  An artificial intelligence (AI) control system is developed to maximize the
mixing rate of a turbulent jet. This system comprises six independently
operated unsteady minijet actuators, two hot-wire sensors placed in the jet,
and genetic programming for the unsupervised learning of a near-optimal control
law. The ansatz of this law includes multi-frequency open-loop forcing,
sensor-feedback and nonlinear combinations thereof. Mixing performance is
quantified by the decay rate of the centreline mean velocity of jet.
Intriguingly, the learning process of AI control discovers the classical
forcings, i.e. axisymmetric, helical and flapping achievable from conventional
control techniques, one by one in the order of increased performance, and
finally converges to a hitherto unexplored forcing. Careful examination of the
control landscape unveils typical control laws, generated in the learning
process, and their evolutions. The best AI forcing produces a complex turbulent
flow structure that is characterized by periodically generated mushroom
structures, helical motion and oscillating jet column, all enhancing the mixing
rate and vastly outperforming others. Being never reported before, this flow
structure is examined in various aspects, including the velocity spectra, mean
and fluctuating velocity fields and their downstream evolution, and flow
visualization images in three orthogonal planes, all compared with other
classical flow structures. Along with the knowledge of the minijet-produced
flow and its effect on the initial condition of the main jet, these aspects
cast valuable insight into the physics behind the highly effective mixing of
this newly found flow structure. The results point to the great potential of AI
in conquering the vast opportunity space of control laws for many actuators and
sensors and in optimizing turbulence.
