Coarse grained computational fluid dynamic simulation of sands and biomass fluidization with a hybrid drag
Abstract
We report the bubbling fluidized bed reactor is widely used in fast pyrolysis of biomass. Discrete simulation of this reactor is challenging due to many sand particles and lack of accurate drag corrections accounting for the interaction of two different solid particles with different properties. In this research, the computational cost is reduced by using the coarse-grained computational fluid dynamic-discrete element method, where many sand particles are lumped into a larger numerical parcel. The Syamlal–O'Brien drag model is used for sand, while Ganser correction coupled with Gidaspow model is used for the nonspherical biomass particles. This hybrid approach shows superior behavior over other drag models using pressure drops as a benchmark. The predicted bed height and pressure fluctuating frequencies compare well with experiment. Finally, the mixing of biomass is close to perfect if the superficial velocity is larger than four times the minimum fluidization velocity.
- Authors:
-
- National Energy Technology Lab. (NETL), Morgantown, WV (United States); West Virginia University Research Corporation, Morgantown, WV (United States)
- National Energy Technology Lab. (NETL), Morgantown, WV (United States)
- Publication Date:
- Research Org.:
- National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR (United States)
- Sponsoring Org.:
- USDOE Office of Fossil Energy (FE); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Sustainable Transportation Office. Bioenergy Technologies Office
- OSTI Identifier:
- 1607761
- Alternate Identifier(s):
- OSTI ID: 1576650
- Resource Type:
- Accepted Manuscript
- Journal Name:
- AIChE Journal
- Additional Journal Information:
- Journal Volume: 66; Journal Issue: 4; Journal ID: ISSN 0001-1541
- Publisher:
- American Institute of Chemical Engineers
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 09 BIOMASS FUELS
Citation Formats
Lu, Liqiang, Gao, Xi, Shahnam, Mehrdad, and Rogers, William A. Coarse grained computational fluid dynamic simulation of sands and biomass fluidization with a hybrid drag. United States: N. p., 2019.
Web. doi:10.1002/aic.16867.
Lu, Liqiang, Gao, Xi, Shahnam, Mehrdad, & Rogers, William A. Coarse grained computational fluid dynamic simulation of sands and biomass fluidization with a hybrid drag. United States. https://doi.org/10.1002/aic.16867
Lu, Liqiang, Gao, Xi, Shahnam, Mehrdad, and Rogers, William A. Tue .
"Coarse grained computational fluid dynamic simulation of sands and biomass fluidization with a hybrid drag". United States. https://doi.org/10.1002/aic.16867. https://www.osti.gov/servlets/purl/1607761.
@article{osti_1607761,
title = {Coarse grained computational fluid dynamic simulation of sands and biomass fluidization with a hybrid drag},
author = {Lu, Liqiang and Gao, Xi and Shahnam, Mehrdad and Rogers, William A.},
abstractNote = {We report the bubbling fluidized bed reactor is widely used in fast pyrolysis of biomass. Discrete simulation of this reactor is challenging due to many sand particles and lack of accurate drag corrections accounting for the interaction of two different solid particles with different properties. In this research, the computational cost is reduced by using the coarse-grained computational fluid dynamic-discrete element method, where many sand particles are lumped into a larger numerical parcel. The Syamlal–O'Brien drag model is used for sand, while Ganser correction coupled with Gidaspow model is used for the nonspherical biomass particles. This hybrid approach shows superior behavior over other drag models using pressure drops as a benchmark. The predicted bed height and pressure fluctuating frequencies compare well with experiment. Finally, the mixing of biomass is close to perfect if the superficial velocity is larger than four times the minimum fluidization velocity.},
doi = {10.1002/aic.16867},
journal = {AIChE Journal},
number = 4,
volume = 66,
place = {United States},
year = {2019},
month = {11}
}
Web of Science
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