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Prediction of wall-bounded turbulence from wall quantities using convolutional neural networks (1912.12969v1)

Published 30 Dec 2019 in physics.flu-dyn, physics.comp-ph, and stat.ML

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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Authors (5)
  1. L. Guastoni (7 papers)
  2. M. P. Encinar (1 paper)
  3. P. Schlatter (20 papers)
  4. H. Azizpour (8 papers)
  5. R. Vinuesa (22 papers)
Citations (31)

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