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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Optimizing Solar PV Deployment in Manufacturing: A Morphological Matrix and Fuzzy TOPSIS Approach

The growing energy demand of the industrial sector and the need for sustainable solutions highlight the importance of efficient decision making in solar photovoltaic (PV) implementation. Selecting optimal PV configuration is complex due to the interdependent technical, economic, environmental, and social factors involved. This study introduces an integrated decision-making method combining a morphological matrix and fuzzy TOPSIS to systematically select and rank optimal PV system configurations for manufacturing firms. While the morphological matrix exhaustively examines possible design solutions based on sensing, smart, sustainable, and social (S4) attributes, the fuzzy TOPSIS method ranks the alternatives by handling uncertainty in decision making. A case study conducted in a Mexican manufacturing company validates the methodology’s effectiveness. The optimal PV configuration identified comprehensively addresses operational and sustainability criteria, covering all lifecycle stages. This approach demonstrates quantitative superiority and greater robustness compared to existing fuzzy TOPSIS-based methods for solar PV applications. The findings highlight the practical value of data-driven, multi-criteria decision making for industrial solar energy adoption, enhancing project feasibility, cost efficiency, and environmental compliance. Future research will incorporate discrete event simulation (DES) to further refine energy consumption strategies in manufacturing.

Briceño, Citlaly Pérez↗
DOE OSTI2026

SORCER TEST UTQIAĠVIK DEPLOYMENT FOR IONOGRAMS (STUDI) 2021-2024

The Air Force Research Laboratory (AFRL) has developed a passive broadband radio frequency (RF) receiver, called a Sounder Receiver (SoRcer), as an ionospheric diagnostic instrument. These systems can produce ionospheric specifications to characterize the local ionosphere. The specifications from the SoRcers can then be used to inform data-assimilation models; monitor diurnal, seasonal, or other cyclical changes; and detect irregularities or abnormalities in the ionosphere, such as traveling ionospheric disturbances (TIDs), sporadic E, and spread F.

frequency↗
DOE OSTI2026

Bridging Control and Deployment: A Cross-Layer Analysis of Scalable Building Cluster Control

Building cluster control has emerged as a promising approach for enabling flexible and coordinated operation of distributed building systems, yet its transition from pilot demonstrations to routine grid-interactive operation remains limited. This paper argues that this gap cannot be explained by control algorithms alone. Instead, it arises from interacting barriers in communication infrastructure, data and semantic interoperability, uncertainty management, stakeholder participation, market design, and policy support. Accordingly, the paper reviews both technical and non-technical barriers to building cluster control. Technical challenges include heterogeneous devices and protocols, communication latency and reliability, distributed decision-making, and uncertainty propagation across aggregated loads. Non-technical barriers include user participation, stakeholder coordination, incentive allocation, and data governance. Existing solution approaches are synthesized, including semantic interoperability frameworks, edge and hierarchical communication architectures, distributed and transactive control strategies, uncertainty-aware optimization, policy mechanisms, and market reforms. Based on this analysis, two research directions are identified: testing infrastructures that can evaluate control performance under realistic multi-building conditions, and abstraction methods that allow building clusters to interact with other energy sectors through standardized flexibility representations. Overall, the paper provides a structured review of how building cluster control can move from isolated demonstrations toward reproducible, market-compatible, and grid-relevant implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Cold Climate Degradation: An Analysis of Double-Axis Tracked, E-W Vertical, and Fixed-Tilt Photovoltaic Deployments in Alaska

As countries around the world transition towards renewable energy, there is increasing interest in using photovoltaic (PV) technologies to help decarbonize remote northern communities due to their scalability and affordability. However, a major barrier towards large-scale adoption of PV in cold climates is performance uncertainty under extreme environmental conditions including snowfall, freeze-thaw cycles, and high wind loads. Existing literature on PV degradation rates in the North is relatively limited, with published degradation rates varying between -0.2%/year (Sweden) to -1.3%/year (Scotland). At this workshop, we will present preliminary results on the long-term performance of two diverse photovoltaic sites located in Fairbanks, Alaska at 64.8 degrees N: a monofacial Al-BSF double-axis tracking site maintained by the Cold Climate Housing Research Center (CCHRC), and a bifacial PERC/SHJ E-W vertical and south-facing fixed-tilt site maintained by the Alaska Center Energy and Power (ACEP). CCHRC data has been collected over a period of 15 years, while ACEP site data has been collected over 4 years. Using the degradation analysis tool, RdTools, we will present annual system degradation rates, seasonal performance ratio, and identify potential cold climate failure mechanisms for commercially available PV technologies. This analysis will add to existing literature by directly comparing the performance of multiple PV configurations in Alaska.

bifacial↗
DOE OSTI2024

Deploying a New AI Software Tool for Rapid Characterization & Quantification of Unconventional Sources of Critical Minerals

Poster for the 2024 NETL Resource Sustainability Meeting. The poster presents a new project award by the Office of Technology Transitions to accelerate application and commercial utilization of an NETL-developed technology to rapidly characterize critical mineral occurrences within secondary and/or unconventional feedstocks, such as coal refuse or waste impoundments.

Creason, Christopher↗
DOE OSTI2024

Sustainability Criteria for Hydrogen Deployments

This presentation is an update of progress to date on the work done on developing sustainability metrics for hydrogen projects. Here we outline the motivation and objective for the work as well as the process we use to derive a sustainability framework and metrics to be considered when evaluating the sustainability of a hydrogen infrastructure project.

DEIA↗
DOE OSTI2024

The NREL Sensor Laboratory: Hydrogen Leak Detection for Large Scale Deployments: Preprint

The NREL Hydrogen Sensor Laboratory was commissioned in 2010 as a resource for sensor developers, end-users, and regulatory agencies within the national and international hydrogen community. The Laboratory continues to provide as its core capability the unbiased verification of hydrogen sensor performance to assure sensor availability and their proper use. However, the mission and strategy of the NREL Sensor Laboratory has evolved to meet the needs of the growing hydrogen market. The Sensor Laboratory program has expanded to support research in conventional and alternative detection methods as hydrogen use expands to large-scale markets as envisioned by the DOE National Clean Hydrogen Strategy and Roadmap. Current research encompasses advanced methods of hydrogen leak detection including stand-off and wide area monitoring approaches for large scale and distributed applications. In addition to safety applications, low-level detection strategies to support the potential environmental impacts of hydrogen and hydrogen product losses along the value chain are being explored. Many of these applications utilize detection strategies that supplement and may supplant the use of traditional point sensors. The latest results of the hydrogen detection strategy research at NREL will be presented.

detection↗
DOE OSTI2024

Merefa Community Microgrid: Supporting Distributed Energy Resource Deployment in Ukraine

A conceptual design is described for a community microgrid in Ukraine. Microgrid resources include solar photovoltaics, battery energy storage, and conventional natural gas fueled reciprocating engine generators. The conceptual architecture was informed by the microgrid developer, NREL subject matter experts, and the application of REopt, an NREL-developed software tool created for identification of least-cost combination of resources for achieving cost savings, resilience, and renewable energy goals. This fact sheet is a summary of a previously published technical report; see NREL/TP-7A40-89527, which includes conceptual architecture, estimates of key summary financial metrics, and sequence of operations.

battery storage↗
DOE OSTI2024

Graduating Sustainable Industrial Decarbonization Solutions from the Laboratory to Real-World Deployments

Accelerating innovation and integration of industrial decarbonization technology solutions is crucial for achieving industry-wide net-zero greenhouse gas emissions targets. This can be accomplished in various ways through increased utilization of low-embodied carbon materials and the adoption of advanced manufacturing techniques in construction practices. At the National Renewable Energy Laboratory (NREL), we facilitate dialogue and action across all sectors to uncover industry needs to inform innovation, and explore ways we can help with implementation of emerging solutions in the real world. As an applied laboratory, NREL sits between academia and industry to fully bridge the gap from foundational science research to examining the feasibility of market applications. Integration of innovative technologies and solutions that are efficient, resilient, and grid-interactive will play a pivotal role in supporting a clean energy future. However, it is imperative that these solutions prioritize equitable outcomes, addressing the challenges faced by all communities. By embracing decarbonization, the construction industry can revolutionize itself, promoting sustainability, reducing carbon emissions, conserving resources, while enhancing performance and durability - all major steps toward a greener and more sustainable future.

cement↗
DOE OSTI2024

Emerging Jets Search, Triton Server Deployment, and Track Quality Development: Machine Learning Applications in High Energy Physics

Machine learning is becoming prevalent in high energy physics, with numerous applications in physics analyses and event reconstruction showing great improvements compared to traditional computing methods. This thesis studies three projects which each propose new avenues for machine learning applications within the high energy physics CMS experiment located at CERN. In the first project, a search for a dark matter signal called “emerging jets” is performed, using graph neural networks to greatly increase sensitivity to the signal’s signature within the data. The result of this dark matter search sets the most stringent exclusion limits to date on theoretical emerging jet models. Motivated by inefficiencies encountered when processing the emerging jet graph neural network at Fermi National Accelerator Laboratory’s computing centers, the second project re-optimizes the computing centers for machine learning inference. This re-optimization uses NVIDIA Triton Inference Servers to process users’ analysis code heterogeneously, therefore achieving high processing throughput and decreasing user time-to-insight. The last project focuses on an upgrade to the CMS experiment’s real-time event selection system which improves physics object reconstruction under harsh processing conditions. A boosted decision tree is used to quickly and efficiently quantify a reconstructed particle’s “track quality” in order to remove particle tracks reconstructed erroneously. In summary, this thesis will not only present examples of how high energy physics can greatly benefit by leveraging machine learning techniques for physics analysis and reconstruction, but will also provide guidance on how the field can prepare for the inevitable increase in machine learning applications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗