Analysis of the information overlap between the PIV and [formula omitted] chemiluminescence signals in turbulent flames using a sparse sensing framework

Abstract

The purpose of this study is to quantify the information overlap between the OH* chemiluminescence signal and the three-dimensional Particle Image Velocimetry (PIV) signal for a set of bluff-body stabilized, swirling and non-swirling turbulent methane/air flames issued from the Cambridge/Sandia Stratified Swirl Burner. The time-resolved velocity data was collected using high-speed stereoscopic PIV operated at 3 kHz. This sampling rate was sufficiently high to enable accurate tracking of the large-scale spatial and temporal features of the flow. To investigate flame-flow interactions, high-speed (8 kHz) OH* chemiluminescence imaging was applied on the same set of flames. The two datasets are first independently analyzed using multi-scale Proper Orthogonal Decomposition (mPOD) for the purpose of detecting the relevant flow and heat release rate structures. The resulting modes are qualitatively compared to assess if it is possible to identify the same flow structures from the two datasets. Finally, the information overlap is quantified by predicting the velocity field from the chemiluminescence signal using a sparse sensing framework. Sparse sensing is a mathematical technique that leverages the low-dimensional representation of a physical phenomenon to predict the entire state of the system using few measurements. The results show that the velocity and chemiluminescence mPOD modes display broadly similar features, from which is possible to retrieve the same flow instabilities in the corresponding frequency range. Moreover, the prediction of the velocity field obtained using chemiluminescence as input of the sparse sensing model achieves a good level of accuracy, pointing to an important degree of information overlap between the two signals.