固体力学与飞行器总体设计

蜂窝夹层板BLE的一种增强型协同优化建模方法

  • 贾光辉 ,
  • 段枭
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  • 北京航空航天大学 宇航学院, 北京 100191
段枭 男, 硕士研究生。主要研究方向: 飞行器设计。 Tel: 010-82313324 E-mail: duanxiao@sa.buaa.edu.cn

收稿日期: 2014-09-16

  修回日期: 2015-01-14

  网络出版日期: 2015-03-02

基金资助

国家空间碎片专题项目(K020110-1/3/6)

Enhanced collaborative optimization modeling method of BLE about honeycomb sandwich panel

  • JIA Guanghui ,
  • DUAN Xiao
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  • School of Astronautics, Beihang University, Beijing 100191, China

Received date: 2014-09-16

  Revised date: 2015-01-14

  Online published: 2015-03-02

Supported by

National Space Debris Thematic Project of China (K020110-1/3/6)

摘要

弹道极限方程(BLE)是进行飞行器防护结构设计与空间碎片撞击风险评估的关键技术,基于超高速撞击物理实验数据对已知形式的弹道极限方程进行修正,是获得高可信度新方程的一种常用方法。为了快速准确地获取新方程,以国外131个碳纤维复合材料(CFRP)面板的蜂窝夹层板实验数据为对象,运用增强型协同优化(ECO)方法对Christiansen方程进行优化。结果显示增强型协同优化方法与穷举法的优化结果一致,并给出了计算效率提升比例。为考核修正后方程的适用性,利用铝合金面板的蜂窝夹层板的25个实验数据对修正方程进行检测,结果显示修正方程可以将总体预测率从68%提升至84%,安全预测率从76%提升至92%,绝对误差平方和从0.046 2下降至0.006 3,相对误差平方和从1.046 0下降至0.109 0。

本文引用格式

贾光辉 , 段枭 . 蜂窝夹层板BLE的一种增强型协同优化建模方法[J]. 航空学报, 2015 , 36(7) : 2260 -2268 . DOI: 10.7527/S1000-6893.2015.0022

Abstract

Ballistic limit equation (BLE) is a key technology to spacecraft protection structure design and orbital debris impact risk assessment.Modifying present BLE based on experimental data is a commonly used method to obtain new equation with high reliability.In order to obtain new equation quickly and accurately, selecting the experimental result of 131 honeycomb sandwich panels of carbon fiber composite materials (CFRP) as data, the Christiansen equation is modified with enhanced collaborative optimization (ECO) method.The optimal results of ECO method and exhaustion method are consistent and the improvement of computational efficiency is given.To assess the applicability of the modified equation, experimental data of 25 honeycomb sandwich panels of aluminum alloy material are used.The prediction results show that the totality predicted rate increases from 68% to 84%, the safety predicted rate increases from 76% to 92%, the absolute error sum of square decreases from 0.046 2 to 0.006 3 and the relative error sum of square decreases from 1.046 0 to 0.109 0.

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