超高速碰撞下航天器夹层板弹道极限的新预测模型

A. Cherniaev, R. Carriere
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引用次数: 0

摘要

在轨道碎片超高速撞击(HVI)的情况下,蜂窝芯夹层板(HCSP)的弹道性能受到蜂窝芯尺寸、箔厚度和芯材的影响。本研究开发了两个预测模型来解释这种影响:一个专用的弹道极限方程(BLE)和一个人工神经网络(ANN),用于预测HVI对HCSP的影响。BLE是惠普尔屏蔽BLE的改进版本,在针对一组新的模拟数据进行测试时,在预测HCSP的弹道极限方面显示出出色的准确性,误差范围仅为1.13%至5.58%。该人工神经网络是使用MATLAB的深度学习工具箱框架开发的,并使用与BLE拟合相同的HCSP HVI数据库进行训练,当针对一组以前未用于网络训练的模拟数据进行测试时,显示出非常好的预测精度,差异范围为0.67%至7.27%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
NEW PREDICTIVE MODELS FOR THE BALLISTIC LIMIT OF SPACECRAFT SANDWICH PANELS SUBJECTED TO HYPERVELOCITY IMPACT
Cell size, foil thickness, and the material of the core, influence the ballistic performance of honeycomb-core sandwich panels (HCSP) in the case of hypervelocity impact (HVI) by orbital debris. Two predictive models that account for this influence have been developed in this study: a dedicated ballistic limit equation (BLE) and an artificial neural network (ANN) trained to predict the outcomes of HVI on HCSP. The BLE is a modified version of the Whipple shield BLE and demonstrated excellent accuracy in predicting the ballistic limits of HCSP, when tested against a new set of simulation data, with the discrepancy ranging from 1.13% to 5.58% only. The ANN was developed using MATLAB’s Deep Learning Toolbox framework and was trained utilizing the same HCSP HVI database as that employed for the BLE fitting and demonstrated a very good predictive accuracy, when tested against a set of simulation data not previously used in the training of the network, with the discrepancy ranging from 0.67% to 7.27%.
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