Acceleration of turbulent combustion DNS via principal component transport

Abstract

We investigate the implementation of principal component (PC) transport to accelerate the direct numerical simulation (DNS) of turbulent combustion flows. The acceleration is achieved using the transport of PCs and the tabulation of the closure terms in the PC-transport equations using machine learning. Further acceleration is achieved by a treatment for bottlenecks associated with the acoustic time steps for low Mach number flows. The approach is implemented in 2D and 3D on a laboratory scale lean premixed methane-air flame stabilized on a slot burner. DNS based on the transport of thermochemical scalars (species and energy) is also carried out, first to develop a 2D DNS database for PC-transport equations’ closure terms and, second, to validate the approach against species DNS in 2D and 3D, a principal goal of the present effort. The results show that surrogate PC DNS can reproduce instantaneous profiles as well as statistics associated with turbulence, flame topology properties and measures of flame-turbulence interactions. The study also demonstrates that parametric simulations with surrogate PC DNS can be implemented at a fraction of the cost of a full 3D DNS with species and energy transport.