NEW PREDICTIVE MODELS FOR THE BALLISTIC LIMIT OF SPACECRAFT SANDWICH PANELS SUBJECTED TO HYPERVELOCITY IMPACT

A. Cherniaev, R. Carriere
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Abstract

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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超高速碰撞下航天器夹层板弹道极限的新预测模型
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%。
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