  We experimentally optimize mixing of a turbulent round jet using machine
learning control (MLC) following Li et al (2017). The jet is manipulated with
one unsteady minijet blowing in wall-normal direction close to the nozzle exit.
The flow is monitored with two hotwire sensors. The first sensor is positioned
on the centerline 5 jet diameters downstream of the nozzle exit, i.e. the end
of the potential core, while the second is located 3 jet diameters downstream
and displaced towards the shear-layer. The mixing performance is monitored with
mean velocity at the first sensor. A reduction of this velocity correlates with
increased entrainment near the potential core. Machine Learning Control (MLC)
is employed to optimize sensor feedback, a general open-loop broadband
frequency actuation and combinations of both. MLC has identified the optimal
periodic forcing with a small duty cycle as the best control policy employing
only 400 actuation measurements, each lasting for 5 seconds. This learning rate
is comparable if not faster than typical optimization of periodic forcing with
two free parameters (frequency and duty cycle). In addition, MLC results
indicate that neither new frequencies nor sensor feedback improves mixing
further-contrary to many of other turbulence control experiments. The
optimality of pure periodic actuation may be attributed to the simple jet
flapping mechanism in the minijet plane. The performance of sensor feedback is
shown to face a challenge for small duty cycles. The jet mixing results
demonstrate the untapped potential of MLC in quickly learning optimal general
control policies, even deciding between open- and closed-loop control.
