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Scheme optimization for a turbine blade under multiple working conditions based on the entropy weight vague set

机译:基于熵重模糊的多个工作条件下的涡轮叶片的方案优化

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The deformation of blades under complex loads of multiple working conditions will reduce the energy conversion efficiency. To reduce the deviation of the blade shape in practical working conditions, a combination and optimization method of blade design schemes under multiple working conditions, based on the entropy weight vague sets, is proposed. The sensitivity of each working condition index is analyzed based on the information entropy, and the satisfaction degree of the design scheme based on the design requirements and experiences is described with the vague set. The matching degree of different design schemes for multiple working conditions is quantified according to the scoring function. The combination and optimization of the design scheme are verified by numerical simulation analysis. The results show that the proposed design scheme has a smaller blade shape deviation than the traditional design scheme under multiple working conditions.
机译:在多个工作条件的复杂负载下叶片的变形将降低能量转换效率。 为了减小叶片形状在实际工作条件下的偏差,提出了基于熵重模的多个工作条件下的叶片设计方案的组合和优化方法。 基于信息熵分析每个工作条件指标的灵敏度,并且基于设计要求和经验的设计方案的满意度描述了模糊集。 根据评分功能量化多个工作条件的不同设计方案的匹配程度。 通过数值模拟分析验证了设计方案的组合和优化。 结果表明,所提出的设计方案具有比在多个工作条件下的传统设计方案较小的叶片形状偏差。

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