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Fluids
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Drag Reduction Through Traditional or Machine Learning-Based Flow Control
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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 599

Special Issue Editors

Dr. Bingfu Zhang

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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
Prof. Dr. Hui Tang

E-Mail Website
Guest Editor
Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Interests: flow control; fluid-structure interaction; AI for fluid dynamics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mingming Zhang

E-Mail Website
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
Dr. Xiaowei Deng

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Fluids is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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 222
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
(This article belongs to the Special Issue Drag Reduction Through Traditional or Machine Learning-Based Flow Control)
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