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An Engineering Model for Static Yawed Wind Turbines Based on Actuator Line Simulations and Symbolic Regression.

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Title: An Engineering Model for Static Yawed Wind Turbines Based on Actuator Line Simulations and Symbolic Regression.
Authors: Sun, Haoyuan1 (AUTHOR) H.Y.Sun@tudelft.nl, Sciacchitano, Andrea1 (AUTHOR), Yu, Wei1 (AUTHOR)
Source: Wind Energy. May2026, Vol. 29 Issue 5, p1-24. 24p.
Subjects: Engineering models, Computational fluid dynamics, Wind turbines, Regression analysis, National Renewable Energy Laboratory (U.S.), Aerodynamics
Abstract: Yaw engineering models are commonly used as add‐ons to the industrial blade element momentum (BEM) framework to improve load and power predictions by accounting for the skewed wake effect. However, existing yaw engineering models show noticeable limitations in accurately predicting the induced velocity distribution across the blade span. In this study, we employ a genetic symbolic regression （SR） approach to develop a new set of yaw engineering models for both the normal and tangential induced velocities of a static yawed wind turbine. The model regression is performed using simulation data from Reynolds‐averaged Navier–Stokes (RANS) simulations with an actuator line model (ALM) of the NREL 5‐MW wind turbine, covering a range of yaw angles (γ$$ \gamma $$) and thrust coefficients (CT$$ {C}_T $$) over which the skewed wake effect is dominant. The regressed models are selected based on an optimal trade‐off between accuracy and complexity, with complexity constrained to remain comparable with Branlard's yaw engineering model. The selected models are subsequently verified using three unseen cases that span different operating conditions and wind turbine models. Verification is performed through a series of evaluations, including generalization performance tests, implementation within the BEM framework to assess their aerodynamic performances, and quantitative errors and loading analyses. The results demonstrate that the proposed models improve both the amplitude accuracy and azimuthal phase of induced velocities compared with the existing models of Coleman and Branlard, enabling it to accurately capture the phase of the peak aerodynamic forces across each annulus and to predict the nonrestoring yaw moment occurring in the inboard region of the turbine, which other models fail to reproduce. [ABSTRACT FROM AUTHOR]
Copyright of Wind Energy is the property of Wiley-Blackwell 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.)
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An: 193226023
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  Data: An Engineering Model for Static Yawed Wind Turbines Based on Actuator Line Simulations and Symbolic Regression.
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  Data: <searchLink fieldCode="AR" term="%22Sun%2C+Haoyuan%22">Sun, Haoyuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> H.Y.Sun@tudelft.nl</i><br /><searchLink fieldCode="AR" term="%22Sciacchitano%2C+Andrea%22">Sciacchitano, Andrea</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yu%2C+Wei%22">Yu, Wei</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Wind+Energy%22">Wind Energy</searchLink>. May2026, Vol. 29 Issue 5, p1-24. 24p.
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  Data: <searchLink fieldCode="DE" term="%22Engineering+models%22">Engineering models</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+fluid+dynamics%22">Computational fluid dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Wind+turbines%22">Wind turbines</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22National+Renewable+Energy+Laboratory+%28U%2ES%2E%29%22">National Renewable Energy Laboratory (U.S.)</searchLink><br /><searchLink fieldCode="DE" term="%22Aerodynamics%22">Aerodynamics</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Yaw engineering models are commonly used as add‐ons to the industrial blade element momentum (BEM) framework to improve load and power predictions by accounting for the skewed wake effect. However, existing yaw engineering models show noticeable limitations in accurately predicting the induced velocity distribution across the blade span. In this study, we employ a genetic symbolic regression （SR） approach to develop a new set of yaw engineering models for both the normal and tangential induced velocities of a static yawed wind turbine. The model regression is performed using simulation data from Reynolds‐averaged Navier–Stokes (RANS) simulations with an actuator line model (ALM) of the NREL 5‐MW wind turbine, covering a range of yaw angles (γ$$ \gamma $$) and thrust coefficients (CT$$ {C}_T $$) over which the skewed wake effect is dominant. The regressed models are selected based on an optimal trade‐off between accuracy and complexity, with complexity constrained to remain comparable with Branlard's yaw engineering model. The selected models are subsequently verified using three unseen cases that span different operating conditions and wind turbine models. Verification is performed through a series of evaluations, including generalization performance tests, implementation within the BEM framework to assess their aerodynamic performances, and quantitative errors and loading analyses. The results demonstrate that the proposed models improve both the amplitude accuracy and azimuthal phase of induced velocities compared with the existing models of Coleman and Branlard, enabling it to accurately capture the phase of the peak aerodynamic forces across each annulus and to predict the nonrestoring yaw moment occurring in the inboard region of the turbine, which other models fail to reproduce. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Wind Energy is the property of Wiley-Blackwell 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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        Value: 10.1002/we.70118
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        Text: English
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        PageCount: 24
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      – SubjectFull: Engineering models
        Type: general
      – SubjectFull: Computational fluid dynamics
        Type: general
      – SubjectFull: Wind turbines
        Type: general
      – SubjectFull: Regression analysis
        Type: general
      – SubjectFull: National Renewable Energy Laboratory (U.S.)
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      – SubjectFull: Aerodynamics
        Type: general
    Titles:
      – TitleFull: An Engineering Model for Static Yawed Wind Turbines Based on Actuator Line Simulations and Symbolic Regression.
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            NameFull: Sun, Haoyuan
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            NameFull: Sciacchitano, Andrea
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            NameFull: Yu, Wei
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            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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