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 best-performing 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 8.8%. The best-performing 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.

翻译：厢式货车与卡车等平背车辆的空气动力学阻力主要源于其低压尾流区，控制该区域对降低油耗至关重要。本文在$Re_W\approx 78,300$条件下开展实验研究，通过在钝体模型后缘布置四个脉冲射流进行主动流动控制。研究采用混合遗传算法（结合全局搜索与基于梯度的局部优化器），在实验闭环设置中确定性能最优的射流驱动参数。成本函数的设计旨在通过同时最小化气动阻力与惩罚驱动能耗来实现净节能。优化过程成功识别出一种控制策略，可实现约8.8%的阻力降低。最优控制律表现为底部射流产生强低频驱动以针对主涡脱落，而顶部与侧向射流则处理更高频率、更低能量的流动现象。粒子图像测速分析表明，尾流显著上移并趋于稳定，导致模型底部基础区域压力大幅恢复。最终，本研究表明无模型优化方法能够成功识别出非直观、多层面的驱动策略，实现显著且能量高效的阻力降低。