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A GPU Accelerated Method for 3-D Nonlinear Kelvin Ship Wave Patterns Simulation
Version 1
: Received: 9 October 2023 / Approved: 9 October 2023 / Online: 10 October 2023 (03:12:28 CEST)
A peer-reviewed article of this Preprint also exists.
Sun, X.; Cai, M.; Ding, J. A GPU-Accelerated Method for 3D Nonlinear Kelvin Ship Wake Patterns Simulation. Appl. Sci. 2023, 13, 12148. Sun, X.; Cai, M.; Ding, J. A GPU-Accelerated Method for 3D Nonlinear Kelvin Ship Wake Patterns Simulation. Appl. Sci. 2023, 13, 12148.
Abstract
Ship wave is of great interest for wave drag, coastal erosion and ship detection. In this paper, a highly-paralleled numerical scheme is proposed for simulating three-dimensional (3-D) nonlinear Kelvin ship waves effectively. First, a numerical model for nonlinear ship waves is established based on potential flow theory, boundary integral method and Jacobian-free Newton-Krylov (JFNK) method. In order to improve computational efficiency and reduce data storage of JFNK method, a banded preconditioner method is then developed by formulating the optimal bandwidth selection rule. After that, a Graphics Process Unit (GPU) based parallel computing framework is designed, and a GPU solver is developed by using Compute Unified Device Architecture (CUDA) language. Finally, numerical simulations of 3-D nonlinear ship waves under multiple scales are performed by using the GPU and CPU solvers. Simulation results show that the proposed GPU solver is more efficient than the CPU solver with the same accuracy. More than 66% GPU memory can be saved and the computational speed can be accelerated up to 20 times. Hence, the computation time for Kelvin ship waves simulation can be significantly reduced by applying the GPU parallel numerical scheme, which lays a solid foundation for practical ocean engineering.
Keywords
Kelvin wake pattern; GPU acceleration; Boundary integral method; JFNK method; Banded preconditioner method
Subject
Engineering, Marine Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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