PeleLM-FDF large eddy simulator of turbulent reacting flows

Abstract

A new computational methodology, termed ‘PeleLM-FDF’ is developed and utilised for high fidelity large eddy simulation (LES) of complex turbulent combustion systems. This methodology is constructed via a hybrid scheme combining the Eulerian PeleLM base flow solver with the Lagrangian Monte Carlo simulator of the filtered density function (FDF) for the subgrid scale reactive scalars. The resulting methodology is capable of simulating some of the most intricate physics of complex turbulence-combustion interactions. This is demonstrated by LES of a non-premixed CO/H${}_2$ temporally evolving jet flame. The chemistry is modelled via a skeletal kinetics model, and the results are appraised via a posteriori comparisons against direct numerical simulation (DNS) data of the same flame. Excellent agreements are observed for the time evolution of various statistics of the thermo-chemical quantities, including the manifolds of the multi-scalar mixing. The new methodology is capable of capturing the complex phenomena of flame-extinction and re-ignition at a 1/512 of the computational cost of the DNS. The high fidelity and the computational affordability of the new PeleLM-FDF solver warrants its consideration for LES of practical turbulent combustion systems.

Keywords:

• large eddy simulation
• turbulent combustion
• filtered density function
• low mach number approximation

Acknowledgments

We are grateful to Professor Evatt R. Hawkes of the University of New South Wales for providing the DNS data as used for comparative studies here. We are indebted to Dr. Marcus Day of National Renewable Energy Laboratory, the original developer of the PeleLM for excellent comments on the draft of this manuscript. Computational resources are provided by the University of Pittsburgh Center for Research Computing.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work is sponsored by the National Science Foundation [grant numbers CBET-2042918 and CBET-2152803].