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Experimental optimization of plasma actuation for cylinder drag reduction using genetic algorithms.

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Title: Experimental optimization of plasma actuation for cylinder drag reduction using genetic algorithms.
Authors: Yu, Yi1 (AUTHOR), Ju, Enbo1 (AUTHOR), Ali, Kashif1 (AUTHOR), Wang, Lingxi1 (AUTHOR), Meng, Xuanshi1,2 (AUTHOR) mxsbear@nwpu.edu.cn
Source: Experiments in Fluids. May2026, Vol. 67 Issue 5, p1-15. 15p.
Subjects: Drag reduction, Plasma flow, Genetic algorithms, Closed loop systems, Cylinder (Shapes), Particle image velocimetry, Wind tunnel testing
Abstract: This study develops a closed-loop optimization system that combines plasma flow control with a genetic algorithm to minimize the drag coefficient of a circular cylinder based on wind tunnel experiments. The system optimizes the plasma actuation parameters, namely the duty-cycle frequency f and the duty-cycle ratio τ , by using experimental feedback data and significantly enhances the optimization efficiency at a Reynolds number of approximately 22,500. A pair of alternating surface dielectric barrier discharge plasma actuators is symmetrically positioned at azimuthal angles of ± 90 ∘ on both sides of the cylinder. The system communicates in real time with a signal generator and a pressure scanner through a LabVIEW program to adjust the actuator parameters. To reduce the influence of experimental errors during the optimization process, the drag coefficients corresponding to each parameter combination are cumulatively averaged and then incorporated into the genetic algorithm iteration. This averaging strategy increases the reliability of the optimization and shortens the required experimental time. The closed-loop system identifies and adapts to experimental asymmetries caused by slight errors, obtaining the true physical optimum rather than an idealized theoretical value. The optimization results show a drag reduction rate of 21.8%. Additionally, particle image velocimetry (PIV) confirms that optimized actuation suppresses vortex shedding, reduces wake velocity deficit, and stabilizes the wake structure, thereby lowering energy loss and improving aerodynamic performance. This study demonstrates the feasibility of machine learning-based closed-loop optimization in plasma flow control, providing a foundation for more robust, intelligent flow control strategies. [ABSTRACT FROM AUTHOR]
Copyright of Experiments in Fluids is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 193949942
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Experimental optimization of plasma actuation for cylinder drag reduction using genetic algorithms.
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  Data: <searchLink fieldCode="AR" term="%22Yu%2C+Yi%22">Yu, Yi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ju%2C+Enbo%22">Ju, Enbo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ali%2C+Kashif%22">Ali, Kashif</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Lingxi%22">Wang, Lingxi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Meng%2C+Xuanshi%22">Meng, Xuanshi</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> mxsbear@nwpu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Experiments+in+Fluids%22">Experiments in Fluids</searchLink>. May2026, Vol. 67 Issue 5, p1-15. 15p.
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  Data: <searchLink fieldCode="DE" term="%22Drag+reduction%22">Drag reduction</searchLink><br /><searchLink fieldCode="DE" term="%22Plasma+flow%22">Plasma flow</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Closed+loop+systems%22">Closed loop systems</searchLink><br /><searchLink fieldCode="DE" term="%22Cylinder+%28Shapes%29%22">Cylinder (Shapes)</searchLink><br /><searchLink fieldCode="DE" term="%22Particle+image+velocimetry%22">Particle image velocimetry</searchLink><br /><searchLink fieldCode="DE" term="%22Wind+tunnel+testing%22">Wind tunnel testing</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This study develops a closed-loop optimization system that combines plasma flow control with a genetic algorithm to minimize the drag coefficient of a circular cylinder based on wind tunnel experiments. The system optimizes the plasma actuation parameters, namely the duty-cycle frequency f and the duty-cycle ratio τ , by using experimental feedback data and significantly enhances the optimization efficiency at a Reynolds number of approximately 22,500. A pair of alternating surface dielectric barrier discharge plasma actuators is symmetrically positioned at azimuthal angles of ± 90 ∘ on both sides of the cylinder. The system communicates in real time with a signal generator and a pressure scanner through a LabVIEW program to adjust the actuator parameters. To reduce the influence of experimental errors during the optimization process, the drag coefficients corresponding to each parameter combination are cumulatively averaged and then incorporated into the genetic algorithm iteration. This averaging strategy increases the reliability of the optimization and shortens the required experimental time. The closed-loop system identifies and adapts to experimental asymmetries caused by slight errors, obtaining the true physical optimum rather than an idealized theoretical value. The optimization results show a drag reduction rate of 21.8%. Additionally, particle image velocimetry (PIV) confirms that optimized actuation suppresses vortex shedding, reduces wake velocity deficit, and stabilizes the wake structure, thereby lowering energy loss and improving aerodynamic performance. This study demonstrates the feasibility of machine learning-based closed-loop optimization in plasma flow control, providing a foundation for more robust, intelligent flow control strategies. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Experiments in Fluids is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1007/s00348-026-04210-5
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 15
        StartPage: 1
    Subjects:
      – SubjectFull: Drag reduction
        Type: general
      – SubjectFull: Plasma flow
        Type: general
      – SubjectFull: Genetic algorithms
        Type: general
      – SubjectFull: Closed loop systems
        Type: general
      – SubjectFull: Cylinder (Shapes)
        Type: general
      – SubjectFull: Particle image velocimetry
        Type: general
      – SubjectFull: Wind tunnel testing
        Type: general
    Titles:
      – TitleFull: Experimental optimization of plasma actuation for cylinder drag reduction using genetic algorithms.
        Type: main
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          Name:
            NameFull: Yu, Yi
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            NameFull: Ju, Enbo
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            NameFull: Ali, Kashif
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            NameFull: Wang, Lingxi
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            NameFull: Meng, Xuanshi
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          Dates:
            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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              Value: 07234864
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              Value: 67
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              Value: 5
          Titles:
            – TitleFull: Experiments in Fluids
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