Multi-objective Structural Optimization of a Composite Wind Turbine Blade
- Multi-objective Structural Optimization of a Composite Wind Turbine Blade
- Dong-Hoon Choi
- Issue Date
- This paper presents an extensively automated optimization procedure for Horizontal Axis Wind Turbine (HAWT) blades based on integrated structural analysis, which involves extreme gust loaded analysis and stochastic normal wind loaded analysis. To analyze these two aspects, a FEM blade model is built in SAMCEF, a commercial CAE tool. According to IEC61400-1 International Standard of design requirements of wind turbines, an Extreme Operating Gust (EOG) is loaded on the blade surface. Different from traditional structural analysis based on uniform wind pressure, the blade in this research is divided into different elements which are subject to different wind pressures as the rotor rotates. Even for the same blade element, the pressure applied will change as the rotor rotates. The pressures of all blade elements are generated by a Matlab code interfaced with SAMCEF. The proposed structure analysis is called Blade Element Structural Analysis (BESA) in which the blade loads are rotated while the blade is fixed. For fatigue life prediction, a time-varying fluctuating wind is simulated. After obtaining the dynamic stress results, fatigue life is predicted based on Rainflow Counting Algorithm and Miner’s Rule.
In the structural model, two composite materials named Glass Fiber Reinforced Plastic (GFRP) and Carbon Fiber Reinforced Plastic (CFRP) are applied for main materials. For minimizing the material cost and blade weight, the cost and total mass of the blade are set to be a multi-objective subjected to the constraints of stress ratio, tip deflection, and predicted fatigue life limitation, as well as the side constraints of design variables. Three kinds of design variables, which are laminate layer thickness, material type and orientation angle of reinforcements are tailored the structural performance. In order to reduce the number of design variables, Design Variable Linking (DVL) method is applied. Due to the discreteness of design variables, Evolutionary Algorithm (EA) is selected as an optimizer which minimizes both the cost and total mass. By combined the FEM analysis and a Process Integration and Design Optimization (PIDO) tool, named PIAnO, the proposed optimization approach has dramatically reduced the design cost and improved the blade performance.
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- GRADUATE SCHOOL[S](대학원) > MECHANICAL ENGINEERING(기계공학과) > Theses (Master)
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