Wind Turbine Airfoil Noise Prediction Method Based on Generalized Airfoil Database and Residual Neural Network
To address the limitations of existing wind turbine airfoil databases, the high computational cost, and low efficiency of noise prediction, this paper proposes a wind turbine airfoil noise prediction method based on generalized airfoil sets and residual neural networks. Firstly, taking a database of...
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MDPI AG
2025-05-01
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| author | Quan Wang Haoran Zhang Xiaodi Wang Yang Ni |
| author_facet | Quan Wang Haoran Zhang Xiaodi Wang Yang Ni |
| author_sort | Quan Wang |
| collection | DOAJ |
| description | To address the limitations of existing wind turbine airfoil databases, the high computational cost, and low efficiency of noise prediction, this paper proposes a wind turbine airfoil noise prediction method based on generalized airfoil sets and residual neural networks. Firstly, taking a database of 31 commonly used wind turbine airfoils as a reference, a generalized airfoil set with diverse geometric contours was generated. This was achieved by employing airfoil functional integration theory, B-spline curves, and the Class function/Shape function Transformation (CST) method while varying coefficients and control vector parameters. Secondly, the BPM semi-empirical model was used to compute the noise for the generalized airfoil set, which served as the data labels for deep learning. Finally, classical machine learning models were utilized to construct the airfoil noise prediction model. The results demonstrate that the airfoil noise prediction model constructed with the residual neural network (ResNet-18) achieved the highest prediction accuracy, with a mean squared error (MSE) of 0.0282 and a coefficient of determination (<i>R</i><sup>2</sup>) of 0.99972. Additionally, the trained model exhibited computational efficiency that was 17.5 times higher than the BPM model. |
| format | Article |
| id | doaj-art-a08b44ec150047d2b85beb02da3f7acb |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-a08b44ec150047d2b85beb02da3f7acb2025-08-20T03:49:22ZengMDPI AGApplied Sciences2076-34172025-05-01159512310.3390/app15095123Wind Turbine Airfoil Noise Prediction Method Based on Generalized Airfoil Database and Residual Neural NetworkQuan Wang0Haoran Zhang1Xiaodi Wang2Yang Ni3School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, ChinaSchool of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, ChinaSchool of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, ChinaSchool of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, ChinaTo address the limitations of existing wind turbine airfoil databases, the high computational cost, and low efficiency of noise prediction, this paper proposes a wind turbine airfoil noise prediction method based on generalized airfoil sets and residual neural networks. Firstly, taking a database of 31 commonly used wind turbine airfoils as a reference, a generalized airfoil set with diverse geometric contours was generated. This was achieved by employing airfoil functional integration theory, B-spline curves, and the Class function/Shape function Transformation (CST) method while varying coefficients and control vector parameters. Secondly, the BPM semi-empirical model was used to compute the noise for the generalized airfoil set, which served as the data labels for deep learning. Finally, classical machine learning models were utilized to construct the airfoil noise prediction model. The results demonstrate that the airfoil noise prediction model constructed with the residual neural network (ResNet-18) achieved the highest prediction accuracy, with a mean squared error (MSE) of 0.0282 and a coefficient of determination (<i>R</i><sup>2</sup>) of 0.99972. Additionally, the trained model exhibited computational efficiency that was 17.5 times higher than the BPM model.https://www.mdpi.com/2076-3417/15/9/5123wind turbineairfoil noisedeep learningresidual neural network |
| spellingShingle | Quan Wang Haoran Zhang Xiaodi Wang Yang Ni Wind Turbine Airfoil Noise Prediction Method Based on Generalized Airfoil Database and Residual Neural Network Applied Sciences wind turbine airfoil noise deep learning residual neural network |
| title | Wind Turbine Airfoil Noise Prediction Method Based on Generalized Airfoil Database and Residual Neural Network |
| title_full | Wind Turbine Airfoil Noise Prediction Method Based on Generalized Airfoil Database and Residual Neural Network |
| title_fullStr | Wind Turbine Airfoil Noise Prediction Method Based on Generalized Airfoil Database and Residual Neural Network |
| title_full_unstemmed | Wind Turbine Airfoil Noise Prediction Method Based on Generalized Airfoil Database and Residual Neural Network |
| title_short | Wind Turbine Airfoil Noise Prediction Method Based on Generalized Airfoil Database and Residual Neural Network |
| title_sort | wind turbine airfoil noise prediction method based on generalized airfoil database and residual neural network |
| topic | wind turbine airfoil noise deep learning residual neural network |
| url | https://www.mdpi.com/2076-3417/15/9/5123 |
| work_keys_str_mv | AT quanwang windturbineairfoilnoisepredictionmethodbasedongeneralizedairfoildatabaseandresidualneuralnetwork AT haoranzhang windturbineairfoilnoisepredictionmethodbasedongeneralizedairfoildatabaseandresidualneuralnetwork AT xiaodiwang windturbineairfoilnoisepredictionmethodbasedongeneralizedairfoildatabaseandresidualneuralnetwork AT yangni windturbineairfoilnoisepredictionmethodbasedongeneralizedairfoildatabaseandresidualneuralnetwork |