An GPU-accelerated particle tracking method for Eulerian–Lagrangian simulations using hardware ray tracing cores

• Published in: Computer physics communications Vol. 271; p. 108221
• Main Authors: Wang, Bin, Wald, Ingo, Morrical, Nate, Usher, Will, Mu, Lin, Thompson, Karsten, Hughes, Richard
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2022
• Subjects: Eulerian-Lagrangian method; GPU parallel computing; Particle tracking; Scalar transport; Scalar transport; Eulerian-Lagrangian method; Particle tracking; GPU parallel computing
• DOI: 10.1016/j.cpc.2021.108221
• ISSN: 0010-4655

To address the high computational cost of particle tracking for realistic Eulerian–Lagrangian simulations, a novel efficient and robust particle tracking method (RT method) for unstructured meshes is presented. The method, for the first time, leverages both hardware ray tracing (RT) cores and GPU parallel computing technology to accelerate Eulerian–Lagrangian simulations. The method includes a hardware-accelerated hosting cell locator using bounding volume hierarchy tree (BVH) and a robust treatment of particle-wall interaction (multiple specular reflection) using an improved neighbor searching approach. The method is implemented in a GPU-accelerated open-source code, which is verified against a reference neighbor-searching particle-tracking method (NS method) and experimental observations. To evaluate the performance of our method, several numerical simulations of fluid-driven scalar transport problem are solved. Using a verification case, we show that the particle distribution simulated by our code is in a good agreement with an experimental observation. Tracking failures and stuck particles are not observed in any simulations. Benchmark results indicate that our RT method leads to a roughly 1.8−2.0× performance improvement compared to the reference NS method for large-scale simulations (millions of mesh cells and particles).