基于深度学习的机翼蒙皮载荷计算方法

Calculation method of wing skin load based on deep learning

针对传统的载荷标定方程计算机翼蒙皮载荷精度低的问题,提出了一种基于深度学习的机翼蒙皮载荷计算新方法。考虑真实机翼蒙皮受力复杂,首先建立了机翼蒙皮试验件模型,使用Ansys仿真软件对试验件进行有限元分析,获得应变与载荷仿真数据,并对仿真数据进行数据清洗与预处理;其次,构建深度神经网络模型,将应变与载荷作为神经网络模型的输入与输出值,采用Adam算法优化提出的载荷计算模型;最后,在测试集上对载荷值进行预测,使用平均相对误差与绝对值差作为评价指标。实验结果显示,基于深度学习的载荷计算方法在小载荷数据上平均绝对误差为0.081 N,在正常载荷数据上的平均相对误差为0.0638%;与传统载荷计算方法比较,本文提出的新方法计算的载荷精度明显优于传统方法。

Aiming at the problem of low accuracy of the traditional load calibration equation for calculating wing skin load,a novel method of wing skin load calculation based on deep learning was proposed.Considering that the force of the real wing skin was complicated,this paper established a simplified wing structure model.Firstly,the finite element analysis on the wing was carried out by using Ansys software to obtain the strain and force simulation data,then the simulation data was cleaned and preprocessed.Secondly,a deep neural network model was constructed,its input and output were the strain and load values,respectively.The Adam optimization algorithm was used to optimize the model for load calculation.Finally,the test set was used to predict the load value,and the average relative error and absolute error were used as evaluation metrics.Experimental results show that the calculation method based on deep learning obtains the average absolute error of 0.081 N for small load data and average relative error of 0.0638%for normal load data,respectively.The load accuracy of new method is obviously better than that of the traditional method comparing with traditional load calibration method.