3D large-scale SPH modeling of vehicle wading with GPU acceleration

作     者：Huashan Zhang Xiaoxiao Li Kewei Feng Moubin Liu 

作者机构：College of EngineeringPeking UniversityBejing 100871China Joint Laboratory of Marine Hydrodynamics and Ocean EngineeringLaoshan LaboratoryQingdao 266237China Nanchang Innovation InstitutePeking UniversityNanchang 330008China Shenzhen TenFong Technology Co.Ltd.Shenzhen 518055China 

出 版 物：《Science China(Physics,Mechanics & Astronomy)》 (中国科学：物理学、力学、天文学（英文版）)

年 卷 期：2023年第66卷第10期

页      面：70-91页

核心收录：

学科分类：080704[工学-流体机械及工程] 080103[工学-流体力学] 082304[工学-载运工具运用工程] 08[工学] 0807[工学-动力工程及工程热物理] 080204[工学-车辆工程] 0802[工学-机械工程] 0801[工学-力学（可授工学、理学学位）] 0823[工学-交通运输工程] 

基　　金：supported by the Laoshan Laboratory(Grant No.LSKJ202202000) National Natural Science Foundation of China(Grant Nos.12032002,and U22A20256) Natural Science Foundation of Beijing(Grant No.L212023) 

主　　题：vehicle wading fluid-structure interaction GPU-based SPH adaptive spatial sort technology 

摘      要：Vehicle wading is a complex fluid-structure interaction(FSI) problem and has attracted great attention recently from the automotive industry, especially for electric vehicles. As a meshless Lagrangian particle method, smoothed particle hydrodynamics(SPH) is one of the most suitable candidates for simulations of vehicle wading due to its inherent advantages in modeling free surface flows, splash, and moving interfaces. Nevertheless, the inevitable neighbor query for the nearest adjacent particles among the support domain leads to considerable computational cost and thus limits its application in 3D large-scale simulations. In this work, a GPU-based SPH method is developed with an adaptive spatial sort technology for simulations of vehicle wading. In addition, a fast, easy-to-implement particle generator is presented for isotropic initialization of the complex vehicle geometry with optimal interpolation properties. A comparative study of vehicle wading on a puddle between the GPUbased SPH with two pieces of commercial software is used to verify the capability of the GPU-based SPH method in terms of convergence analysis, kinematic characteristics, and computing performance. Finally, different conditions of vehicle speeds, water depths, and puddle widths are tested to investigate the vehicle wading numerically. The results demonstrate that the adaptive spatial sort technology can significantly improve the computing performance of the GPU-based SPH method and meanwhile promotes the GPU-based SPH method to be a competitive tool for the study of 3D large-scale FSI problems including vehicle wading. Some helpful findings of the critical vehicle speed, water depth as well as boundary wall effect are also reported in this work.