4.3 Article

Numerical Simulation of Three-Dimensional Violent Free Surface Flows by GPU-Based MPS Method

出版社

WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0219876218430120

关键词

Modified MPS method; CPU acceleration technique; MPSGPU-SJTU solver; 3D violent free surface flows

资金

  1. National Natural Science Foundation of China [51879159, 51490675, 11432009, 51579145]
  2. Chang Jiang Scholars Program [T2014099]
  3. Shanghai Excellent Academic Leaders Program [17XD1402300]
  4. Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning [2013022]
  5. Innovative Special Project of Numerical Tank of Ministry of Industry and Information Technology of China [2016-23/09]
  6. Lloyd's Register Foundation

向作者/读者索取更多资源

The Moving Particle Semi-implicit (MPS) method has been widely used in the field of computational fluid dynamics in recent years. However, the inefficient drawback of MPS method limits its three-dimensional (3D) large-scale applications. In order to overcome this disadvantage, a novel acceleration technique, graphics processing unit (GPU) parallel computing, is applied in MPS. Based on modified NIPS method and GPU technique, an in-house solver MPSGPU-SJTU has been developed by using Compute Unified Device Architecture (CUDA) language. In this paper, 3D dam break and sloshing, two typical violent flows with large deformation and nonlinear fragmentation of free surface are simulated by MPSGPU-SJTU solver. In dam break case, the results of fluid flied, water front, wave height and impact pressure by CPU simulation are compared to those by CPU calculation, experimental research, Smooth Particle Hydrodynamics (SPH) and Boundary Element Method (BEM) simulations. And the comparison of fluid field and impact pressure among GPU, CPU and experiment is made in sloshing flow. The accuracy of CPU solver is verified by these comparisons. Moreover, the computation time of every part in each calculation step is compared between CPU and CPU solvers. The results show that computational efficiency is improved dramatically by employing GPU acceleration technique.

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