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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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DOE OSTI2020

A Non-Invasive Approach for Elucidating the Spatial Distribution of In Situ Stress in Deep Subsurface Geologic Formations Considered for CO 2 Storage (Task 2 Report Extracting Stress Data from Seismic Data)

This report describes research accomplishments achieved with funding provided through Department of Energy (DOE) Contract DE-FE0031686. The purpose of this DOE funding is to develop methods that can provide key information about stress fields that act on deep rocks without invading the earth to acquire that information. The research objective was to demonstrate methods that extract the azimuth directions of SHmax and SHmin stress in deep rocks from traditional seismic reflection data like the data that are used to explore for deep oil and gas reservoirs. Battelle performed two seismic investigations and achieved estimates of SHmax and SHmin azimuths in both efforts that agreed with local, non-seismic, ground-truth measurements of SHmax orientation. The first procedure utilized vertical seismic profiling (VSP) data; the second effort utilized three-dimensional (3D) seismic data.

58 GEOSCIENCES↗

NASA NTRS2019

Evidence-based Approach to Establish Space Suit Carbon Dioxide Limits

A literature survey was conducted to assess if published data (evidence) could help inform a space suit carbon dioxide (CO2) limit. The search identified more than 120 documents about human interaction with elevated CO2. Until now, the guiding philosophy has been to drive space suit CO2 as low as reasonably achievable. NASA’s EVA Office requested an evidencebased approach to support a new generation of exploration-class extravehicular activity (EVA) space suits. Specific literature data about CO2 are not available for EVA in microgravity because EVA is an operational activity and not a research platform. However, enough data from groundbased research are available to facilitate a consensus of expert opinion on space suit CO2 limits. The compilation of data in this report can answer many but not all concerns about the consequences of hypercapnic exercise in a space suit. Inspired partial pressure of CO2 (PICO2) and not dry-gas partial pressure of CO2 (PCO2) is the appropriate metric for hypercapnic dose to establish space suit CO2 limits. The reduction of inspired gas partial pressures by saturation of the inspired gases with water vapor at 37°C is a significant factor under conditions of hypobaric space suit operation. Otherwise healthy EVA astronauts will exhibit wide variability in responses to acute hypercapnia while at rest and during exercise. What is clear from the literature is the absence of prospective (objective) accept or reject criteria for CO2 exposure in general, and no such criteria exist for operating a space suit. There is no absolute “Gold Standard” for an acceptable acute hypercapnic limit, just a gradual decrease in performance as CO2 increases. Acceptable CO2 exposure limits are occupation, situation (learned or novel tasks), and personspecific. Investigators who measured hypercapnic physiology rarely correlated those changes to neurocognitive symptoms, and those that measured hypercapnic neurocognition rarely correlated those changes with physiology. Some answers about changes in neurocognition and functional EVA performance during hypercapnic exercise in a space suit await new research.

Conkin, Johnny↗

DOE OSTI2024

GraphAide: Advanced Graph-Assisted Query and Reasoning System

Curating knowledge from multiple siloed sources that contain both structured and unstructured data is a major challenge in many real-world applications. Pattern matching and querying represent fundamental tasks in modern data analytics that leverage this curated knowledge. The development of such applications necessitates overcoming several research challenges, including data extraction, named entity recognition, data modeling, and designing query interfaces. Moreover, the explainability of these functionalities is critical for their broader adoption. The emergence of Large Language Models (LLMs) has accelerated the development lifecycle of new capabilities. Nonetheless, there is an ongoing need for domain-specific tools tailored to user activities. The creation of digital assistants has gained considerable traction in recent years, with LLMs offering a promising avenue to develop such assistants utilizing domain-specific knowledge and assumptions. In this context, we introduce an advanced query and reasoning system, GraphAide, which constructs a knowledge graph (KG) from diverse sources and allows to query and reason over the resulting KG. GraphAide harnesses both the KG and LLMs to rapidly develop domain-specific digital assistants. It integrates design patterns from retrieval augmented generation (RAG) and the semantic web to create an agentic LLM application. GraphAide underscores the potential for streamlined and efficient development of specialized digital assistants, thereby enhancing their applicability across various domains.

Purohit, Sumit [BATTELLE (PACIFIC NW LAB)] (ORCID:↗

DOE OSTI2022

GridDS: Data Science Toolkit for Energy Grid Data

According to the U.S. Energy Information Administration (EIA), the demand for energy is expected to increase 50% by the year 20501. While energy standards, such as the Institute of Electrical and Electronics Engineers (IEEE) Standard 1547, (Basso 2015) and monitoring with wide area management systems (WAMS) (Liu 2017, Zhou 2016) have enabled large scale data collection and storage, the application of this data in mitigating costs associated with increased consumer demand is an ongoing focus for energy research. This ubiquitous data collection presents a promising opportunity for machine learning and data science to improve efficiency of distributed energy resources (DERs). The GridDS software toolkit is designed to leverage advanced metering infrastructure (AMI), outage management systems data (OMS), Supervisory control Data Acquisition (SCADA), and geographic information systems (GIS) to forecast future energy demands and detect incipient grid failures. GridDS is a python software library designed to be modular and generalizable to data recorded by DERs. In adapting to disparate datasets recorded by various WAMS, GridDS provides a range of unique functionality not presently implemented in current WAMS which have highly specific software infrastructure by design. GridDS functionality ranges from data specification and preparation, to training and validation for state of the art machine learning, to interactive data visualization. For data intake, GridDS combines: Pandera: a library for creating data specifications. TimeScaleDB: a postgresSQL database infrastructure for efficient storage of timeseries data. Dataset class: A custom dataset class / interface that ensures modularity between a range of synthetic and live recorded datasets. Is

Ladd, Alexander↗

DOE OSTI2021

Computed Tomography Scanning and Geophysical Measurements of the Caney Shale Formation from the Tomaney #1-35-34-27 Well

Evaluation of reservoir samples can support resource estimation and determination of effective extraction methodologies. While it is common for commercial entities to perform these characterizations, the resources necessary to conduct these analyses are not always available to the broader interest base, such as state agencies, universities, and research-based consortiums. To meet the growing need for comprehensive and high-quality lithologic data for collaborative research initiatives, the U.S. Department of Energy’s (DOE) National Energy Technology Laboratory (NETL) has used available resources in conjunction with previous techniques and new, innovative methodologies to develop a systematic approach for the evaluation of cores. This report focuses on the Tomaney #1-35-34-27 well. Tomaney #1-35-34-27 was drilled in southwestern Stephens County, Oklahoma within section 35, township 2S, and range 4W. The Tomaney #1-35-34-27 was drilled in January/February of 2020 and targeted the Devonian Woodford Shale Formation and cored the Mississippian Caney Shale formation within the Ardmore Basin.

04 OIL SHALES AND TAR SANDS↗

DOE OSTI2022

DOE Data Days 2022 Report

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

DOE Data Days 2023 Report

The DOE Data Days (D3) workshop brings together data managers, developers, researchers, and program managers across the Department of Energy (DOE) and national laboratories to highlight data management successes, identify potential synergies and common problems, and establish channels for collaboration across the DOE data management community. The fourth D3 workshop was held on October 24th to 26th, 2023 held entirely in-person at Lawrence Livermore National Laboratory (LLNL). The workshop was organized by a multi laboratory committee in an effort to bring data management practitioners at the DOE laboratories together to share their work and results, facilitating knowledge transfers and best practices across project teams. Tools and platforms to support data management and analysis are rapidly evolving and provide enormous opportunities. This report summarizes the important discussions and recommendations from the different working sessions and contains the agenda, submitted abstracts, presentations with links to recorded presentations, breakout session summaries, list of registered attendees, and lessons learned for future organizing committee. The report will be distributed to the DOE, each participating institution’s programmatic stakeholders, and attendees. The dedicated D3 website will host presentations, agenda, and report that is accessible by all labs.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

NASA NTRS1979

Measurement of hurricane winds and waves with a synthetic aperture radar

An analysis of data collected in a hurricane research program is presented. The data were collected with a Synthetic Aperture Radar (SAR) during five aircraft flights in the Atlantic in August and September, 1976. Work was conducted in two areas. The first is an analysis of the L-band SAR data in a scatterometer mode to determine the surface windspeeds in hurricanes, in a similar manner to that done by an X-band scatterometer. The second area was to use the SAR to examine the wave patterns in hurricanes. The wave patterns in all of the storms are similar and show a marked radial asymmetry.

Shemdin, O. H.↗

NASA NTRS1993

A browse facility for Earth science remote sensing data: Center director's discretionary fund final report

An image data visual browse facility is developed for a UNIX platform using the X Windows 11 system. It allows one to visually examine reduced resolution image data to determine which data are applicable for further research. Links with a relational data base manager then allow one to extract not only the full resolution image data, but any other ancillary data related to the case study. Various techniques are examined for compression of the image data in order to reduce data storage requirements and time necessary to transmit the data on the internet. Data used were from the WetNet project.

Meyer, P. J.↗

NASA NTRS2000

A High Resolution Survey of the Disk of M31

This report describes research activities funded for SAO Proposal P3481-5-95, "Monitoring the Center of M31". Related activities for SAO Proposal P3486- 5-95, "A High-Resolution Survey of the Disk of M31" are also described. The research involved the data analysis and interpretation of eleven separate ROSAT (Roentgen Satellite) HRI (High Resolution Imager) observations of the center and inner disk of M31, obtained between July, 1990 and January, 1997. A log of the individual data sets is given. All proposed observations were successfully carried out by the ROSAT Observatory, and standard data products were successfully generated for each observation. There were two basic thrusts to the research. First, we wished to monitor the x-ray source nearest the nucleus of M31, to search for anti-correlated radio/x-ray variability predicted by theoretical models for the source. We would also be able to assess the degree and range of variability of other x-ray sources in the central bulge of M31 and to estimate rates of transients. Secondly, we wished to survey the entire inner disk of M31, and in particular the region covered by the recent MIT wide-band and narrow-band surveys to identify x-ray counterparts to supernova remnants, OB associations, and young blue stars, in areas in which the ROSAT PSPC survey was confused. Results of each research project are summarized. Papers describing the results in more detail are in preparation.

Primini, Francis A.↗

NASA NTRS2000

Investigation of Mesoscale Variability in Convective Boundary Layer Development Using LASE

Throughout this research effort we have analyzed data collected from the Southern Great Plains 1997 (SGP) experiment to evaluate terms in the budget of water vapor in the atmospheric boundary layer (ABL). The analysis is centered on using data obtained from the Lidar Atmospheric Sensing Experiment (LASE) downward-looking differential absorption lidar (DIAL) that was flown on the NASA-P3 aircraft. The DIAL is able to measure vertical profiles of water vapor, as well as aerosol backscatter, throughout the ABL. Initial work on using the DIAL is summarized in the attached meeting preprint, which discusses results from a segment of the 12 July 1997 flight. Additional analysis has been carried out using both the DIAL data and in situ measurements to obtain more complete and detailed estimates of terms in the ABL water vapor budget. This includes data from the Canadian National Research Council Twin Otter aircraft, as well as from a spectrum of surface-based instruments. This work is expected to lead to a refereed article planned for submission to the Journal.

Lenschow, Donald H.↗

NASA NTRS2002

Theoretical Studies of Molecular Spectra

This summary describes the research activities of the principal investigator during the reporting period. The research includes spectroscopy, management of molecular databases, and generation of spectral line profiles and opacity data. The spectroscopy research includes oxygen broadening of nitric oxide (NO), analysis of CO2 spectra, analysis of HNO3 spectra, and analysis of CO spectra.

McKay, Christopher↗

NASA NTRS2001

Scientific Data Purchase Project Overview Presentation

The Scientific Data Purchase (SDP) project acquires science data from commercial sources. It is a demonstration project to test a new way of doing business, tap new sources of data, support Earth science research, and support the commercial remote sensing industry. Phase I of the project reviews simulated/prototypical data sets from 10 companies. Phase II of the project is a 3 year purchase/distribution of select data from 5 companies. The status of several SDP projects is reviewed in this viewgraph presentation, as is the SDP process of tasking, verification, validation, and data archiving. The presentation also lists SDP results for turnaround time, metrics, customers, data use, science research, applications research, and user feedback.

Holekamp, Kara↗

NASA NTRS2014

Development of Super Ensemble-Based Aviation Turbulence Guidance (SEATG) for Air Traffic Management

A new method for forecasting turbulence is developed and evaluated using the high resolution weather model and in situ turbulence observations from commercial aircraft. The new method is an ensemble of various turbulence metrics from multiple time-lagged ensemble forecasts created using a sequence of four procedures. These include weather modeling, calculation of turbulence metrics, mapping the metrics into a common turbulence-scale, and production of final forecast. The new method uses similar methodology as current operational turbulence forecast with three improvements. First, it uses a higher resolution ((delta)x = 3 km) weather model to capture cloud resolving scale phenomena. Second, it computes the metrics for multiple forecasts that are combined at the same valid time resulting in a time-lagged ensemble of multiple turbulence metrics. Finally, it provides both deterministic and probabilistic turbulence forecasts. Results show the new forecasts match well with observed radar reflectivity along a surface front as well as convectively induced turbulence outside the clouds on research period. Overall performance skill of the new turbulence forecast compared with the observed EDR data during the research period is superior to any single turbulence metric. The probabilistic turbulence forecast is used in an example air traffic management application for creating a wind-optimal route considering turbulence information. The wind-optimal route passing through areas of 50% potential for moderate-or-greater turbulence and the lateral turbulence avoidance routes starting from three different waypoints along the wind-optimal route from Los Angeles international airport to John F. Kennedy international airport are calculated using different turbulence forecasts. This example shows additional flight time is required to avoid potential turbulence encounters.

modeling↗

DOE OSTI2024

Model code and data: biomass allocation adjustments induced by elevated CO2 and warming in a C3 brackish marsh, 2017-2022, Maryland

This dataset and R script accompany the published paper Bruns et al. (2024) in Geophysical Research Letters. The data are from the first six years of a field manipulation of whole-ecosystem warming and elevated CO2 experiment (Salt Marsh Accretion Response to Temperature eXperiment, or SMARTX) in the Smithsonian's Global Change Research Wetland (GCReW), a brackish, microtidal wetland site on a subestuary of the Chesapeake Bay. These data were generated to understand how warming and elevated CO2 interact to structure ecosystem-level responses to global change, particularly in terms of carbon sequestration. The dataset covers 2017-2022 and includes peak annual above ground biomass, annual belowground fine root productivity, and porewater NH4 for each experimental plot. The overall experiment is replicated in two locations on the marsh, a lower elevation zone dominated the C3 sedge S. Americanus and a higher elevation plot dominated by the C4 species. This paper and its data release is only for the C3 plot. Variable descriptions for data file is available in variable_descriptions.pdf. The R script Bruns_et_al_2024_GRL_make_figures.Rmd contains model code and other scripts used to generate all paper figures.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Mondo: integrating disease terminology across communities

Precision medicine aims to enhance diagnosis, treatment, and prognosis by integrating multimodal data at the point of care. However, challenges arise due to the vast number of diseases, differing methods of classification, and conflicting terminological coding systems and practices used to represent molecular definitions of disease. This lack of interoperability artificially constrains the potential for diagnosis, clinical decision support, care outcome analysis, as well as data linkage across research domains to support the development or repurposing of therapeutics. There is a clear and pressing need for a unified system for managing disease entities⁠—including identifiers, synonyms, and definitions. To address these issues, we created the Mondo disease ontology—a community-driven, open-source, unified disease classification system that harmonizes diverse terminologies into a consistent, computable framework. Mondo integrates key medical and biomedical terminologies, including Online Mendelian Inheritance in Man (OMIM), Orphanet, Medical Subject Headings (MeSH), National Cancer Institute Thesaurus (NCIt), and more, to provide a comprehensive and accurate representation of disease concepts with fully provenanced and attributed links back to the sources. Mondo can be used as the handle for curation of gene–disease associations utilized in diagnostic applications, research applications such as computational phenotyping, and in clinical coding systems in clinical decision support by pointing the clinician to the numerous knowledge resources linked to the Mondo identifier. Mondo's community-centric approach, stewarded by the Monarch Initiative's expertise in ontologies, ensures that the ontology remains adaptable to the evolving needs of biomedical research and clinical communities, as well as the knowledge providers.

biomedical informatics↗

NASA NTRS2014

Exploring the Utility of the Planned CYGNSS Mission for Investigating the Initiation and Development of the Madden-Julian Oscillation

CYGNSS is a planned constellation consisting of multiple micro-satellites that leverage the Global Positioning System (GPS) to provide rapidly updated, high resolution (approx. 15-50 km, approx. 4 h) surface wind speeds (via bi-static scatterometry) over the tropical oceans in any weather condition, including heavy rainfall. The approach of the work to be presented at this conference is to utilize a limited-domain, cloud-system resolving model (Weather Research and Forecasting or WRF) and its attendant data assimilation scheme (Three-Dimensional Variational Assimilation or 3DVAR) to investigate the utility of the CYGNSS mission for helping characterize key convectiveto- mesoscale processes - such as surface evaporation, moisture advection and convergence, and upscale development of precipitation systems - that help drive the initiation and development of the Madden-Julian Oscillation (MJO) in the equatorial Indian Ocean. The proposed work will focus on three scientific objectives. Objective 1 is to produce a high-resolution surface wind dataset resolution (approx. 0.5 h, approx. 1-4 km) for multiple MJO onsets using WRF-assimilated winds and other data from the DYNAmics of the MJO (DYNAMO) field campaign, which took place during October 2011 - March 2012. Objective 2 is to study the variability of surface winds during MJO onsets at temporal and spatial scales of finer resolution than future CYGNSS data. The goal is to understand how sub-CYGNSS-resolution processes will shape the observations made by the satellite constellation. Objective 3 is to ingest simulated CYGNSS data into the WRF model in order to perform observing system simulation experiments (OSSEs). These will be used to test and quantify the potential beneficial effects provided by CYGNSS, particularly for characterizing the physical processes driving convective organization and upscale development during the initiation and development of the MJO. The proposed research is ideal for answering important questions about the CYGNSS mission, such as the representativeness of surface wind retrievals in the context of the complex airflow processes that occur during heavy precipitation, as well as the tradeoffs in retrieval accuracy that result from finer spatial resolution of the CYGNSS winds versus increased errors/noisiness in those data. Research plans and initial progress toward these objectives will be presented.

Lang, Timothy↗

DOE OSTI2022

WormBase in 2022—data, processes, and tools for analyzing Caenorhabditis elegans

WormBase (www.wormbase.org) is the central repository for the genetics and genomics of the nematode Caenorhabditis elegans. We provide the research community with data and tools to facilitate the use of C. elegans and related nematodes as model organisms for studying human health, development, and many aspects of fundamental biology. Throughout our 22-year history, we have continued to evolve to reflect progress and innovation in the science and technologies involved in the study of C. elegans. We strive to incorporate new data types and richer data sets, and to provide integrated displays and services that avail the knowledge generated by the published nematode genetics literature. Here, we provide a broad overview of the current state of WormBase in terms of data type, curation workflows, analysis, and tools, including exciting new advances for analysis of single-cell data, text mining and visualization, and the new community collaboration forum. Concurrently, we continue the integration and harmonization of infrastructure, processes, and tools with the Alliance of Genome Resources, of which WormBase is a founding member.

59 BASIC BIOLOGICAL SCIENCES↗