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 data at optimally placed points. In this study, the proper orthogonal decomposition (POD) mode calculated from the time-series PSP data is used as a modal basis. Based on the POD modes, the points that effectively represent the features of the pressure distribution are optimally placed by the sensor optimization technique. Then, the time-dependent coefficient vector of the POD modes is determined by minimizing the difference between the time-series pressure data and the reconstructed pressure at the optimal points. Here, the coefficient vector is assumed to be a sparse vector. The advantage of the proposed method is a self-contained method, while existing methods use other data, such as pressure tap data for the reduction of the noise. As a demonstration, we applied the proposed method to the PSP data measuring the Kármán vortex street behind a square cylinder. The reconstructed pressure data agreed very well with the pressures independently measured by pressure transducers. This modal-based approach will be applicable not only to PSP data but other types of experimental data.

Temperature-sensitive paint (TSP) can optically measure a global temperature distribution using a thermal quenching of dye molecules. The TSP measurement is often used in wind tunnel tests to measure the temperature and flow fields in the aerodynamic field. The measurement accuracy is affected by the characteristics of TSP, such as temperature sensitivity, pressure dependency, luminescent intensity, photostability, and surface condition. The characteristics depend on the formulation of TSP. This study investigates the characteristics of the TSP using dichlorotris (1,10-phenanthroline) ruthenium(II) hydrate (Ru-phen). We compare the characteristics of TSPs using different polymers, solvents, and dye concentrations. The TSPs using polyacrylic acid as a polymer shows linear calibration curves, high luminescent intensity, high photostability, and smooth surface. On the other hand, the TSPs using polymethyl methacrylate have nonlinear calibration curves, low luminescent intensity, strong photodegradation, and a rough surface.

CntTSP is a flow visualization technique that combines temperature-sensitive paint (TSP) and carbon nanotube (CNT). TSP is a sprayable temperature sensor, and CNT is used as a thin inner electric heater for the TSP. The cntTSP technique allows the visualization of the movement of the boundary layer transition on a moving airfoil. In this study, cntTSP was applied to a pitch-oscillating airfoil in a low-speed wind tunnel. Three methods were compared as methods for detecting the boundary layer transition position. The transition position was successfully evaluated using a heat transfer coefficient that was calculated by the surface temperature. Moreover, at a high-reduced frequency, the detected transition positions were different between the pitching-up and pitching-down procedure. Moreover, we observed a hysteresis loop of the transition position.

Time-series clustering is a powerful data mining technique for time-series data in the absence of prior knowledge of the clusters. Here we propose a time-series clustering method that leverages an annealing machine, which accurately solves combinatorial optimization problems. The proposed method facilitates an even classification of time-series data into closely located clusters while maintaining robustness against outliers. We compared the proposed method with an existing standard method for clustering an online distributed dataset and found that both methods yielded comparable results. Furthermore, the proposed method was applied to a flow measurement image dataset containing noticeable noise with a signal-to-noise ratio of approximately unity. Despite a small signal variation of approximately 2%, the proposed method effectively classified the data without any overlaps among the clusters. In contrast, the clustering results of the existing methods exhibited overlapping clusters. These results indicate the effectiveness of the proposed method.

Pressure-sensitive paint (PSP) is an optical sensor that can measure global pressure distribution by using the oxygen quenching of dye molecules. In particular, anodized aluminum pressure-sensitive paint (AA-PSP) exhibits a fast time response. AA-PSP has been used in unsteady measurements at supersonic and transonic speeds, such as on the surface of a transonic free-flying sphere or the wall of a shock tube when the shock wave passes. To capture such ultrafast phenomena, the frame rate of the camera must be sufficiently fast, and the exposure time must be sufficiently short. Therefore, it is desirable that the AA-PSP exhibits bright luminescence, high-pressure sensitivity, and fast response time. This study focused on pyrene-based AA-PSPs and investigated their characteristics, such as luminescence intensity and pressure sensitivity, at different anodization times, dipping solvents, and dipping concentrations. Furthermore, a time-response test using a shock tube was conducted on the brightest AA-PSP. Consequently, the time for a 90% rise in pressure was 2.2 μs.