A new GPU-accelerated coupled discrete element and depth-averaged model for simulation of flow-like landslides. (July 2022) - Su, Xiaoli; Liang, Qiuhua; Xia, Xilin
Abstract: Flow-like landslides are a common type of natural hazards that may impose a great risk to people and their properties. Different models have been reported for simulating flow-like landslides, all of which possess limitations due to the underlying assumptions and simplifications. Harnessing the advantages of two types of prevailing modelling approaches, a new coupled model is developed which adopts a discrete element method (DEM) model to simulate the complex collapsing process in the source area and a depth-averaged model (DAM) to predict the predominantly convective movement in the runout and deposition zones. The coupled model is finally implemented on the NVIDIA CUDA programming platform to achieve GPU high-performance computing. Two laboratory tests are considered to validate the model and a field-scale landslide event is simulated to verify its applicability in real-world conditions. Satisfactory results confirm that the coupled model is able to reproduce the dynamic process of real-world flow-like landslides. Highlights: A new modelling framework is developed for flow-like landslide simulation from geomaterial collapse to runout and deposition. The coupled model outperforms the depth-averaged model and discrete element method in reproducing experimental tests. The coupled model is successfully applied to simulate the dynamic process of a real-world flow-like landslide. The coupled model is computationally 2.5 times more efficient than the DEM counterpart.