U.S. Department of EnergyOffice of Scientific and Technical Information
Machine Learning-Enhanced Multiphase CFD for Carbon Capture Modeling Run Data
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.
- Authors:
-
- National Energy Technology Laboratory
- Publication Date:
- Other Number(s):
- b6252153-faff-4074-80ab-b5b47d8b9873
- Research Org.:
- National Energy Technology Laboratory - Energy Data eXchange; NETL
- Sponsoring Org.:
- USDOE Office of Fossil Energy (FE)
- Subject:
- AMReX,Big Data,CFD-DEM,MFIX-Exa
- OSTI Identifier:
- 2344941
- DOI:
- https://doi.org/10.18141/2344941
Citation Formats
Fullmer, William, Musser, Jordan, Gel, Aytekin, and Beetham, Sarah. Machine Learning-Enhanced Multiphase CFD for Carbon Capture Modeling Run Data. United States: N. p., 2024.
Web. doi:10.18141/2344941.
Fullmer, William, Musser, Jordan, Gel, Aytekin, & Beetham, Sarah. Machine Learning-Enhanced Multiphase CFD for Carbon Capture Modeling Run Data. United States. doi:https://doi.org/10.18141/2344941
Fullmer, William, Musser, Jordan, Gel, Aytekin, and Beetham, Sarah. 2024.
"Machine Learning-Enhanced Multiphase CFD for Carbon Capture Modeling Run Data". United States. doi:https://doi.org/10.18141/2344941. https://www.osti.gov/servlets/purl/2344941. Pub date:Fri Jun 28 00:00:00 EDT 2024
@article{osti_2344941,
title = {Machine Learning-Enhanced Multiphase CFD for Carbon Capture Modeling Run Data},
author = {Fullmer, William and Musser, Jordan and Gel, Aytekin and Beetham, Sarah},
abstractNote = {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.},
doi = {10.18141/2344941},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Jun 28 00:00:00 EDT 2024},
month = {Fri Jun 28 00:00:00 EDT 2024}
}