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Stacking sequence optimization of horizontal axis wind turbine blade using FEA, ANN and GA

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Abstract

The requirements for wind energy are significantly increasing for the sources of non-renewable energy is censoriously shortened and the awareness on green energy is emergent. The required energy from the wind turbine can be increased by optimally varying the aerodynamic considerations like aerofoil section, chord length, angle of attack, twist angle and the rotor diameter. However the blade may structurally fail, for the aerodynamic considerations are generally against the structural requirements. For example, the coefficient of lift can be increased with the reduced thickness but the structure may fail due to lacking of bending and torsional strength. Similarly, when the wind turbine blade radius is increased, the structure will have poor buckling strength. As the outer shape of a wind turbine blade and the thickness are determined based on the aerodynamic considerations, they are kept constant in this work and the buckling strength of the wind turbine structure is improved by optimally varying the ply orientations and stacking sequences at each section of the wind turbine blade. The difficulty due to high computational cost in the stacking sequence optimization of wind turbine blade is overcome by replacing finite element analysis using artificial neural network.

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Authors and Affiliations

  1. Department of Mechanical Engineering, PSNA College of Engineering and Technology, Dindigul, 624622, India

    P. Emmanuel Nicholas, D. Vasudevan & T. Ramachandran

  2. Department of Mechanical Engineering, SBM College of Engineering and Technology, Dindigul, 624005, India

    K. P. Padmanaban

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  1. P. Emmanuel Nicholas
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  2. K. P. Padmanaban
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  3. D. Vasudevan
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  4. T. Ramachandran
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Correspondence to P. Emmanuel Nicholas.

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Nicholas, P.E., Padmanaban, K.P., Vasudevan, D. et al. Stacking sequence optimization of horizontal axis wind turbine blade using FEA, ANN and GA. Struct Multidisc Optim 52, 791–801 (2015). https://doi.org/10.1007/s00158-015-1269-1

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  • DOI: https://doi.org/10.1007/s00158-015-1269-1

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