基于神经网络的机翼结构载荷模型建立方法

A Method of Establishing Wing Structural Load Model Based on Neural Network

建立以飞行参数为变量的机翼结构载荷模型是飞行安全监控及飞机疲劳寿命估算的重要技术基础。首先将机翼燃油质量对其结构载荷的影响分离,在此基础上依据飞机结构载荷与飞行参数间的相关性,通过相关分析结合主成分分析的方法确定了低维数且互不相关的建模参数,并采用高斯-伯努利受限玻尔兹曼机预训练的BP神经网络方法实现了模型建立。以飞机跨音速俯仰机动为例,建立了机翼某测载剖面剪力模型,模型验证结果表明,预训练可有效降低模型初始误差,提升建模效率及精度。

The establishment of a wing structural load model with flight parameters as variables is an important technical basis for flight safety monitoring and aircraft fatigue life estimation.Firstly,the influence of wing fuel quality on its structural load is separated.On the basis of this and in accordance with the correlation between aircraft structural load and flight parameters,and the low-dimensional and the uncorrelated modeling parameters determined through correlation analysis combined with principal component analysis,a model is established by the BP neural network method pre-trained by Gauss-Bernoulli restricted Boltzmann machine.Taking the aircraft s transonic pitch maneuver for example,a shear force model of a certain measured load profile of the wing is established.The model verification results show that the pre-training can effectively reduce the initial error of model,and improve the modeling efficiency and accuracy.