Multi-objective material selection for wind turbine blade and tower: Ashby's approach

被引:39
|
作者
Rashedi, A. [1 ]
Sridhar, I. [1 ]
Tseng, K. J. [2 ]
机构
[1] Nanyang Technol Univ, Sch Mech & Aerosp Engn, Singapore 639798, Singapore
[2] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore
来源
MATERIALS & DESIGN | 2012年 / 37卷
关键词
Performance indices; Environmental performance; Material selection charts; Multiple objectives; Weight factors; Scaling; ENGINEERING DESIGN; SYSTEM;
D O I
10.1016/j.matdes.2011.12.048
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The world today is continuously striving towards carbon neutral clean energy technology. Hence, renewable energy sources like wind power system is increasingly receiving the attention of mankind. Energy production is now no more the sole criterion to be considered when installing new megawatt (MW) range of turbines. Rather some important design parameters like structural rigidity, cost effectiveness, life cycle impact, and, above all, reduced mass come into the scenario from new installation point of view. Accordingly, these issues are followed up in this article from wind turbine design perspective. The study, at the outset, aims to establish blade and tower material selection indices on the basis of inherent structural constraints and potential design objectives. Next, it highlights entire blade and tower material selection aspects for small and large scale horizontal axis wind turbines, both for onshore and offshore application. Finally, it distinguishes advanced blade and tower materials in agreement with multiple constraint, compound objective based design optimization procedure. Findings from the study can be deployed to harness massive scale wind energy from structurally more promising, economically more competitive and environmentally more clean and green turbines. (C) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码:521 / 532
页数:12
相关论文
共 50 条
  • [1] Multi-Objective Structural Optimization of a Wind Turbine Tower
    Zheng Y.
    Zhang L.
    Pan Y.
    He Z.
    [J]. Journal of Shanghai Jiaotong University (Science), 2020, 25 (4) : 538 - 544
  • [2] Multi-objective optimization design of small wind turbine blade
    Xie Yongzhi
    Zhang Huan
    Qin Jianhua
    [J]. 2019 4TH INTERNATIONAL CONFERENCE ON INTELLIGENT INFORMATION PROCESSING (ICIIP 2019), 2019, : 288 - 291
  • [3] Multi-objective optimization on blade airfoil of vertical axis wind turbine
    Zhang, Ruiyi
    Li, Deyou
    Chang, Hong
    Wei, Xuntong
    Wang, Hongjie
    [J]. PHYSICS OF FLUIDS, 2024, 36 (08)
  • [4] Validation of the multi-objective structural optimisation of a composite wind turbine blade
    Fagan, E. M.
    Da La Torre, O.
    Leen, S. B.
    Goggins, J.
    [J]. COMPOSITE STRUCTURES, 2018, 204 : 567 - 577
  • [5] Optimization of Wind Turbine Blade Airfoils Using a Multi-Objective Genetic Algorithm
    Chen, Xiaomin
    Agarwal, Ramesh K.
    [J]. JOURNAL OF AIRCRAFT, 2013, 50 (02): : 519 - 527
  • [6] Multi-objective deep reinforcement learning for optimal design of wind turbine blade
    Wang, Zheng
    Zeng, Tiansheng
    Chu, Xuening
    Xue, Deyi
    [J]. RENEWABLE ENERGY, 2023, 203 : 854 - 869
  • [7] A hybrid multi-objective evolutionary algorithm for wind-turbine blade optimization
    Sessarego, M.
    Dixon, K. R.
    Rival, D. E.
    Wood, D. H.
    [J]. ENGINEERING OPTIMIZATION, 2015, 47 (08) : 1043 - 1062
  • [8] Improving wind turbine blade based on multi-objective particle swarm optimization
    Li, Yingjue
    Wei, Kexiang
    Yang, Wenxian
    Wang, Qiong
    [J]. RENEWABLE ENERGY, 2020, 161 : 525 - 542
  • [9] Influence of Reynolds Number on Multi-Objective Aerodynamic Design of a Wind Turbine Blade
    Ge, Mingwei
    Fang, Le
    Tian, De
    [J]. PLOS ONE, 2015, 10 (11):
  • [10] Wind turbine selection for wind farm layout using multi-objective evolutionary algorithms
    Montoya, Francisco G.
    Manzano-Agugliaro, Francisco
    Lopez-Marquez, Sergio
    Hernandez-Escobedo, Quetzalcoatl
    Gil, Consolacion
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2014, 41 (15) : 6585 - 6595