Abstract: This application relates to the technical field of 3D-3C velocity measurement, and provides a method for three-view extraction and particle 3D distribution reconstruction of trichromatic mask PIV, including: color particle image shooting, color channel separation, preliminary 3D reconstruction, and 3D reconstruction refining by using a convolutional neural network model. According to this application, color channel separation is performed on a color particle image using a demosaicing algorithm based on a U-Net++ neural network, enabling high-quality image reconstruction during efficient network extraction. The convolutional neural network model is employed for 3D reconstruction, significantly reducing the particle elongation effect and aligning the spatial position and intensity distribution of particles more closely with the actual conditions. The method enhances computational efficiency and provides higher resolution in the depth of field direction.

Abstract: A method for simultaneous measurement of velocity and temperature fields based on temperature-sensitive phosphorescent particles includes the steps of: establishing a lifetime-temperature curve by measuring lifetimes of temperature-sensitive phosphorescent particles; capturing time-sequential images of the temperature-sensitive phosphorescent particles; processing the time-sequential images via three-view separation to obtain processed images; performing three-dimensional (3D) reconstruction and trajectory fitting on the processed images to obtain a 3D velocity field; acquiring red full-resolution images of the temperature-sensitive phosphorescent particles after illumination; calculating intensities of the temperature-sensitive phosphorescent particles from the red full-resolution images, and determining a phosphorescence lifetime based on the intensities of the particles; determining a 3D temperature field based on the lifetime-temperature curve and the phosphorescence lifetime; and fusing the 3D velocity f

Abstract: This application relates to the technical field of 3D-3C velocity measurement, and provides a method for three-view extraction and particle 3D distribution reconstruction of trichromatic mask PIV, including: color particle image shooting, color channel separation, preliminary 3D reconstruction, and 3D reconstruction refining by using a convolutional neural network model. According to this application, color channel separation is performed on a color particle image using a demosaicing algorithm based on a U-Net++ neural network, enabling high-quality image reconstruction during efficient network extraction. The convolutional neural network model is employed for 3D reconstruction, significantly reducing the particle elongation effect and aligning the spatial position and intensity distribution of particles more closely with the actual conditions. The method enhances computational efficiency and provides higher resolution in the depth of field direction.