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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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The goal of this work is to create an online tool that can be used to search for degradation information and extrapolate PV module performance and durability to field exposure. A graphical user interface will aid in the understanding of the results. The prediction tool will be built modular and published open-source allowing users to expand on the existing framework.
Presentation for Carbon Management Research Project Review Meeting, Pittsburgh, PA, August 28-September 1, 2023.
For presentation at the 2023 FECM / NETL Carbon Management Research Project Review Meeting, Pittsburgh, PA, August 28-September 1, 2023
For presentation at the 2023 FECM/NETL Carbon Management Research Project Review Meeting, Pittsburgh, PA, August 28-September 1, 2023.
US-UK Energy Research Collaboration Workshop, Pittsburgh, PA, April 19-20, 2023
The Energy Storage - Fossil Energy (ES-FE) Dataset Explorer tool allows the user to take a deep dive into existing energy storage plant sites mapped to fossil generation sites. The tool also allows users to explore proposed energy storage sites. The explorer creates a dataset of all proposed and existing energy storage plant sites giving the user the ability to inspect various attributes of generation plants and storage plants, including but not limited to generating capacity, generating technology, and storage capacity.
For presentation at GSA Connects 2023, Pittsburgh, PA, October 15-18, 2023.
For presentation at the Geological Society of America 2023 Annual Meeting, Pittsburgh, PA, October 15-18, 2023.
For presentation at the FECM/ NETL Carbon Management Project Review Meeting, Pittsburgh, PA, August 28-September 1, 2023.
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For presentation at the 2023 American Geophysical Conference, San Francisco, CA, December 11-15, 2023.
This benchmarking study aims to compare residual dose rate predictions from the MARS and DORIAN codes with experimental measurements taken from samples irradiated at CERN's CERF facility. The research evaluates the residual dose rates of these samples over varied cooling intervals. Additionally, it delves into the influence of parameter choices, such as specific physics processes and energy thresholds, on the production and transport of particles and their effects on simulation outcomes. While both MARS and DORIAN largely align with experimental findings, discrepancies were observed in the copper sample. This prompted an in-depth examination of the elevated dose rates from MARS.
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Explore the source record for details and available documents.
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With the goal of improving the performance and reliability of high dependable technological systems such as nuclear power plants, advanced monitoring and health management systems are employed to inform system engineers on observed degradation processes and anomalous behaviors of assets and components. This information is captured in the form of large amount of data which can be heterogenous in nature (e.g., numeric, textual). Such large data availability poses challenges when system engineers are required to parse and analyze them in order to track historic reliability performance of assets and components. This paper tackles directly this challenge by providing means to organize data in the form of a graph: a knowledge graph. The presented approach distinguish itself from current knowledge graph-based methods by the fact that model-based system engineering (MBSE) models are used to “put data into context”. In particular, MBSE models are used as skeleton of a knowledge graph; numeric and textual data elements, once processed, are associated to MBSE model elements. Thus, a knowledge graph captures both system architecture (though MBSE models) and health/performance data. Such feature opens the door to new data analytics methods designed to identify causal relations between observed phenomena.