COMPARATIVE STUDY OF SURROGATE CALCULATION MODELS FOR PERFORMANCE OF BLADE HYDROFOIL OF TIDAL TURBINE BASED ON DATA-DRIVEN

Hu Zhenhui; Yuan Peng; Si Xiancai; Liu Yonghui; Wang Shujie; Zhou Ying

doi:10.19912/j.0254-0096.tynxb.2021-0596

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (12) : 495-502. DOI: 10.19912/j.0254-0096.tynxb.2021-0596

COMPARATIVE STUDY OF SURROGATE CALCULATION MODELS FOR PERFORMANCE OF BLADE HYDROFOIL OF TIDAL TURBINE BASED ON DATA-DRIVEN

Hu Zhenhui¹, Yuan Peng^1,2, Si Xiancai^1,2, Liu Yonghui^1,2, Wang Shujie^1,2, Zhou Ying¹

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Abstract

After the improvement of the application mode of airfoil Bezier-PARSEC parameterization software FanOpt, the data set is established. Taking the Lift-to-Drag Ratio characteristics of airfoil as the target, the machine learning models such as support vector regression （SVR）, decision tree, random forest regression, fully connected neural network and one-dimensional convolution neural network are used to fit the data, and the fitting accuracy of the training models is compared. The results show that the prediction accuracy of lift to drag ratio can reach 97.86% by using fully connected neural network and one-dimensional convolutional neural network as surrogate calculation models in the test set. However, compared with one-dimensional convolutional neural network, fully connected neural network has more advantages in dealing with this kind of data set with uncomplicated structure.

Key words

tidal energy / blades / machine learning / hydrofoils / parameterization / neural networks

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Hu Zhenhui, Yuan Peng, Si Xiancai, Liu Yonghui, Wang Shujie, Zhou Ying. COMPARATIVE STUDY OF SURROGATE CALCULATION MODELS FOR PERFORMANCE OF BLADE HYDROFOIL OF TIDAL TURBINE BASED ON DATA-DRIVEN[J]. Acta Energiae Solaris Sinica. 2022, 43(12): 495-502 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0596

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