# Predicting the near-wall region of turbulence through convolutional neural networks

arXiv: https://arxiv.org/abs/2107.07340 · HTML: https://mlsanity.com/paper/2107.07340/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 15 Jul 2021.

## Builds on (most cited first, 2 of 2)

- [Convolutional-network models to predict wall-bounded turbulence from wall quantities](https://mlsanity.com/paper/2006.12483/) (2020)
- [Prediction of wall-bounded turbulence from wall quantities using convolutional neural networks](https://mlsanity.com/paper/1912.12969/) (2019)

## Built on by (most cited first, 4 of 4)

- [Enhancing Computational Fluid Dynamics with Machine Learning](https://mlsanity.com/paper/2110.02085/) (2021)
- [Improving aircraft performance using machine learning: a review](https://mlsanity.com/paper/2210.11481/) (2022)
- [Emerging trends in machine learning for computational fluid dynamics](https://mlsanity.com/paper/2211.15145/) (2022)
- [Patched Flow Matching: Generative Wall-Pressure Reconstruction Beyond Training-Domain Scales from Sparse Sensors](https://mlsanity.com/paper/2606.22084/) (2026)

## Abstract (arXiv)

Modelling the near-wall region of wall-bounded turbulent flows is a widespread practice to reduce the computational cost of large-eddy simulations (LESs) at high Reynolds number. As a first step towards a data-driven wall-model, a neural-network-based approach to predict the near-wall behaviour in a turbulent open channel flow is investigated. The fully-convolutional network (FCN) proposed by Guastoni et al. [preprint, arXiv:2006.12483] is trained to predict the two-dimensional velocity-fluctuation fields at y+rm target, using the sampled fluctuations in wall-parallel planes located farther from the wall, at y+rm input. The data for training and testing is obtained from a direct numerical simulation (DNS) at friction Reynolds numbers Reτ = 180 and 550. The turbulent velocity-fluctuation fields are sampled at various wall-normal locations, i.e. y+ = 15, 30, 50, 80, 100, 120, 150. At Reτ=550, the FCN can take advantage of the self-similarity in the logarithmic region of the flow and predict the velocity-fluctuation fields at y+ = 50 using the velocity-fluctuation fields at y+ = 100 as input with less than 20% error in prediction of streamwise-fluctuations intensity. These results are an encouraging starting point to develop a neural-network based approach for modelling turbulence at the wall in numerical simulations.