Low-Cost 3D-PTV Utilizing Rainbow Light Illumination And Artificial Neural Network Applied To Rayleigh-Bénard Convection
D. Noto (1,2), Y. Tasaka (2), Y. Murai (2)
(1) Department of Earth and Environmental Science, School of Arts & Sciences, University of Pennsylvania, USA
(2) Laboratory for Flow Control, Division of Mechanical Aerospace Engineering, Faculty of Engineering, Hokkaido University, Japan
Building on the novel methodology for three-dimensional (3D) color particle tracking velocimetry (PTV) proposedearlier by our research group, an improvement and an evaluation of the method is presented in this study. The method utilizes only a color camera and a consumer-grade liquid crystal display projector (LCDP), and thus cost for implementation is greatly lower than that required for a tomographic system. Employment of artificial neural network system enables to eliminate human decisions in constructing a color-to-depth conversion function. That is, the calibration can be performed automatically once relationship between particle color and depth are associated in actual measurement environments. The method was tested in a Rayleigh–Bénard convection of water, which is expected to have horizontally isotropic structures. Two different flow states were successfully measured. The results showed that the 3D color PTV can measure the same statistics of velocity components in the in-plane and out-of-plane directions. Considering all the results, the proposed method using only a single camera and an LCDP has a potential to be an alternative to the state-of-art tomographic measurements.