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@article{faucris.308916596,
abstract = {The objective of this study is to assess the capability of convolution-based neural networks to predict the wall quantities in a turbulent open channel flow, starting from measurements within the flow. Gradually approaching the wall, the first tests are performed by training a fully-convolutional network (FCN) to predict the two-dimensional velocity-fluctuation fields at the inner-scaled wall-normal location ytarget^{+}, using the sampled velocity fluctuations in wall-parallel planes located farther from the wall, at yinput^{+}. The predictions from the FCN are compared against the predictions from a proposed R-Net architecture as a part of the network investigation study. Since the R-Net model is found to perform better than the FCN model, the former architecture is optimized to predict the two-dimensional streamwise and spanwise wall-shear-stress components and the wall pressure from the sampled velocity-fluctuation fields farther from the wall. The data for training and testing is obtained from direct numerical simulation (DNS) of open channel flow at friction Reynolds numbers Reτ=180 and 550. The turbulent velocity-fluctuation fields are sampled at various inner-scaled wall-normal locations, i.e. y^{+}={15,30,50,100,150}, along with the wall-shear stress and the wall pressure. At Reτ=550, both FCN and R-Net 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 about 10% error in prediction of streamwise-fluctuations intensity. Further, the network model trained in this work is also able to predict the wall-shear-stress and wall-pressure fields using the velocity-fluctuation fields at y^{+}=50 with around 10% error in the intensity of the corresponding fluctuations at both Reτ=180 and 550. These results are an encouraging starting point to develop neural-network-based approaches for modelling turbulence near the wall in numerical simulations, especially large-eddy simulations (LESs).},
author = {Balasubramanian, A. G. and Guastoni, L. and Schlatter, Philipp and Azizpour, H. and Vinuesa, R.},
doi = {10.1016/j.ijheatfluidflow.2023.109200},
faupublication = {yes},
journal = {International Journal of Heat and Fluid Flow},
keywords = {Deep learning; Fully convolutional network; Self-similarity; Turbulent channel flow; Wall-shear stress},
note = {CRIS-Team Scopus Importer:2023-08-11},
peerreviewed = {Yes},
title = {{Predicting} the wall-shear stress and wall pressure through convolutional neural networks},
volume = {103},
year = {2023}
}