A simultaneous surface pressure and displacement measurement method that integrates pressure-sensitive paint (PSP) and binocular stereophotogrammetry is proposed. The assays were completed on the Φ4 m rotor test stand at China Aerodynamic Research and Development Center (CARDC). A single-shot lifetime approach was utilized to acquire the instantaneous pressure field on a rotor blade coated with PSP. At the same time, the PSP feature points were used to obtain the 3D coordinates of stereo cameras, which yielded the blade displacement field. The experimental results showed that the displacement measuring accuracy was better than 0.2 mm, and the pressure measurement accuracy was not affected, with Standard Deviation (STD) values below 700 Pa. The advantages of the proposed system are its simple structure, low cost, high accuracy and high test efficiency, which will offer a practical solution for the exploration of fluid–structure interplay. Hence, such a system is a prospective for the wind tunnel tests of helicopter rotor blades.

The characterization of pressure-sensitive paint (PSP) is affected by many physical and chemical factors, making it is difficult to analyze the relationship between characterization and influencing factors. An artificial neural network (ANN)-based method for predicting pressure sensitivity using paint thickness and roughness was proposed in this paper. The mean absolute percentage error (MAPE) for predicting pressure sensitivity is 6.5088%. The difference of paint thickness and roughness between sample and model surface may be a source of experimental error in PSP pressure measurement tests. The Stern-Volmer coefficients A and B are strongly linked. Pressure sensitivity is approximately equal to coefficient B, so coefficient A is predicted using pressure sensitivity based on the same ANN, and the MAPE of predicting A is 2.1315%. Then, we try to calculate the pressure by using the thickness and roughness on a model to predict pressure sensitivity and Stern-Volmer coefficient A. The PSP pressure measurement test was carried out at the China Aerodynamic Research and Development Center. Using the Stern-Volmer coefficient calculated by the in situ method, the method in this paper, and the sample calibration experiment, the root mean square errors (RMSE) of the pressure are 47.4431 Pa, 63.4736 Pa, and 73.0223 Pa, respectively.