Convolutional-network models to predict wall-bounded turbulence from wall quantities

• URL: http://arxiv.org/abs/2006.12483v1
• Date: Mon, 22 Jun 2020 17:57:40 GMT
• Title: Convolutional-network models to predict wall-bounded turbulence from wall quantities
• Authors: L. Guastoni, A. G\"uemes, A.Ianiro, S. Discetti, P. Schlatter, H. Azizpour, R. Vinuesa
• Abstract summary: Two models are trained to predict the two-dimensional velocity-fluctuation fields at different wall-normal locations in a turbulent open channel flow. The first model is a fully-convolutional neural network (FCN) which directly predicts the fluctuations. The second one reconstructs the flow fields using a linear combination of orthonormal basis functions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Two models based on convolutional neural networks are trained to predict the two-dimensional 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), hence named FCN-POD. Both models are trained using data from two direct numerical simulations (DNS) at friction Reynolds numbers $Re_{\tau} = 180$ and $550$. Thanks to their ability to predict the nonlinear interactions in the flow, both models show a better prediction performance than the extended proper orthogonal decomposition (EPOD), which establishes a linear relation between input and output fields. The performance of the various models is compared based on predictions of the instantaneous fluctuation fields, turbulence statistics and power-spectral densities. The FCN exhibits the best predictions closer to the wall, whereas the FCN-POD model provides better predictions at larger wall-normal distances. We also assessed the feasibility of performing transfer learning for the FCN model, using the weights from $Re_{\tau}=180$ to initialize those of the $Re_{\tau}=550$ case. Our results indicate that it is possible to obtain a performance similar to that of the reference model up to $y^{+}=50$, with $50\%$ and $25\%$ of the original training data. These non-intrusive sensing models will play an important role in applications related to closed-loop control of wall-bounded turbulence.

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