Incompressible versus compressible large eddy simulation for the identification of premixed flame dynamics

The present work compares the respective advantages and disadvantages of compressible and incompressible computational fluid dynamics (CFD) formulations when used for the estimation of the acoustic flame response. The flame transfer function of a turbulent premixed swirl-stabilized burner is determined by applying system identification (SI) to time series data extracted from large eddy simulation (LES). By analyzing the quality of the results, the present study shows that incompressible simulations exhibit several advantages over their compressible counterpart with equal prediction of the flame dynamics. On the one hand, the forcing signals can be designed in such a way that desired statistical properties can be enhanced, while maintaining optimal values in the amplitude. On the other hand, computational costs are reduced and the implementation is fundamentally simpler due to the absence of acoustic wave propagation and corresponding resonances in the flame response or even self-excited acoustic oscillations. Such an increase in efficiency makes the incompressible CFD/SI modeling approach very appealing for the study of a wide variety of systems that rely on premixed combustion. In conclusion, the present study reveals that both methodologies predict the same flame dynamics, which confirms that incompressible simulation can be used for thermoacoustic analyses of acoustically compact velocity-sensitive flames.

Automated summary

This study compares the advantages and disadvantages of compressible and incompressible computational fluid dynamics (CFD) formulations for estimating acoustic flame response. By applying system identification (SI) to time series data extracted from large eddy simulation (LES), the flame transfer function of a swirl-stabilized burner is determined. The results show that incompressible simulations have several advantages over compressible simulations in terms of desired statistical properties enhancement, reduced computational costs, and simpler implementation.