航空学报 > 2020, Vol. 41 Issue (10): 223852-223852   doi: 10.7527/S1000-6893.2020.23852

某机翼的安全预测载荷模型建立

赵燕1, 宋江涛2, 唐宁3   

  1. 1. 中国飞行试验研究院 总体所, 西安 710089;
    2. 中国飞行试验研究院 发动机所, 西安 710089;
    3. 中国飞行试验研究院 飞机所, 西安 710089
  • 收稿日期:2020-01-22 修回日期:2020-04-03 发布日期:2020-03-26
  • 通讯作者: 赵燕 E-mail:zhaoyan1031@mail.nwpu.edu.cn

Construction of safety-predicting load model on certain wing

ZHAO Yan1, SONG Jiangtao2, TANG Ning3   

  1. 1. General Institute, Chinese Flight Test Establishment, Xi'an 710089, China;
    2. Engine Institute, Chinese Flight Test Establishment, Xi'an 710089, China;
    3. Aircraft Institute, Chinese Flight Test Establishment, Xi'an 710089, China
  • Received:2020-01-22 Revised:2020-04-03 Published:2020-03-26

摘要: 基于试飞阶段全V-N包线的实测飞行载荷,将改进遗传算法、线性回归与BP神经网络融合,给出了一种适用于全寿命周期的自适应安全预测载荷模型建立方法。将该方法应用于某飞机机翼的安全预测载荷模型建立,并对所建立的载荷模型进行了全V-N包线的验证。分析了样本空间与载荷模型精度的关系。结果表明:建立的弯矩预测载荷全包线最大误差为10.6%、平均误差为1.0%,剪力的最大误差为9.1%、平均误差为0.4%,比优化线性和分段线性的误差小,比神经网络的收敛性好。随着建模数据从全样本、1/2、1/3、…、1/10样本的变化,弯矩和剪力方程的全V-N包线的最大误差整体呈增大趋势,弯矩最大误差变化范围为10.6%~19.6%,最大剪力误差变化范围为9.1%~27.9%。

关键词: 飞行试验, 飞行载荷, 安全预测, 全寿命周期载荷模型, 全V-N包线

Abstract: Using measured flight loads in full V-N envelope during flight tests, an adaptive safety-predicting load model for life cycles was built based on the combination of an improved genetic algorithm, a linear regression and BP neural network. The above adaptive method was used to build the safety-predicting load model of certain wing which was validated in full V-N envelope. Moreover, the effects of the sample on the model accuracy were analyzed. The results showed that the maximum and average errors of the predicted bending-moment in full V-N envelope are 10.6% and 1.0%, and those of the predicted shear in full V-N envelope are 9.1% and 0.4%, respectively. These errors are lower than those from optimized-linear and piece-wise-linear methods, and the convergence is better than that from the neural network. With the sample varying from full, 1/2, 1/3,…, 1/10, the maximum errors of the bending-moment and the shear change from 10.6% to 19.6% and from 9.1% to 27.9%, respectively.

Key words: flight test, flight load, safety-predicting, load model for life cycles, full V-N envelope

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