Accelerating unstructured large eddy simulation solver with GPU

摘要

Purpose Adopting large eddy simulation (LES) to simulate the complex flow in turbomachinery is appropriate to overcome the limitation of current Reynolds-Averaged Navier-Stokes modelling and it provides a deeper understanding of the complicated transitional and turbulent flow mechanism; however, the large computational cost limits its application in high Reynolds number flow. This study aims to develop a three-dimensional GPU-enabled parallel-unstructured solver to speed up the high-fidelity LES simulation.Design/methodology/approach Compared to the central processing units (CPUs), graphics processing units (GPUs) can provide higher computational speed. This work aims to develop a three-dimensional GPU-enabled parallel-unstructured solver to speed up the high-fidelity LES simulation. A set of low-dissipation schemes designed for unstructured mesh is implemented with compute unified device architecture programming model. Several key parameters affecting the performance of the GPU code are discussed and further speed-up can be obtained by analysing the underlying finite volume-based numerical scheme.Findings The results show that an acceleration ratio of approximately 84 (on a single GPU) for double precision algorithm can be achieved with this unstructured GPU code. The transitional flow inside a compressor is simulated and the computational efficiency has been improved greatly. The transition process is discussed and the role of K-H instability playing in the transition mechanism is verified.Practical/implications The speed-up gained from GPU-enabled solver reaches 84 compared to original code running on CPU and the vast speed-up enables the fast-turnaround high-fidelity LES simulation.Originality/value The GPU-enabled flow solver is implemented and optimized according to the feature of finite volume scheme. The solving time is reduced remarkably and the detail structures including vortices are captured.