Sensing the turbulent large-scale motions with their wall signature

Güemes, A.; Discetti, Stefano; Ianiro, Andrea

doi:10.1063/1.5128053

Cited by 40 publications

(35 citation statements)

References 65 publications

(48 reference statements)

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“…On the other hand, FCN-POD applies POD only on the output flow fields, using the spatial modes from the training set and predicting the corresponding temporal coefficients using a neural network. This is a development of the model used by Güemes et al (2019), note, however, that, in that study, the domain employed and reconstructed provided a compact POD eigenspectrum, here, the availability of a larger domain in the streamwise and spanwise directions spreads the energy content over a wider set of POD modes. This makes the predictions of temporal coefficients more difficult, especially those associated with the least energetic modes.…”

Section: Summary Of the Methodsmentioning

confidence: 99%

“…The FCN-POD model is built upon the previous work by Güemes et al (2019). In the present work a different neural-network architecture, a fully convolutional one, is used instead of a CNN.…”

Section: Pod-based Predictions With Convolutional Neural Networkmentioning

confidence: 99%

“…In this work, all the velocity components are predicted and a higher Re is investigated as well. Furthermore, in the work by Güemes, Discetti & Ianiro (2019) the information of the most-energetic scales was encoded into a POD basis, and a CNN was used to predict that information at different wall-normal locations from streamwise wall-shear-stress measurements. Their results demonstrated that CNNs can significantly outperform linear methods in the prediction of POD time coefficients for low-order reconstruction of the velocity fields.…”

Section: Introductionmentioning

confidence: 99%

“…The methods proposed by Guastoni et al (2020) and Güemes et al (2019), henceforth referred to as fully convolutional network (FCN) and FCN-POD respectively, are extended in the present study. Both models are able to provide a nonlinear characterization of the relation between wall features and the flow on wall-parallel planes.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Summary Of the Methodsmentioning

confidence: 99%

Section: Pod-based Predictions With Convolutional Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…In recent years, machine learning, especially deep learning (LeCun, Bengio & Hinton 2015), has shown remarkable performance. Güemes, Discetti & Ianiro (2019) applied an extended POD and convolutional neural networks, respectively, to reconstruct large- and very large-scale motions in a turbulent channel flow based on the wall shear stress measurement, and showed that the convolutional neural networks performed significantly better than the extended POD. Kim & Lee (2020) used a nine-layer convolutional neural network (CNN) to predict the heat flux at the wall using wall variables (, and ), and showed that the CNN outperformed a linear regression.…”

Section: Introductionmentioning

confidence: 99%