Hydrodynamic simulations in shallow water environments require careful consideration of the Wetting and Drying (WD) processes, which poses challenges to accurately modeling moving boundaries. This study introduces a novel method called the flow resistance method (FRM), which builds upon the foundation of the Negative-Depth Method (NDM) to tackle the intricacies of the moving boundary problem. Inspired by the Navier-Stokes/Brinkman (NSB) model from porous media theory, FRM incorporates a continuous function related to additional flow resistance that is proportional to the flow velocity. This approach facilitates a seamless transition between the exposed bed and fluid area wherein the additional flow resistance becomes 0 within the fluid area and approaches infinity in the exposed bed. Consequently, FRM adeptly and implicitly manages the moving boundary problem, causing a rapid decay of flow velocity to 0 in the exposed bed. In order to test the performance of FRM, four typical numerical experiments were conducted, along with an examination of a real-life case. Accuracy, robustness, and computational efficiency were assessed as key performance indicators. The simulations demonstrate that FRM adeptly tracks the moving water front, yielding precise results. Furthermore, when compared to established methods such as the Element Removal Method (ERM) and NDM, FRM exhibits broader applicability and achieves significant enhancements in the key performance indicators. These findings underscore the promising potential and broad applications of FRM in the field.

中文翻译：

---

具有流阻项的稳健良好平衡方法，可在浅水模拟中进行准确的润湿和干燥建模


浅水环境中的水动力模拟需要仔细考虑润湿和干燥（WD）过程，这对精确建模移动边界提出了挑战。本研究引入了一种称为流阻法 (FRM) 的新方法，该方法建立在负深度法 (NDM) 的基础上，用于解决复杂的移动边界问题。受多孔介质理论中的 Navier-Stokes/Brinkman (NSB) 模型的启发，FRM 结合了与附加流阻相关的连续函数，该流阻与流速成正比。这种方法有利于暴露床和流体区域之间的无缝过渡，其中附加流阻在流体区域内变为0并且在暴露床中接近无穷大。因此，FRM 巧妙且隐式地管理了移动边界问题，导致暴露床中的流速快速衰减至 0。为了测试 FRM 的性能，进行了四个典型的数值实验以及对现实生活案例的检查。准确性、鲁棒性和计算效率被作为关键绩效指标进行评估。模拟表明 FRM 能够熟练地跟踪移动的海滨，产生精确的结果。此外，与元素去除法（ERM）和NDM等现有方法相比，FRM表现出更广泛的适用性，并在关键性能指标上实现了显着增强。这些发现强调了 FRM 在该领域的巨大潜力和广泛应用。