Jet mixing enhancement using machine learning control
Résumé
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. 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 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 furtherâcontrary 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.