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Intelligence algorithm for optimization design of the low wind speed airfoil for wind turbine

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Abstract

In order to develop wind resources in low wind speed (LWS) area, a new intelligence algorithm based on the airfoil profile expressed by B-spline for LWS airfoil is proposed. Considering the design requirements for LWS wind turbine airfoil design and taking the DU airfoil as original design coefficients, the new LWS airfoil families with the thickness of 18, 21, 25, 30, 35, 40% were obtained by the particle swarm optimization based on the improved inertia factor and mode. The results show that, compared with the original DU airfoils, all the LWS airfoil families have better aerodynamic performance under free and fixed transition. Performance of the 18% thickness airfoil is improved most significantly: Under fixed transition condition, the maximum lift coefficient increases by 13.53%, and the maximum lift to drag ratio increases by 10.77%; under the free transition condition, the maximum lift coefficient increases by 18.84%, and the maximum lift to drag ratio increases by 11.92%. The aerodynamic performance of a new airfoil named CQUL-180, taken as an example, was analyzed and validated by the computational fluid dynamics compared with DU96-W-180 airfoil, which verifies the reliability of the intelligence algorithm.

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Acknowledgements

The authors acknowledge the National Natural Science Foundation of China (Grant: 51475056).

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Authors and Affiliations

  1. The State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, China

    Xiaoping Pang, Haoyu Wang & Jin Chen

  2. Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, Sichuan, China

    Haoyu Wang

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  1. Xiaoping Pang
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  2. Haoyu Wang
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  3. Jin Chen
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Correspondence to Xiaoping Pang.

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Pang, X., Wang, H. & Chen, J. Intelligence algorithm for optimization design of the low wind speed airfoil for wind turbine. Cluster Comput 22 (Suppl 4), 8119–8129 (2019). https://doi.org/10.1007/s10586-017-1635-4

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  • DOI: https://doi.org/10.1007/s10586-017-1635-4

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