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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Big Data Challenges at the Community Coordinated Modeling Center (CCMC)

Like other research centers, the Community Coordinated Modeling Center (CCMC, https://ccmc.gsfc.nasa.gov) at NASA Goddard Space Flight Center (GSFC) is also experiencing the big data challenges. CCMC hosts over 80 space weather models for Runs On Request (ROR), Continuous Runs and Instant Runs simulation services for the research community. In addition, CCMC has started to support simulation output onboarding in response to the Open Science initiative. Overall, we have accumulated over petabytes of simulation output data and are rapidly growing. In this presentation, we will discuss our data and storage challenges. We will present our attempts to address our challenges and any associated lessons learned. CCMC uses Apache Airflow to ensure data transfer is consistent. We will give a brief overview on how we leverage Apache Airflow to enhance our environment.

space weather↗
DOE OSTI2020

Big Sky Regional Carbon Sequestration Partnership (Phase III Final Scientific/Technical Report)

The Big Sky Carbon Sequestration Partnership (BSCSP) pursued a Phase III demonstration project at Kevin Dome in north central Montana. Kevin Dome covers approximately 700 square miles and is a naturally occurring CO 2 reservoir that is flanked by oil and gas fields. The carbon dioxide (CO 2 ) is in the upper Devonian Duperow (carbonate) formation and does not reach the spill point of the dome; therefore, the dome has potential as a CO 2 sequestration reservoir, a CO 2 supply, or as both if anthropogenic sources and enhanced oil recovery (EOR) operations are associated with the dome. Kevin Dome could potentially act as a buffer to continue accepting anthropogenic CO 2 when EOR flooding operations are interrupted or completed. The project objective was to produce one million tonnes of CO 2 from the gas cap of Kevin Dome, pipe it laterally, inject, and re-store it in the brine leg of the same formation to test the hub / buffer storage concept. This was to be accomplished by drilling up to five production wells, building a short pipeline and compression facilities, and drilling an injection well and several monitoring wells. BSCSP commenced outreach and site characterization activities including acquiring baseline data for near-surface insurance monitoring, acquiring 3-dimensional, 9-component surface seismic over the project area, drilling two test wells (one in the production area and one in the injection area), coring key intervals, and performing comprehensive logging. Well tests of those wells revealed two barriers to the project. The production (Danielson 33-17) well was perforated in multiple zones but failed to produce any significant CO 2 . This was despite being drilled in the near vicinity of a historic well that had produced 3700 MCF per day in a drill stem test. Modeling indicated that this was likely due to a phase change during production causing a temperature drop resulting in hydrate and/or water ice formation that clogged the formation. Tests of the injection zone (Wallewein 22-1) well indicated total dissolved solids (TDS) slightly below the EPA required 10,000 parts per million (ppm) for a Class VI underground injection control permit. While the project was initiated before Class VI rules were promulgated, and this was an experimental project (seemingly qualified for a Class V permit), the Environmental Protection Agency (EPA) indicated that injection would require a Class VI permit. The low salinity result was unexpected as contours plotted based on regional formation water quality data indicated an expected TDS above 20,000 ppm, and wells between the recharge zone and the Wallewein well tested above 10,000 ppm. Faced with the inability to obtain an injection permit, the demonstration project could not proceed. However, the project had generated valuable samples and data on a large natural analog including 32 sq. mi. of 3-D, 9-C seismic, 430 ft. of carbonate core covering seven different depositional environments taken from areas with, and without the presence of CO 2 , 30 ft. of core of two caprocks, a tight carbonate and an anhydrite, a full set of modern logs on both wells, and well tests. DOE decided to re-scope the project around completing studies utilizing this data. This report covers both the initial scope and the re-scope (Task / Section numbers preceded with an R). While the report covers a wide range of project activities, highlights of this work include: Development of a geostatic model using neural nets to match well logs to facies and using multi-waveform seismic to inform reservoir heterogeneity; Unique mechanical testing of permeability – stress relationship in two caprock materials; Development of full waveform inversion to generate a high resolution velocity model; Model development for dual permeability (fracture and matrix) systems to better account for matrix-matrix interactions; Joint seismic wave inversion (including the first quadr-joint inversion) exhibiting better imaging of a challenging reservoir zone in stiff rock; Core flow and core flood results on a reactive carbonate; and Innovative laboratory measurements of seismic response of fractured core as a function of fluid fill.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

CHARACTERIZATION ON ANISOTROPIC THERMAL CONDUCTIVITY FOR BIG AREA ADDITIVE MANUFACTURING WITH POLYMERS

Additive manufacturing with polymers has been used mainly for prototyping. A recent development of Big Area Additive Manufacturing (BAAM) at Oak Ridge National Laboratory has opened its applications in the mold and die industry. A numerical simulation and prediction for a mold heating performance requires accurate anisotropic thermal properties of the printed material, which are challenging to obtain, and often requires the use of multiple techniques. The transient plane source (TPS) technique has been widely used due to its ability to measure the thermal properties of an extensive range of materials (solids, liquids, powder). Despite the capability to characterize thermal conductivity k of isotropic and anisotropic materials, the measurements of latter materials are limited to the cases, where the samples have the same thermal conductivity k along x- and y-axis that form the radial plane. In this work, the method for a characterization of k in all three dimensions is developed, and the application of TPS is extended to the determination of thermal properties along the x-, y-, and z-axis individually. The materials are represented by additively manufactured polymers including polylactic acid (PLA) and styrene maleic anhydride (SMA). The developed method consists of (1) a determination of the heat capacity of the polymers by means of TPS in combination with the developed in this work data analysis procedure, (2) a machining three types of cylindrical samples from the same material, with the height corresponding either to x-, y-, or z-direction of printing, and (3) a determination of axial thermal conductivity employing anisotropic model and using previously determined heat capacity

Trofimov, Artem↗
DOE OSTI2021

Unleashing the Power of Industrial Big Data through Scalable Manual Labeling

Big Data plays a central role in the remarkable results achieved by Machine Learning (ML) and especially Deep Learning (DL) in the recent years. However, the difficulty in obtaining a reasonable amount of labeled samples limits ML/DL application in various domains, including industrial equipment and system monitoring. In this paper the need for methods that turn manual labeling into a scalable process is highlighted. A real world problem is analyzed for which weak supervision methods, successfully employed in other domains, did not produce acceptable results. An alternative approach based on clustering ensembles is described and tested, achieving good performance.

Paes Leao, Bruno↗
DOE OSTI2024

Fostering Geothermal Machine Learning Success: Elevating Big Data Accessibility and Automated Data Standardization in the Geothermal Data Repository: Preprint

The Department of Energy's (DOE) Geothermal Data Repository (GDR) has implemented improvements to both its data lakes and its data standards and automated data pipelines. The GDR data lakes have reduced storage and compute-related barriers to using large geothermal datasets, enabling these large datasets to be accessed by anyone with a modern computer and internet access. More recently, the GDR has been working to further reduce barriers through streamlining the data intake process, educating users on the process and requirements, and aiding users in accessing data from the data lakes. These improvements have augmented the quantity of datasets the GDR is able to accept into its data lakes and have enabled users who are new to cloud tools to access these datasets more easily, overall increasing the accessibility of big geothermal data for use in machine learning and other projects. In addition, the GDR now has built-in data standards and pipelines for drilling data, geospatial data, and distributed acoustic sensing (DAS) data. These standardization efforts aim to enhance the real-world applicability of geothermal machine learning outcomes by improving the quality of training data. Specifically, through standardizing high-value datasets, the GDR is reducing project-specific data curation requirements, thus allowing more time for actual research. By automating this process, the burden of standardization is lifted from the user, ultimately increasing the availability of standardized data.

accessibility↗
DOE OSTI2025

Fostering Geothermal Machine Learning Success: Elevating Big Data Accessibility and Automated Data Standardization in the Geothermal Data Repository

The Department of Energy's (DOE's) Geothermal Data Repository (GDR) has implemented improvements to both its data lakes and its data standards and automated data pipelines. The GDR data lakes have reduced storage and compute-related barriers to using large geothermal datasets, enabling these large datasets to be accessed by anyone with a modern computer and internet access. More recently, the GDR has been working to further reduce barriers through streamlining the data intake process, educating users on the process and requirements, and helping users access data from the data lakes. These improvements have augmented the quantity of datasets the GDR is able to accept into its data lakes and have enabled users who are new to cloud tools to access these datasets more easily, overall increasing the accessibility of big geothermal data for use in machine learning and other projects. In addition, the GDR now has built-in data standards and pipelines for drilling data, geospatial data, and distributed acoustic sensing (DAS) data. These standardization efforts aim to enhance the real-world applicability of geothermal machine learning outcomes by improving the quality of training data. Specifically, through standardizing high-value datasets, the GDR is reducing project-specific data curation requirements, thus allowing more time for actual research. By automating this process, the burden of standardization is lifted from the user, ultimately increasing the availability of standardized data. This paper provides an update on recent improvements made to the GDR's data lakes and automated data pipelines, including: (1) streamlining the data lake intake process, (2) better educating users on the process and requirements through a new data lakes page, (3) adding data lake direct access links to GDR data lake submission pages, (4) implementing a DAS data pipeline to convert DAS data uploaded in SEG-Y format to a standardized hierarchical data format v5 (HDF5), (5) extending this pipeline to encompass data in the GDR data lake, (6) adding metadata requirements for geospatial data, (7) making user interface/user experience (UX) enhancements to the data pipelines' documentation pages, and (8) improving the GDR's data standards and pipelines pages to better guide users in ensuring that their data is standardized by the GDR's automated data pipelines. 2024 Geothermal Resources Council. All rights reserved.

accessibility↗
DOE OSTI2026

Foreign Entity of Concern Requirements in the One Big Beautiful Bill Act

The One Big Beautiful Bill Act (OBBB), enacted July 4, 2025, makes billions of dollars in federal energy tax credits conditional on supply chain independence from China and other foreign entities of concern. The OBBB simultaneously creates powerful economic incentives to reshore energy supply chains to the United States and allied nations. Through such incentives, the OBBB elevates digital assurance and supply chain verification from voluntary best practices into critical capabilities for demonstrating tax credit eligibility. The OBBB uses tax credit eligibility requirements to simultaneously address national security concerns regarding foreign supply chain dependencies and incentivize domestic energy manufacturing. This brief details how organizations should operationalize these requirements through baseline compliance audits, interim documentation systems, supply chain diversification strategies, and long-term institutional integration of digital assurance capabilities that turn compliance burdens into competitive advantages

29 - ENERGY PLANNING, POLICY AND ECONOMY↗
DOE OSTI2026

Leveling-Up for Big-Format Modules

Like Mario grabbing a super mushroom, PV modules just keep getting bigger! As they grow, so do the challenges of handling, installing, and testing them in the field. At NREL, we've embarked on our own New Hope - adapting to this size revolution across our tools, transportation, ergonomics, and field compatibility. Join us as we navigate this galactic expansion and keep PV testing at the cutting edge.

14 SOLAR ENERGY↗
DOE OSTI2023

Wetlands Delineation Report: Big Eddy-Ostrander Transmission Line

Pacific Northwest National Labs (PNNL) was contracted by BPA to prepare a Wetland Delineation Report to support project planning and compliance with state and federal wetland and waterway removal/fill regulations. This report documents the wetlands and waterways of the U.S./State located along the 66.5 miles of existing transmission line, the access road network, and in areas where hazard trees would be removed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2015

Taming Big Data Variety in the Earth Observing System Data and Information System

Although the volume of the remote sensing data managed by the Earth Observing System Data and Information System is formidable, an oft-overlooked challenge is the variety of data. The diversity in satellite instruments, science disciplines and user communities drives cost as much or more as the data volume. Several strategies are used to tame this variety: data allocation to distinct centers of expertise; a common metadata repository for discovery, data format standards and conventions; and services that further abstract the variations in data.

Information Systems↗
NASA NTRS2016

An Integrated Gate Turnaround Management Concept Leveraging Big Data/Analytics for NAS Performance Improvements

The Integrated Gate Turnaround Management (IGTM) concept was developed to improve the gate turnaround performance at the airport by leveraging relevant historical data to support optimization of airport gate operations, which include: taxi to the gate, gate services, push back, taxi to the runway, and takeoff, based on available resources, constraints, and uncertainties. By analyzing events of gate operations, primary performance dependent attributes of these events were identified for the historical data analysis such that performance models can be developed based on uncertainties to support descriptive, predictive, and prescriptive functions. A system architecture was developed to examine system requirements in support of such a concept. An IGTM prototype was developed to demonstrate the concept using a distributed network and collaborative decision tools for stakeholders to meet on time pushback performance under uncertainties.

Big Data and Net-enabled ATM↗
NASA NTRS2017

NASA's Big Earth Data Initiative Accomplishments

The goal of NASA's effort for BEDI is to improve the usability, discoverability, and accessibility of Earth Observation data in support of societal benefit areas. Accomplishments: In support of BEDI goals, datasets have been entered into Common Metadata Repository(CMR), made available via the Open-source Project for a Network Data Access Protocol (OPeNDAP), have a Digital Object Identifier (DOI) registered for the dataset, and to support fast visualization many layers have been added in to the Global Imagery Browse Services (GIBS).

CMR↗