A non-uniform grid approach for high-resolution flood inundation simulation based on GPUs

In view of the frequent occurrence of floods due to climate change, and the fact that a large calculation domain, with complex land types, is required for solving the problem of the flood simulations, this paper proposes an optimized non-uniform grid model combined with a high-resolution model based on the graphics processing unit (GPU) acceleration to simulate the surface water flow process. For the grid division, the topographic gradient change is taken as the control variable and different optimization criteria are designed according to different land types. In the numerical model, the Godunov-type method is adopted for the spatial discretization, the TVD-MUSUL and Runge-Kutta methods are used to improve the model’s spatial and temporal calculation accuracies, and the simulation time is reduced by leveraging the GPU acceleration. The model is applied to ideal and actual case studies. The results show that the numerical model based on a non-uniform grid enjoys a good stability. In the simulation of the urban inundation, approximately 40%–50% of the urban average topographic gradient change to be covered is taken as the threshold for the non-uniform grid division, and the calculation efficiency and accuracy can be optimized. In this case, the calculation efficiency of the non-uniform grid based on the optimized parameters is 2–3 times of that of the uniform grid, and the approach can be adopted for the actual flood simulation in large-scale areas.

A graphics processing unit-based robust numerical model for solute transport driven by torrential flow condition

Solute transport simulations are important in water pollution events.This paper introduces a finite volume Godunovtype model for solving a 4×4 matrix form of the hyperbolic conservation laws consisting of 2D shallow water equations and transport equations.The model adopts the Harten-Lax-van Leer-contact(HLLC)-approximate Riemann solution to calculate the cell interface fluxes.It can deal well with the changes in the dry and wet interfaces in an actual complex terrain,and it has a strong shock-wave capturing ability.Using monotonic upstream-centred scheme for conservation laws(MUSCL)linear reconstruction with finite slope and the Runge-Kutta time integration method can achieve second-order accuracy.At the same time,the introduction of graphics processing unit(GPU)-accelerated computing technology greatly increases the computing speed.The model is validated against multiple benchmarks,and the results are in good agreement with analytical solutions and other published numerical predictions.The third test case uses the GPU and central processing unit(CPU)calculation models which take 3.865 s and 13.865 s,respectively,indicating that the GPU calculation model can increase the calculation speed by 3.6 times.In the fourth test case,comparing the numerical model calculated by GPU with the traditional numerical model calculated by CPU,the calculation efficiencies of the numerical model calculated by GPU under different resolution grids are 9.8–44.6 times higher than those by CPU.Therefore,it has better potential than previous models for large-scale simulation of solute transport in water pollution incidents.It can provide a reliable theoretical basis and strong data support in the rapid assessment and early warning of water pollution accidents.

A TensorFIow-based new high-performance computational framework for CFD

In this study,a computational framework in the field of artificial intelligence was applied in computational fluid dynamics(CFD)field.This Framework,which was initially proposed by Google Al department,is called"TensorFlow".An improved CFD model based on this framework was developed with a high-order difference method,which is a constrained interpolation profile(CIP)scheme for the base flow solver of the advection term in the Navier-Stokes equations,and preconditioned conjugate gradient(PCG)method was implemented in the model to solve the Poisson equation.Some new features including the convolution,vectorization,and graphics processing unit(GPU)acceleration were implemented to raise the computational efficiency.The model was tested with several benchmark cases and shows good performance.Compared with our former CIP-based model,the present Tensor Flow-based model also shows significantly higher computational efficiency in large-scale computation.The results indicate TensorFlow could be a promising framework for CFD models due to its ability in the computational acceleration and convenience for programming.