Full Length Article
A particle-resolved heat-particle-fluid coupling model by DEM-IMB-LBM

https://doi.org/10.1016/j.jrmge.2023.02.030Get rights and content
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Abstract

Multifield coupling is frequently encountered and also an active area of research in geotechnical engineering. In this work, a particle-resolved direct numerical simulation (PR-DNS) technique is extended to simulate particle-fluid interaction problems involving heat transfer at the grain level. In this extended technique, an immersed moving boundary (IMB) scheme is used to couple the discrete element method (DEM) and lattice Boltzmann method (LBM), while a recently proposed Dirichlet-type thermal boundary condition is also adapted to account for heat transfer between fluid phase and solid particles. The resulting DEM-IBM-LBM model is robust to simulate moving curved boundaries with constant temperature in thermal flows. To facilitate the understanding and implementation of this coupled model for non-isothermal problems, a complete list is given for the conversion of relevant physical variables to lattice units. Then, benchmark tests, including a single-particle sedimentation and a two-particle drafting-kissing-tumbling (DKT) simulation with heat transfer, are carried out to validate the accuracy of our coupled technique. To further investigate the role of heat transfer in particle-laden flows, two multiple-particle problems with heat transfer are performed. Numerical examples demonstrate that the proposed coupling model is a promising high-resolution approach for simulating the heat-particle-fluid coupling at the grain level.

Keywords

Particle-fluid interaction
Heat transfer
Discrete element method (DEM)
Lattice Boltzmann method (LBM)
Dirichlet-type thermal boundary
Direct numerical simulation

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Dr. Ming Xia is an Associate Professor in the College of Civil Engineering at Xiangtan University, China. He obtained his BSc, MSc and PhD degrees from Central South University, China, in 2007, 2009 and 2014, respectively. During November 2019 to November 2020, he conducted research work on the combined DEM and LBM under supervision of Professor Y.T. Feng at the Zienkiewicz Centre for Computational Engineering, Swansea University (UK). His research interests include: (1) multifield/multiphysics coupling of rock mechanics and its application, and (2) computational methods in porous media. He presided 2 projects funded by the National Natural Science Foundation of China and 4 projects funded by the Hunan Province.

Dr. Jinlong Fu is a postdoctoral researcher at the Zienkiewicz Institute for Modelling, Data and AI at Swansea University. He obtained his PhD in computational mechanics from Swansea University in 2020, and he won the Best PhD Thesis Award in the UK Association for Computational Mechanics (the Roger Owen Prize, 2021). He has a multidisciplinary research background with research experience at the interface of computational mechanics, numerical simulation, data science, physics-based engineering modeling, and AI. His broad research interests focus on pore-scale flow modeling, computational fluid dynamics, model order reduction and high-performance computing. The goal of his research is to model and simulate physical systems at different scales by integrating simulation, modeling and AI; and to provide strategies for system learning, prediction, optimization and decision-making in real time.

Peer review under responsibility of Institute of Rock and Soil Mechanics, Chinese Academy of Sciences.