Enhancement of signal-to-noise ratio of schlieren visualization measurements in low-density wind tunnel tests using modal decomposition

Abstract: Signal processing methods that remove noise due to atmospheric fluctuation and image sensors and extract fluid phenomena from schlieren images obtained in the low-density wind tunnel test were developed together with the highly sensitive schlieren measurement setup. Time-series schlieren images of the flow around a triangular airfoil were analyzed, and the effectiveness of noise reduction methods using the randomized singular value decomposition and band-pass filtering using the fast Fourier transform (FFT) and the inverse FFT were investigated. The proposed method succeeded in removing noise by taking advantage of the frequency difference between the noise and fluid phenomena, and the fluid phenomena around the airfoil were clearly visualized at a Reynolds number of 3000 and a Mach number of 0.15. Graphical abstract: [Figure not available: see fulltext.]