Fully convolutional networks for velocity-field predictions based on the wall heat flux in turbulent boundary layers

Guastoni L, Balasubramanian AG, Foroozan F, Güemes A, Ianiro A, Discetti S, Schlatter P, Azizpour H, Vinuesa R (2025)

Publication Type: Journal article

Publication year: 2025

Journal

Theoretical and Computational Fluid Dynamics Springer Verlag (Germany)

Book Volume: 39

Article Number: 13

Journal Issue: 1

DOI: 10.1007/s00162-024-00732-y

Abstract

Fully-convolutional neural networks (FCN) were proven to be effective for predicting the instantaneous state of a fully-developed turbulent flow at different wall-normal locations using quantities measured at the wall. In Guastoni et al. (J Fluid Mech 928:A27, 2021. https://doi.org/10.1017/jfm.2021.812), we focused on wall-shear-stress distributions as input, which are difficult to measure in experiments. In order to overcome this limitation, we introduce a model that can take as input the heat-flux field at the wall from a passive scalar. Four different Prandtl numbers Pr=ν/α=(1,2,4,6) are considered (where ν is the kinematic viscosity and α is the thermal diffusivity of the scalar quantity). A turbulent boundary layer is simulated since accurate heat-flux measurements can be performed in experimental settings: first we train the network on aptly-modified DNS data and then we fine-tune it on the experimental data. Finally, we test our network on experimental data sampled in a water tunnel. These predictions represent the first application of transfer learning on experimental data of neural networks trained on simulations. This paves the way for the implementation of a non-intrusive sensing approach for the flow in practical applications.

Authors with CRIS profile

Philipp Schlatter

Involved external institutions

Royal Institute of Technology / Kungliga Tekniska Högskolan (KTH) SE Sweden (SE) Universidad Carlos III de Madrid (UC3M) ES Spain (ES) Swedish e-Science Research Centre (SeRC) SE Sweden (SE)

How to cite

APA:

Guastoni, L., Balasubramanian, A.G., Foroozan, F., Güemes, A., Ianiro, A., Discetti, S.,... Vinuesa, R. (2025). Fully convolutional networks for velocity-field predictions based on the wall heat flux in turbulent boundary layers. Theoretical and Computational Fluid Dynamics, 39(1). https://doi.org/10.1007/s00162-024-00732-y

MLA:

Guastoni, Luca, et al. "Fully convolutional networks for velocity-field predictions based on the wall heat flux in turbulent boundary layers." Theoretical and Computational Fluid Dynamics 39.1 (2025).

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