  The convective heat transfer in a turbulent boundary layer (TBL) on a flat
plate is enhanced using an artificial intelligence approach based on linear
genetic algorithms control (LGAC). The actuator is a set of six slot jets in
crossflow aligned with the freestream. An open-loop optimal periodic forcing is
defined by the carrier frequency, the duty cycle and the phase difference
between actuators as control parameters. The control laws are optimised with
respect to the unperturbed TBL and to the actuation with a steady jet. The cost
function includes the wall convective heat transfer rate and the cost of the
actuation. The performance of the controller is assessed by infrared
thermography and characterised also with particle image velocimetry
measurements. The optimal controller yields a slightly asymmetric flow field.
The LGAC algorithm converges to the same frequency and duty cycle for all the
actuators. It is noted that such frequency is strikingly equal to the inverse
of the characteristic travel time of large-scale turbulent structures advected
within the near-wall region. The phase difference between multiple jet
actuation has shown to be very relevant and the main driver of flow asymmetry.
The results pinpoint the potential of machine learning control in unravelling
unexplored controllers within the actuation space. Our study furthermore
demonstrates the viability of employing sophisticated measurement techniques
together with advanced algorithms in an experimental investigation.
