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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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Buildings are where we live, work, go to school, gather as a community, and so much more. Globally, buildings are responsible for 30% of energy consumption and 26% of energy-related emissions. The National Renewable Energy Laboratory (NREL) is committed to adapting tools and capabilities to support emerging economies and improve the energy efficiency and flexibility of their buildings.
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NREL hosts computing facilities for the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE). In 2024, NREL introduced Kestrel, the 3rd generation, EERE-sponsored supercomputer dedicated to renewable energy and energy efficiency research. Kestrel has already been used for hundreds of research projects by NREL, other national laboratories, and university partners. This includes HPC4EI-sponsored industrial partnerships.
Computational methods underpin advancing the science and engineering of energy efficiency, sustainable transportation, renewable power technologies, and developing a knowledge base to optimize energy systems. NREL's Computational Science Center (CSC) proudly focuses on providing the service of computing, advancing the science of computing, and enabling NREL's clean energy mission.
Overview of Net Zero World's program capabilities and offerings for partners.
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A method for converting amorphous boron nitride to crystalline boron nitride, the method comprising immersing the amorphous boron nitride into anhydrous molten magnesium chloride maintained within a temperature range of 720° C.-820° C. while the amorphous boron nitride is cathodically polarized at a voltage within a range of −2.2V to −2.8V for a period of time of at least 2 minutes to result in conversion of the amorphous boron nitride to the crystalline form. Also described herein is a method for converting an amorphous carbon material to a crystalline carbon material, the method comprising immersing said amorphous carbon material into anhydrous molten magnesium chloride maintained within a temperature range of 780° C.-820° C. while the amorphous carbon material is cathodically polarized at a voltage within a range of −2.2V to −2.8V for a period of time of at least 2 minutes to result in conversion of the amorphous carbon material to the crystalline form.
This paper describes a simplified engineering model based on a one-dimensional thermal resistance network. The model is used to develop a new method to relate film cooling effectiveness and heat transfer augmentation to local overall cooling effectiveness in a conjugate flat plate experiment. This paper presents experimental proof-of-concept data to demonstrate the potential for this model. In contrast to previous approaches, neither the wall heat flux nor the adiabatic wall temperature is required to estimate the local film cooling performance parameters. The model predicts surface temperatures that are within the experimental uncertainties over the range for which the model is trained, and to within five percent when the model is extrapolated to higher coolant channel Reynolds numbers. This paper is relevant to conjugate test rigs that can measure the hot surface temperature distribution with and without film cooling. This information may also be relevant to designers as a method to approximate surface temperatures or used as an approximate heat transfer model for optimization studies.
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3 page evaluation abstract for PVSC 52
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NOvA is a long-baseline neutrino oscillation experiment that detects neutrino particles from the NuMI beam at Fermilab. Before data from this experiment can be used in analyses, raw hits in the detector must be matched to their source particles, and the type of each particle must be identified. This task has commonly been done using a mix of traditional clustering approaches and convolutional neural networks (CNNs). Due to the construction of the detector, the data is presented as two sparse 2D images: an XZ and a YZ view of the detector, rather than a 3D representation. We propose a point set neural network that operates on the sparse matrices with an operation that mixes information from both views. Our model uses less than 10% of the memory required using previous methods while achieving a 96.8% AUC score, a higher score than obtained when both views are processed independently (85.4%).