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.

引用

页码：495 / 502

页数：7

共 16 条

[1]

HUANG J H, SU H L, ZHAO X H., Aerody-namic coefficient prediction of airfoil using BP neural network, Advances in aeronautical science and engineering, 1, 1, pp. 36-39, (2010)

[2]

SANTOS M, MATTOS B, GIRARDI R., Aerodynamic coefficient prediction of airfoils using neural networks, 46th AIAA Aerospace Sciences Meeting and Exhibit, (2008)

[3]

YILMAZ E, GERMAN B., A convolutional neural network approach to training predictors for airfoil performance, 18th AIAA/Issmo Multidisciplinary Analysis & Optimization Conference, (2017)

[4]

SEKAR V, ZHANG M Q, SHU C, Et al., Inverse design of airfoil using a deep convolutional neural network, AIAA journal, 57, 2, pp. 1-11, (2019)

[5]

CHEN H, HE L, QIAN W, Et al., Multiple aerodynamic coefficient prediction of airfoils using a convolutional neural network, Symmetry, 12, 4, pp. 544-558, (2020)

[6]

WANG B X, LU J G, WANG J T., Application of neural network to aerodynamic performance optimization of wind turbine, Machinery design and manufacture, 349, 3, pp. 243-247, (2020)

[7]

ZHU G J, FENG J J, GUO P C, Et al., Optimization of hydrofoil for marine current turbine based on radial basis function neural network and genetic algorithm, Transactions of the Chinese Society of Agricultural Engineering, 30, 8, pp. 65-73, (2014)

[8]

PATRI A, PATNAIK Y., Random forest and stochastic gradient tree boosting based approach for the prediction of airfoil self-noise, Procedia computer science, 46, pp. 109-121, (2015)

[9]

SUN Q., Research on optimal design of airfoil for horizontal-axis wind turbines, (2013)

[10]

ZHU Y H, YUAN S Q, ZHANG J F, Et al., Research status for parameterization hydro-turbine blade airfoil, China rural water and hydropower, 1, pp. 129-132, (2016)

← 1 2 →