mlsanityData to 7 Oct 2026

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

arXiv 2006.12483 ↗First posted 22 Jun 2020physics.flu-dyn physics.comp-ph stat.MLcited by 46

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Abstract (arXiv)

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 Reτ = 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 Reτ=180 to initialize those of the Reτ=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.

Key facts. Cited by 46 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Jun 2020.

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Citing papers per year: 2021: 10; 2022: 8; 2023: 9; 2024: 6; 2025: 6; 2026: 7.

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