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

Guastoni L, Encinar MP, Schlatter P, Azizpour H, Vinuesa R (2020)

Publication Type: Conference contribution

Publication year: 2020

Publisher: Institute of Physics Publishing

Book Volume: 1522

Conference Proceedings Title: Journal of Physics: Conference Series

Event location: Madrid, ESP

DOI: 10.1088/1742-6596/1522/1/012022

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.

Authors with CRIS profile

Philipp Schlatter

Involved external institutions

Royal Institute of Technology / Kungliga Tekniska Högskolan (KTH) SE Sweden (SE) Universidad Politécnica de Madrid (UPM) ES Spain (ES) Swedish e-Science Research Centre (SeRC) SE Sweden (SE)

How to cite

APA:

Guastoni, L., Encinar, M.P., Schlatter, P., Azizpour, H., & Vinuesa, R. (2020). Prediction of wall-bounded turbulence from wall quantities using convolutional neural networks. In Javier Jimenez (Eds.), Journal of Physics: Conference Series. Madrid, ESP: Institute of Physics Publishing.

MLA:

Guastoni, Luca, et al. "Prediction of wall-bounded turbulence from wall quantities using convolutional neural networks." Proceedings of the 4th Madrid Summer School on Turbulence, Madrid, ESP Ed. Javier Jimenez, Institute of Physics Publishing, 2020.

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