CAS OpenIR
Coupling Artificial Neural Network with EMMS drag for simulation of dense fluidized beds
Yang, Zhuo1,2; Lu, Bona1,2; Wang, Wei1,2
2021-12-31
Source PublicationCHEMICAL ENGINEERING SCIENCE
ISSN0009-2509
Volume246Pages:12
AbstractThe previous sub-grid, energy-minimization multi-scale (EMMS) drag models were all established at cer-tain fixed operating conditions and material properties. In this study, we developed a generic EMMS drag for simulating dense fluidized beds by using the Artificial Neural Network (ANN) to cover a wide range of operating conditions and material properties. To this end, the algorithm of the EMMS model was opti-mized to provide a huge dataset efficiently and the performance of ANN was tested by training with dif-ferent numbers of data and hidden layer structures. The EMMS-ANN model was determined by balancing the training precision and computational time and then applied to the simulation of five fluidized beds under different operating conditions and material properties. It was found that the simulation with the EMMS-ANN drag enables reasonable prediction and shows good applicability to a wide range of dense fluidization. (C) 2021 Elsevier Ltd. All rights reserved.
KeywordEMMS Drag coefficient Artificial neural network Fluidized bed CFD simulation
DOI10.1016/j.ces.2021.117003
Language英语
WOS KeywordGAS-SOLID FLOWS ; NUMERICAL-SIMULATION ; CONSTITUTIVE MODELS ; EULERIAN SIMULATION ; CFD SIMULATION ; 2-FLUID MODEL ; PARTICLES ; GELDART
Funding ProjectNational Natural Science Foundation of China[22078331] ; National Natural Science Foundation of China[91834302] ; National Natural Science Foundation of China[21625605] ; National Natural Science Foundation of China[21821005] ; State Key Laboratory of Multiphase Complex Systems of China[MPCS-2021-D05] ; Chinese Academy of Sciences[XDA21030700]
WOS Research AreaEngineering
WOS SubjectEngineering, Chemical
Funding OrganizationNational Natural Science Foundation of China ; State Key Laboratory of Multiphase Complex Systems of China ; Chinese Academy of Sciences
WOS IDWOS:000704401400010
PublisherPERGAMON-ELSEVIER SCIENCE LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ipe.ac.cn/handle/122111/50538
Collection中国科学院过程工程研究所
Corresponding AuthorLu, Bona; Wang, Wei
Affiliation1.Chinese Acad Sci, Inst Proc Engn, State Key Lab Multiphase Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Yang, Zhuo,Lu, Bona,Wang, Wei. Coupling Artificial Neural Network with EMMS drag for simulation of dense fluidized beds[J]. CHEMICAL ENGINEERING SCIENCE,2021,246:12.
APA Yang, Zhuo,Lu, Bona,&Wang, Wei.(2021).Coupling Artificial Neural Network with EMMS drag for simulation of dense fluidized beds.CHEMICAL ENGINEERING SCIENCE,246,12.
MLA Yang, Zhuo,et al."Coupling Artificial Neural Network with EMMS drag for simulation of dense fluidized beds".CHEMICAL ENGINEERING SCIENCE 246(2021):12.
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