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Fully-convolutional neural networks (FCN) were proven to be effective forpredicting the instantaneous state of a fully-developed turbulent flow atdifferent wall-normal locations using quantities measured at the wall. InGuastoni et al. [J. Fluid Mech. 928, A27 (2021)], we focused onwall-shear-stress distributions as input, which are difficult to measure inexperiments. In order to overcome this limitation, we introduce a model thatcan take as input the heat-flux field at the wall from a passive scalar. Fourdifferent Prandtl numbers $Pr = \nu/\alpha = (1,2,4,6)$ are considered (where$\nu$ is the kinematic viscosity and $\alpha$ is the thermal diffusivity of thescalar quantity). A turbulent boundary layer is simulated since accurateheat-flux measurements can be performed in experimental settings: first wetrain the network on aptly-modified DNS data and then we fine-tune it on theexperimental data. Finally, we test our network on experimental data sampled ina water tunnel. These predictions represent the first application of transferlearning on experimental data of neural networks trained on simulations. Thispaves the way for the implementation of a non-intrusive sensing approach forthe flow in practical applications.

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