A feature-preserving parallel particle generation method for complex geometries

TL;DR

This paper introduces a parallel particle generation method that preserves geometric features for complex geometries, improving efficiency and accuracy over existing methods, and demonstrating its application in industrial scenarios.

Contribution

The paper presents a novel feature-preserving particle generation method using explicit geometry, with new algorithms for mapping, feature extraction, and parallelization, enhancing performance and scalability.

Findings

Achieves ~10X speedup with multi-threading.

Outperforms existing methods in runtime and accuracy.

Successfully applied to industrial models like vehicle wading and gearbox oiling.

Abstract

In this paper, a Feature-preserving Particle Generation (FPPG) method for arbitrary complex geometry is proposed. Instead of basing on implicit geometries, such as level-set, FPPG employs an explicit geometric representation for the parallel and automatic generation of high-quality surface and volume particles, which enables the full preservation of geometric features, such as sharp edges, singularities and etc. Several new algorithms are proposed in this paper to achieve the aforementioned objectives. First, a particle mapping and feature line extraction algorithm is proposed to ensure the adequate representation of arbitrary complex geometry. An improved and efficient data structure is developed too to maximize the parallel efficiency and to optimize the memory footprint. Second, the physics-based particle relaxation procedure is tailored for the explicit geometric representation to achieve a uniform particle distribution. Third, in order to handle large-scale industrial models, the proposed FPPG method is entirely parallelized on shared memory systems and Boolean operations are allowed to tackle structures with multiple assemblies. Intensive numerical tests are carried out to demonstrate the capabilities of FPPG. The scalability tests show that a speedup of ~10X is achieved through multi-threading parallelization with various models. Comparative studies with other particle generation methods show that FPPG achieves better performance in both runtime and accuracy. Last, two industrial cases of vehicle wading and gearbox oiling are studied to illustrate that FPPG is applicable to complex geometries.