Reducing base drag on road vehicles using pulsed jets optimized by hybrid genetic algorithms

Aerodynamic drag on flat-backed vehicles like vans and trucks is dominated by a low-pressure wake, whose control is critical for reducing fuel consumption. This paper presents an experimental study at $Re_W\approx 78,300$ on active flow control using four pulsed jets at the rear edges of a bluff body model. A hybrid genetic algorithm, combining a global search with a local gradient-based optimizer, was used to determine the optimal jet actuation parameters in an experiment-in-the-loop setup. The cost function was designed to achieve a net energy saving by simultaneously minimizing aerodynamic drag and penalizing the actuation's energy consumption. The optimization campaign successfully identified a control strategy that yields a drag reduction of approximately 10%. The optimal control law features a strong, low-frequency actuation from the bottom jet, which targets the main vortex shedding, while the top and lateral jets address higher-frequency, less energetic phenomena. Particle Image Velocimetry analysis reveals a significant upward shift and stabilization of the wake, leading to substantial pressure recovery on the model's lower base. Ultimately, this work demonstrates that a model-free optimization approach can successfully identify non-intuitive, multi-faceted actuation strategies that yield significant and energetically efficient drag reduction.

Fast and robust parametric and functional learning with Hybrid Genetic Optimisation (HyGO)

The Hybrid Genetic Optimisation framework (HyGO) is introduced to meet the pressing need for efficient and unified optimisation frameworks that support both parametric and functional learning in complex engineering problems. Evolutionary algorithms are widely employed as derivative-free global optimisation methods but often suffer from slow convergence rates, especially during late-stage learning. HyGO integrates the global exploration capabilities of evolutionary algorithms with accelerated local search for robust solution refinement. The key enabler is a two-stage strategy that balances exploration and exploitation. For parametric problems, HyGO alternates between a genetic algorithm and targeted improvement through a degradation-proof Dowhill Simplex Method (DSM). For function optimisation tasks, HyGO rotates between genetic programming and DSM. Validation is performed on (a) parametric optimisation benchmarks, where HyGO demonstrates faster and more robust convergence than standard genetic algorithms, and (b) function optimisation tasks, including control of a damped Landau oscillator. Practical relevance is showcased through aerodynamic drag reduction of an Ahmed body via Reynolds-Averaged Navier-Stokes simulations, achieving consistently interpretable results and reductions exceeding 20% by controlled jet injection in the back of the body for flow reattachment and separation bubble reduction. Overall, HyGO emerges as a versatile hybrid optimisation framework suitable for a broad spectrum of engineering and scientific problems involving parametric and functional learning.