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

Lu, Liqiang; Gao, Xi; Dietiker, Jean-François; Shahnam, Mehrdad; Rogers, William A.

doi:10.1021/acs.iecr.1c03644

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

Journal Article · 25 December 2021 · Industrial and Engineering Chemistry Research

DOI: https://doi.org/10.1021/acs.iecr.1c03644 · OSTI ID:1868072

^[1]; Gao, Xi ^[2]; Dietiker, Jean-François ^[1]; Shahnam, Mehrdad ^[1]; Rogers, William A. ^[1]

National Energy Technology Lab. (NETL), Morgantown, WV (United States)
Guangdong Technion-Israel Institute of Technology (China)

Here, the accuracy of coarse-grained Euler-Lagrangian simulations of fluidized beds heavily depends on the mesoscale drag models to account for the influences of the unresolved sub-grid structures. Traditional filtered drag models are regressed with mesoscale markers such as voidage and slip velocities. In this research, a filtered drag was regressed with both mesoscale and macro-scale markers using fine grid Computational Fluid Dynamics - Discrete Element Method (CFD-DEM) simulations. The traditional non-linear regression method was compared with machine learning regression using an Artificial Neural Network (ANN) implemented in PyTorch and coupled with MFiX. The new drag showed higher accuracy than the Wen-Yu drag and another filtered drag derived from the two-fluid model. The nonlinear regression shows slightly better results than ANN regression in cases with similar R² values. The utilization of the gas inlet velocity as an additional macro-scale marker reduced the errors by up to 55.3% in the tested cases.

View Accepted Manuscript (DOE)

Cite

Export

Save

Research Organization:: National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR (United States)

Sponsoring Organization:: USDOE Office of Fossil Energy (FE)

Grant/Contract Number:: 89243318CFE000003

OSTI ID:: 1868072

Journal Information:: Industrial and Engineering Chemistry Research, Journal Name: Industrial and Engineering Chemistry Research Journal Issue: 1 Vol. 61; ISSN 0888-5885

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

References (58)

Filtered two-fluid models for fluidized gas-particle suspensions Igci, Yesim; Andrews, Arthur T.; Sundaresan, Sankaran AIChE Journal, Vol. 54, Issue 6 https://doi.org/10.1002/aic.11481	journal	January 2008
A functional subgrid drift velocity model for filtered drag prediction in dense fluidized bed Parmentier, Jean-François; Simonin, Olivier; Delsart, Olivier AIChE Journal, Vol. 58, Issue 4 https://doi.org/10.1002/aic.12647	journal	May 2011
Filtered two-fluid models of fluidized gas-particle flows: New constitutive relations Milioli, Christian C.; Milioli, Fernando E.; Holloway, William AIChE Journal, Vol. 59, Issue 9 https://doi.org/10.1002/aic.14130	journal	May 2013
A spatially-averaged two-fluid model for dense large-scale gas-solid flows Schneiderbauer, Simon AIChE Journal, Vol. 63, Issue 8 https://doi.org/10.1002/aic.15684	journal	March 2017
Validation study on spatially averaged two-fluid model for gas-solid flows: I. A priori analysis of wall bounded flows Schneiderbauer, Simon AIChE Journal, Vol. 64, Issue 5 https://doi.org/10.1002/aic.16142	journal	March 2018
A gas pressure gradient‐dependent subgrid drift velocity model for drag prediction in fluidized gas–particle flows Jiang, Ming; Chen, Xiao; Zhou, Qiang AIChE Journal, Vol. 66, Issue 4 https://doi.org/10.1002/aic.16884	journal	December 2019
Machine learning to assist filtered two‐fluid model development for dense gas–particle flows Zhu, Li‐Tao; Tang, Jia‐Xun; Luo, Zheng‐Hong AIChE Journal, Vol. 66, Issue 6 https://doi.org/10.1002/aic.16973	journal	March 2020
MP-PIC modeling of CFB risers with homogeneous and heterogeneous drag models Xie, Jun; Zhong, Wenqi; Yu, Aibing Advanced Powder Technology, Vol. 29, Issue 11 https://doi.org/10.1016/j.apt.2018.08.007	journal	November 2018
CFD simulation of concurrent-up gas–solid flow in circulating fluidized beds with structure-dependent drag coefficient Yang, Ning; Wang, Wei; Ge, Wei Chemical Engineering Journal, Vol. 96, Issue 1-3 https://doi.org/10.1016/j.cej.2003.08.006	journal	December 2003
A grid-independent EMMS/bubbling drag model for bubbling and turbulent fluidization Luo, Hao; Lu, Bona; Zhang, Jingyuan Chemical Engineering Journal, Vol. 326 https://doi.org/10.1016/j.cej.2017.04.145	journal	October 2017
Influence of grid resolution, parcel size and drag models on bubbling fluidized bed simulation Lu, Liqiang; Konan, Arthur; Benyahia, Sofiane Chemical Engineering Journal, Vol. 326 https://doi.org/10.1016/j.cej.2017.06.002	journal	October 2017
Bubbling/slugging flow behavior in a cylindrical fluidized bed: ECT measurements and two-fluid simulations Singh, Brajesh K.; Roy, Shantanu; Buwa, Vivek V. Chemical Engineering Journal, Vol. 383 https://doi.org/10.1016/j.cej.2019.123120	journal	March 2020
Long-time simulation of catalytic MTO reaction in a fluidized bed reactor with a coarse-grained discrete particle method — EMMS-DPM Liu, Xingchi; Xu, Ji; Ge, Wei Chemical Engineering Journal, Vol. 389 https://doi.org/10.1016/j.cej.2020.124135	journal	June 2020
Simulation of gas–solid two-phase flow by a multi-scale CFD approach—of the EMMS model to the sub-grid level Wang, Wei; Li, Jinghai Chemical Engineering Science, Vol. 62, Issue 1-2 https://doi.org/10.1016/j.ces.2006.08.017	journal	January 2007
A discrete particle model for particle–fluid flow with considerations of sub-grid structures Xu, Ming; Ge, Wei; Li, Jinghai Chemical Engineering Science, Vol. 62, Issue 8 https://doi.org/10.1016/j.ces.2006.12.008	journal	April 2007
Eulerian simulation of heterogeneous gas–solid flows in CFB risers: EMMS-based sub-grid scale model with a revised cluster description Wang, Junwu; Ge, Wei; Li, Jinghai Chemical Engineering Science, Vol. 63, Issue 6 https://doi.org/10.1016/j.ces.2007.11.023	journal	March 2008
Why the two-fluid model fails to predict the bed expansion characteristics of Geldart A particles in gas-fluidized beds: A tentative answer Wang, Junwu; van der Hoef, M. A.; Kuipers, J. A. M. Chemical Engineering Science, Vol. 64, Issue 3 https://doi.org/10.1016/j.ces.2008.09.028	journal	February 2009
Searching for a mesh-independent sub-grid model for CFD simulation of gas–solid riser flows Lu, Bona; Wang, Wei; Li, Jinghai Chemical Engineering Science, Vol. 64, Issue 15 https://doi.org/10.1016/j.ces.2009.04.024	journal	August 2009
An advanced EMMS scheme for the prediction of drag coefficient under a 1.2MWth CFBC isothermal flow—Part I: Numerical formulation Nikolopoulos, A.; Papafotiou, D.; Nikolopoulos, N. Chemical Engineering Science, Vol. 65, Issue 13 https://doi.org/10.1016/j.ces.2010.03.054	journal	July 2010
A bubble-based EMMS model for gas–solid bubbling fluidization Shi, Zhansheng; Wang, Wei; Li, Jinghai Chemical Engineering Science, Vol. 66, Issue 22 https://doi.org/10.1016/j.ces.2011.07.020	journal	November 2011
MP-PIC simulation of CFB riser with EMMS-based drag model Li, Fei; Song, Feifei; Benyahia, Sofiane Chemical Engineering Science, Vol. 82 https://doi.org/10.1016/j.ces.2012.07.020	journal	September 2012
A structure-dependent multi-fluid model (SFM) for heterogeneous gas–solid flow Hong, Kun; Shi, Zhansheng; Wang, Wei Chemical Engineering Science, Vol. 99 https://doi.org/10.1016/j.ces.2013.05.050	journal	August 2013
Direct concurrent multi-scale CFD modeling: The effect of intraparticle transfer on the flow field in a MTO FBR Chen, Xiao-Min; Luo, Zheng-Hong; Zhu, Ya-Ping Chemical Engineering Science, Vol. 104 https://doi.org/10.1016/j.ces.2013.09.055	journal	December 2013
A drag model for filtered Euler–Lagrange simulations of clustered gas–particle suspensions Radl, Stefan; Sundaresan, Sankaran Chemical Engineering Science, Vol. 117 https://doi.org/10.1016/j.ces.2014.07.011	journal	September 2014
EMMS-based discrete particle method (EMMS–DPM) for simulation of gas–solid flows Lu, Liqiang; Xu, Ji; Ge, Wei Chemical Engineering Science, Vol. 120 https://doi.org/10.1016/j.ces.2014.08.004	journal	December 2014
CFD study of mixing and segregation in CFB risers: Extension of EMMS drag model to binary gas–solid flow Zhou, Quan; Wang, Junwu Chemical Engineering Science, Vol. 122 https://doi.org/10.1016/j.ces.2014.10.025	journal	January 2015
Filtered model for the cold-model gas–solid flow in a large-scale MTO fluidized bed reactor Zhu, Li-Tao; Xie, Le; Xiao, Jie Chemical Engineering Science, Vol. 143 https://doi.org/10.1016/j.ces.2016.01.006	journal	April 2016
Filtered sub-grid constitutive models for fluidized gas-particle flows constructed from 3-D simulations Sarkar, Avik; Milioli, Fernando E.; Ozarkar, Shailesh Chemical Engineering Science, Vol. 152 https://doi.org/10.1016/j.ces.2016.06.023	journal	October 2016
Fluid and particle coarsening of drag force for discrete-parcel approach Ozel, Ali; Kolehmainen, Jari; Radl, Stefan Chemical Engineering Science, Vol. 155 https://doi.org/10.1016/j.ces.2016.08.014	journal	November 2016
Assessment of different coarse graining strategies to simulate polydisperse gas-solids flow Lu, Liqiang; Xu, Yupeng; Li, Tingwen Chemical Engineering Science, Vol. 179 https://doi.org/10.1016/j.ces.2018.01.003	journal	April 2018
Development and validation of an enhanced filtered drag model for simulating gas-solid fluidization of Geldart A particles in all flow regimes Gao, Xi; Li, Tingwen; Sarkar, Avik Chemical Engineering Science, Vol. 184 https://doi.org/10.1016/j.ces.2018.03.038	journal	July 2018
An effective three-marker drag model via sub-grid modeling for turbulent fluidization Zhu, Li-Tao; Liu, Yuan-Xing; Luo, Zheng-Hong Chemical Engineering Science, Vol. 192 https://doi.org/10.1016/j.ces.2018.08.026	journal	December 2018
Macro-scale effects over filtered and residual stresses in gas-solid riser flows Mouallem, Joseph; Niaki, Seyed R. A.; Chavez-Cussy, Norman Chemical Engineering Science, Vol. 195 https://doi.org/10.1016/j.ces.2018.09.054	journal	February 2019
Influences of operating parameters on the fluidized bed coal gasification process: A coarse-grained CFD-DEM study Hu, Chenshu; Luo, Kun; Wang, Shuai Chemical Engineering Science, Vol. 195 https://doi.org/10.1016/j.ces.2018.10.015	journal	February 2019
Extending the EMMS-bubbling model to fluidization of binary particle mixture: Parameter analysis and model validation Ahmad, Nouman; Tong, Ying; Lu, Bona Chemical Engineering Science, Vol. 200 https://doi.org/10.1016/j.ces.2019.02.016	journal	June 2019
A material-property-dependent sub-grid drag model for coarse-grained simulation of 3D large-scale CFB risers Zhu, Li-Tao; Liu, Yuan-Xing; Tang, Jia-Xun Chemical Engineering Science, Vol. 204 https://doi.org/10.1016/j.ces.2019.04.026	journal	August 2019
Meso-scale drag model designed for coarse-grid Eulerian-Lagrangian simulation of gas-solid flows Yu, Yaxiong; Li, Yu; Jiang, Ming Chemical Engineering Science, Vol. 223 https://doi.org/10.1016/j.ces.2020.115747	journal	September 2020
Modeling of the filtered drag force in gas–solid flows via a deep learning approach Zhang, Yu; Jiang, Ming; Chen, Xiao Chemical Engineering Science, Vol. 225 https://doi.org/10.1016/j.ces.2020.115835	journal	November 2020
Introducing an artificial neural network energy minimization multi-scale drag scheme for fluidized particles Nikolopoulos, Aristeidis; Samlis, Christos; Zeneli, Myrto Chemical Engineering Science, Vol. 229 https://doi.org/10.1016/j.ces.2020.116013	journal	January 2021
Development of data-driven filtered drag model for industrial-scale fluidized beds Jiang, Yundi; Chen, Xiao; Kolehmainen, Jari Chemical Engineering Science, Vol. 230 https://doi.org/10.1016/j.ces.2020.116235	journal	February 2021
CFD-DEM modeling of filtered fluid-particle drag and heat transfer in bidisperse gas-solid flows Lei, He; Liao, Jia-Wei; Zhu, Li-Tao Chemical Engineering Science, Vol. 246 https://doi.org/10.1016/j.ces.2021.116896	journal	December 2021
Bubble-induced mesoscale drag model for the simulation of gas-solid bubbling fluidization Zhang, Kai; Wang, Shuai; He, Yurong Chemical Engineering Science, Vol. 246 https://doi.org/10.1016/j.ces.2021.116990	journal	December 2021
Coupling Artificial Neural Network with EMMS drag for simulation of dense fluidized beds Yang, Zhuo; Lu, Bona; Wang, Wei Chemical Engineering Science, Vol. 246 https://doi.org/10.1016/j.ces.2021.117003	journal	December 2021
Mesoscale drag modeling: a critical review Wang, Wei; Lu, Bona; Geng, Jingwei Current Opinion in Chemical Engineering, Vol. 29 https://doi.org/10.1016/j.coche.2020.07.001	journal	September 2020
Development of filtered Euler–Euler two-phase model for circulating fluidised bed: High resolution simulation, formulation and a priori analyses Ozel, A.; Fede, P.; Simonin, O. International Journal of Multiphase Flow, Vol. 55 https://doi.org/10.1016/j.ijmultiphaseflow.2013.04.002	journal	October 2013
A dynamic anisotropic Spatially-Averaged Two-Fluid Model for moderately dense gas-particle flows Rauchenzauner, Stefanie; Schneiderbauer, Simon International Journal of Multiphase Flow, Vol. 126 https://doi.org/10.1016/j.ijmultiphaseflow.2020.103237	journal	May 2020
Particle clustering (mesoscale structure) of high-flux gas–solid circulating fluidized bed Wang, Chengxiu; Li, Chengxiang; Lan, Xingying Particuology, Vol. 48 https://doi.org/10.1016/j.partic.2018.12.003	journal	February 2020
GPU accelerated MFiX-DEM simulations of granular and multiphase flows Lu, Liqiang Particuology, Vol. 62 https://doi.org/10.1016/j.partic.2021.08.001	journal	March 2022
Do we need sub-grid scale corrections for both continuum and discrete gas-particle flow models? Benyahia, Sofiane; Sundaresan, Sankaran Powder Technology, Vol. 220 https://doi.org/10.1016/j.powtec.2011.10.052	journal	April 2012
Impact of operating parameters on biomass gasification in a fluidized bed reactor: An Eulerian-Lagrangian approach Wang, Shuai; Luo, Kun; Hu, Chenshu Powder Technology, Vol. 333 https://doi.org/10.1016/j.powtec.2018.04.027	journal	June 2018
Neural-network-based filtered drag model for gas-particle flows Jiang, Yundi; Kolehmainen, Jari; Gu, Yile Powder Technology, Vol. 346 https://doi.org/10.1016/j.powtec.2018.11.092	journal	March 2019
EMMS-based solid stress model for the multiphase particle-in-cell method Jiang, Yong; Li, Fei; Ge, Wei Powder Technology, Vol. 360 https://doi.org/10.1016/j.powtec.2019.09.031	journal	January 2020
Validation of a filtered drag model for solid residence time distribution (RTD) prediction in a pilot-scale FCC riser Yu, Jia; Gao, Xi; Lu, Liqiang Powder Technology, Vol. 378 https://doi.org/10.1016/j.powtec.2020.10.007	journal	January 2021
Numerical Investigation of the Ability of Salt Tracers to Represent the Residence Time Distribution of Fluidized Catalytic Cracking Particles Lu, Liqiang; Gao, Xi; Li, Tingwen Industrial & Engineering Chemistry Research, Vol. 56, Issue 46 https://doi.org/10.1021/acs.iecr.7b03773	journal	November 2017
Computational Fluid Dynamics/Discrete Element Method Investigation on the Biomass Fast Pyrolysis: The Influences of Shrinkage Patterns and Operating Parameters Hu, Chenshu; Luo, Kun; Wang, Shuai Industrial & Engineering Chemistry Research, Vol. 58, Issue 3 https://doi.org/10.1021/acs.iecr.8b05279	journal	December 2018
On the Subgrid Behavior of Accelerated Riser Flows for a High Stokes Number Particulate Milioli, Christian C.; Milioli, Fernando E. Industrial & Engineering Chemistry Research, Vol. 50, Issue 23 https://doi.org/10.1021/ie1020975	journal	November 2011
Constitutive Models for Filtered Two-Fluid Models of Fluidized Gas–Particle Flows Igci, Yesim; Sundaresan, Sankaran Industrial & Engineering Chemistry Research, Vol. 50, Issue 23 https://doi.org/10.1021/ie200190q	journal	December 2011
Toward Constitutive Models for Momentum, Species, and Energy Transport in Gas–Particle Flows Sundaresan, Sankaran; Ozel, Ali; Kolehmainen, Jari Annual Review of Chemical and Biomolecular Engineering, Vol. 9, Issue 1 https://doi.org/10.1146/annurev-chembioeng-060817-084025	journal	June 2018

Similar Records

Small-scale validation tests for MFIX-Exa CFD-DEM

Technical Report · 2025 · OSTI ID:2589328

Fullmer, William D.

An Investigation into Solution Verification for CFD-DEM

Technical Report · 2017 · OSTI ID:1427020

Fullmer, William D.; Musser, Jordan

Assessing the capability of continuum and discrete particle methods to simulate gas-solids flow using DNS predictions as a benchmark

Journal Article · 2017 · Powder Technology · OSTI ID:1440342

Lu, Liqiang; Liu, Xiaowen; Li, Tingwen; +3 more

Related Subjects

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
artificial neural network
computational fluid dynamics
discrete element modeling
filtered drag
machine learning

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

Citation Formats

References (58)

Similar Records

Related Subjects