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Related Experiment Video

Updated: Jan 15, 2026

Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing
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Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing

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Multi-fidelity Bayesian Optimisation of Wind Farm Wake Steering using Wake Models and Large Eddy Simulations.

Andrew Mole1, Sylvain Laizet1

  • 1Department of Aeronautics, Imperial College London, London, SW7 2AZ UK.

Flow, Turbulence and Combustion
|October 13, 2025
PubMed
Summary

This study shows that high-fidelity simulations like large eddy simulations (LES) optimize wind farm yaw configurations better than analytical models. A multi-fidelity Bayesian optimization framework reduces computational cost for improved wind farm power output.

Keywords:
Bayesian OptimisationMulti-fidelityTurbulenceWind Farm

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Area of Science:

  • Renewable Energy Engineering
  • Computational Fluid Dynamics
  • Aerodynamics

Background:

  • Wind turbine wakes reduce efficiency and increase fatigue in wind farms.
  • Wake steering strategies can enhance total wind farm power output.
  • Analytical wake models are commonly used but have limitations.

Purpose of the Study:

  • To compare the effectiveness of analytical wake models and high-fidelity large eddy simulations (LES) for optimizing wind farm yaw configurations.
  • To introduce a multi-fidelity Bayesian optimization framework to reduce computational costs associated with LES.

Main Methods:

  • Utilized large eddy simulations (LES) for higher-fidelity wind farm flow field modeling.
  • Developed a multi-fidelity Bayesian optimization framework with surrogate models.
  • Employed a multi-fidelity acquisition function to guide optimization iterations.

Main Results:

  • Large eddy simulations identified more optimal yaw configurations for full wind farms compared to analytical models.
  • The multi-fidelity Bayesian optimization framework achieved comparable results to single-fidelity optimization with fewer expensive LES runs.
  • Significant reduction in computational cost was demonstrated for achieving optimal wind farm power improvements.

Conclusions:

  • High-fidelity LES capture complex wake interactions more effectively for yaw optimization.
  • The proposed multi-fidelity Bayesian optimization framework offers a computationally efficient approach to wind farm optimization.
  • This method enables achieving substantial wind farm power gains at a reduced computational expense.