损失及落后角代理模型在多级轴流 压气机特性预测中的应用

In order to improve the design capability of axial compressor and characteristics of the engine, researchers need to master a method which can better predict pressure ratio and efficiency of axial compressor. Combining the data of compressor flow field calculated by theory of three-dimensional flow and empirical loss and deviation angle formulas, a new loss and deviation angle model was established by using regularized radial basis function neural network instead of empirical formulas, and the characteristics of E³ 10-stage high pressure compressor were calculated. The effects of non-regularization and regularization on loss and deviation angle prediction were studied, respectively, as well as the influence of compressor efficiency and pressure ratio prediction was investigated. The results showed that in a multistage compressor, under the conditions of distinguishing rotor and stator, rotating speed and operating conditions, the regularized radial basis function neural network surrogate model could better predict the loss and deviation angle and overall characteristics of a multistage compressor in most cases. However, this kind of work could not have a satisfying performance on the prediction of loss and deviation angle from shroud to hub.