Predicting the near-wall region of turbulence through convolutional neural networks
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Modelling the near-wall region of wall-bounded turbulent flows is a widespread practice to reduce the computational cost of large-eddy simulations (LESs) at high Reynolds number. As a first step towards a data-driven wall-model, a neural-network-based approach to predict the near-wall behaviour in a turbulent open channel flow is investigated. The fully-convolutional network (FCN) proposed by Guastoni et al. [preprint, arXiv:2006.12483] is trained to predict the two-dimensional velocity-fluctuation fields at y^+_ target, using the sampled fluctuations in wall-parallel planes located farther from the wall, at y^+_ input. The data for training and testing is obtained from a direct numerical simulation (DNS) at friction Reynolds numbers Re_τ = 180 and 550. The turbulent velocity-fluctuation fields are sampled at various wall-normal locations, i.e. y^+ = {15, 30, 50, 80, 100, 120, 150}. At Re_τ=550, the FCN can take advantage of the self-similarity in the logarithmic region of the flow and predict the velocity-fluctuation fields at y^+ = 50 using the velocity-fluctuation fields at y^+ = 100 as input with less than 20 in prediction of streamwise-fluctuations intensity. These results are an encouraging starting point to develop a neural-network based approach for modelling turbulence at the wall in numerical simulations.
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