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High-resolution fluid–particle interactions: a machine learning approach

Published online by Cambridge University Press:  15 March 2022

Tsimur Davydzenka  and
Pejman Tahmasebi Open the ORCID record for Pejman Tahmasebi [Opens in a new window]
Tsimur Davydzenka
Affiliation:
College of Engineering and Applied Science, University of Wyoming, Laramie, WY82071, USA
Pejman Tahmasebi*
Affiliation:
College of Engineering and Applied Science, University of Wyoming, Laramie, WY82071, USA
*
† Email address for correspondence: [email protected]
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Abstract

Modelling of fluid–particle interactions is a major area of research in many fields of science and engineering. There are several techniques that allow modelling of such interactions, among which the coupling of computational fluid dynamics (CFD) and the discrete element method (DEM) is one of the most convenient solutions due to the balance between accuracy and computational costs. However, the accuracy of this method is largely dependent upon mesh size, where obtaining realistic results always comes with the necessity of using a small mesh and thereby increasing computational intensity. To compensate for the inaccuracies of using a large mesh in such modelling, and still take advantage of rapid computations, we extended the classical modelling by combining it with a machine learning model. We have conducted seven simulations where the first one is a numerical model with a fine mesh (i.e. ground truth) with a very high computational time and accuracy, the next three models are constructed on coarse meshes with considerably less accuracy and computational burden and the last three models are assisted by machine learning, where we can obtain large improvements in terms of observing fine-scale features yet based on a coarse mesh. The results of this study show that there is a great opportunity in machine learning towards improving classical fluid–particle modelling approaches by producing highly accurate models for large-scale systems in a reasonable time.

JFM classification

Granular media: Wet granular material Geophysical and Geological Flows: Coastal engineering Geophysical and Geological Flows: River dynamics
Type
JFM Papers
Information
Journal of Fluid Mechanics , Volume 938 , 10 May 2022 , A20
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Copyright
© The Author(s), 2022. Published by Cambridge University Press

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References

Aghaei Jouybari, M., Yuan, J., Brereton, G.J. & Murillo, M.S. 2021 Data-driven prediction of the equivalent sand-grain height in rough-wall turbulent flows. J. Fluid Mech. 912, A8.10.1017/jfm.2020.1085CrossRefGoogle Scholar
Antony, S.J., Zhou, C.H. & Wang, X. 2006 An integrated mechanistic-neural network modelling for granular systems. Appl. Math. Model. 30, 116–128.CrossRefGoogle Scholar
Ariana, M.A., Vaferi, B. & Karimi, G. 2015 Prediction of thermal conductivity of alumina water-based nanofluids by artificial neural networks. Powder Technol. 278, 1–10.10.1016/j.powtec.2015.03.005CrossRefGoogle Scholar
Benvenuti, L., Kloss, C. & Pirker, S. 2016 Identification of DEM simulation parameters by artificial neural networks and bulk experiments. Powder Technol. 291, 456–465.10.1016/j.powtec.2016.01.003CrossRefGoogle Scholar
Bertuola, D., Volpato, S., Canu, P. & Santomaso, A.C. 2016 Prediction of segregation in funnel and mass flow discharge. Chem. Engng Sci 150, 16–25.10.1016/j.ces.2016.04.054CrossRefGoogle Scholar
Brenner, M.P., Eldredge, J.D. & Freund, J.B. 2019 Perspective on machine learning for advancing fluid mechanics. Phys. Rev. Fluids 4, 100501.CrossRefGoogle Scholar
Brevis, I., Muga, I. & van der Zee, K.G. 2020 Data-driven finite elements methods: machine learning acceleration of goal-oriented computations. arXiv:2003.04485v1.Google Scholar
Brunton, S.L., Noack, B.R. & Koumoutsakos, P. 2020 Machine learning for fluid mechanics. Annu. Rev. Fluid Mech. 52, 477–508.10.1146/annurev-fluid-010719-060214CrossRefGoogle Scholar
Chaurasia, R.C. & Nikkam, S. 2017 Application of artificial neural network to study the performance of multi-gravity separator (MGS) treating iron ore fines. Part. Sci. Technol. 35, 93–102.CrossRefGoogle Scholar
Chen, F., Drumm, E.C. & Guiochon, G. 2011 Coupled discrete element and finite volume solution of two classical soil mechanics problems. Comput. Geotech. 38, 638–647.CrossRefGoogle Scholar
Chen, J., Orozovic, O., Williams, K., Meng, J. & Li, C. 2020 A coupled DEM-SPH model for moisture migration in unsaturated granular material under oscillation. Intl J. Mech. Sci. 169, 105313.10.1016/j.ijmecsci.2019.105313CrossRefGoogle Scholar
Chen, W. & Qiu, T. 2012 Numerical simulations for large deformation of granular materials using smoothed particle hydrodynamics method. Intl J. Geomech. 12, 127–135.10.1061/(ASCE)GM.1943-5622.0000149CrossRefGoogle Scholar
Chen, X., Zhong, W., Zhou, X., Jin, B. & Sun, B. 2012 CFD–DEM simulation of particle transport and deposition in pulmonary airway. Powder Technol. 228, 309–318.10.1016/j.powtec.2012.05.041CrossRefGoogle Scholar
Crowe, C.T., Schwarzkopf, J.D., Sommerfeld, M. & Tsuji, Y. 2011 Multiphase Flows with Droplets and Particles. CRC Press.10.1201/b11103CrossRefGoogle Scholar
Cui, Y., Chan, D. & Nouri, A. 2017 Discontinuum modeling of solid deformation pore-water diffusion coupling. Intl J. Geomech. 17, 04017033.10.1061/(ASCE)GM.1943-5622.0000903CrossRefGoogle Scholar
Di Felice, R. 1994 The voidage function for fluid-particle interaction systems. Intl J. Multiphase Flow 20, 153–159.CrossRefGoogle Scholar
Ding, W.-T. & Xu, W.-J. 2018 Study on the multiphase fluid-solid interaction in granular materials based on an LBM-DEM coupled method. Powder Technol. 335, 301–314.CrossRefGoogle Scholar
Farizhandi, A.A.K., Zhao, H. & Lau, R. 2016 Modeling the change in particle size distribution in a gas-solid fluidized bed due to particle attrition using a hybrid artificial neural network-genetic algorithm approach. Chem. Engng Sci. 155, 210–220.CrossRefGoogle Scholar
Figueiredo, E., Moldovan, I., Santos, A., Campos, P. & Costa, J.C.W.A. 2019 Finite element–based machine-learning approach to detect damage in bridges under operational and environmental variations. J. Bridge Engng. 24, 04019061.10.1061/(ASCE)BE.1943-5592.0001432CrossRefGoogle Scholar
Fukami, K., Fukagata, K. & Taira, K. 2019 Super-resolution reconstruction of turbulent flows with machine learning. J. Fluid Mech. 870, 106–120.10.1017/jfm.2019.238CrossRefGoogle Scholar
Fukami, K., Fukagata, K. & Taira, K. 2020 a Assessment of supervised machine learning methods for fluid flows. Theor. Comput. Fluid Dyn. 34, 497–519.10.1007/s00162-020-00518-yCrossRefGoogle Scholar
Fukami, K., Fukagata, K. & Taira, K. 2020 b Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows. J. Fluid Mech. 909, A9.Google Scholar
Fukami, K., Hasegawa, K., Nakamura, T., Morimoto, M. & Fukagata, K. 2021 Model order reduction with neural networks: application to laminar and turbulent flows. SN Comput. Sci. 2, 467.CrossRefGoogle Scholar
Garbaa, H., Jackowska-Strumillo, L., Grudzien, K. & Romanowski, A. 2014 Neural network approach to ECT inverse problem solving for estimation of gravitational solids flow. In 2014 Federated Conference on Computer Science and Information Systems (FedCSIS) vol. 34, pp. 19–26.Google Scholar
Gidaspow, D. 1994 Multiphase Flow and Fluidization-Continuum and Kinetic Theory Descriptions. Academic Press.Google Scholar
Hager, A., Kloss, C., Pirker, S. & Goniva, C. 2011 Parallel open source CFD-DEM for resolved particle-fluid interaction. In 9th International Conference on Computational Fluid Dynamics in the Minerals and Process Industries. Melbourne.Google Scholar
Hassanpour, A., Tan, H., Bayly, A., Gopalkrishnan, P., Ng, B. & Ghadiri, M. 2011 Analysis of particle motion in a paddle mixer using discrete element method (DEM). Powder Technol. 206, 189–194.CrossRefGoogle Scholar
Hertz, H. 1882 Ueber die Berührung fester elastischer Körper. J. Für Die Reine Und Angew. Math. (Crelles Journal) 1882, 156–171.Google Scholar
Hinch, E.J. & Serayssol, J.M. 1986 The elastohydrodynamic collision of two spheres. J. Fluid Mech. 163, 479–497.Google Scholar
Höhner, D., Wirtz, S. & Scherer, V. 2013 Experimental and numerical investigation on the influence of particle shape and shape approximation on hopper discharge using the discrete element method. Powder Technol. 235, 614–627.CrossRefGoogle Scholar
Houlsby, G.T.T. 2009 Potential particles: a method for modelling non-circular particles in DEM. Comput. Geotech. 36, 953–959.CrossRefGoogle Scholar
Jayasundara, C.T., Yang, R.Y., Guo, B.Y., Yu, A.B., Govender, I., Mainza, A., van der Westhuizen, A. & Rubenstein, J. 2011 CFD–DEM modelling of particle flow in IsaMills – comparison between simulations and PEPT measurements. Miner. Engng 24, 181–187.CrossRefGoogle Scholar
Ji, S., Chen, X. & Liu, L. 2019 Coupled DEM-SPH method for interaction between dilated polyhedral particles and fluid. Math. Probl. Engng 2019, 4987801.Google Scholar
Jiang, Y., Kolehmainen, J., Gu, Y., Kevrekidis, Y.G., Ozel, A. & Sundaresan, S. 2019 Neural-network-based filtered drag model for gas-particle flows. Powder Technol. 346, 403–413.10.1016/j.powtec.2018.11.092CrossRefGoogle Scholar
Kachrimanis, K., Karamyan, V. & Malamataris, S. 2003 Artificial neural networks (ANNs) and modeling of powder flow. Intl J. Pharm. 250, 13–23.CrossRefGoogle ScholarPubMed
Khan, A.R. & Richardson, J.R. 1989 Fluid-particle interactions and flow characteristics of fluidized beds and settling suspensions of spherical particles. Chem. Engng Commun. 78, 111–130.CrossRefGoogle Scholar
Kim, H., Kim, J., Won, S. & Lee, C. 2021 Unsupervised deep learning for super-resolution reconstruction of turbulence. J. Fluid Mech. 910, A29.CrossRefGoogle Scholar
Kingma, D.P. & Ba, J.L. 2015 Adam: A method for stochastic optimization. In 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings. arXiv:1412.6980v9.Google Scholar
Koch, D.L. & Hill, R.J. 2001 Inertial effects in suspension and porous-media flows. Annu. Rev. Fluid Mech. 33, 619–647.CrossRefGoogle Scholar
Ku, X., Li, T. & Løvås, T. 2015 CFD–DEM simulation of biomass gasification with steam in a fluidized bed reactor. Chem. Engng Sci. 122, 270–283.CrossRefGoogle Scholar
Kutz, J.N. 2017 Deep learning in fluid dynamics. J. Fluid Mech. 814, 1–4.CrossRefGoogle Scholar
Li, B., Yang, Z., Zhang, X., He, G., Deng, B.-Q. & Shen, L. 2020 a Using machine learning to detect the turbulent region in flow past a circular cylinder. J. Fluid Mech. 905, A10.CrossRefGoogle Scholar
Li, X., Zhao, J. & Kwan, J.S.H. 2020 b Assessing debris flow impact on flexible ring net barrier: a coupled CFD-DEM study. Comput. Geotech. 128, 103850.CrossRefGoogle Scholar
Liang, L., Liu, M., Martin, C. & Sun, W. 2018 a A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis. J. R. Soc. Interface 15, 20170844.CrossRefGoogle ScholarPubMed
Liang, L., Liu, M., Martin, C. & Sun, W. 2018 b A machine learning approach as a surrogate of finite element analysis-based inverse method to estimate the zero-pressure geometry of human thoracic aorta. Intl J. Numer. Meth. Biomed. Engng 34, e3103.CrossRefGoogle Scholar
Liashchynskyi, P. & Liashchynskyi, P. 2019 Grid search, random search, genetic algorithm: a big comparison for NAS. arXiv:1912.06059v1.Google Scholar
Ling, Y., Wagner, J.L., Beresh, S.J., Kearney, S.P. & Balachandar, S. 2012 Interaction of a planar shock wave with a dense particle curtain: modeling and experiments. Phys. Fluids 24, 113301.CrossRefGoogle Scholar
Lu, L.-S. & Hsiau, S.-S. 2008 DEM simulation of particle mixing in a sheared granular flow. Particuology 6, 445–454.CrossRefGoogle Scholar
Mahdi, F.M. & Holdich, R.G. 2017 Using statistical and artificial neural networks to predict the permeability of loosely packed granular materials. Sep. Sci. Technol. 52, 1–12.CrossRefGoogle Scholar
Mandal, S. & Khakhar, D.V. 2016 A study of the rheology of planar granular flow of dumbbells using discrete element method simulations. Phys. Fluids 28, 103301.CrossRefGoogle Scholar
Mitra, P., Venkatesan, V., Jangid, N., Nambiar, A., Kumar, D., Roa, V., Santo, N.D., Haghshenas, M., Mitra, S. & Schmidt, D. 2021 Network compression for machine-learnt fluid simulations. arXiv:2103.00754v1.Google Scholar
O'Sullivan, C. 2011 Particulate Discrete Element Modelling. Taylor & Francis.CrossRefGoogle Scholar
Park, J. & Choi, H. 2020 Machine-learning-based feedback control for drag reduction in a turbulent channel flow. J. Fluid Mech. 904, A24.CrossRefGoogle Scholar
Payne, F.C., Quinnan, J.A., Potter, S.T. & Press, C.R.C. 2008 Remediation Hydraulics. CRC Press.10.1201/9781420006841CrossRefGoogle Scholar
Prieto, J.L. 2020 Viscoelastic effects on drop deformation using a machine learning-enhanced, finite element method. Polymers (Basel) 12, 1652.CrossRefGoogle ScholarPubMed
Radl, S. & Sundaresan, S. 2014 A drag model for filtered Euler-Lagrange simulations of clustered gas-particle suspensions. Chem. Engng Sci. 117, 416–425.CrossRefGoogle Scholar
Ramezani, M., Sun, B., Subramaniam, S. & Olsen, M.G. 2018 Detailed experimental and numerical investigation of fluid–particle interactions of a fixed train of spherical particles inside a square duct. Intl J. Multiphase Flow 103, 16–29.CrossRefGoogle Scholar
Rycroft, C.H., Orpe, A.V. & Kudrolli, A. 2009 Physical test of a particle simulation model in a sheared granular system. Phys. Rev. E 80, 031305.CrossRefGoogle Scholar
Suzuki, K., Bardet, J.P., Oda, M., Iwashita, K., Tsuji, Y., Tanaka, T. & Kawaguchi, T. 2007 Simulation of upward seepage flow in a single column of spheres using discrete-element method with fluid-particle interaction. J. Geotech. Geoenviron. Engng 133, 104–109.CrossRefGoogle Scholar
Tang, Y., Chan, D.H. & Zhu, D.Z. 2017 A coupled discrete element model for the simulation of soil and water flow through an orifice. Intl J. Numer. Anal. Methods Geomech. 41, 1477–1493.CrossRefGoogle Scholar
Tong, Z.B., Zheng, B., Yang, R.Y., Yu, A.B. & Chan, H.K. 2013 CFD-DEM investigation of the dispersion mechanisms in commercial dry powder inhalers. Powder Technol. 240, 19–24.CrossRefGoogle Scholar
Topin, V., Monerie, Y., Perales, F. & Radjaï, F. 2012 Collapse dynamics and runout of dense granular materials in a fluid. Phys. Rev. Lett. 109, 188001.CrossRefGoogle Scholar
Tsuji, Y., Kawaguchi, T. & Tanaka, T. 1993 Discrete particle simulation of two-dimensional fluidized bed. Powder Technol. 77, 79–87.CrossRefGoogle Scholar
Vidyapati, V. & Subramaniam, S. 2013 Granular flow in silo discharge: discrete element method simulations and model assessment. Ind. Engng Chem. Res. 52, 13171–13182.CrossRefGoogle Scholar
Wagner, J.J., Shu, H., Kilambi, R. & Higgs, C.F. 2019 Experimental investigation of fluid-particle interaction in binder jet 3D printing. In Proceedings of the 30th Annual International Solid Freeform Fabrication Symposium - An Additive Manufacturing Conference SFF 2019, pp. 134–147. Preprints.Google Scholar
Wan, Z.Y. & Sapsis, T.P. 2018 Machine learning the kinematics of spherical particles in fluid flows. J. Fluid Mech. 857, R2.CrossRefGoogle Scholar
Wan, J., Wang, F., Yang, G., Zhang, S., Wang, M., Lin, P. & Yang, L. 2018 The influence of orifice shape on the flow rate: a DEM and experimental research in 3D hopper granular flows. Powder Technol. 335, 147–155.CrossRefGoogle Scholar
Wu, C., Cheng, Y., Ding, Y. & Jin, Y. 2010 CFD–DEM simulation of gas–solid reacting flows in fluid catalytic cracking (FCC) process. Chem. Engng Sci. 65, 542–549.CrossRefGoogle Scholar
Xu, W.J., Dong, X.Y. & Ding, W.T. 2019 Analysis of fluid-particle interaction in granular materials using coupled SPH-DEM method. Powder Technol. 353, 459–472.10.1016/j.powtec.2019.05.052CrossRefGoogle Scholar
Xu, B.H., Feng, Y.Q., Yu, A.B., Chew, S.J. & Zulli, P. 2001 A numerical and experimental study of the gas-solid flow in a fluid bed reactor. Powder Handl. Process. 13, 71–76.Google Scholar
Xu, W.-J., Hu, L.-M. & Gao, W. 2016 Random generation of the meso-structure of a soil-rock mixture and its application in the study of the mechanical behavior in a landslide dam. Intl J. Rock Mech. Min. Sci. 86, 166–178.CrossRefGoogle Scholar
Xu, B.H. & Yu, A.B. 1997 Numerical simulation of the gas-solid flow in a fluidized bed by combining discrete particle method with computational fluid dynamics. Chem. Engng Sci. 52, 2785–2809.CrossRefGoogle Scholar
Yang, G.C., Jing, L., Kwok, C.Y. & Sobral, Y.D. 2019 A comprehensive parametric study of LBM-DEM for immersed granular flows. Comput. Geotech. 114, 103100.CrossRefGoogle Scholar
Zhang, Y., Jia, F., Zeng, Y., Han, Y. & Xiao, Y. 2018 a DEM study in the critical height of flow mechanism transition in a conical silo. Powder Technol. 331, 98–106.CrossRefGoogle Scholar
Zhang, X. & Tahmasebi, P. 2019 Effects of grain size on deformation in porous media. Transp. Porous Media 129, 321–341.10.1007/s11242-019-01291-1CrossRefGoogle Scholar
Zhang, Z., Zhang, X., Qiu, H. & Daddow, M. 2016 Dynamic characteristics of track-ballast-silty clay with irregular vibration levels generated by high-speed train based on DEM. Constr. Build. Mater. 125, 564–573.CrossRefGoogle Scholar
Zhang, Z.-H., Zhang, X., Tang, Y. & Cui, Y. 2018 b Discrete element analysis of a cross-river tunnel under random vibration levels induced by trains operating during the flood season. J. Zhejiang Univ. A 19, 346–366.10.1631/jzus.A1700002CrossRefGoogle Scholar
Zhao, J. & Shan, T. 2013 a Coupled CFD-DEM simulation of fluid-particle interaction in geomechanics. Powder Technol. 239, 248–258.CrossRefGoogle Scholar
Zhao, J. & Shan, T. 2013 b Numerical modeling of fluid-particle interaction in granular media. Theor. Appl. Mech. Lett. 3, 021007.CrossRefGoogle Scholar
Zhu, H.P., Zhou, Z.Y., Yang, R.Y. & Yu, A.B. 2007 Discrete particle simulation of particulate systems: theoretical developments. Chem. Engng Sci. 62, 3378–3396.CrossRefGoogle Scholar
Zhu, H.P., Zhou, Z.Y., Yang, R.Y. & Yu, A.B. 2008 Discrete particle simulation of particulate systems: a review of major applications and findings. Chem. Engng Sci. 63, 5728–5770.CrossRefGoogle Scholar