Abstract
We have proposed a new method using artificial neural network for estimating the whole flow velocity vector fields from measured velocity vectors. The neural network is trained by using measured velocity vectors as teaching data so that the derivatives of a certain scalar function agree well with the measured data. The continuity equation of flow is consequently satisfied in the estimated vector fields and the scalar function gives the stream function.
In this paper, the method is evaluated using the PIV standard images which are able to give a correct vector field. The following facts are consequently proved.
1. The method is effective to measured fields with sparse velocity vector data.
2. Error estimation rate is improved by patry eliminating estimated data on the periphery of the image.
