Bayesian optimisation of an opposition control strategy in a fully turbulent channel flow

The proposed study aims to optimise a real-time opposition control strategy to reduce the intensity of near-wall sweep events by applying a Bayesian optimisation algorithm. The experiments were conducted in a fully turbulent channel flow characterised by a friction Reynolds number of 350. Sweep events were identified using a gradient-based detection technique and controlled via a wall-normal jet. An open-loop control logic was implemented and the control parameters (frequency, voltage amplitude and delay time) were optimised, within the bounds imposed by the experimental set-up, to bring the maximum sweep events intensity reduction up to 54\, \%, with a robust cost function. The effects of the control were observed by analysing the conditionally averaged sweep events at various streamwise locations downstream of the actuation point. Moreover, the conditional analysis was applied to the cross-correlation function of velocity signals highlighting the large reduction of the sweep event convection velocity during the blowing phase of the jet. An overall energy increase has been found in the conditionally averaged energy spectra for the controlled case. The analysis of conditionally averaged wavelet spectra revealed that the control, by interrupting the natural evolution of the sweep event, initially leads to a reduction in the energy associated with it, followed by a subsequent increase during the development of the jet-blowing phase.