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在叶片气动设计中,叶片叶尖部分翼型的升阻比和失速迎角对叶片的气动性能有很大影响。根据不同样本叶尖翼型的几何特征和气动特征,利用SOM神经网络对其进行分组,形成系统的叶尖翼型专家数据库。在此基础上,采用置信度推理法建立了翼型几何参数与气动参数之间的关系,并给出了优化方向,由此产生优化设计外形。研究结果表明:SOM神经网络能够有效地区分有相同特征的一类翼型,分类灵活,可以为风机叶片设计工作提供方向性指导;最终得到的设计翼型与基准翼型相比,有效地提高了升阻比和失速迎角,具有较优的综合气动性能。
In the aerodynamic design of the blade, the ratio of lift to drag and the angle of attack of attack on the blade tip have a great influence on the aerodynamic performance of the blade. According to the geometrical and aerodynamic characteristics of tip airfoils of different samples, the SOM neural network is used to group them to form a system of tip airfoils expert database. On this basis, the relationship between airfoil geometric parameters and aerodynamic parameters was established by confidence reasoning method, and the direction of optimization was given, which resulted in the optimal design shape. The results show that the SOM neural network can effectively differentiate a type of airfoil with the same characteristics and can be classified flexibly, which can provide direction guidance for the design of the fan blade. Compared with the reference airfoil, the SOM neural network can effectively improve The lift-drag ratio and stalling angle of attack, with better overall aerodynamic performance.