Affiliations: Osaka Electro-Communication University, Hatsu-cho,
Neyagawa, Osaka 572-8530, Japan. | Department of Environmental Engineering, Osaka
University, Yamadaoka, Suita, Osaka 565-0871, Japan. | Department of Electronics and Information Science,
Kyoto Institute of Technology, Matsugasaki, Sakyo-ku, Kyoto 606-8585,
Japan.
Abstract: This paper proposes a new gradient-based PIV using an artificial
neural network for acquiring the characteristics of a two-dimensional flow
field. The neural network can effectively realize an accurate approximation of
the vector field by introducing some knowledge on the characteristic property.
The neural network is trained by using spatial and temporal image gradients so
that the basic equation of the gradient-based method is satisfied. Since the
neural network itself learns the stream function, the continuity equation of
flow is consequently satisfied in the measured velocity vector field. The new
gradient-based PIV can be applied to even partly lacking visualized images.
Keywords: PIV, neural networks, gradient-based method, stream function
