Development of a Filtered CFD-DEM Drag Model with Multiscale Markers Using an Artificial Neural Network and Nonlinear Regression-Reference-Cited by-同舟云学术

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Development of a Filtered CFD-DEM Drag Model with Multiscale Markers Using an Artificial Neural Network and Nonlinear Regression

Published:2021-12-26 Issue:1 Volume:61 Page:882-893
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Lu Liqiang¹²^ORCID,Gao Xi³,Dietiker Jean-François¹²,Shahnam Mehrdad¹,Rogers William A.¹

Affiliation:

1. National Energy Technology Laboratory, 3610 Collins Ferry Road, Morgantown, West Virginia 26507, United States

2. NETL Support Contractor, 3610 Collins Ferry Road, Morgantown, West Virginia 26507, United States

3. Department of Chemical Engineering, Guangdong Technion-Israel Institute of Technology, 241 Daxue Road, Shantou, Guangdong 515063, China

Funder

U.S. Department of Energy

Publisher

American Chemical Society (ACS)

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.1c03644

Reference61 articles.

1. Impact of operating parameters on biomass gasification in a fluidized bed reactor: An Eulerian-Lagrangian approach

2. Computational Fluid Dynamics/Discrete Element Method Investigation on the Biomass Fast Pyrolysis: The Influences of Shrinkage Patterns and Operating Parameters

3. Direct concurrent multi-scale CFD modeling: The effect of intraparticle transfer on the flow field in a MTO FBR

4. Filtered model for the cold-model gas–solid flow in a large-scale MTO fluidized bed reactor

5. Long-time simulation of catalytic MTO reaction in a fluidized bed reactor with a coarse-grained discrete particle method — EMMS-DPM

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