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Machine Learning-Enhanced Multiphase CFD for Carbon Capture Modeling run-32

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.

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Last Updated June 13, 2024, 11:21 (LMT)
Created June 13, 2024, 09:53 (LMT)
AI/ML Product no
Citation William Fullmer, Jordan Musser, Aytekin Gel, Sarah Beetham, Machine Learning-Enhanced Multiphase CFD for Carbon Capture Modeling, 11/29/2023, https://edx.netl.doe.gov/dataset/mfix-exa-alcc2324-run-32, DOI: 10.18141/2344941
Geospatial no
Netl Product yes
Poc Email mehrdad.shahnam@netl.doe.gov
Point Of Contact Mehrdad Shahnam
Program Or Project RIC, CSE, CDE
Project Number FWP-1022463
Publication Date 2024-06-28