A vector-measurement-sensor problem for the least squares estimation is considered, by extending a previous novel approach in this paper. An extension of the vector-measurement-sensor selection of the greedy algorithm is proposed and is applied to particle-image-velocimetry data to reconstruct the full state based on the information given by sparse vector-measurement sensors.

Polymer/ceramic pressure-sensitive paint (PC-PSP), which incorporates a high percentage of particles in the binder layer, is proposed in order to improve the characteristics of PSP. The procedure for embedding particles into the binder layer was modified. In the conventional procedure, dye is adsorbed onto a polymer/ceramic coating film (denoted herein as a dye-adsorbed (D-adsorbed) PSP). In the new procedure, the mixture of a dye and particles is adsorbed onto a polymer coating film (denoted herein as the particle/dye-adsorbed (PD-adsorbed) PSP). The effect of particle mass content on PSP characteristics was investigated. In addition, the effect of solvent on PSP characteristics and film structure were evaluated for the PD-adsorbed PSP. As a result, the difference in the PSP characteristics between the two types of PSP was clarified. Although surface roughness and time response increase with increased mass content of particles for both D- and PD-adsorbed PSPs, the critical pigment volume concentration (CPVC) for the PD-adsorbed PSP is smaller than that of the D-adsorbed PSP (88 wt% and 93 wt%, respectively). The PD-adsorbed PSP has a higher frequency response comparing with the D-adsorbed PSP while maintaining the same surface roughness. Observation by scanning electron microscope showed that the CPVC of the PC-PSP is governed primarily by surface structure. The coating film structure can be roughly classified into two states depending on the particle mass content. One is a state in which the coating film consisted of two layers: a lower particle-rich layer and an upper polymer-rich layer. This type of structure was observed in the PD-adsorbed PSP as well as in the D-adsorbed PSP. In the other state, polymer and particles are homogeneously distributed in the film, and pores are formed. This difference in the coating structure results in a change in the time response.

The characteristics of fast-response pressure-sensitive paints (PSPs) in low-pressure conditions were evaluated. Three representative porous binders were investigated: polymer-ceramic PSP (PC-PSP), anodized-aluminum PSP (AA-PSP), and thin-layer chromatography PSP (TLC-PSP). For each PSP, two types of luminophores, Pt(II) meso-tetra (pentafluorophenyl) porphine (PtTFPP) and tris(bathophenanthroline) ruthenium dichloride (Ru(dpp)${}_3$), were used as sensor molecules. Pressure sensitivities, temperature sensitivities, and photodegradation rates were measured and evaluated using a pressure chamber. The effect of ambient pressure on the frequency response was investigated using an acoustic resonance tube. The diffusivity coefficients of PSPs were estimated from the measured frequency response and luminescent lifetime, and the governing factor of the frequency response under low-pressure conditions was identified. The results of static calibration show that PC-PSP/PtTFPP, AA-PSP/Ru(dpp)${}_3$, and TLC-PSP/PtTFPP have high pressure sensitivities that exceed 4%/kPa under low-pressure conditions and that temperature sensitivity and photodegradation rates become lower as the ambient pressure decreases. Dynamic calibration results show that the dynamic characteristics of PSPs with PtTFPP are dependent on the ambient pressure, whereas those of PSPs with Ru(dpp)${}_3$ are not influenced by the ambient pressure. This observation indicates that the governing factor in the frequency response under low-pressure conditions is the lifetime for PC-PSP and TLC-PSP, whereas the governing factor for AA-PSP is diffusion.

A data analysis method based on the linear least-squares (LLS) method was developed for the extraction of high-resolution skin friction fields from global luminescent oil film (GLOF) visualization images of a surface in an aerodynamic flow. In this method, the oil film thickness distribution and its spatiotemporal development are measured by detecting the luminescence intensity of the thin oil film. From the resulting set of GLOF images, the thin oil film equation is solved to obtain an ensemble-averaged (steady) skin friction field as an inverse problem. In this paper, the formulation of a discrete linear system of equations for the LLS method is described, and an error analysis is given to identify the main error sources and the relevant parameters. Simulations were conducted to evaluate the accuracy of the LLS method and the effects of the image patterns, image noise, and sample numbers on the results in comparison with the previous snapshot-solution-averaging (SSA) method. An experimental case is shown to enable the comparison of the results obtained using conventional oil flow visualization and those obtained using both the LLS and SSA methods. The overall results show that the LLS method is more reliable than the SSA method and the LLS method can yield a more detailed skin friction topology in an objective way.

In this study, the propagation time and attenuation rate distributions of each sound source grid point (200 × 200) to a microphone of an arbitrary position across the shear layer, which are required for beamforming, were reconstructed by the reduced-order model with sparse sampling for acceleration of the computation. First, the propagation time and attenuation rate distributions, including the refraction of sound by the shear layer were calculated over 100 patterns of combinations of the wind speed and the microphone position, as training data. The dominant modes and optimum sampling points were discovered from the training data. Subsequently, data-driven sparse sampling for reconstruction was applied and the propagation time and the attenuation rate from each grid point (200 × 200) to a microphone were quickly calculated for the given microphone position and wind speed. The error of the obtained calculation result is 1% or less, and the approximation by data-driven sparse sampling is concluded to be effective.