Multi-objective optimization of vertical-axis wind turbine’s blade structure using genetic algorithm

被引：0
作者：
Geneid A.A. [1 ]
Atia M.R.A. [2 ]
Badawy A. [3 ]
机构：
[1] Ain Shams University, Cairo
[2] Arab Academy for Science, Technology and Maritime Transport, Cairo
[3] October University for Modern Sciences and Arts MSA, Giza
来源：
Journal of Engineering and Applied Science | 2022年 / 69卷 / 1期
关键词：
Genetic algorithm; Multi-objective optimization; Sustainable design; Vertical wind turbines; Wind turbine blade structure;
D O I：
10.1186/s44147-022-00150-z
中图分类号：
学科分类号：
摘要：
Through the field of renewable energy, the vertical-axis wind turbine is preferable, especially when the wind speed is low to medium. The optimization of blade structure design is essential to enhance the usability of the vertical-axis wind turbine. This paper introduces an optimization approach for the uniform blade structure design used in the vertical-axis wind turbine. The blade cost represents 20% of the turbine overall cost, and inertia load is the dominating design load. This approach aims to optimize the weight and the cost while maintaining structural integrity. Designs of blade structure are based on a multi-objective model, including the composite material and geometric parameters, where multiple design parameters are included. The model enhances the requirement of computation time and resources by approximation cross-sectional properties and loading calculations. The cost index concept is investigated to introduce an efficient method for approximation, normalizing the cost from currency exchange and price changes. The formulated model is then validated using a finite element analysis package, where the model is the integration between the numerical geometric model and the classical laminate theory. Optimization models are then formulated based on genetic algorithm and Pareto frontier analysis. Blade design parameters are included in the optimization to cover a wide range of parameters. The geometric cross-sectional properties are estimated using empirical formulas to reduce computation time and resources. The presented approach augmented the blade design parameters and genetic algorithm optimization. Optimum results for NACA 0021 shows the blade mass range between 2.5 and 3 kg and the cost index from 40 to 90. © 2022, The Author(s).
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