摘要
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.
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 algo- rithm 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.
基金
supported by the National Basic Research Program of China (2007CB714600)
the Priority Academic Program Development of Jiangsu Higher Education Institutions and the EU Seventh Framework Program (FP7-PEOPLE-2010-IRSES-269202)
