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

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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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OSTI Info

Field Value
Dataset Type AS
Contract Nos FWP-1022463
Org Research Org NETL
Language English
Country US
Sponsor Org USDOE Office of Fossil Energy (FE)
Site Url https://edx.netl.doe.gov/dataset/mfix-exa-alcc2324-run-data
Contact Name William Fullmer
Contact Org National Energy Technology Laboratory
Doi 10.18141/2344941

Additional Info

Field Value
Last Updated June 13, 2024, 11:37 (LMT)
Created November 29, 2023, 09:35 (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/machine-learning-enhanced-multiphase-cfd-for-carbon-capture-modeling, DOI: 10.18141/2344941
Geospatial no
Netl Product yes
Osti yes
Poc Email mehrdad.shahnam@netl.doe.gov
Point Of Contact Mehrdad Shahnam
Program Or Project RIC, CSE, CDE
Project Number FWP-1022463