Computationally efficient modeling of hydro-sediment-morphodynamic processes using a hybrid local time step/global maximum time step

• Published in: Advances in water resources Vol. 127; pp. 26 - 38
• Main Authors: Hu, Peng, Lei, Yunlong, Han, Jianjian, Cao, Zhixian, Liu, Huaihan, He, Zhiguo
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01-05-2019; Elsevier Science Ltd
• Subjects: Accuracy; Computational efficiency; Computer applications; Computer simulation; Computing time; Dam failure; Efficiency; Elevation; Finite volume method; Global maximum-time-step; Hydro-sediment-morphological modeling; Local-time-step; Mathematical models; Model accuracy; Modelling; Norms; Riemann solver; Rivers; Sediment; Sediments; Water levels; Local-time-step; Finite volume method; Global maximum-time-step; Computational efficiency; Hydro-sediment-morphological modeling
• ISSN: 0309-1708; 1872-9657
• DOI: 10.1016/j.advwatres.2019.03.006

•A computationally efficient hydro-sediment-morphodynamic model is developed.•The computational efficiency of the model can be faster by an order of magnitude than previous model of similar type.•The high computational efficiency is achieved by a new hybrid LTS/GMaTS method.
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 L2 norms of water level and bed elevation between the new model and the traditional model, as compared to the L2 norms between the traditional model and the measured data.