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Title: 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:
ORCiD logo [1]; ORCiD logo [2];  [2];  [2]
  1. National Energy Technology Lab. (NETL), Morgantown, WV (United States); West Virginia University Research Corporation, Morgantown, WV (United States)
  2. National Energy Technology Lab. (NETL), Morgantown, WV (United States)
Publication Date:
Research Org.:
National Energy Technology Lab. (NETL), Morgantown, WV (United States)
Sponsoring Org.:
USDOE Office of Fossil Energy (FE); USDOE Office of Energy Efficiency and Renewable Energy (EERE), 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; biomass; CFD‐DEM; coarse grain; fast pyrolysis; nonspherical drag

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. https://doi.org/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}
}

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Cited by: 4 works
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