Computationally efficient LES-TPDF turbulent combustion models

• Applicant: Dr. Yipeng Ge, Ph.D.
• Subject Area: Fluid Mechanics
• Term: from 2016 to 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 319178666

TThe combustion in many technical burners occurs in turbulent flow. Due to the overlap of chemical and turbulent and time scales and because of the non-linear dependence of the reaction rate on the concentrations of chemical species, the turbulence chemistry interaction must be taken into account e.g. by using transported transported probability density functions (TPDF's). The turbulent flow can be calculated very well using Large Eddy Simulations (LES), however usually not all spatial scales of chemical reactions can be resolved. The direct coupling of TPDF's with LES is computationally intensive because the TPDF must be represented by many stochastic particles in each LES-computational cell. The calculation of the evolution of the chemical species is the computationally most expensive part of the code. The aim of this project is to develop methods that provides a similar quality of prediction of the evolution of chemical species at a significantly reduced computational effort. Two related TPDF approaches will be developed, which take into account the close correlation between the mixture fraction and species. Basis in both cases is a LES for the simulation of turbulent flow field. In one approach the TPDF are represented with a low number of stochastic particles (sparse Lagrangian method). The mixture model uses larger areas in physical space and mixes preferably particles of similar mixture fraction. In the second approach the TPDF is derived from a RANS simulation, which is performed using the time-averaged LES-flow field. The RANS simulation is cheaper since it needs only be stationary and a coarser computational grid can be applied. In both cases the feedback to the LES occurs by means of a density conditioned on the mixture fraction. To further reduce the computational time, the solution of chemical evolution on GPU's and the use of tables with systematically reduced chemical models are examined.

• DFG Programme: Research Grants
• Co-Investigator: Professor Dr. Michael Pfitzner