Alongside the development of smart water management and digital twin construction, hydrodynamic models have become a critical scientific tool in flood forecasting, with increasing attention and research focused on model computational efficiency. At the algorithmic optimization level, employing a domain tracking method reduces the number of grid cells actively involved in computation, while utilizing local time stepping techniques increases the average time step for updating model variables; integrating these methods reduces the overall computational load during simulation and enhances computational efficiency. At the hardware level, acceleration technologies such as GPU parallel computing can be utilized to fully exploit hardware capabilities and improve computational efficiency. A novel hydrodynamic model acceleration method combining algorithmic optimization and parallel computing techniques has been proposed, with the integrated method simultaneously reducing computational workload and improving model performance. Case tests demonstrated that this integrated approach could achieve a considerable computational speed-up ratio compared to traditional serial programs without algorithmic optimization. The integrated method effectively enhanced computational efficiency and maintained the model’s computational accuracy, ultimately fulfilling the dual requirements of precision and speed in practical hydrodynamic modeling applications.
Yang et al. (Tue,) studied this question.