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

2025-10-30

AI Summary

Bluff-body trucks suffer from high aerodynamic drag due to a low-pressure wake beneath the rear underbody. Method: This study proposes a pulsed-jet-based active flow control strategy, employing four pulsed jets positioned at the rear edge. A composite cost function—balancing drag reduction and energy consumption—is formulated using PIV measurements and a closed-loop experimental optimization platform. A novel model-free hybrid genetic algorithm is introduced, integrating global search with local gradient-based optimization to solve multi-frequency cooperative control laws in real time. Contribution/Results: Low-frequency, high-amplitude excitation effectively suppresses primary vortex shedding, lifts and stabilizes the wake, and enhances base pressure recovery—achieving ~10% drag reduction with net positive energy gain. This work reveals, for the first time, a non-intuitive multi-frequency cooperative control mechanism and establishes an engineering-feasible paradigm for active flow control toward energy-efficient commercial vehicles.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Intelligent Robots: Behavior Learning & Control

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• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

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 $Re_Wapprox 78,300$ 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.

Problem

• Optimizing pulsed jets to reduce aerodynamic drag on bluff vehicles
• Developing hybrid genetic algorithm for energy-efficient flow control
• Achieving net energy savings through wake stabilization and drag reduction

Innovation

• Hybrid genetic algorithms optimize pulsed jet parameters
• Active flow control reduces drag by 10 percent
• Model-free approach identifies non-intuitive actuation strategies