Drag Reduction Through Traditional or Machine Learning-Based Flow Control

A special issue of Fluids (ISSN 2311-5521). This special issue belongs to the section "Turbulence".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 444

Special Issue Editors


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Guest Editor
School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, China
Interests: aerodynamics; wakes; drag reduction; turbulent boundary layer; artificial intelligence for flow control

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Guest Editor
School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen Campus, Shenzhen 518055, China
Interests: wind energy; solar energy; hydrogen energy; distributed energy system; low altitude economy

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Guest Editor
Department of Civil Engineering, The University of Hong Kong, Hong Kong, China
Interests: wind engineering; artificial intelligence; machine learning; wake modeling; computational fluid dynamics
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Special Issue Information

Dear Colleagues,

With the increasing demands on the performance of high-speed trains, cars, air and submarine vehicles, as well as wind turbines, there is a growing interest in exploring new technologies to reduce their flow-induced drag. The goals are to decrease power consumption, develop faster vehicles, or enhance the safety of wind turbines. Drag reduction approaches can be classified into active and passive means, depending on whether energy input is required for flow control. Passive devices, such as flaps, deflectors, and vortex generators, effectively reduce form drag, while riblets and micro-patterned superhydrophobic surfaces are successful in reducing skin friction. When passive techniques reach their limits through shape modification, active techniques offer further potential for drag reduction and adaptive control. Recently, machine learning has proven to be a powerful tool in developing optimal control strategies to maximize drag reduction.

This Special Issue aims to showcase recent advances in both passive and active drag reduction techniques, including experimental and numerical investigations into pressure and skin-friction drag reduction in turbulent flows. Research on traditional flow control approaches is welcome. Contributions from artificial intelligence-based shape optimization and machine learning-based active flow control studies are also encouraged.

Dr. Bingfu Zhang
Dr. Hui Tang
Prof. Dr. Mingming Zhang
Dr. Xiaowei Deng
Guest Editors

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Keywords

  • aerodynamic drag reduction for high-speed trains, cars, and aircrafts
  • hydrodynamic drag reduction for ships and underwater vehicles
  • skin-friction drag reduction in turbulent flows
  • aerodynamics and flow control for wind turbines
  • application of machine learning for flow control
  • artificial intelligence-based fluid dynamic shape optimization

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Published Papers (1 paper)

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Research

19 pages, 5624 KiB  
Article
Research on the Improvement of BEM Method for Ultra-Large Wind Turbine Blades Based on CFD and Artificial Intelligence Technologies
by Shiyu Yang, Mingming Zhang, Yu Feng, Haikun Jia, Na Zhao and Qingwei Chen
Fluids 2025, 10(5), 112; https://doi.org/10.3390/fluids10050112 - 27 Apr 2025
Viewed by 99
Abstract
With the development of the wind power industry, wind turbine blades are increasingly adopting ultra-large-scale designs. However, as the size of blades continues to increase, existing aerodynamic calculation methods struggle to achieve both relatively high computational accuracy and efficiency simultaneously. To tackle this [...] Read more.
With the development of the wind power industry, wind turbine blades are increasingly adopting ultra-large-scale designs. However, as the size of blades continues to increase, existing aerodynamic calculation methods struggle to achieve both relatively high computational accuracy and efficiency simultaneously. To tackle this challenge, this research focuses on the low accuracy issues of the traditional Blade Element Momentum theory (BEM) in predicting the aerodynamic performance of wind turbine blades. Consequently, a correction framework is proposed, to integrate the Computational Fluid Dynamics (CFD) method with the Multilayer Perceptron (MLP) neural network. In this approach, the CFD method is used to predict the airflow characteristics around the blades, and the MLP neural network is employed to model the intricate functional relationships between multiple influencing factors and key aerodynamic parameters. This process results in high-precision predictive functions for key aerodynamic parameters, which are then used to correct the traditional BEM. When this correction framework is applied to the rotor of the IEA 15 MW wind turbine, the effectiveness of MLP in predicting key aerodynamic parameters is demonstrated. The research findings suggest that this framework can enhance the accuracy of BEM aerodynamic load predictions to a level comparable to that of RANS. Full article
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