| description abstract | The estimation of flame transfer functions (FTF) from time series data generated by large eddy simulation (LES) via system identification (SI) is an important element of thermoacoustic analysis. A continuous time series of adequate length is required to achieve low uncertainty, especially when dealing with turbulent noise. Limited scalability of LES codes implies that the wall-clock-time required for generating such time series may be excessive. The present paper tackles this challenge by exploring how the superposition of multiple simulations with the same excitation signal, but varying initial conditions, increases signal-to-noise ratio (SNR) and leads to more robust identification. In addition, the established SI approach, which relies on broadband excitation, is compared to excitation with approximate Dirac and Heaviside signals, promising simpler pre- and postprocessing. Results demonstrate that the proposed workflow reduces significantly the wall-clock-time required for robust FTF identification. This reduction in wall-clock-time requires more parallel computational resources, but it does not significantly increase the overall computational cost while also enabling FTF estimation using Heaviside excitation. The proposed method is assessed on a partially premixed, steam enriched water-enhanced turbofan (“WET”) swirl burner with significant turbulent noise levels. Steam enrichment is a combustion concept that reduces harmful emissions such as NOx and CO2 while increasing engine efficiency. However, the effect of steam on the flame response needs to be better understood. To this end, a combustion model including an optimized global chemical mechanism for partially premixed wet methane combustion is presented and validated against experimental data. | |
