Reducing base drag on road vehicles using pulsed jets optimized by hybrid genetic algorithms
- URL: http://arxiv.org/abs/2510.26718v1
- Date: Thu, 30 Oct 2025 17:20:44 GMT
- Title: Reducing base drag on road vehicles using pulsed jets optimized by hybrid genetic algorithms
- Abstract summary: 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.<n>A model-free optimization approach can successfully identify non-intuitive, multi-faceted actuation strategies that yield significant and energetically efficient drag reduction.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- 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 $Re_W\approx 78,300$ 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.
Related papers
- High-lift Wing Separation Control via Bayesian Optimization and Deep Reinforcement Learning [2.5104533767972974]
Two optimization strategies are explored: open-loop Bayesian optimization (BO) and closed-loop deep reinforcement learning (DRL)<n>BO successfully identified steady jet velocities that increased efficiency by +10.9% through a -9.7% drag reduction while maintaining lift.<n>DRL agent, despite leveraging instantaneous flow information from distributed sensors, achieved only minor improvements in lift and drag, with negligible efficiency gain.
arXiv Detail & Related papers (2026-05-12T11:34:46Z) - Optimal control of differentially flat underactuated planar robots in the perspective of oscillation mitigation [0.0]
Underactuated robots are characterized by a larger number of degrees of freedom than actuators.<n>This structural property enables the development of lightweight and cost-effective robotic systems with enhanced dexterity.
arXiv Detail & Related papers (2026-03-16T16:55:11Z) - A 26-Gram Butterfly-Inspired Robot Achieving Autonomous Tailless Flight [61.21669716392821]
textitAirPulse is a 26-gram butterfly-inspired robot that achieves the first onboard, closed-loop controlled flight for a tailless two-winged platform at this scale.<n>It replicates key biomechanical traits of butterfly flight, utilizing low-aspect-ratio, compliant carbon-fiber-reinforced wings and low-frequency flapping that reproduces characteristic biological body undulations.
arXiv Detail & Related papers (2026-02-06T15:58:50Z) - Physics-informed Neural-operator Predictive Control for Drag Reduction in Turbulent Flows [109.99020160824553]
We propose an efficient deep reinforcement learning framework for modeling and control of turbulent flows.<n>It is model-based RL for predictive control (PC), where both the policy and the observer models for turbulence control are learned jointly.<n>We find that PINO-PC achieves a drag reduction of 39.0% under a bulk-velocity Reynolds number of 15,000, outperforming previous fluid control methods by more than 32%.
arXiv Detail & Related papers (2025-10-03T00:18:26Z) - Optimizing Diffusion Models for Joint Trajectory Prediction and Controllable Generation [49.49868273653921]
Diffusion models are promising for joint trajectory prediction and controllable generation in autonomous driving.
We introduce Optimal Gaussian Diffusion (OGD) and Estimated Clean Manifold (ECM) Guidance.
Our methodology streamlines the generative process, enabling practical applications with reduced computational overhead.
arXiv Detail & Related papers (2024-08-01T17:59:59Z) - Airport take-off and landing optimization through genetic algorithms [55.2480439325792]
This research addresses the crucial issue of pollution from aircraft operations, focusing on optimizing both gate allocation and runway scheduling simultaneously.
The study presents an innovative genetic algorithm-based method for minimizing pollution from fuel combustion during aircraft take-off and landing at airports.
arXiv Detail & Related papers (2024-02-29T14:53:55Z) - A GOA-Based Fault-Tolerant Trajectory Tracking Control for an Underwater
Vehicle of Multi-Thruster System without Actuator Saturation [9.371458775465825]
This paper proposes an intelligent fault-tolerant control (FTC) strategy to tackle the trajectory tracking problem of an underwater vehicle (UV) under thruster damage (power loss) cases.
In the proposed control strategy, the trajectory tracking component is formed by a refined backstepping algorithm that controls the velocity variation and a sliding mode control deducts the torque/force outputs.
arXiv Detail & Related papers (2023-01-04T21:30:16Z) - Driver Assistance Eco-driving and Transmission Control with Deep
Reinforcement Learning [2.064612766965483]
In this paper, a model-free deep reinforcement learning (RL) control agent is proposed for active Eco-driving assistance.
It trades-off fuel consumption against other driver-accommodation objectives, and learns optimal traction torque and transmission shifting policies from experience.
It shows superior performance in minimizing fuel consumption compared to a baseline controller that has full knowledge of fuel-efficiency tables.
arXiv Detail & Related papers (2022-12-15T02:52:07Z) - A Hybrid Tracking Control Strategy for an Unmanned Underwater Vehicle
Aided with Bioinspired Neural Dynamics [14.66072990853587]
This paper presents a novel hybrid control strategy for an unmanned underwater vehicle (UUV) based on a bioinspired neural dynamics model.
An enhanced backstepping kinematic control strategy is first developed to avoid sharp velocity jumps and provides smooth velocity commands.
Then, a novel sliding mode control is proposed, which is capable of providing smooth and continuous torque commands.
arXiv Detail & Related papers (2022-09-03T19:18:54Z) - Controlled Sparsity via Constrained Optimization or: How I Learned to
Stop Tuning Penalties and Love Constraints [81.46143788046892]
We focus on the task of controlling the level of sparsity when performing sparse learning.
Existing methods based on sparsity-inducing penalties involve expensive trial-and-error tuning of the penalty factor.
We propose a constrained formulation where sparsification is guided by the training objective and the desired sparsity target in an end-to-end fashion.
arXiv Detail & Related papers (2022-08-08T21:24:20Z) - Unified Automatic Control of Vehicular Systems with Reinforcement
Learning [64.63619662693068]
This article contributes a streamlined methodology for vehicular microsimulation.
It discovers high performance control strategies with minimal manual design.
The study reveals numerous emergent behaviors resembling wave mitigation, traffic signaling, and ramp metering.
arXiv Detail & Related papers (2022-07-30T16:23:45Z) - Motion Planning and Control for Multi Vehicle Autonomous Racing at High
Speeds [100.61456258283245]
This paper presents a multi-layer motion planning and control architecture for autonomous racing.
The proposed solution has been applied on a Dallara AV-21 racecar and tested at oval race tracks achieving lateral accelerations up to 25 $m/s2$.
arXiv Detail & Related papers (2022-07-22T15:16:54Z) - Optimizing Airborne Wind Energy with Reinforcement Learning [0.0]
Reinforcement Learning is a technique that learns to associate observations with profitable actions without requiring prior knowledge of the system.
We show that in a simulated environment Reinforcement Learning finds an efficient way to control a kite so that it can tow a vehicle for long distances.
arXiv Detail & Related papers (2022-03-27T10:28:16Z) - Safe Model-based Off-policy Reinforcement Learning for Eco-Driving in
Connected and Automated Hybrid Electric Vehicles [3.5259944260228977]
This work proposes a Safe Off-policy Model-Based Reinforcement Learning algorithm for the eco-driving problem.
The proposed algorithm leads to a policy with a higher average speed and a better fuel economy compared to the model-free agent.
arXiv Detail & Related papers (2021-05-25T03:41:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.