Drag Reduction Through Traditional or Machine Learning-Based Flow Control

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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