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

Lu, Liqiang; Liu, Xiaowen; Li, Tingwen; Wang, Limin; Ge, Wei; Benyahia, Sofiane

doi:10.1016/j.powtec.2017.08.034

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

Journal Article · Sat Aug 12 00:00:00 EDT 2017 · Powder Technology

DOI:https://doi.org/10.1016/j.powtec.2017.08.034· OSTI ID:1440342

Lu, Liqiang ^[1]; Liu, Xiaowen ^[2]; Li, Tingwen ^[3]; Wang, Limin ^[4]; Ge, Wei ^[2]; Benyahia, Sofiane ^[1]

National Energy Technology Lab. (NETL), Morgantown, WV (United States)
Chinese Academy of Sciences (CAS), Beijing (China). State Key Lab. of Multiphase Complex Systems and Inst. of Process Engineering (IPE); Univ. of Chinese Academy of Sciences, Beijing (China)
National Energy Technology Lab. (NETL), Morgantown, WV (United States); AECOM, Morgantown, WV (United States)
Chinese Academy of Sciences (CAS), Beijing (China). State Key Lab. of Multiphase Complex Systems and Inst. of Process Engineering (IPE)

For this study, gas–solids flow in a three-dimension periodic domain was numerically investigated by direct numerical simulation (DNS), computational fluid dynamic-discrete element method (CFD-DEM) and two-fluid model (TFM). DNS data obtained by finely resolving the flow around every particle are used as a benchmark to assess the validity of coarser DEM and TFM approaches. The CFD-DEM predicts the correct cluster size distribution and under-predicts the macro-scale slip velocity even with a grid size as small as twice the particle diameter. The TFM approach predicts larger cluster size and lower slip velocity with a homogeneous drag correlation. Although the slip velocity can be matched by a simple modification to the drag model, the predicted voidage distribution is still different from DNS: Both CFD-DEM and TFM over-predict the fraction of particles in dense regions and under-predict the fraction of particles in regions of intermediate void fractions. Also, the cluster aspect ratio of DNS is smaller than CFD-DEM and TFM. Since a simple correction to the drag model can predict a correct slip velocity, it is hopeful that drag corrections based on more elaborate theories that consider voidage gradient and particle fluctuations may be able to improve the current predictions of cluster distribution.

View Accepted Manuscript (DOE)

Research Organization:: National Energy Technology Laboratory (NETL), Pittsburgh, PA, and Morgantown, WV (United States); Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Fossil Energy (FE); National Natural Science Foundation of China (NSFC)

OSTI ID:: 1440342

Alternate ID(s):: OSTI ID: 1549707

Report Number(s):: NETL-PUB--21175; PII: S0032591017306782

Journal Information:: Powder Technology, Journal Name: Powder Technology Journal Issue: C Vol. 321; ISSN 0032-5910

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (42)

Drag force of intermediate Reynolds number flow past mono- and bidisperse arrays of spheres Beetstra, R.; van der Hoef, M. A.; Kuipers, J. A. M. AIChE Journal, Vol. 53, Issue 2 https://doi.org/10.1002/aic.11065	journal	January 2007
An approach for drag correction based on the local heterogeneity for gas-solid flows Li, Tingwen; Wang, Limin; Rogers, William AIChE Journal, Vol. 63, Issue 4 https://doi.org/10.1002/aic.15507	journal	October 2016
An efficient and reliable predictive method for fluidized bed simulation Lu, Liqiang; Benyahia, Sofiane; Li, Tingwen AIChE Journal, Vol. 63, Issue 12 https://doi.org/10.1002/aic.15832	journal	June 2017
Gas-particle flow in a vertical pipe with particle-particle interactions Sinclair, J. L.; Jackson, R. AIChE Journal, Vol. 35, Issue 9 https://doi.org/10.1002/aic.690350908	journal	September 1989
Multiscale Discrete Supercomputing - A Game Changer for Process Simulation? Ge, Wei; Lu, Liqiang; Liu, Shiwen Chemical Engineering & Technology, Vol. 38, Issue 4 https://doi.org/10.1002/ceat.201400746	journal	March 2015
Discrete particle simulation of two-dimensional fluidized bed Tsuji, Y.; Kawaguchi, T.; Tanaka, T. Powder Technology, Vol. 77, Issue 1 https://doi.org/10.1016/0032-5910(93)85010-7	journal	October 1993
Physical mapping of fluidization regimes—the EMMS approach Ge, Wei; Li, Jinghai Chemical Engineering Science, Vol. 57, Issue 18 https://doi.org/10.1016/S0009-2509(02)00234-8	journal	September 2002
Numerical simulation of the gas-solid flow in a fluidized bed by combining discrete particle method with computational fluid dynamics Xu, B. H.; Yu, A. B. Chemical Engineering Science, Vol. 52, Issue 16 https://doi.org/10.1016/S0009-2509(97)00081-X	journal	August 1997
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
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
Review of discrete particle modeling of fluidized beds Deen, N. G.; Van Sint Annaland, M.; Van der Hoef, M. A. Chemical Engineering Science, Vol. 62, Issue 1-2 https://doi.org/10.1016/j.ces.2006.08.014	journal	January 2007
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
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
Discrete particle simulation of particulate systems: A review of major applications and findings Zhu, H. P.; Zhou, Z. Y.; Yang, R. Y. Chemical Engineering Science, Vol. 63, Issue 23 https://doi.org/10.1016/j.ces.2008.08.006	journal	December 2008
Eulerian simulation of gas–solid flows with particles of Geldart groups A, B and D using EMMS-based meso-scale model Lu, Bona; Wang, Wei; Li, Jinghai Chemical Engineering Science, Vol. 66, Issue 20 https://doi.org/10.1016/j.ces.2011.06.026	journal	October 2011
Large-scale DNS of gas–solid flows on Mole-8.5 Xiong, Qingang; Li, Bo; Zhou, Guofeng Chemical Engineering Science, Vol. 71 https://doi.org/10.1016/j.ces.2011.10.059	journal	March 2012
Fully resolved simulation of a gas-fluidized bed: A critical test of DEM models Kriebitzsch, S. H. L.; van der Hoef, M. A.; Kuipers, J. A. M. Chemical Engineering Science, Vol. 91 https://doi.org/10.1016/j.ces.2012.12.038	journal	March 2013
Structure-dependent drag in gas–solid flows studied with direct numerical simulation Zhou, Guofeng; Xiong, Qingang; Wang, Limin Chemical Engineering Science, Vol. 116 https://doi.org/10.1016/j.ces.2014.04.025	journal	September 2014
Review of direct numerical simulation of fluid–particle mass, momentum and heat transfer in dense gas–solid flows Deen, Niels G.; Peters, E. A. J. F.; Padding, Johan T. Chemical Engineering Science, Vol. 116 https://doi.org/10.1016/j.ces.2014.05.039	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 simulation of dense particulate reaction system: Approaches, recent advances and applications Zhong, Wenqi; Yu, Aibing; Zhou, Guanwen Chemical Engineering Science, Vol. 140 https://doi.org/10.1016/j.ces.2015.09.035	journal	February 2016
Computer virtual experiment on fluidized beds using a coarse-grained discrete particle method—EMMS-DPM Lu, Liqiang; Xu, Ji; Ge, Wei Chemical Engineering Science, Vol. 155 https://doi.org/10.1016/j.ces.2016.08.013	journal	November 2016
Assessment of polydisperse drag models for the size segregation in a bubbling fluidized bed using discrete particle method Zhang, Yong; Zhao, Yuemin; Lu, Liqiang Chemical Engineering Science, Vol. 160 https://doi.org/10.1016/j.ces.2016.11.028	journal	March 2017
Effect of granular temperature and solid concentration fluctuation on the gas-solid drag force: A CFD test Wang, Junwu Chemical Engineering Science, Vol. 168 https://doi.org/10.1016/j.ces.2017.04.031	journal	August 2017
Extension of a coarse grained particle method to simulate heat transfer in fluidized beds Lu, Liqiang; Morris, Aaron; Li, Tingwen International Journal of Heat and Mass Transfer, Vol. 111 https://doi.org/10.1016/j.ijheatmasstransfer.2017.04.040	journal	August 2017
A review of multiscale CFD for gas–solid CFB modeling Wang, Wei; Lu, Bona; Zhang, Nan International Journal of Multiphase Flow, Vol. 36, Issue 2 https://doi.org/10.1016/j.ijmultiphaseflow.2009.01.008	journal	February 2010
Quasi-real-time simulation of rotating drum using discrete element method with parallel GPU computing Xu, Ji; Qi, Huabiao; Fang, Xiaojian Particuology, Vol. 9, Issue 4 https://doi.org/10.1016/j.partic.2011.01.003	journal	August 2011
The effect of particle shape on simple shear flows Cleary, Paul W. Powder Technology, Vol. 179, Issue 3 https://doi.org/10.1016/j.powtec.2007.06.018	journal	January 2008
Open-source MFIX-DEM software for gas–solids flows: Part I—Verification studies Garg, Rahul; Galvin, Janine; Li, Tingwen Powder Technology, Vol. 220 https://doi.org/10.1016/j.powtec.2011.09.019	journal	April 2012
Open-source MFIX-DEM software for gas-solids flows: Part II — Validation studies Li, Tingwen; Garg, Rahul; Galvin, Janine Powder Technology, Vol. 220 https://doi.org/10.1016/j.powtec.2011.09.020	journal	April 2012
Applying uncertainty quantification to multiphase flow computational fluid dynamics Gel, A.; Garg, R.; Tong, C. Powder Technology, Vol. 242 https://doi.org/10.1016/j.powtec.2013.01.045	journal	July 2013
A modified cohesion model for CFD–DEM simulations of fluidization Gu, Yile; Ozel, Ali; Sundaresan, Sankaran Powder Technology, Vol. 296 https://doi.org/10.1016/j.powtec.2015.09.037	journal	August 2016
Direct numerical simulation of low-Reynolds-number flow past arrays of rotating spheres Zhou, Qiang; Fan, Liang-Shih Journal of Fluid Mechanics, Vol. 765 https://doi.org/10.1017/jfm.2014.732	journal	January 2015
Coarse-Grained-Particle Method for Simulation of Liquid–Solids Reacting Flows Lu, Liqiang; Yoo, Kisoo; Benyahia, Sofiane Industrial & Engineering Chemistry Research, Vol. 55, Issue 39 https://doi.org/10.1021/acs.iecr.6b02688	journal	September 2016
Assessment of Different Discrete Particle Methods Ability To Predict Gas-Particle Flow in a Small-Scale Fluidized Bed Lu, Liqiang; Gopalan, Balaji; Benyahia, Sofiane Industrial & Engineering Chemistry Research, Vol. 56, Issue 27 https://doi.org/10.1021/acs.iecr.7b01862	journal	June 2017
Fluid Mechanical Description of Fluidized Beds. Equations of Motion Anderson, T. B.; Jackson, Roy Industrial & Engineering Chemistry Fundamentals, Vol. 6, Issue 4 https://doi.org/10.1021/i160024a007	journal	November 1967
Estimation of Numerical Errors Related to Some Basic Assumptions in Discrete Particle Methods Benyahia, Sofiane; Galvin, Janine E. Industrial & Engineering Chemistry Research, Vol. 49, Issue 21 https://doi.org/10.1021/ie100662z	journal	November 2010
A Review of Eulerian Simulation of Geldart A Particles in Gas-Fluidized Beds Wang, Junwu Industrial & Engineering Chemistry Research, Vol. 48, Issue 12 https://doi.org/10.1021/ie900247t	journal	June 2009
Use of the Boltzmann Equation to Simulate Lattice-Gas Automata McNamara, Guy R.; Zanetti, Gianluigi Physical Review Letters, Vol. 61, Issue 20 https://doi.org/10.1103/PhysRevLett.61.2332	journal	November 1988
Mathematical Structures Senechal, M. Science, Vol. 260, Issue 5111 https://doi.org/10.1126/science.260.5111.1170	journal	May 1993
What Don't We Know? Kennedy, D. Science, Vol. 309, Issue 5731 https://doi.org/10.1126/science.309.5731.75	journal	July 2005
Discrete simulation of granular and particle-fluid flows: from fundamental study to engineering application Ge, Wei; Wang, Limin; Xu, Ji Reviews in Chemical Engineering, Vol. 33, Issue 6 https://doi.org/10.1515/revce-2015-0079	journal	November 2017

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Voidage correction algorithm for unresolved Euler–Lagrange simulations Askarishahi, Maryam; Salehi, Mohammad-Sadegh; Radl, Stefan Computational Particle Mechanics, Vol. 5, Issue 4 https://doi.org/10.1007/s40571-018-0193-8	journal	April 2018
Quantifying the non-equilibrium characteristics of heterogeneous gas–solid flow of smooth, inelastic spheres using a computational fluid dynamics–discrete element method Wang, Jing; Chen, Xizhong; Bian, Wei Journal of Fluid Mechanics, Vol. 866 https://doi.org/10.1017/jfm.2019.156	journal	March 2019

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