Gradient-based PIV using neural networks
| Autor: | Akikazu Kaga, Ichiro Kimura, Yasuaki Kuroe, R. Kiyohara, Y. Susaki |
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| Rok vydání: | 2002 |
| Předmět: |
Artificial neural network
Field (physics) business.industry Condensed Matter Physics chemistry.chemical_compound chemistry Continuity equation Flow (mathematics) Stream function Vector field Artificial intelligence Electrical and Electronic Engineering Stochastic neural network Characteristic property business Algorithm Mathematics |
| Zdroj: | Journal of Visualization. 5:363-370 |
| ISSN: | 1875-8975 1343-8875 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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