Exploration and application of the endoscopic pressure-sensitive paint measurement method in confined compressor cascade environments

To address the challenge of measuring blade surface pressures in confined and enclosed compressor environments, this research develops a high-spatial-resolution endoscopic pressure-sensitive paint (PSP) measurement method and preliminarily explores its applicability. An integrated optical path for endoscopic PSP excitation and imaging is designed, upon which an endoscopic PSP measurement system is constructed. Furthermore, factors influencing endoscopic measurements are investigated, and the patterns of their effects are revealed. The effectiveness and applicability of the endoscopic PSP measurement method are validated in a compressor cascade environment. A nonlinear transformation (NLT) method is proposed for the three-dimensional reconstruction of complex models under endoscopic distortion, and its sensitivity is analyzed. Additionally, a strategy for utilizing NLT is proposed to reduce reconstruction errors. The results show that improving spatial resolution leads to reduced light intensity, which is inversely proportional to the square of the magnification factor. A combined linear-time and exponential-length attenuation model is proposed to accurately describe the variation of light intensity with exposure time and propagation distance. Under long exposure, the endoscopic PSP images exhibit satisfactory sharpness and signal-to-noise ratio without noticeable blurring. The endoscopic PSP measurement method successfully measured the pressure field on compressor blade surfaces. The NLT method achieves three-dimensional reconstruction of a compressor blade in confined compressor environments, effectively correcting image distortion and enhancing reconstruction accuracy. The endoscopic PSP results agree well with pressure taps, and the maximum relative deviation of the NLT method is <1%. The endoscopic PSP measurements capture the structure of the suction spike at the blade leading edge, providing further insights into leading-edge flow structures and flow separation.