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Stratified premixed combustion, known for its capability to expandflammability limits and reduce overall-lean combustion instability, has beenwidely adopted to comply with increasingly stringent environmental regulations.Numerous numerical simulations with different combustion models and meshresolutions have been conducted on laboratory-scale flames to furtherunderstand the stratified premixed combustion. However, the trade-off betweenthe high-fidelity and low computational cost for simulating laboratory-scaleflames still remains, particularly for those combustion models involving directcoupling of chemistry and flow. In the present study, a GPU-based solver isemployed to solve partial differential equations and calculate the thermal andtransport properties, while an artificial neural network (ANN) is introduced toreplace reaction rate calculation. Particular emphasis is placed on evaluatingthe proposed GPU-ANN approach through the large eddy simulation of theCambridge stratified flame. The simulation results show good agreement for theflow and flame statistics between the GPU-ANN approach and the conventionalCPU-based solver with direct integration (DI). The comparison suggests that theGPU-ANN approach can achieve the same level of accuracy as the conventionalCPU-DI solver. In addition, the overall speed-up factor for the GPU-ANNapproach is over two orders of magnitude. This study lays the potentialgroundwork for fully resolved laboratory-scale flame simulations based ondetailed chemistry with much more affordable computational cost.

GPU-accelerated Large Eddy Simulation of turbulent stratified flames with machine learning chemistry