轮胎刚度智能预测及在起落架摆振分析中的应用

Intelligent prediction of tire stiffness and application in landing gear shimmy analysis

为分析飞机起落架摆振问题,需要获取航空轮胎的侧向、扭转刚度。建立了一套航空轮胎侧向与扭转刚度智能预测方法,并在某无人机起落架防摆设计中成功应用。收集了503组轮胎侧向、扭转刚度试验测试数据,来自3个轮胎厂商,7种轮胎型号,涵盖不同气压、载荷、压缩量、直径、宽度等特征参数的组合。建立了一个全连接人工神经网络模型,通过已有轮胎刚度测试数据对神经网络模型进行了训练,从随机分配的验证集训练效果可以看出,训练到1000次以后,均值误差基本收敛,预测值与真实值基本一致。利用训练后的模型对某无人机轮胎侧向与扭转刚度进行了预测,并将预测结果应用于某无人机起落架摆振稳定性分析,确定起落架临界阻尼,指导减摆器的设计。

It is necessary to obtain the lateral and torsional stiffness of aviation tire to analyze the shimmy of landing gear.In this pa⁃per,a set of intelligent prediction methods of aircraft tire lateral and torsional stiffness are established and successfully applied in the anti-shimmy design of landing gear of a UAV.503 groups of tire lateral and torsional stiffness test data were collected,including 3 tire manufacturers,7 tire models,different combinations of air pressure,load,compression,diameter and width.A fully connect⁃ed deep learning neural network model is established.The neural network model is trained through the existing tire stiffness test da⁃ta.From the training effect of the randomly assigned verification set,it can be seen that after 1000 times of training,the mean error basically converges,and the predicted value is basically consistent with the real value.The trained model is used to predict the later⁃al and torsional stiffness of the tire of a UAV,and the prediction results are applied to the analysis of the shimmy of the landing gear to determine the critical damping of the landing gear and guide the design of the shimmy damper.