摘要
建立了一套适用于轴流涡轮非轴对称端壁的优化系统,该系统主要基于人工神经网络方法和遗传算法.为了降低高维优化设计的计算规模,采用了设计空间分区方法,结合D-最优试验设计方法建立计算样本数据库;使用人工神经网络方法建立代理模型,以等熵效率为优化目标函数,优化方法为遗传算法.采用上述优化策略对Aachen一级半涡轮进行优化设计,端壁布置了25个控制点,数据库样本规模为300,优化后等熵效率增加了0.43%.计算结果表明:采用空间分区方法在保持足够代理模型精度的同时,可以降低样本数据库的规模,并且在试验设计方法选择时带来更多的灵活性.另外,非轴对称端壁造型在静叶上的应用效果相较于动叶要好,且在第二级静叶的应用效果最佳.
An optimization design method based on genetic algorithms and artificial neural networks(ANN)is developed to improve the performance of non-axisymmetric endwall profiling,which has been proved to be an effective method to control the secondary flows and improve flow structures of turbomachinery.For a 1.5-stage axial turbine,aparameterized model of non-axisymmetric endwall with 25 control points on the hub is established,the surrogate model is built using subspace strategy,D-optimal experimental design method and ANN model with a database of 300 samples.The isentropic efficiency is selected as optimal objective,and genetic algorithms is chosen as the optimization algorithm.The total-to-total isentropic efficiency of the turbine increase about 0.43%after optimization.The results show that the subspace strategy used to build the database brings more flexibility and indicated that the non-axisymmetric endwall contouring has a significant influence on stators than rotors.
引文
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