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GPU-Accelerated Developments for the Realistic Simulation of Large-Scale Mud/Debris Flows

Author(s): Sergio Martinez-Aranda; Reinaldo Garcia; Pilar Garcia-Navarro

Linked Author(s): Sergio Martínez-aranda

Keywords: Finite Volume methods; Conservative numerical schemes; Mud/debris flow; GPU-accelerated algorithms

Abstract: Mud/debris flows are among the most challenging gravity-driven geophysical flows. Natural muddy slurries and debris are two-phase flows with non-Newtonian rheology where solids represent about 40-80% of the flow volume, creating marked density gradients and non-hydrostatic pore-fluid pressures. This dynamic pore pressures affect the effective normal stress along the flow column, modifying the frictional shear stress between grains and causing the solid phase dilation. Furthermore, these unsteady flows occur along steep and irregular terrains which require a refined non-structured spatial discretization in order to capture the terrain complexity, increasing exponentially the computational times of the models. In this work, a novel GPU-accelerated two-dimensional Efficient Simulation Tools (EST) for multi-grain two-phase shallows-flows running in non-structured triangular meshes is presented. The system of depth-averaged equations is formed by the conservation equations for the mass and linear momentum of the compressible two-phase mixture and the mass transport equations for the different solid phases involved in the flow. A new closure relation for the shear-induced pore-fluid pressure distribution during the movement of dense-packed solid-liquid mixtures has also been proposed, which accounts for the shear-induced separation of the solid and liquid phases. The system is solved using a Finite Volume scheme supported by a novel Riemann solver which allows the bulk flow density to participate in the definition of the characteristic wave structure. The proposed tool is faced to a real-scale catastrophic mining tailings flow, demonstrating its robustness, accuracy and efficiency. The GPU-accelerated tool shows a computational performance 280 times faster than the CPU-based algorithm.

DOI: https://doi.org/10.3850/IAHR-39WC252171192022553

Year: 2022

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