Journal
JOURNAL OF VISUALIZATION
Volume 5, Issue 4, Pages 363-370Publisher
IOS PRESS
DOI: 10.1007/BF03182351
Keywords
PIV; neural networks; gradient-based method; stream function
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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.
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