Comparative study of surrogate calculation models for performance of blade hydrofoil of tidal turbine based on data-driven

被引：0
|
作者：
Hu Z. [1 ]
Yuan P. [1 ,2 ]
Si X. [1 ,2 ]
Liu Y. [1 ,2 ]
Wang S. [1 ,2 ]
Zhou Y. [1 ]
机构：
[1] College of Engineering, Ocean University of China, Qingdao
[2] Ocean Engineering Key Laboratory of Qingdao, Qingdao
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2022年 / 43卷 / 12期
关键词：
Blades; Hydrofoils; Machine learning; Neural networks; Parameterization; Tidal energy;
D O I：
10.19912/j.0254-0096.tynxb.2021-0596
中图分类号：
学科分类号：
摘要：
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. © 2022, Solar Energy Periodical Office Co., Ltd. All right reserved.
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页码：495 / 502
页数：7
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