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Wind turbine airfoil noise prediction using dedicated airfoil database and deep learning technology

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  • Yang, Han
  • Yuan, Weimin
  • Zhu, Weijun
  • Sun, Zhenye
  • Zhang, Yanru
  • Zhou, Yingjie

Abstract

Noise emission is a major issue in wind turbine airfoil design, particularly for large scale wind turbines in low wind speed sites adjacent to urban areas. Conventional methods for addressing aerodynamic noise involve computational aeroacoustics or measurements in anechoic wind tunnels, which are both time-consuming and costly. Some surrogate methods can help reducing the cost, but most of them still have the same problems as specific parameterization techniques. In this study, we introduce a data-driven approach for predicting the aerodynamic noise of wind turbine airfoils, utilizing Convolutional Neural Network (CNN) technology. A major issue is the insufficient number of existing wind turbine airfoils to meet the requirements for deep learning training. To establish a dedicated airfoil database, we systematically sampled 11,700 profiles from renowned wind turbine airfoil series, including NREL, NACA, DU, RISØ, etc., utilizing an integrated airfoil approach. The noise performance of each airfoil was determined through a semi-empirical method. After training is finished, our CNN model enables the calculation of sound pressure levels for new airfoils, circumventing the need for computationally intensive physical equations. With a mean absolute percentage error of less than 0.0531%, our results demonstrate the promising predictive ability of the proposed model. This establishes the model as a suitable tool for airfoil noise evaluation and the design of low-noise airfoils.

Suggested Citation

  • Yang, Han & Yuan, Weimin & Zhu, Weijun & Sun, Zhenye & Zhang, Yanru & Zhou, Yingjie, 2024. "Wind turbine airfoil noise prediction using dedicated airfoil database and deep learning technology," Applied Energy, Elsevier, vol. 364(C).
    • Handle: RePEc:eee:appene:v:364:y:2024:i:c:s0306261924005488
    DOI: 10.1016/j.apenergy.2024.123165
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    References listed on IDEAS

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