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Chinese Science Bulletin

, Volume 57, Issue 5, pp 466–472

First Online: 28 November 2011Received: 27 August 2011Accepted: 11 October 2011

Abstract

Incorporating controlled elitism and dynamic distance crowding strategies, a modified NSGA-II algorithm based on a fast and genetic non-dominated sorting algorithm is developed with the aim of obtaining a novel multi-objective optimization design algorithm for wind turbine blades. As an example, a high-performance 1.5 MW wind turbine blade, taking maximum annual energy production and minimum blade mass as the optimization objectives, was designed. A 1-16-scale model of this blade was tested in a 12 m × 16 m wind tunnel and the experimental results validated the high performance. Moreover, both the computational fluid dynamics CFD method and a free-vortex method FVM were applied to calculating the aerodynamic performance, which was consistent with the experimental data. For completeness, the CFD and FVM were used to analyze the wake structure, and good and consistent results were obtained between them.

Keywordswind turbine multi-objective optimization wind tunnel test CFD free-vortex method This article is published with open access at Springerlink.com

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Autor: TongGuang Wang - Long Wang - Wei Zhong - BoFeng Xu - Li Chen

Fuente: https://link.springer.com/article/10.1007/s11434-011-4856-6







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