基于神经网络模型的动态非线性气动力辨识方法

 在标准径向基函数（RBF）神经网络模型的基础上发展了带输出反馈的RBF神经网络。将计算流体力学(CFD)方法计算的时域气动载荷作为输入信号,建立跨声速非定常非线性气动力模型,并进一步运用CFD方法验证模型的精度。算例表明带输出反馈的RBF神经网络较标准RBF神经网络精度更高,能更准确描述跨声速激波大幅振荡时的非线性和非定常特性,并可推广用于多自由度运动的动态非线性气动力建模。用多级信号训练,预测简谐信号输入下的气动力算例表明带输出反馈的RBF神经网络能够预测不同振幅、不同频率的信号激励下的非线性气动力。

Unsteady Nonlinear Aerodynamics Identification Based on Neural Network Model

This article develops an auto-regressive radial basis function (AR-RBF) neural network model based on the standard RBF neural network model. The computed aerodynamic loads by the time domain CFD method are set as the input signals, and an unsteady nonlinear aerodynamic model can be constructed by the AR-RBF. The direct CFD results are used to validate the precision of the model. Comparison of the two neural network models in prediction performance shows that the AR-RBF neural network model performs better in precision and that it can fit well the unsteady nonlinear characteristics of the transonic flow under large amplitude oscillations of a shock wave. In addition, this model can be easily extended to multi-dimension models. The results of predicting the aerodynamic forces excited by periodic signals show that the AR-RBF neural network model trained with multi-step input signals has the ability of predicting nonlinear aerodynamic forces under harmonic vibrations of different amplitudes or different frequencies.