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For: Jiang Y, Chen X, Kolehmainen J, Kevrekidis IG, Ozel A, Sundaresan S. Development of data-driven filtered drag model for industrial-scale fluidized beds. Chem Eng Sci 2021;230:116235. [DOI: 10.1016/j.ces.2020.116235] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Number Cited by Other Article(s)
1
Milioli CC, Milioli FE. A scale sensitive filtered sub-grid drag model for fluidized gas-particle flows. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2022.118266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
2
Ouyang B, Zhu LT, Luo ZH. Machine learning for full spatiotemporal acceleration of gas-particle flow simulations. POWDER TECHNOL 2022. [DOI: 10.1016/j.powtec.2022.117701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
3
Chew JW, LaMarche WCQ, Cocco RA. 100 years of scaling up fluidized bed and circulating fluidized bed reactors. POWDER TECHNOL 2022. [DOI: 10.1016/j.powtec.2022.117813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
4
Zhu LT, Chen XZ, Ouyang B, Yan WC, Lei H, Chen Z, Luo ZH. Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c01036] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
5
Du C, Han C, Yang Z, Wu H, Luo H, Niedzwiecki L, Lu B, Wang W. Multiscale CFD Simulation of an Industrial Diameter-Transformed Fluidized Bed Reactor with Artificial Neural Network Analysis of EMMS Drag Markers. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c00396] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
6
Machine learning-aided optimization of coal decoupling combustion for lowering NO and CO emissions simultaneously. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
7
Using mesoscale drag model-augmented coarse-grid simulation to design fluidized bed reactor: Effect of bed internals and sizes. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2022.117547] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
8
Yang Y, Zhang Q, Pan T, Qu Y, Liu X, Li X, Cheng Y, Wang L, Liu W. The hydrodynamics of low-density particles and optimization of the EMMS calculation process. POWDER TECHNOL 2022. [DOI: 10.1016/j.powtec.2022.117511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
9
Geng J, Tian Y, Wang W. Exploring a unified EMMS drag model for gas-solid fluidization. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2022.117444] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
10
Benyahia S. Selecting the best approach for fluidized bed simulation of aeratable particles. POWDER TECHNOL 2022. [DOI: 10.1016/j.powtec.2022.117178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
11
Ouyang B, Zhu LT, Su YH, Luo ZH. A hybrid mesoscale closure combining CFD and deep learning for coarse-grid prediction of gas-particle flow dynamics. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2021.117268] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
12
MFiX based multi-scale CFD simulations of biomass fast pyrolysis: A review. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2021.117131] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
13
A dynamic multiphase turbulence model for coarse-grid simulations of fluidized gas-particle suspensions. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2021.117104] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
14
Tian Y, Geng J, Wang W. On the choice of mesoscale drag markers. AIChE J 2021. [DOI: 10.1002/aic.17558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
15
Lu L, Gao X, Dietiker JF, Shahnam M, Rogers WA. Development of a Filtered CFD-DEM Drag Model with Multiscale Markers Using an Artificial Neural Network and Nonlinear Regression. Ind Eng Chem Res 2021. [DOI: 10.1021/acs.iecr.1c03644] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
16
Yang Z, Lu B, Wang W. Coupling Artificial Neural Network with EMMS drag for simulation of dense fluidized beds. Chem Eng Sci 2021. [DOI: 10.1016/j.ces.2021.117003] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
17
A scale-independent modeling method for filtered drag in fluidized gas-particle flows. POWDER TECHNOL 2021. [DOI: 10.1016/j.powtec.2021.08.092] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
18
Zhu L, Ouyang B, Lei H, Luo Z. Conventional and data‐driven modeling of filtered drag, heat transfer, and reaction rate in gas–particle flows. AIChE J 2021. [DOI: 10.1002/aic.17299] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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