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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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Microbial decontamination of particulate food using a pilot-scale atmospheric plasma jet treatment system
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Factors controlling groundwater response to earth and atmospheric tides
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Steam oxidation of uranium mononitride in pure and reducing steam atmospheres to 1200 °C
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Polarized infrared radiative transfer in an optically anisotropic medium: A Markov chain solution for remote sensing of atmospheric suspended particles
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Toward prediction of turbulent atmospheric flows over propagating oceanic waves via machine-learning augmented large-eddy simulation
Wind-wave interactions have important effects on the energy harvesting of offshore wind farms. High-fidelity large-eddy simulation (LES) is a powerful approach for investigating wind-wave interactions in turbulent oceanic environments. Due to the large scale of the flow domain and the high grid resolution required to resolve multi-scale flow motions, however, brute-force LES of wind-wave interactions is computationally very expensive. We propose augmenting brute-force LES via machine-learning data-driven modeling (ML-LES) to dramatically reduce the computational time required to obtain converged turbulence statistics when the brute-force approach is employed. Namely, we employ a convolutional neural network (CNN) autoencoder trained and validated with LES data sets to develop a highly efficient ML-LES approach for computing turbulence statistics from just a few snapshots of instantaneous LES flow fields. Further, our results demonstrate the accuracy and efficiency of ML-LES in predicting the mean velocity, velocity fluctuations, and turbulence kinetic energy in highly stretched computational grid systems required to carry out simulations in real-life oceanic environments.
Injection of meteoric phosphorus into planetary atmospheres
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The importance of ammonia for springtime atmospheric new particle formation and aerosol number abundance over the United States
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Predictive modeling of atmospheric nuclear fallout microphysics
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The effect of atmosphere on the flash-sintering of nanoscale titania ceramics
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Environmental atmosphere effect on lubrication performance of gallium-based liquid metal
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