  We study the adaptability of deep reinforcement learning (DRL)-based active
flow control (AFC) technology for bluff body flows with complex geometries. It
is extended from a cylinder with Ar=1 to a flat elliptical cylinder with Ar=2,
slender elliptical cylinders with Ar less than 1, and a flat plate with Ar=0.
The robustness and adaptability of DRL-based control technology will be
assessed under varying levels of flow instability around bluff bodies. We
utilize the Proximal Policy Optimization (PPO) algorithm to precisely control
the mass flow rates of synthetic jets located on the upper and lower surfaces
of a cylinder to achieve reduction in drag, minimization of lift, and
suppression of vortex shedding. Our research findings indicate that, for
elliptical cylinders with Ar between 1.75 and 0.75, the reduction in drag
coefficient ranges from 0.9% to 15.7%, and the reduction in lift coefficient
ranges from 95.2% to 99.7%. The DRL-based control strategy not only
significantly reduces lift and drag, but also completely suppresses vortex
shedding while using less than 1% of external excitation energy, demonstrating
its efficiency and energy-saving capabilities. Additionally, for Ar from 0.5 to
0, the reduction in drag coefficient ranges from 26.9% to 43.6%, and the
reduction in lift coefficient from 50.2% to 68.0%. This reflects the control
strategy's significant reduction in both drag and lift coefficients, while also
alleviating vortex shedding. The interaction and nonlinear development of
vortices in the wake of elliptical cylinders lead to complex flow instability,
and DRL-based AFC technology shows adaptability and potential in addressing
flow control problems for this type of bluff body flow.
