Abstract

This study addresses the challenge of simulating realistic particle systems by proposing a novel particle decomposition scheme that improves the parallel performance of surface resolved particle simulations. Realistic particle systems often involve large numbers of particles and complex particle shapes. The resulting need to account for shape factors requires the inclusion of even more particles to obtain statistically meaningful results. However, the computational cost increases with the number of particles, making efficient parallelization crucial. Therefore, the proposed scheme aims to improve the scalability by optimizing the communication and data management between processors. Through hindered settling experiments, the applicability and performance of the novel particle decomposition scheme are thoroughly investigated using the homogenized lattice Boltzmann method. The results show that the proposed method significantly improves the performance, especially in scenarios with a large number of particles, by reducing communication constraints and improving scalability. This research contributes to the advancement of computational methods for efficient study of complex particle systems and provides valuable insights for future developments in this field.

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