Optimization Design of Blades Based on Multi-Objective Particle Swarm Optimization Algorithm

被引:0
|
作者
Li, Zihao [1 ]
Wang, Wei [1 ]
Xie, Yonghe [1 ]
Li, Detang [1 ]
机构
[1] Zhejiang Ocean Univ, Sch Naval Architecture & Maritime, Zhoushan 316000, Peoples R China
关键词
floating offshore wind turbines; multi-objective PSO algorithm; aerodynamic efficiency; structural strength; collaborative optimization;
D O I
10.3390/jmse13030486
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
Among renewable energy sources derived from the ocean, wind power has developed rapidly. This article proposes an optimization algorithm framework that integrates two objectives: aerodynamic shape optimization and structural optimization. For practical reasons, the 5-MW wind turbine blade was selected as the research object, and the sea conditions near the East China Sea were chosen as the environmental parameters for its service environment. The FAST simulation software was employed for verification purposes. The results indicated that the optimized blade not only meets the target power output but also possesses unique economic advantages, such as being lightweight and exhibiting low aerodynamic force.
引用
收藏
页数:27
相关论文
共 50 条
  • [1] Multi-objective optimization design of the wind-to-heat system blades based on the Particle Swarm Optimization algorithm
    Qian, Jing
    Sun, Xiangyu
    Zhong, Xiaohui
    Zeng, Jiajun
    Xu, Fei
    Zhou, Teng
    Shi, Kezhong
    Li, Qingan
    APPLIED ENERGY, 2024, 355
  • [2] Robust Design Optimization Based on Multi-Objective Particle Swarm Optimization
    Yu Yan
    Dai Guangming
    Chen Liang
    Zhou Chong
    Peng Lei
    2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2016, : 4918 - 4925
  • [3] Modified Multi-Objective Particle Swarm Optimization Algorithm for Multi-objective Optimization Problems
    Qiao, Ying
    ADVANCES IN SWARM INTELLIGENCE, ICSI 2012, PT I, 2012, 7331 : 520 - 527
  • [4] Design optimization of APMEC using chaos multi-objective particle swarm optimization algorithm
    Pan, Pengyi
    Wang, Dazhi
    Niu, Bowen
    ENERGY REPORTS, 2021, 7 : 531 - 537
  • [5] Research on Multi-Objective Multidisciplinary Design Optimization Based on Particle Swarm Optimization
    Wang, Yangyang
    Han, Minghong
    PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON RELIABILITY SYSTEMS ENGINEERING (ICRSE 2017), 2017,
  • [6] An improved multi-objective particle swarm optimization algorithm
    Zhang, Qiuming
    Xue, Siqing
    ADVANCES IN COMPUTATION AND INTELLIGENCE, PROCEEDINGS, 2007, 4683 : 372 - +
  • [7] Improved multi-objective particle swarm optimization algorithm
    College of Automation, Northwestern Polytechnical University, Xi'an 710129, China
    不详
    Liu, B. (lbn1987113@163.com), 2013, Beijing University of Aeronautics and Astronautics (BUAA) (39):
  • [8] A simplified multi-objective particle swarm optimization algorithm
    Vibhu Trivedi
    Pushkar Varshney
    Manojkumar Ramteke
    Swarm Intelligence, 2020, 14 : 83 - 116
  • [9] Constrained Multi-objective Particle Swarm Optimization Algorithm
    Gao, Yue-lin
    Qu, Min
    EMERGING INTELLIGENT COMPUTING TECHNOLOGY AND APPLICATIONS, 2012, 304 : 47 - 55
  • [10] A particle swarm algorithm for multi-objective optimization problem
    Institute of Information Engineering, Xiangtan University, Xiangtan 411105, China
    Moshi Shibie yu Rengong Zhineng, 2007, 5 (606-611):