Reducing base drag on road vehicles using pulsed jets optimized by hybrid genetic algorithms
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arXiv
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| Format: | Preprint |
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2025
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| author | Robledo, Isaac Alfaro, Juan Duro, Víctor Solera-Rico, Alberto Castellanos, Rodrigo Vila, Carlos Sanmiguel |
| author_facet | Robledo, Isaac Alfaro, Juan Duro, Víctor Solera-Rico, Alberto Castellanos, Rodrigo Vila, Carlos Sanmiguel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_26718 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reducing base drag on road vehicles using pulsed jets optimized by hybrid genetic algorithms Robledo, Isaac Alfaro, Juan Duro, Víctor Solera-Rico, Alberto Castellanos, Rodrigo Vila, Carlos Sanmiguel Fluid Dynamics Neural and Evolutionary Computing Optimization and Control 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. |
| title | Reducing base drag on road vehicles using pulsed jets optimized by hybrid genetic algorithms |
| topic | Fluid Dynamics Neural and Evolutionary Computing Optimization and Control |
| url | https://arxiv.org/abs/2510.26718 |