Development and evaluation of Gappy-POD for noisy PIV measurements in gas turbine combustors

Abstract

Gappy proper orthogonal decomposition (GPOD) is assessed here as a data reconstruction technique for particle image velocimetry (PIV) measurements, specifically those applicable to gas turbine combustors (GTC). At practical operating conditions, PIV measurements are plagued with issues, such as low signal-to-noise ratios, that result in significant gaps in data. Four GPOD methods are studied here, including a new method that makes use of a median filter (MF) outlier detection technique to adaptively update reconstructions between iterations. The analysis of the performance of the GPOD methods is done by implementing them on a non-gappy PIV data set. Artificial gaps of varying amounts are added to this non-gappy data set in a manner similar to the gaps found in real experimental data. Additionally, two criteria to check for GPOD convergence are investigated. It was found that the MF method produced the lowest reconstruction error regardless of the amount of gappiness. Furthermore, the MF method was found to be relatively insensitive to the accuracy of the convergence criterion. The MF GPOD therefore is an effective method for filling in missing data in PIV measurements of gas turbine combustor flows.