A GPU-based numerical model coupling hydrodynamical and morphological processes

摘要

Sediment transport simulations are important in practical engineering.In this study,a graphics processing unit(GPU)-based numerical model coupling hydrodynamical and morphological processes was developed to simulate water flow,sediment transport,and morphological changes.Aiming at accurately predicting the sediment transport and sediment scouring processes,the model resolved the realistic features of sediment transport and used a GPU-based parallel computing technique to the accelerate calculation.This model was created in the framework of a Godunov-type finite volume scheme to solve the shallow water equations(SWEs).The SWEs were discretized into algebraic equations by the finite volume method.The fluxes of mass and momentum were computed by the Harten,Lax,and van Leer Contact(HLLC) approximate Riemann solver,and the friction source terms were calculated by the proposed a splitting point-implicit method.These values were evaluated using a novel 2 D edge-based MUSCL scheme.The code was programmed using C++and CUDA,which could run on GPUs to substantially accelerate the computation.The aim of the work was to develop a GPU-based numerical model to simulate hydrodynamical and morphological processes.The novelty is the application of the GPU techniques in the numerical model,making it possible to simulate the sediment transport and bed evolution in a high-resolution but efficient manner.The model was applied to two cases to evaluate bed evolution and the effects of the morphological changes on the flood patterns with high resolution.This indicated that the GPU-based high-resolution hydro-geomorphological model was capable of reproducing morphological processes.The computational times for this test case on the GPU and CPU were298.1 and 4531.2 s,respectively,indicating that the GPU could accelerate the computation 15.2 times.Compared with the traditional CPU high-grid resolution,the proposed GPU-based high-resolution numerical model improved the reconstruction speed more than 2.0-12.83 times for different grid resolutions while remaining computationally efficient.