Markerless Image Alignment Method for Pressure-Sensitive Paint Image

We propose a markerless image alignment method for pressure-sensitive paint measurement data replacing the time-consuming conventional alignment method in which the black markers are placed on the model and are detected manually. In the proposed method, feature points are detected by a boundary detection method, in which the PSP boundary is detected using the Moore-Neighbor tracing algorithm. The performance of the proposed method is compared with the conventional method based on black markers, the difference of Gaussian (DoG) detector, and the Hessian corner detector. The results by the proposed method and the DoG detector are equivalent to each other. On the other hand, the performances of the image alignment using the black marker and the Hessian corner detector are slightly worse compared with the DoG and the proposed method. The computational cost of the proposed method is half of that of the DoG method. The proposed method is a promising for the image alignment in the PSP application in the viewpoint of the alignment precision and computational cost.

Dynamic-stall measurements using time-resolved pressure-sensitive paint on double-swept rotor blades

The study presents an optimized pressure-sensitive paint (PSP) measurement system that was applied to investigate unsteady surface pressures on recently developed double-swept rotor blades in the rotor test facility at the German Aerospace Center (DLR) in Göttingen. The measurement system featured an improved version of a double-shutter camera that was designed to reduce image blur in PSP measurements on fast rotating blades. It also comprised DLR’s PSP sensor, developed to capture transient flow phenomena (iPSP). Unsteady surface pressures were acquired across the outer 65% of the rotor blade with iPSP and at several radial blade sections by fast-response pressure transducers at blade-tip Mach and Reynolds numbers of $$\mathrm {M}_\mathrm{tip} = 0.282-0.285$$ M tip = 0.282 - 0.285 and $$\mathrm {Re}_\mathrm{tip}= 5.84-5.95 \times 10^5$$ Re tip = 5.84 - 5.95 × 10 5 . The unique experimental setup allowed for scanning surface pressures across the entire pitch cycle at a phase resolution of $${0.225}\,{\mathrm{deg}}$$ 0.225 deg azimuth for different collective and cyclic-pitch settings. Experimental results of both investigated cyclic-pitch settings are compared in detail to a delayed detached eddy simulation using the flow solver FLOWer and to flow visualizations from unsteady Reynolds-averaged Navier–Stokes (URANS) computations with DLR’s TAU code. The findings reveal a detailed and yet unseen insight into the pressure footprint of double-swept rotor blades undergoing dynamic stall and allow for deducing “stall maps”, where confined areas of stalled flow on the blade are identifiable as a function of the pitch phase. Graphical abstract