Unsteady separated flow behind a bluff body causes fluctuating drag and transverse forces on the body, which is of great significance in many offshore and marine engineering applications. While physical experimental and computational techniques provide valuable physics insight, they are generally time-consuming and expensive for design space exploration and flow control of such practical scenarios. We present an efficient Convolutional Neural Network (CNN) based deep-learning technique to predict the unsteady fluid forces for different bluff body shapes. The discrete convolution process with a non-linear rectification is employed to approximate the mapping between the bluff-body shape and the fluid forces. The deep neural network is fed by the Euclidean distance function as the input and the target data generated by the full-order Navier-Stokes computations for primitive bluff body shapes. The convolutional networks are iteratively trained using a stochastic gradient descent method to predict the fluid force coefficients of different geometries and the results are compared with the full-order computations. We have extended this CNN-based technique to predict the variation of force coefficients with the Reynolds number as well. Within the error threshold, the predictions based on our deep convolutional network have a speed-up nearly three orders of magnitude compared to the full-order results and consumes an insignificant fraction of computational resources. The deep CNN-based model can predict the hydrodynamic coefficients required for the well-known Lighthill’s force decomposition in almost real time which is extremely advantageous for offshore applications. Overall, the proposed CNN-based approximation procedure has a profound impact on the parametric design of bluff bodies and the feedback control of separated flows.

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