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 2D 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 were 298.1 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.

泥沙运移模拟在实际工程中很重要。在这项研究中，基于图形处理单元（GPU）的数值模型耦合了水动力和形态过程，以模拟水流，泥沙输送和形态变化。为了准确预测泥沙输送和泥沙冲刷过程，该模型解决了泥沙输送的现实特征，并使用基于GPU的并行计算技术来加快计算速度。该模型是在Godunov型有限体积方案的框架内创建的，用于求解浅水方程（SWE）。通过有限体积法将SWE离散为代数方程。质量和动量的通量由Harten，Lax和van Leer Contact（HLLC）近似Riemann求解器计算，提出了分裂点隐式方法，计算了摩擦源项。使用新颖的基于2D边缘的MUSCL方案评估这些值。该代码是使用C ++和CUDA编程的，可以在GPU上运行以大大加快计算速度。这项工作的目的是开发一个基于GPU的数值模型来模拟流体动力学和形态过程。新颖之处在于GPU技术在数值模型中的应用，从而有可能以高分辨率但有效的方式模拟沉积物的输送和床的演化。将该模型应用于两个案例，以高分辨率评估河床演化和形态变化对洪水模式的影响。这表明基于GPU的高分辨率水文地貌模型能够再现形态过程。此测试用例在GPU和CPU上的计算时间分别为298.1和4531.2 s，这表明GPU可以将计算速度提高15.2倍。与传统的CPU高网格分辨率相比，基于GPU的高分辨率数值模型针对不同的网格分辨率将重建速度提高了2.0–12.83倍，同时保持了计算效率。