一种新型带槽式Savonius叶轮参数多目标优化  

Multi-objective Optimization of A New Savonius-type Impeller Parameters with Slots

作　　者：杨状状 宋瑞银[2] 刘博宇 任聪杰 林躜[2] 盛勇 YANG Zhuang-zhuang;SONG Rui-yin;LIU Bo-yu;REN Cong-jie;LIN Zuan;SHENG Yong(School of Mechanical and Energy Engineering,Zhejiang University of Science and Technology,Hangzhou 310023,China;NingboTech University,Ningbo 315100,China;School of Mechanical Engineering,Zhejiang University,Hangzhou 310030,China)

机构地区：[1]浙江科技学院机械与能源工程学院,浙江杭州310023 [2]浙大宁波理工学院,浙江宁波315100 [3]浙江大学机械工程学院,浙江杭州310030

出　　处：《水电能源科学》2023年第12期203-206,共4页Water Resources and Power

基　　金：国家自然科学基金项目(52001276);浙江省“领雁”重大专项研发项目(2023C03122)。

摘　　要：为了提高Savonius型(S型)叶轮的捕能效率和消波效率,提出了一种新型的带导流槽结构的MBC型叶轮。通过数值模拟、试验测试及BP神经网络结合NSGA-Ⅱ的多目标优化算法优化MBC型叶轮的重叠率、间隙率、内外圆弧夹角、内圆弧半径、导流槽尺寸等5个参数,对比分析了最优MBC型、S型和MB型叶轮的性能表现。结果表明,相关性能的预测值与实际值的误差小于10%,验证了所建立的预测模型的性能良好,得到的最优参数为重叠率0.136、间隙率0.003、内外圆弧夹角5.23°、内圆弧半径41.42 mm、导流槽尺寸2.83 mm;最优MBC型叶轮相较于传统S型叶轮,捕能效率、消波效率分别提升了35.8%、11.0%。A new type of MBC impellerr with a guide vane slot structure was proposed to improve the energy capture and wave reduction efficiency of Savonius-type(S-type)impeller.A multi-objective optimization algorithm combined BP neural network,NSGA-Ⅱ,numerical simulation and experimental test was used to optimize five parameters of the MBC impeller including overlap ratio,gap ratio,internal and external arc angle,internal arc radius,and guide vane size.The performance of the optimal MBC impeller was compared with that of the S-type and MB-type impellers.The results show that the prediction error of the relevant performance values is less than 10%compared to the actual values,verifying the good performance of the established prediction model.The obtained optimal parameters are:overlap ratio of 0.136,gap ratio of 0.003,internal and external arc angle of 5.23°,internal arc radius of 41.42 mm,and guide vane size of 2.83 mm.Compared with the traditional S-type impeller,the optimal MBC impeller has increased energy capture efficiency by 35.8%and wave reduction efficiency by 11%.

关 键 词：Savonius型叶轮 BP神经网络 NSGA-Ⅱ遗传算法 多目标优化 

分 类 号：TV734[水利工程—水利水电工程] TK79[动力工程及工程热物理—流体机械及工程]