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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Bibliographic Details
Published in:	Journal of fluid mechanics Vol. 928
Main Authors:	Guastoni, Luca, Güemes, Alejandro, Ianiro, Andrea, Discetti, Stefano, Schlatter, Philipp, Azizpour, Hossein, Vinuesa, Ricardo
Format:	Journal Article
Language:	English
Published:	Cambridge, UK Cambridge University Press 10.12.2021
Subjects:	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
Online Access:	Get full text
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