基于数据驱动的复杂进气下风扇转子叶根损失模型  

A data-driven based hub region loss model of fan rotor under complex inflow condition

作　　者：石凯凯 鹿哈男 潘天宇 李秋实[1,4] SHI Kaikai;LU Ha’nan;PAN Tianyu;LI Qiushi(School of Energy and Power Engineering,Beihang University,Beijing 100191,China;Research Institute of Aero-Engine,Beihang University,Beijing 100191,China;Collaborative Innovation Center for Advanced Aero-Engine,Beijing 100191,China;School of Aeronautics and Astronautics,Xihua University,Chengdu 610039,China)

机构地区：[1]北京航空航天大学能源与动力工程学院,北京100191 [2]北京航空航天大学航空发动机研究院,北京100191 [3]先进航空发动机协同创新中心,北京100191 [4]西华大学航空航天学院,成都610039

出　　处：《航空动力学报》2023年第7期1637-1647,共11页Journal of Aerospace Power

基　　金：中央高校基本科研业务费专项资金。

摘　　要：发展了一种基于数据驱动的复杂进气下风扇转子叶根损失预测方法。提取了影响风扇转子叶根损失的关键气动参数作为输入变量,熵损失系数作为输出参数;采用计算耗时小的单叶片通道定常模型,通过给定不同边界条件并进行组合来构建样本数据库,使得数据库中样本点尽可能覆盖更广的复杂进气工况;采用径向基神经网络训练并构建输入变量与输出参数之间的映射,实现叶根损失的快速预测。计算结果表明:该损失模型能够准确捕捉叶根损失的径向分布趋势,并且相比于传统损失模型能够大幅提升预测精度。在不同流量、进气旋流以及畸变强度工况下,叶根流动损失平均预测误差基本小于10%。A data-driven based hub loss prediction model was developed for the fan rotor under complex inflow conditions.The key aerodynamic parameters were extracted as input parameters and the entropy loss as output parameter.The sample database was constructed based on computation-efficient single-blade-passage steady computational method.Different boundary conditions were set and combined to make the database samples cover a wide range of complex inflows as far as possible.The RBF neural network was used to construct the mapping between input and output parameters to realize rapid prediction of hub loss.Results showed that the loss model can accurately capture the radial distributions of hub loss and significantly improve the prediction accuracy.Meanwhile,the averaged loss prediction error in the rotor hub region was mostly lower than 10%under different inlet mass flow,inlet swirl angle and inflow distortion conditions.

关 键 词：数据驱动 损失模型 神经网络 复杂来流 跨声速风扇 

分 类 号：V231.3[航空宇航科学与技术—航空宇航推进理论与工程]