A parallel dual-grid multiscale approach to CFD-DEM couplings

TL;DR

This paper introduces a parallel dual-grid multiscale CFD-DEM coupling method that enhances computational efficiency and scalability for large-scale simulations by reducing communication costs and enabling complex domain partitioning.

Contribution

The paper proposes a novel parallelization strategy for dual-grid multiscale CFD-DEM couplings, improving scalability and flexibility while maintaining accuracy and reducing communication overhead.

Findings

Maintains good parallel performance over 1000 processes.

Achieves grid-convergent solutions with improved accuracy.

Reduces inter-process communication between CFD and DEM.

Abstract

In this work, a new parallel dual-grid multiscale approach for CFD-DEM couplings is investigated. Dual- grid multiscale CFD-DEM couplings have been recently developed and successfully adopted in different applications still, an efficient parallelization for such a numerical method represents an open issue. Despite its ability to provide grid convergent solutions and more accurate results than standard CFD-DEM couplings, this young numerical method requires good parallel performances in order to be applied to large-scale problems and, therefore, extend its range of application. The parallelization strategy here proposed aims to take advantage of the enhanced complexity of a dual-grid coupling to gain more flexibility in the domain partitioning while keeping a low inter-process communication cost. In particular, it allows avoiding inter- process communication between CFD and DEM software and still allows adopting complex partitioning strategies thanks to an optimized grid-based communication. It is shown how the parallelized multiscale coupling holds all its natural advantages over a mono-scale coupling and can also have better parallel performance. Three benchmark cases are presented to assess the accuracy and performance of the strategy. It is shown how the proposed method allows maintaining good parallel performance when operated over 1000 processes.