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Accurate and affordable simulation of supercritical reacting flow is ofpractical importance for developing advanced engine systems for liquid rockets,heavy-duty powertrains, and next-generation gas turbines. In this work, wepresent detailed numerical simulations of LOX/GCH4 flame-vortex interactionunder supercritical conditions. The well-established benchmark configuration ofthree-dimensional Taylor-Green vortex (TGV) embedded with a diffusion flame ismodified for real fluid simulations. Both ideal gas and Peng-Robinson (PR)cubic equation of states are studied to reveal the real fluid effects on theTGV evolution and flame-vortex interaction. The results show intensified flamestretching and quenching arising from the intrinsic large density gradients ofreal gases, as compared to that for the idea gases. Furthermore, to reduce thecomputational cost associated with real fluid thermophysical propertycalculations, a machine learning-based strategy utilising deep neural networks(DNNs) is developed and then assessed using the three-dimensional reactive TGV.Generally good prediction accuracy is achieved by the DNN, meanwhile providinga computational speed-up of 13 times over the convectional approach. Theprofound physics involved in flame-vortex interaction under supercriticalconditions demonstrated by this study provides a benchmark for future relatedstudies, and the machine learning modelling approach proposed is promising forpractical high-fidelity simulation of supercritical combustion.

Detailed simulation of LOX/GCH4 flame-vortex interaction in supercritical Taylor-Green flows with machine learning