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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Geo Thermal Cloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The project is motivated by the challenges, risks, and costs associated with geothermal exploration and production. Many processes and parameters impacting geothermal conditions are poorly understood. Diverse datasets are available to help characterize subsurface geothermal conditions (public and proprietary; satellite, airborne surveys, vegetation/water sampling, geological, geophysical, etc.). Yet, it is not clear how to properly leverage these datasets for geothermal exploration due to an incomplete understanding of how physical processes impacting subsurface geothermal conditions are represented in these observations. Recent advancements in machine learning (ML) provide great promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. Our goals and work under Phases 1 and 2 (as proposed) of this project address all these needs.

15 GEOTHERMAL ENERGY↗
NASA NTRS1990

A unified view of convective transports by stratocumulus clouds, shallow cumulus clouds, and deep convection

A bulk planetary boundary layer (PBL) model was developed with a simple internal vertical structure and a simple second-order closure, designed for use as a PBL parameterization in a large-scale model. The model allows the mean fields to vary with height within the PBL, and so must address the vertical profiles of the turbulent fluxes, going beyond the usual mixed-layer assumption that the fluxes of conservative variables are linear with height. This is accomplished using the same convective mass flux approach that has also been used in cumulus parameterizations. The purpose is to show that such a mass flux model can include, in a single framework, the compensating subsidence concept, downgradient mixing, and well-mixed layers.

Randall, David A.↗
NASA NTRS1993

The distance to the Large Magellanic Cloud - Constraints from Cepheids in Large Magellanic Cloud star clusters

We have used recent observational data for the Cepheids in the rich, young LMC clusters NGC 1866 and NGC 2031 to constrain the cluster distances with the mass-equivalency (ME) method. The basis of this approach is to fix the cluster distances by requiring the Cepheid evolutionary and pulsational masses to be equal. Using evolutionary models incorporating a mild amount of core and envelope overshooting along with recent pulsational models, we derive distance moduli of 18.51 +/- 0.21 and 18.32 +/- 0.20 for NGC 1866 and NGC 2031, respectively. The quoted errors are dominated by the uncertainties in the heavy element abundances of the clusters (assumed to be 0.3 dex for both clusters), with a smaller contribution due to the apparently intrinsic spread in the masses of the Cepheids in each cluster. For the ME method, we find that Delta(m-M)(0)/Delta sub Z(1) = 0.69, where Z(1) = log (Z/0.016). This result implies that the cluster distances can be determined to better than +/- 5 percent if the cluster abundances can be measured to better than about +/- 0.15 dex.

Bertelli, Gianpaolo↗