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DOE OSTI · 2344941

Machine Learning-Enhanced Multiphase CFD for Carbon Capture Modeling Run Data

Fullmer, William·Musser, Jordan·Gel, Aytekin·Beetham, Sarah
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Abstract

Repository for the data generated as part of the 2023-2024 ALCC project "Machine Learning-Enhanced Multiphase CFD for Carbon Capture Modeling." The data was generated with MFIX-Exa's CFD-DEM model. The problem of interest is gravity driven, particle-laden, gas-solid flow in a triply-periodic domain of length 2048 particle diameters with an aspect ratio of 4. The mean particle concentration ranges from 1% to 40% and the Archimedes number ranges from 18 to 90. The particle-to-fluid density ratio, particle-particle restitution and friction coefficients and domain aspect ratio are held constant at values of 1000, 0.9, 0.25 and 4, respectively. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 using NERSC award ALCC-ERCAP0025948.

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Fullmer, William, Musser, Jordan, Gel, Aytekin, Beetham, Sarah. 2024-06-28. Machine Learning-Enhanced Multiphase CFD for Carbon Capture Modeling Run Data. https://doi.org/10.18141/2344941

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