Re-Oriented POD for Feature Extraction From Time Resolved Reacting Flow Datasets

Abstract

Abstract Recent improvements in computational and experimental combustion have led to an increased availability of high-fidelity data sets. With the surge of high-quality data, the reduction and analysis of this information become a key aspect in improving our understanding of highly dynamic, reacting flow fields. This work utilizes simultaneous high-speed stereoscopic particle image velocimetry (sPIV), OH-planar laser induced fluorescence (PLIF) and fuel-PLIF measurements in a high pressure, liquid fueled swirl combustor. This work extends one of the most commonly used modal analysis techniques, proper orthogonal decomposition (POD). Instead of decomposing the data as a series of time coefficients and spatial modes, which is the result of snap-shot POD, the data is re-oriented such that the coefficients are a function of the transverse distance and the modes are a function of axial distance and time. This reoriented POD method, referred to as spatio-temporal POD, allows us to directly target convective flow structures while still extracting the same types of information as from snap-shot POD. Additionally, since the modes contain temporal information, phase speeds associated with the convective structures can easily be calculated from the phase portraits. This paper presents results of this analysis, allowing us to track the growth of coherent structures, their phase speeds, and their subsequent decay.