Convolutional-network models to predict wall-bounded turbulence from wall quantities. (10th December 2021) - Guastoni, Luca; Güemes, Alejandro; Ianiro, Andrea; Discetti, Stefano; Schlatter, Philipp; Azizpour, Hossein; Vinuesa, Ricardo
Abstract: Abstract : Two models based on convolutional neural networks are trained to predict the two-dimensional instantaneous velocity-fluctuation fields at different wall-normal locations in a turbulent open-channel flow, using the wall-shear-stress components and the wall pressure as inputs. The first model is a fully convolutional neural network (FCN) which directly predicts the fluctuations, while the second one reconstructs the flow fields using a linear combination of orthonormal basis functions, obtained through proper orthogonal decomposition (POD), and is hence named FCN-POD. Both models are trained using data from direct numerical simulations at friction Reynolds numbers $Re_{τ } = 180$ and 550. Being able to predict the nonlinear interactions in the flow, both models show better predictions than the extended proper orthogonal decomposition (EPOD), which establishes a linear relation between the input and output fields. The performance of the models is compared based on predictions of the instantaneous fluctuation fields, turbulence statistics and power-spectral densities. FCN exhibits the best predictions closer to the wall, whereas FCN-POD provides better predictions at larger wall-normal distances. We also assessed the feasibility of transfer learning for the FCN model, using the model parameters learned from the $Re_{τ }=180$ dataset to initialize those of the model that is trained on the $Re_{τ }=550$ dataset. After training the initialized model at the new $Re_{τ }$, our results indicate the possibility of matching the reference-model performance up to $y^{+}=50$, with $50\, %$ and $25\, %$ of the original training data. We expect that these non-intrusive sensing models will play an important role in applications related to closed-loop control of wall-bounded turbulence.