A double-objective prediction and optimization method for buoys performance based on the artificial neural network

被引:4
作者
Jiang, Chunyu [1 ]
Cao, Feifei [1 ,2 ,4 ]
Li, Demin [1 ]
Wei, Zhiwen [1 ]
Shi, Hongda [1 ,2 ,3 ,4 ]
机构
[1] Ocean Univ China, Coll Engn, 238 Songling Rd, Qingdao 266100, Peoples R China
[2] Shandong Prov Key Lab Ocean Engn, 238 Songling Rd, Qingdao 266100, Peoples R China
[3] Pilot Natl Lab Marine Sci & Technol Qingdao, 1 Wenhai Rd, Qingdao 266237, Peoples R China
[4] Ocean Univ China, Qingdao Municipal Key Lab Ocean Renewable Energy, Qingdao 266100, Peoples R China
基金
中国国家自然科学基金;
关键词
Buoy optimization; Bessel equation; Numerical simulation; Artificial neural network;
D O I
10.1016/j.oceaneng.2023.114969
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
Determining the optimal geometry of the buoy is an important step in the design of oscillating wave energy converters, its main problem of which is the complexity and computational cost of the buoy shape parameterization process. To solve the above problems, the Genetic Algorism Back Propagation (GABP) neural network is used to optimize the longitudinal section of the buoy, and the Bessel equation is used to simplify the coordinates. 280 buoys with regular shapes are used as the training set, and 5% of them are selected as the test set. The trained neural network can accurately predict both the maximum heave displacement and additional mass of the buoy under a uniform wave condition, with a prediction accuracy of 93%. An optimal buoy geometry shape is constructed by randomly varying Bessel control points which enables the trained neural network to perform doubleobjective optimization of the buoy longitudinal section. The method proposed in this study can provide a new idea for the optimization of buoy geometry shape and performance prediction.
引用
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页数:11
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