Image-based fluid data assimilation with deep neural network

Abstract

Estimating unknown parameters or conditions based on observation in a numerical model is a problem considered in data assimilation. In this study, we investigate a data assimilation approach using an image-processing deep neural network (DNN) as a likelihood function. The DNN is trained by observed images and tests visualized images generated by numerical simulation. This approach does not provide exact comparison of observed and simulated images for constructing the likelihood function. However, it would be effective when exact comparison is difficult due to noise or complex measurement procedures. The effectiveness of this approach is investigated by a vortex flow around a circular cylinder moving freely in the crossflow direction, realizing vortex-induced vibration (VIV). The DNN trained by wake visualization images from the VIV experiment tests pseudo-wake images from the numerical simulation to estimate VIV amplitude and frequency parameters. By evaluating the confidence score of the DNN from the simulated images with different amplitude and frequency values, these input parameters can be estimated based on the experimental VIV images, resulting in data assimilation with a likelihood function based on the DNN. The results showed that the amplitude and frequency of the VIV can be approximated from the experimental images.