基于RBF神经网络的压气机叶片面压力场预测研究

RBF Neural Network Based Prediction on Blade Surface Pressure Fields in Compressors

航空发动机压气机内部流道气流特性复杂,叶片所处的涡状流场具有高压、高速、旋转和非定常等特点,因此,亟需高效、准确地计算和预测压气机叶片复杂流场的气动特性.该文针对航空发动机叶片复杂流场的研究,通过计算流体动力学(computational fluid dynamics,CFD)方法,生成不同工作状态下的叶片表面气动载荷分布.采用径向基函数(radial based function,RBF)神经网络建立压力面表面气动载荷预测模型,将神经网络建模方法与流场计算相结合,神经网络方法能够对基于CFD的数据集进行学习和训练,适当地弥补来自计算流体动力学的误差,为有效预测航空发动机压气机叶片复杂流场提供了参考渠道.

The airflow characteristics of the internal flow path of an aero-engine compressor are complex,and the vortex flow field around the blade is characterized by high pressure,high speed,rotation,and unsteadiness.Therefore,there is an urgent need to calculate and predict the aerodynamic characteristics of the complex flow field around the compressor blade efficiently and accurately.The computational fluid dynamics(CFD)method was used to generate the aerodynamic load distribution on the blade surface under different operating conditions for the study of the complex flow fields around aero-engine blades.The radial based function(RBF)neural network was applied to establish the pressure surface aerodynamic load prediction model,and the neural network modeling method was combined with the flow field calculation.The neural network method can learn and train the CFD-based data set to properly compensate the errors from the CFD,which provides a reference for the effective prediction of the complex flow fields around aero-engine compressor blades.