Fully convolutional networks for velocity-field predictions based on the wall heat flux in turbulent boundary layers - Guastoni, Luca; Balasubramanian, Arivazhagan G.; Foroozan, Firoozeh; Güemes, Alejandro; Ianiro, Andrea; Discetti, Stefano; Schlatter, Philipp; Azizpour, Hossein; Vinuesa, Ricardo (Theoretical and Computational Fluid Dynamics, 2025)
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.
CRIS-Team Scopus Importer:2025-02-17
Keywords: Machine learning; Turbulence simulation; Turbulent boundary layers