Data-driven approach for noise reduction in pressure-sensitive paint data based on modal expansion and time-series data at optimally placed points

We propose a noise reduction method for unsteady pressure-sensitive paint (PSP) data based on modal expansion, the coefficients of which are determined from time-series at optimally placed points. In this study, proper orthogonal decomposition (POD) mode calculated PSP is used as basis. Based POD modes, points that effectively represent features pressure distribution by sensor optimization technique. Then, time-dependent coefficient vector modes minimizing difference between and reconstructed optimal Here, assumed to be sparse vector. The advantage proposed self-contained method, while existing methods use other such tap noise. As demonstration, we applied measuring K\'arm\'an vortex street behind square cylinder. agreed very well with pressures independently measured transducers.