnited States Government nor any agency thereof, nor Battelle Memorial Institute, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, complete- ness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial products, process, or service by trade name,trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. Pacific Northwest National Laboratory operated by Battelle for the United States Department of Energy Available from:Office of Scientific and Technical Information http://www.OSTI.gov multisectordynamics.org This work is made available under the terms of the Creative Commons Attribution- NonCommercial 4.0 International (CC BY-NC 4.0) https://creativecommons.org/licenses/by-nc/4 Suggested citation: Monier, E., Reed, P.M., Vernon, C.R., Hadjimichael, A., Brelsford, C.M., Burleyson, C.B., Dyreson, A.R., Fletcher, S.M., Giang, A., Gupta, R.S., Jackson, N.D., Jones, A.D., Lamontagne, J.R., McCollum, D.L., Morris, J.F., Moss, R.H., Peng, W., Saari, R.K., Srikrishnan, V., Szinai, J.K., Yoon, J. (2024) MultiSector Dynamics: 2023 Inaugural Workshop Report. MSD-LIVE Data Repository. doi:10.57931/2371710.

Monier, Erwan↗
NASA NTRS1992

Mosquito Lagoon environmental resources inventory

This document provides a synopsis of biotic and abiotic data collected in the Mosquito Lagoon area in relation to water quality. A holistic ecological approach was used in this review to allow for summaries of climate, land use, vegetation, geohydrology, water quality, fishes, sea turtles, wading birds, marine mammals, invertebrates, shellfish, and mosquito control. The document includes a bibliographic database list of 157 citations that have references to the Mosquito Lagoon, many of which were utilized in development of the text.

Provancha, Jane A.↗
NASA NTRS2003

A Multi-Use Airborne Research Facility

Much of our progress in understanding the Earth system comes from measurements made in the atmosphere. Aircraft are widely used to collect in situ measurements of the troposphere and lower stratosphere, and they also serve as platforms for many remote sensing instruments. Airborne field measurement campaigns require a capable aircraft, a specially trained support team, a suite of basic instrumentation, space and power for new instruments, and data analysis and processing capabilities (e.g. Veal et al., 1977). However, these capabilities are expensive and there is a need to reduce costs while maintaining the capability to perform this type of research. To this end, NASA entered a Cooperative Agreement with the University of North Dakota (UND) to help support the operations of the UND Cessna Citation research aircraft. This Cooperative Agreement followed in form and substance a previous agreement. The Cooperative Agreement has benefited both NASA and UND. In part because of budget reductions, the NASA Airborne Science Office has elected to take advantage of outside operators of science research platforms to off-load some science requirements (Huning, 1996). UND has worked with NASA to identify those requirements that could be met more cost effectively with the UND platform. This has resulted in significant cost savings to NASA while broadening the base of researchers in the NASA science programs. At the same time, the Agreement has provided much needed support to UND to help sustain the Citation research facility. In this report, we describe the work conducted under this Cooperative Agreement.

Poellot, Michael R.↗
NASA NTRS1987

Status report on a natural laminar-flow nacelle flight experiment

The natural laminar flow (NLF) nacelle experiment is part of a drag reduction production program, and has the dual objectives of studying the extent of NLF on full scale nacelles in a flight environment and the effect of acoustic disturbance on the location of transition on the nacelle surface. The experiment is being conducted in two phases: (1) an NLF fairing was flown on a full scale Citation nacelle to develop the experiment technique and establish feasibility; (2) full scale, flow through, NLF nacelles located below the right wing of an experimental NASA OV-1 aircraft are evaluated. The measurements of most interest are the static pressure distribution and transition location on the nacelle surface, and the fluctuating pressure levels associated with the noise sources. Data are collected in combinations of acoustic frequencies and sound pressure levels. The results of phase 2 tests to date indicate that on shape GE2, natural laminar flow was maintained as far aft as the afterbody joint at 50 percent of the nacelle length. An aft facing step at this joint caused premature transition at this station. No change was observed in the transition pattern when the noise sources were operated.

Hastings, Earl C., Jr.↗
NASA NTRS1973

Bibliography of information on mechanics of structural failure

A bibliography of approximately 1500 reference citations related to six problem areas in the mechanics of failure in aerospace structures is presented. The bibliography represents a search of the literature published in the ten year period 1962-1972 and is largely limited to documents published in the United States. Listings are subdivided into the six problem areas: (1) life prediction of structural materials; (2) fracture toughness data; (3) fracture mechanics analysis; (4) hydrogen embrittlement; (5) protective coatings; and (6) composite materials. An author index is included.

Carpenter, J. L., Jr.↗
DOE OSTI2025

Knowledge Oriented Graph Unified Transformer (KOGUT) v0.1

KOGUT — Knowledge Oriented Graph Unified Transformer KOGUT implements the Relational Graph Transformer (RelGT) architecture for knowledge graph link prediction in biological domains, with a primary focus on microbial growth media prediction. While the original RelGT (arXiv:2505.10960) targets relational tables, time series, and multi-table databases, KOGUT adapts this architecture for heterogeneous biological knowledge graphs, providing first-in-class AI predictive models for microbial cultivation. Key Adaptations Beyond Original RelGT: - Knowledge Graph Focus: Applied to biological KGs with semantic node types (taxa, chemicals, media, phenotypes, environments) versus generic relational database tables, trained on the KG-Microbe knowledge graph (1.3M entities, 2.9M edges, 24 relation types). - Multimodal Node Encoding: Integrates node labels, categories, descriptions, and synonyms from KG metadata through learned embedding layers—adapting relational column features to graph node attributes with textual semantics. - Extended K-Hop Subgraph Strategy: Optimized neighborhood sampling (3-hop default, configurable up to 200 nodes) tuned for sparse biological networks, building on the original local-global attention framework with biological relation preservation. - Biolink Predicate Preservation: Type-specific transformations for 24 biological edge semantics (occurs_in, consumes, produces, has_phenotype, subclass_of) beyond standard relational foreign keys, enabling multi-relation link prediction. - Inductive Learning Support: Enables zero-shot predictions for novel taxa through feature-based embeddings (temperature, oxygen requirements, gram stain, cell shape), extending the original transductive relational benchmark scope to uncultured microorganisms. CheapSOTA Performance Optimizations (This Distribution): - VQ-EMA Centroid Attention: Vector quantization with exponential moving average for improved global context modeling (+5-10% MRR improvement). - HDF5 Precomputed Data Loading: One-time preprocessing of k-hop subgraphs to eliminate redundant graph traversals (2-5× training speedup). - Distributed Data Parallel Training: Multi-GPU support for scaling to larger knowledge graphs (tested on 4× NVIDIA A100 GPUs at NERSC Perlmutter). - Mixed Precision Training: Automatic mixed precision (AMP) for memory efficiency and faster training. Advantages Over Standard Knowledge Graph Embedding Models: Combines RelGT's proven multi-element tokenization (features, type, hop, structure) with graph-native biological representations, enabling interpretable link prediction across heterogeneous entities that standard embedding models (TransE, RotatE, ComplEx) and table-based transformers cannot directly model. Achieves near-perfect performance on microbial growth media prediction (MRR: 0.9966, Precision@1: 0.9932, Hit@10: 1.0000) while maintaining explainability through attention-based reasoning over biological pathways. Training Data: - KG-Microbe merged knowledge graph: 1,379,337 nodes, 2,960,472 edges - 24 biological relation types including taxonomic hierarchies, metabolic interactions, phenotype associations, and environmental relationships - Primary prediction task: Growth media suitability for microbial taxa (biolink:occurs_in, 50K edges) - Multi-relation capability: Predicts links for any of the 24 relation types, including chemical consumption/production, phenotype associations, and taxonomic classification Citation: Original RelGT Architecture: Dwivedi et al., "Relational Graph Transformer", arXiv:2505.10960, 2025 KOGUT Implementation: Knowledge Oriented Graph Unified Transformer for Microbial Growth Media Prediction Developed at Lawrence Berkeley National Laboratory (LBNL) Trained on NERSC Perlmutter supercomputer

Joachimiak, Marcin [Lawrence Berkeley National Lab↗