Machine learning accelerated discrete element modeling of granular flows

TY - JOUR

T1 - Machine learning accelerated discrete element modeling of granular flows

AU - Lu, Liqiang

AU - Gao, Xi

AU - Dietiker, Jean François

AU - Shahnam, Mehrdad

AU - Rogers, William A.

N1 - Publisher Copyright: © 2021 Elsevier Ltd

PY - 2021/12/14

Y1 - 2021/12/14

N2 - Granular flows are widely encountered in many industrial processes and natural phenomena. Discrete Element Modeling (DEM) is a useful tool for understanding and troubleshooting devices processing granular materials. However, its applicability is significantly limited by the huge computational cost associated with detecting and computing collisions. In this research, the computation speed of DEM was accelerated by orders of magnitude using a convolutional neural network to replace the direct calculation of particle–particle and particle-boundary collisions. The MFiX software was used to generate the training and testing dataset. A GPU accelerated TensorFlow model was used to train the neural network and test the results. The model fluctuations caused by different training steps were reduced with a multi-scale loss function. The accuracy was improved with more frames within one training step. The modeling of a rotating drum and a hopper demonstrated the accuracy and efficiency of this machine learning accelerated DEM in the simulation of granular flows.

AB - Granular flows are widely encountered in many industrial processes and natural phenomena. Discrete Element Modeling (DEM) is a useful tool for understanding and troubleshooting devices processing granular materials. However, its applicability is significantly limited by the huge computational cost associated with detecting and computing collisions. In this research, the computation speed of DEM was accelerated by orders of magnitude using a convolutional neural network to replace the direct calculation of particle–particle and particle-boundary collisions. The MFiX software was used to generate the training and testing dataset. A GPU accelerated TensorFlow model was used to train the neural network and test the results. The model fluctuations caused by different training steps were reduced with a multi-scale loss function. The accuracy was improved with more frames within one training step. The modeling of a rotating drum and a hopper demonstrated the accuracy and efficiency of this machine learning accelerated DEM in the simulation of granular flows.

KW - Convolutional neural network

KW - Discrete Element Modeling

KW - Granular flow

KW - Machine learning

KW - TensorFlow

UR - http://www.scopus.com/inward/record.url?scp=85107823990&partnerID=8YFLogxK

U2 - 10.1016/j.ces.2021.116832

DO - 10.1016/j.ces.2021.116832

M3 - 文章

AN - SCOPUS:85107823990

SN - 0009-2509

VL - 245

JO - Chemical Engineering Science

JF - Chemical Engineering Science

M1 - 116832

ER -