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Multiobjective optimisation and integrated design of wind turbine blades using WTBM-ANSYS for high fidelity structural analysis

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  • Maheri, Alireza

Abstract

Multiobjective optimisation and integrated aerodynamic-structural design of wind turbine blades are emerging approaches, both requiring significant number of high fidelity analyses. Designer-in-the-loop blade modelling and pre/post-processing using specialised software is the bottleneck of high fidelity analysis and therefore a major obstacle in performing a robust optimisation, where thousands of high fidelity analyses are required to find the optimum solution. Removing this bottleneck is the driver for the development of WTBM, an automated wind turbine blade modeller. WTBM takes parameters defining the blade and its operating condition as inputs and generates pre-processor, solver and post-processor APDL files required by ANSYS for high fidelity analysis. The inputs can be generated automatically within an optimisation process, hence so can be the APDL files, allowing a fully automated optimisation in which any of the parameters which are required to define the size, topology, structure and material of a blade to be treated as a design variable. The solver parameters will be also updated automatically as necessary. The performance of WTBM-ANSYS in conducting hundreds of automated high fidelity analyses within an optimisation process is shown through multiobjective structural design and multiobjective integrated design case studies.

Suggested Citation

  • Maheri, Alireza, 2020. "Multiobjective optimisation and integrated design of wind turbine blades using WTBM-ANSYS for high fidelity structural analysis," Renewable Energy, Elsevier, vol. 145(C), pages 814-834.
  • Handle: RePEc:eee:renene:v:145:y:2020:i:c:p:814-834
    DOI: 10.1016/j.renene.2019.06.013
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    Cited by:

    1. Patricio A. Corbalán & Luciano E. Chiang, 2024. "Fast Power Coefficient vs. Tip–Speed Ratio Curves for Small Wind Turbines with Single-Variable Measurements following a Single Test Run," Energies, MDPI, vol. 17(5), pages 1-23, March.
    2. Jia, Liangyue & Hao, Jia & Hall, John & Nejadkhaki, Hamid Khakpour & Wang, Guoxin & Yan, Yan & Sun, Mengyuan, 2021. "A reinforcement learning based blade twist angle distribution searching method for optimizing wind turbine energy power," Energy, Elsevier, vol. 215(PA).

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