Prediction of wall-bounded turbulence from wall quantities using convolutional neural networks

• URL: http://arxiv.org/abs/1912.12969v1
• Date: Mon, 30 Dec 2019 15:34:41 GMT
• Title: Prediction of wall-bounded turbulence from wall quantities using convolutional neural networks
• Authors: L. Guastoni, M. P. Encinar, P. Schlatter, H. Azizpour, R. Vinuesa
• Abstract summary: A fully-convolutional neural-network model is used to predict the streamwise velocity fields at several wall-normal locations. Various networks are trained for predictions at three inner-scaled locations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A fully-convolutional neural-network model is used to predict the streamwise velocity fields at several wall-normal locations by taking as input the streamwise and spanwise wall-shear-stress planes in a turbulent open channel flow. The training data are generated by performing a direct numerical simulation (DNS) at a friction Reynolds number of $Re_{\tau}=180$. Various networks are trained for predictions at three inner-scaled locations ($y^+ = 15,~30,~50$) and for different time steps between input samples $\Delta t^{+}_{s}$. The inherent non-linearity of the neural-network model enables a better prediction capability than linear methods, with a lower error in both the instantaneous flow fields and turbulent statistics. Using a dataset with higher $\Delta t^+_{s}$ improves the generalization at all the considered wall-normal locations, as long as the network capacity is sufficient to generalize over the dataset. The use of a multiple-output network, with parallel dedicated branches for two wall-normal locations, does not provide any improvement over two separated single-output networks, other than a moderate saving in training time. Training time can be effectively reduced, by a factor of 4, via a transfer learning method that initializes the network parameters using the optimized parameters of a previously-trained network.

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