Correlation between Pitch Impregnation Pressure and Pore Sizes of Graphite Block

This study aimed to investigate the effect of impregnation pressure on the decrease in porosity of impregnated bulk graphite. The correlation between pitch impregnation behavior and the pore sizes of the bulk graphite block was studied to determine the optimal impregnation pressure. The densities and porosities of the bulk graphite before and after pitch impregnation under various pressures between 10 and 50 bar were evaluated based on the Archimedes method and a mercury porosimeter. The density increased rates increased by 1.93–2.44%, whereas the impregnation rate calculated from the rate of open porosity decreased by 15.15–24.48%. The density increase rate and impregnation rate were significantly high when the impregnation pressures were 40 and 50 bar. Compared with impregnation pressures of 10, 20, and 30 bar, the minimum impregnatable pore sizes with impregnation pressures of 40 and 50 bar were 30–39 and 24–31 nm, respectively. The mercury intrusion porosimeter analysis results demonstrated that the pressure-sensitive pore sizes of the graphite blocks were in the range of 100–4500 nm. Furthermore, the ink-bottle-type pores in this range contributed predominantly to the effect of impregnation under pressure, given that the pitch-impregnated-into-ink-bottle-type pores were difficult to elute during carbonization.

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.