Explorative gradient method for active drag reduction of the fluidic pinball and slanted Ahmed body

We address a challenge of active flow control: the optimization many actuation parameters guaranteeing fast convergence and avoiding suboptimal local minima. This is addressed by new optimizer, called explorative gradient method (EGM). EGM alternatively performs one exploitive downhill simplex step an Latin hypercube sampling iteration. Thus, rate based guaranteed while, at same time, better minima are explored. For analytical multi-modal test function, shown to significantly outperform method, random restart simplex, sampling, Monte Carlo genetic algorithm. applied minimize net drag power two-dimensional fluidic pinball benchmark with three cylinder rotations as parameters. The reduced 29 % employing direct numerical simulations Reynolds number $100$ on diameter. optimal leads 52 reduction Coanda forcing for boat tailing partial stabilization vortex shedding. price energy corresponding 23 unforced parasitic power. also used $35^\circ$ slanted Ahmed body distributed steady blowing 10 inputs. 17 achieved using Reynolds-averaged Navier–Stokes $Re_H=1.9 \times 10^5$ height body. wake controlled seven jet-slot actuators all trailing edges. Symmetric operation corresponds five independent actuator groups top, middle, bottom, top sides bottom sides. Each slot produces uniform jet velocity angle free parameters, yielding emulates inward-directed velocities which comparable oncoming velocity. expect that will be employed efficient optimizer in future control plants alternative or augmentation pure search methods.