神经网络模型在压气机通流特性分析中的应用

为了解决通流特性分析程序中原始模型对压气机性能预测精度不足的问题，提高压气机通流特性分析过程的可靠性，基于对大量多圆弧叶栅的数值模拟结果建立了压气机叶栅性能数据库，并以该数据库为依托，采用神经网络建模方法建立了压气机叶栅基准损失系数和基准落后角模型。结果显示:两模型对叶栅基准损失系数和基准落后角的预测精度均满足工程应用要求，其精度分别为±0.002和±1°。在对采用神经网络模型的通流特性分析程序校验过程中发现，其无论对压气机整机性能还是对流动细节的预测精度上都获得了显著提高，尤其是在主流区。此外从压气机整体特性上看，基准损失系数和基准落后角精度的提高对非设计工况损失系数和落后角的预测精度影响是积极的。 

Application of neural network model in compressor through-flow analysis

To solve the problem of insufficient prediction accuracy of the compressor performance by the original model in the through-flow analysis program，and to improve the reliability of the compressor through-flow analysis process，a compressor cascade performance database was established based on the numerical simulation results of a large number of multiple circular arc cascades.Based on this database，neural network modeling method was used to establish the baseline loss coefficient and baseline deviation angle models of compressor cascade.Results showed that，the prediction accuracy of the two models for the baseline loss coefficient and baseline deviation angle of cascade met the requirements of engineering applications，with the accuracy of ±0.002 and ±1°，respectively.During the verification process，it could be found that the neural network models significantly improved the prediction accuracy of both compressor's overall performance and the flow details，especially at the core flow region.Moreover，the improvement of the accuracy of baseline loss coefficient and baseline deviation angle had a positive effect on the prediction accuracy of loss coefficient and deviation angle at off-design conditions.