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Multi-objective genetic algorithms based structural optimization and experimental investigation of the planet carrier in wind turbine gearbox

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Abstract

To improve the dynamic performance and reduce the weight of the planet carrier in wind turbine gearbox, a multi-objective optimization method, which is driven by the maximum deformation, the maximum stress and the minimum mass of the studied part, is proposed by combining the response surface method and genetic algorithms in this paper. Firstly, the design points’ distribution for the design variables of the planet carrier is established with the central composite design (CCD) method. Then, based on the computing results of finite element analysis (FEA), the response surface analysis is conducted to find out the proper sets of design variable values. And a multi-objective genetic algorithm (MOGA) is applied to determine the direction of optimization. As well, this method is applied to design and optimize the planet carrier in a 1.5MW wind turbine gearbox, the results of which are validated by an experimental modal test. Compared with the original design, the mass and the stress of the optimized planet carrier are respectively reduced by 9.3% and 40%. Consequently, the cost of planet carrier is greatly reduced and its stability is also improved.

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Authors and Affiliations

  1. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Pengxing Yi, Lijian Dong & Tielin Shi

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  1. Pengxing Yi
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  2. Lijian Dong
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  3. Tielin Shi
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Correspondence to Pengxing Yi.

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Yi, P., Dong, L. & Shi, T. Multi-objective genetic algorithms based structural optimization and experimental investigation of the planet carrier in wind turbine gearbox. Front. Mech. Eng. 9, 354–367 (2014). https://doi.org/10.1007/s11465-014-0319-5

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  • DOI: https://doi.org/10.1007/s11465-014-0319-5

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