Prediction of wall-bounded turbulence from wall quantities using convolutional neural networks. (April 2020)

• Record Type: Journal Article
• Authors: Guastoni, Luca; Encinar, Miguel P.; Schlatter, Philipp; Azizpour, Hossein; Vinuesa, Ricardo
• Is Part Of: Journal of physics. Volume 1522(2020)
• Publication Date: 2020-04
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1522/1/012022
• Languages: English
• ISSNs: 1742-6588

Abstract

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 Reτ = 180. Various networks are trained for predictions at three inner-scaled locations (y + = 15, 30, 50) and for different time steps between input samples Δt + 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 Δt + 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.