Gradient-based PIV using neural networks

被引：0

作者：

I. Kimura

Y. Susaki

R. Kiyohara

A. Kaga

Y. Kuroe

机构：

[1] Osaka Electro-Communication University,Department of Environmental Engineering

[2] Osaka University,Department of Electronics and Information Science

[3] Kyoto Institute of Technology,undefined

来源：

Journal of Visualization | 2002年 / 5卷

关键词：

PIV; neural networks; gradient-based method; stream function;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

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.

引用

页码：363 / 370

页数：7