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We address a challenge of active flow control: the optimization of manyactuation parameters guaranteeing fast convergence and avoiding suboptimallocal minima. This challenge is addressed by a new optimizer, calledexplorative gradient method (EGM). EGM alternatively performs one exploitivedownhill simplex step and an explorative Latin hypercube sampling iteration.Thus, the convergence rate of a gradient based method is guaranteed while, atthe same time, better minima are explored. For an analytical multi-modal testfunction, EGM is shown to significantly outperform the downhill simplex method,the random restart variant, Latin hypercube sampling, Monte Carlo iterationsand the genetic algorithm. EGM is applied to minimize the net drag power of thetwo-dimensional fluidic pinball benchmark with three cylinder rotations asactuation parameters. The net drag power is reduced by $40\%$ employing directnumerical simulations at a Reynolds number of $100$ based on the cylinderdiameter. This optimal actuation leads to $98\%$ drag reduction employingCoanda forcing for boat tailing and partial stabilization of vortex shedding.The price is an actuation energy corresponding to $58\%$ of the unforcedparasitic drag power. EGM is also used to minimize drag of the $35^\circ$slanted Ahmed body employing distributed steady blowing with 10 inputs. $17\%$drag reduction are achieved using Reynolds-Averaged Navier-Stokes simulations(RANS) at the Reynolds number $Re_H=1.9 \times 10^5$ based on the height of theAhmed body. The optimal actuation emulates boat tailing by inward-directedblowing with velocities which are comparable to the oncoming velocity. Weexpect that EGM will be employed as efficient optimizer in many future activeflow control plants.

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