  Accurate and affordable simulation of supercritical reacting flow is of
practical importance for developing advanced engine systems for liquid rockets,
heavy-duty powertrains, and next-generation gas turbines. In this work, we
present detailed numerical simulations of LOX/GCH4 flame-vortex interaction
under supercritical conditions. The well-established benchmark configuration of
three-dimensional Taylor-Green vortex (TGV) embedded with a diffusion flame is
modified for real fluid simulations. Both ideal gas and Peng-Robinson (PR)
cubic equation of states are studied to reveal the real fluid effects on the
TGV evolution and flame-vortex interaction. The results show intensified flame
stretching and quenching arising from the intrinsic large density gradients of
real gases, as compared to that for the idea gases. Furthermore, to reduce the
computational cost associated with real fluid thermophysical property
calculations, 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 providing
a computational speed-up of 13 times over the convectional approach. The
profound physics involved in flame-vortex interaction under supercritical
conditions demonstrated by this study provides a benchmark for future related
studies, and the machine learning modelling approach proposed is promising for
practical high-fidelity simulation of supercritical combustion.
