Development and evaluation of gappy-POD as a data reconstruction technique for noisy PIV measurements in gas turbine combustors

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Low signal-to-noise in particle image velocimetry (PIV) measurements in systems such as high pressure gas turbine combustors can result in significant data gaps that negatively affect subsequent analysis. Here, gappy proper orthogonal decomposition (GPOD) is evaluated as a method of filling such missing data. Four GPOD methods are studied, including a new method that utilizes a median filter (MF) to adaptively select whether a local missing data point is updated after each iteration. These methods also are compared against local Kriging interpolation. The GPOD methods are tested using PIV data without missing vectors that were obtained in atmospheric pressure swirl flames. Parameters studied include the turbulence intensity, amount of missing data, and the amount of noise in the valid data. Two criteria to check for GPOD convergence also were investigated. The MF method filled in the missing data with the lowest error across all parameters tested, with approximately one-third the computational cost of Kriging. Furthermore, the accuracy of MF GPOD was relatively insensitive to the quality of the convergence criterion. Therefore, compared to the three other GPOD methods and Kriging interpolation, the MF GPOD method is an effective method for filling missing data in PIV measurements in the studied gas turbine combustor flows.