Computationally efficient modeling of hydro-sediment-morphodynamic processes using a hybrid local time step/global maximum time step
Computationally efficient modeling of hydro-sediment-morphodynamic processes using a hybrid local time step/global maximum time step
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DOI：
10.1016/j.advwatres.2019.03.006
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发表时间：
2019-05
影响因子：
4.7
通讯作者：
P. Hu;Y. Lei;Jianjian Han;Z. Cao;Huai-han Liu;Zhiguo He
中科院分区：
文献类型：
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作者：
P. Hu;Y. Lei;Jianjian Han;Z. Cao;Huai-han Liu;Zhiguo He
提出了一种局部时间步长/全局最大时间步长（LTS/GMaTS）混合方法，以提高水-沉积-形态动力学过程的计算效率。采用平衡激波捕获有限体积法和HLLC近似黎曼求解器在非结构三角形网格上对控制方程进行了数值求解。通过实现LTS来求解控制泥沙流动的方程（即水-泥沙部分），实现gmat来求解控制河床物质的方程（即形态动力学部分），实现了较高的计算效率。通过对长江中游太平口航道2个可蚀河床溃坝基准试验和1个野外实例的模拟，证明了该方法具有较高的计算效率和较好的定量精度。结果表明，该模型的计算效率比采用全局最小时间步长（GMiTS）的传统模型提高了一个数量级。与传统模型与实测资料之间的l2范数相比，新模型与传统模型之间的水位和河床高程的l2范数可以忽略不计，证明了新模型在当前情况下的定量精度令人满意。
A hybrid local time step/global maximum time step (LTS/GMaTS) method is proposed for computationally efficient modeling of hydro-sediment-morphodynamic processes. The governing equations are numerically solved on unstructured triangular meshes using a well-balanced shock-capturing finite volume method with the HLLC approximate Riemann solver. High computational efficiency is achieved by implementing the LTS to solve equations governing sediment-laden flows (i.e., the hydro-sediment part), and implementing the GMaTS to solve equations governing bed materials (i.e., the morphodynamic part). Two benchmark experimental dam-break flows over erodible beds and one field case of the Taipingkou waterway, Middle Yangtze River, are simulated to demonstrate the high computational efficiency and the satisfactory quantitative accuracy. It is shown that the computational efficiency of the new model can be faster by an order of magnitude than a traditional model of similar type but implementing the global minimum time step (GMiTS). The satisfactory quantitative accuracy of the new model for the present cases is demonstrated by the negligible L2norms of water level and bed elevation between the new model and the traditional model, as compared to the L2norms between the traditional model and the measured data.