We improve convergence speed by two orders of magnitude and the global exploration capabilities of particle swarm optimization (PSO) through targeted position-mutated elitism (TPME). The proposed fast-converging TPME operator requires a fitness-based classification technique to categorize the particles. The introduced classification is motivated by its simplicity, low memory requirements, and automated termination criteria based on convergence. The three key innovations address particle classification, elitism, and mutation in the cognitive and social model. PSO-TPME is benchmarked against five popular PSO variants for multi-dimensional functions, which are extensively adopted in the optimization field, In particular, the convergence accuracy, convergence speed, and the capability to find global minima are investigated. The statistical error is assessed by numerous repetitions. The simulations confirmed that in ten of the thirteen investigated functions, the proposed PSO variant outperforms other variants in terms of convergence rate and accuracy by at least two orders of magnitude. On the other hand, the simulations demonstrated the early exploration capabilities of PSO-TPME in all tested functions. In the first ten iterations, PSO-TPME outperformed all the investigated PSO variants by at least two orders of magnitude.

We optimize jet mixing using large eddy simulations (LES) at a Reynolds number of $3000$ . Key methodological enablers consist of Bayesian optimization, a surrogate model enhanced by deep learning and persistent data topology for physical interpretation. The mixing performance is characterized by an equivalent jet radius ( $R_{eq}$ ) derived from the streamwise velocity in a plane located $8$ diameters downstream. The optimization is performed in a 22-dimensional actuation space that comprises most known excitations. This search space parameterizes the distributed actuation imposed on the bulk flow and at the periphery of the nozzle in the streamwise and radial directions. The momentum flux measures the energy input of the actuation. The optimization quadruples the jet radius $R_{eq}$ with a $7$ -armed blooming jet after around $570$ evaluations. The control input requires $2\,\%$ momentum flux of the main flow, which is one order of magnitude lower than an ad hoc dual-mode excitation. Intriguingly, a pronounced suboptimum in the search space is associated with a double-helix jet, a new flow pattern. This jet pattern results in a mixing improvement comparable to the blooming jet. A state-of-the-art Bayesian optimization converges towards this double-helix solution. The learning is accelerated and converges to another better optimum by including a deep-learning-enhanced surrogate model trained along the optimization. Persistent data topology extracts the global and many local minima in the actuation space. These minima can be identified with flow patterns beneficial to the mixing.

In this work, we report on a closed-loop flow control strategy that consistently reduces the drag of a D-shaped bluff body under variable freestream velocity conditions. The control strategy is guided by open-loop tests with pulsed Coanda blowing at two freestream velocities that yield optimal frequencies (Strouhal number of 0.33 and 1.3), which reduce the drag by up to 40%. The strong correlation between drag coefficient (C_d) and the wake fluctuations is exploited for the feedback signal, where a microphone signal is used to measure the pressure fluctuations at the model base. The results demonstrate the ability to perform accurate and robust $H_{\infty }$ -based control for drag reduction using solely the wake pressure fluctuations at the model base as feedback signal. The robust control strategy at constant freestream velocity is shown to improve output stability and enhance performance in terms of settling time, even when employing simple models of the flow response with large uncertainty. Building on that success, an $H_{\infty }$ -based linear parameter varying controller is designed and implemented to reduce drag under free stream variations and/or fluctuations. Similarly, the results demonstrate improved robustness and performance enhancements.