An investigation of hybrid CPU-GPU solvers for supersonic reacting flow simulation with detailed chemical kinetics

Abstract

Accurate simulations of supersonic reacting flow require the use of detailed chemical kinetics to capture the complex phenomena such as auto-ignition and broken, thickened flamelets. However, the detailed chemical mechanisms for hydrocarbon fuels used in supersonic combustion typically have a great number of species and reactions. This paper reports on the implementation of a hybrid CPU-GPU solver by using the graphics processing unit (GPU) as a coprocessor to accelerate the computation of chemical source terms in modeling supersonic reacting flows with detailed chemical kinetics. The chemical kinetics are solved explicitly via a fully-coupled method with an implicit scheme. Three parallel strategies with different storage schemes were explored in implementing the highly parallel GPU algorithms for accelerating combustion modeling. Significant acceleration was obtained by using these parallel algorithms for chemical source terms evaluation. In order to verify the viability of the solver, two supersonic combustion flow test cases were conducted. The performance of these algorithms is evaluated by modeling four chemical reaction mechanisms on different grids compared with the CPU-only solver. The computation cost of these algorithms is basically proportional to the size of the grids. The performance gain of these parallel algorithms depends on the size of the reaction mechanism. A maximum speedup factor of over 80 was obtained by modeling the mechanism consisting of 348 species and 2163 reactions. The significant performance improvement provided by these parallel algorithms can provide a significant perspective for designing GPU-accelerated algorithms for applications in simulating supersonic reacting flows.