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

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 ful...

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Veröffentlicht in:Journal of fluid mechanics Jg. 928
Hauptverfasser: Guastoni, Luca, Güemes, Alejandro, Ianiro, Andrea, Discetti, Stefano, Schlatter, Philipp, Azizpour, Hossein, Vinuesa, Ricardo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cambridge, UK Cambridge University Press 10.12.2021
Schlagworte:
Artificial neural networks
Basis functions
Channel flow
Datasets
Decomposition
Direct numerical simulation
Feasibility studies
Fields
Fluid flow
Fluid mechanics
JFM Papers
Mathematical models
Neural networks
Open channel flow
Predictions
Proper Orthogonal Decomposition
Reynolds number
Statistical methods
Training
Transfer learning
Turbulence
turbulence simulation
Wall pressure
turbulence simulation
ISSN:0022-1120, 1469-7645, 1469-7645
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Zusammenfassung: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 $Re_{\tau } = 180$ 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 $Re_{\tau }=180$ dataset to initialize those of the model that is trained on the $Re_{\tau }=550$ dataset. After training the initialized model at the new $Re_{\tau }$, 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.
Bibliographie:ObjectType-Article-1
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ISSN:0022-1120
1469-7645
1469-7645
DOI:10.1017/jfm.2021.812