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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 19 records

DuraMAT Data Hub

The DuraMAT Data Hub has been supporting the consortium for the past six years. The Data Hub has had success in supporting the projects, providing a platform for sharing data within projects and to the public, and learning how to better leverage the existing software platform and the available Amazon Web Services environment. During this new generation of the Data Hub, we are looking at ways to help improve the data hub architecture, user experience, and improve operations by taking advantage of new technology platforms and software that will be more impactful on the consortium researchers and the broader scientific community. In this poster we will look at the current operational capabilities, data dissemination, and development that will improve the system in the near and far future.

14 SOLAR ENERGY↗

The Wind Data Hub, also known as The Data Archive and Portal.

The Wind Data Hub, or WDH (formerly known as the Data Archive and Portal), is established by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy’s Wind Energy Technologies Office (WETO). The WDH provides secure, timely, easy, and open access to all laboratory, field, and benchmark model data produced by land-based and offshore projects funded by WETO.

17 WIND ENERGY↗

The Role of Mobility Data Hubs in an Integrated Decarbonized Transportation Future

The landscape for connected mobility ecosystems is evolving rapidly as information and communication technologies lower the cost and complexity of connecting people to places, integrating transportation modes and collecting data regarding such movements. These developments have been key to unlocking new business opportunities, particularly through mobility services. While the mechanisms for data collection, processing, and transfer have made significant advances in the past decade, the broader landscape of mobility data architectures and data users remains largely unresolved. It is unclear as to whether the result will converge towards a framework that resembles a coherent quilt or a disjointed patchwork of competing visions. Initial approaches to mobility data collection and provisioning have been largely siloed - by mode or software - or held for exclusive use, however several key players are quickly realizing the need and opportunities enabled through integrated mobility data eco-systems, or mobility data hubs as referred to in this paper. As the business case for hosting mobility data hubs evolves, there is great uncertainty regarding their impact to either advance or exacerbate sustainable mobility (e.g., seamless connectivity across modes, decreased energy consumption and greenhouse gas emissions, etc.). Groups such as the United Nations and World Bank have identified data platforms as a key enabler of realizing environmental and social benefits. If designed with decarbonization in mind, we hypothesize that enhanced observability provided by these ever-expanding mobility data hubs can facilitate energy and emissions reductions that are otherwise limited by transactional barriers and knowledge asymmetry that is inherent to a more siloed approach. In this sense, integration of mobility data can help to create a competitive playing field where value is not determined by exclusivity of data, but rather the quality and uniqueness of a given service. The goals of this paper are to 1) identify key players and data architectures that are emerging in a service-based mobility market, 2) explore several use cases where mobility data hubs have enabled greater sustainability outcomes, and 3) discuss key issues that will need to be resolved to fully leverage emerging mobility data hubs towards a sustainable transportation future.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗

HydroGEN and H2NEW Data Hub

HydroGEN is a multi-lab consortium focused on early-stage R&D in H2 production, supported by the U.S. Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy (EERE), Hydrogen and Fuel Cell Technologies Office (HFTO). The consortium advances research and development (R&D) of innovative materials for advanced water splitting (AWS) technologies to enable clean, sustainable and low-cost ($1/kg H2) hydrogen production and fosters cross-cutting innovation using theory-guided applied materials R&D to advance all emerging water-splitting pathways for hydrogen production. Hydrogen from Next-generation Electrolyzers of Water (H2NEW) is a consortium of nine U.S. Department of Energy (DOE) national laboratories focused on making large-scale electrolyzers, which produce hydrogen from electricity and water, more durable, efficient, and affordable. The presentation introduces the HydroGEN data hub to the H2NEW consortium and encourages H2NEW members to use it to store and share data with team members and eventually the public. The two consortia share this data hub.

advanced water splitting technologies↗

BETO 2021 Peer Review - ChemCatBio Data Hub 2.6.2.500

The goal of the ChemCatBio DataHub project is to accelerate the catalyst and process development cycle by developing transformational tools for prediction and collaboration in catalyst R&D. The project is currently focused on the development of the Catalyst Property Database (CPD), a free and public resource released in September 2020. The CPD was designed in response to the observation that when data, such as computed reaction energetics, is used in catalyst design, it is almost always generated by the researchers seeking to use it, even if similar data has been published previously. One barrier to data reuse that results in this duplication of effort is the difficult process of finding and applying published data, which can be slow, error-prone, and manual. The CPD seeks to overcome these challenges by creating a centralized, searchable database of quality catalyst property data. At present, the CPD contains computed adsorption energies for intermediates along catalytic pathways. During FY21 and FY22, development of the CPD continues with a focus on external users and meeting their requirements. A batch upload capability, training and curation procedures, user interviews, and a demonstration of the CPD's utility in accelerating catalyst research are planned. Overall, the Data Hub project and CPD aim to reduce the time and cost of catalyst research by harnessing the power of data in catalyst discovery.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Energy Material Network Data Hubs

In early 2015 the United States Department of Energy conceived of a consortium of collaborative bodies based on shared expertise, data, and resources that could be targeted towards the more difficult problems in energy materials research. The concept of virtual laboratories had been envisioned and discussed earlier in the decade in response to the advent of the Materials Genome Initiative and similar scientific thrusts. To be effective, any virtual laboratory needed a robust method for data management, communication, security, data sharing, dissemination, and demonstration to work efficiently and effectively for groups of remote researchers. With the accessibility of new, easily deployed cloud technology and software frameworks, such individual elements could be integrated, and the required collaboration architecture is now possible. The developers have leveraged open-source software frameworks, customized them, and merged them into a platform to enable collaborative energy materials science, regardless of the geographic dispersal of the people and resources. After five years in operations, the systems are demonstratively an effective platform for enabling research within the Energy Material Networks (EMN). This paper will show the design and development of a secured scientific data sharing platform, the ability to customize the system to support diverse workflows, and examples of the enabled research and results connected with some of the Energy Material Networks.

97 MATHEMATICS AND COMPUTING↗

Knowledge-based assistance for science visualization and analysis using large distributed databases

Within this decade, the growth in complexity of exploratory data analysis and the sheer volume of space data require new and innovative approaches to support science investigators in achieving their research objectives. To date, there have been numerous efforts addressing the individual issues involved in inter-disciplinary, multi-instrument investigations. However, while successful in small scale, these efforts have not proven to be open and scaleable. This proposal addresses four areas of significant need: scientific visualization and analysis; science data management; interactions in a distributed, heterogeneous environment; and knowledge-based assistance for these functions. The fundamental innovation embedded within this proposal is the integration of three automation technologies, namely, knowledge-based expert systems, science visualization and science data management. This integration is based on the concept called the Data Hub. With the Data Hub concept, NASA will be able to apply a more complete solution to all nodes of a distributed system. Both computation nodes and interactive nodes will be able to effectively and efficiently use the data services (access, retrieval, update, etc.) with a distributed, interdisciplinary information system in a uniform and standard way. This will allow the science investigators to concentrate on their scientific endeavors, rather than to involve themselves in the intricate technical details of the systems and tools required to accomplish their work. Thus, science investigators need not be programmers. The emphasis will be on the definition and prototyping of system elements with sufficient detail to enable data analysis and interpretation leading to publishable scientific results. In addition, the proposed work includes all the required end-to-end components and interfaces to demonstrate the completed concept.

Handley, Thomas H., Jr.↗

Vibratory hub load data reduction and analysis from the reverse velocity rotor wind tunnel test, phase 2B

The vibratory hub loads data analysis from the reverse velocity rotor wind tunnel test is reported. Vibratory loads were obtained from the rotating hub balance and also by synthesis of generalized coordinates from the blade flap bending moments. Load trends were defined as a function of speed, rotor thrust and 2 per rev cyclic from each of the data methods. These trends were compared to determine the degree of agreement between each method and provide substantiation for the generalized coordinate approach.

Taylor, R. B.↗

Design and implementation considerations of a MSAT packet data network

The Mobile Data System, which is intended to provide for packet switched data services is currently under development. The system is based on a star network topology consisting of a centralized Data Hub (DH) serving a large number of mobile terminals. Through the Data Hub, end-to-end connections can be established between terrestrial users on public or private data networks and mobile users. The MDS network will be capable of offering a variety of services some of which are based on the standard X.25 network interface protocol, and others optimized for short messages and broadcast messages. A description of these services and the trade-offs in the DH design are presented.

Karam, Fouad G.↗

Reducing Uncertainty of Fielded Photovoltaic Performance (Final Technical Report)

Improved analysis and reporting of photovoltaic (PV) field performance increases the certainty of owners and financiers that systems will perform as expected. Advanced module technologies (e.g., PERC, HJT, and bifacial) introduce new degradation mechanisms and performance characteristics. The FY19-21 Reducing Uncertainty project leveraged data from the ever-increasing PV fleet to develop models and understanding of the field performance of existing and new technologies. Specifically, we accomplished: report on field performance and degradation rates for high-efficiency silicon (HJT, PERC, IBC) and more conventional technologies; developed automated analysis techniques to quantify system performance (performance ratio, energy yield) and production shortfalls (soiling, degradation, availability); refined the RdTools software toolkit to bring standard, validated analysis techniques to bear on third-party data; analyzed and reported on large datasets including Treasury data and Lawrence Berkeley National Laboratory's Utility-Scale dataset to expand the high-quality degradation-rate histogram published previously; worked with industry partners and the DuraMAT data hub to enable private parties to share and aggregate PV production data anonymously, leveraging cloud-based data analysis infrastructure and publishing on US fleet-scale performance comprising over 7GW of operating systems. (https://www.nrel.gov/pv/fleet-performance-data-initiative.html). Through our industry collaborations we have engaged in NDA-covered data transfer with twelve PV fleet owners as of January 2022, with more agreements in negotiation. Our scalable cloud-based time series database contains over 30 billion rows (20TB) of PV time series data, representing over 1700 commercial and utility-scale systems, and over 7.2 GW of DC capacity (Fig 1). Initial field performance results have been distributed in several public reports. Because our fleet composition and data quality methods are continually improving, annual updates to these results are published to our PV Fleet webpage [ https://www.nrel.gov/pv/fleet-performance-data-initiative.html ] and DuraMAT data hub [DOI: 10.21948/1842958]. Another existing dissemination channel used for observed soiling losses is a map we maintain for soiling losses. Additional products developed include a report detailing fleet-wide performance index, availability, startup loss and snow loss factors, a detailed report on the 1603 grant dataset comprising over 100,000 PV systems with failure and performance details and a utility-scale report coauthored with LBNL on 31 GW of system performance.

14 SOLAR ENERGY↗

MIRACL Co-Simulation Platform Lab assets and tools integration

Pacific Northwest National Laboratory's (PNNL) co-simulation platform (CSP) for the Microgrids, Infrastructure Resilience, and Advanced Controls Launchpad (MIRACL) project, also known as MIRACL-CSP, is a functional layer designed and developed to oversee the operational exchanges of data at the application level to and from different resources residing on the MIRACL Data Hub shared platform. MIRACL-CSP allows virtual interactions between various data hub resources during co-simulation runtime.

24 POWER TRANSMISSION AND DISTRIBUTION↗

An AI-Enabled Chat Bot for DuraMAT

The DuraMAT Data Hub has evolved to meet the ever expanding research demands by integrating a chatbot interface that enhances FAIR compliance and simplifies discovery across our growing archive of projects and datasets. With the Data Hubs increasing use by both consortium researchers and the international community, traditional navigation methods have become unwieldy. Building on last year's feasibility study, we now report the successful implementation of the chatbot system, detailing its architecture and demonstrating its ability to deliver a more intuitive and rewarding user experience.

14 SOLAR ENERGY↗

Barge Site - Avian Radar System / Derived Data

This is a combined data set of 67,410 bird/bat tracks from an avian radar system deployed on a research barge (MERLIN True3D, DeTect, Panama City, Florida, USA) and concurrent wind measurements from two scanning lidars (WindCube v2.1, Vaisala, Vantaa, Finland, and Halo XR+, Halo Photonics, Lannion, France). The research barge (16.5 m x 61 m) was deployed as part of the Wind Forecast Improvement Project (WFIP-3) off the northeast coast of the United States south of Massachusetts (40.9 deg N, 70.79 deg W). This data set comprises 5 weeks of data between August 27th 2024 and September 27th 2024. Radar data were provided by DeTect and Lidar data were accessed through the Wind Data Hub (wfip3/barg.WINDPROF.z01.a0) The data have been filtered and sorted into two size groups ("big" and "small") based on a clustering approach. See Snortland, A., Clerc, J., Hein, C., & Cotter, E. (2025). Wind as Driver of Bird and Bat Abundance, Flight Direction, Altitude, and Speed on the North Atlantic Shelf. arXiv preprint arXiv:2511.14983 for complete details. Data are provided in 2 files: "Birds" and "Birds_hourly" Birds: This file contains information about each of the 67,410 flying animal tracks detected by the radar during the data collection period, including parameters measured by the radar and wind information interpolated from the lidar wind measurements. We note that the raw radar dataset contained 301,618 tracks; tracks in this processed dataset were filtered based on the requirements described in Snortland et al. (2025). Birds_hourly: This file contains timeseries of the number of tracks detected per hour over the course of the data collection period, including wind conditions and sun position for each hour. These data were used for generalized additive modeling in Snortland et al. (2025).

17 WIND ENERGY↗

Principles of the Battery Data Genome

Batteries are central to modern society. They are no longer just a convenience but a critical enabler of the transition to a resilient, low-carbon economy. Battery development capabilities are provided by communities spanning materials discovery, battery chemistry and electrochemistry, cell and pack design, scale-up, manufacturing, and deployments. Despite their relative maturity, data-science practices among these diverse groups are far behind the state of the art in other fields, which have demonstrated an ability to significantly improve innovation and economic impact. The negative consequences of the present paradigm include incremental improvements but few breakthroughs, significant manufacturing uncertainties, and cascading investment risks that collectively slow deployments. The primary roadblock to a battery-data-science renaissance is the requirement for large amounts of high-quality data, which are not available in the current fragmented ecosystem. Here, in this study, we identify gaps and propose principles that enable the solution by building a robust community of data hubs with standardized practices and flexible sharing options that will seed advanced tools spanning innovation to deployment. Precedents are offered that demonstrate that both public good and immense economic gains will arise from sharing valuable battery data. The proposed Battery Data Genome looks to broadly transform innovations and revolutionize their translation from research to societal impact.

25 ENERGY STORAGE↗

Data Release and Project Closeout

A dataset containing procedures to: 1.) Make a dataset public 2.) Closeout a project on the Data Hub

EMN, HydroGEN, data governance, PUID, Data Hub, Da↗

Data Release and Project Closeout

A dataset containing procedures to: 1.) Make a dataset public 2.) Closeout a project on the Data Hub

EMN, ElectroCat, data governance, PUID, Data Hub, ↗

Data Release and Project Closeout

A dataset containing procedures to: 1.) Make a dataset public 2.) Closeout a project on the Data Hub

EMN, HyMARC, data governance, PUID, Data Hub, Data↗