Dynamic modeling and damage analysis of debris cloud fragments produced by hypervelocity impacts via image processing

Abstract

It is always a challenging task to model the trajectory and make an efficient damage estimation of debris clouds produced by hypervelocity impact (HVI) on thin-plates due to the difficulty in obtaining high-quality fragment images from experiments. To improve the damage estimation accuracy of HVIs on a typical double-plate Whipple shield configuration, we investigate the distributive characteristic of debris clouds in successive shadowgraphs using image processing techniques and traditional numerical methods. The aim is to extract the target movement parameters of a debris cloud from the acquired shadowgraphs using image processing techniques and construct a trajectory model to estimate the damage with desirable performance. In HVI experiments, eight successive frames of fragment shadowgraphs are derived from a hypervelocity sequence laser shadowgraph imager, and four representative frames are selected to facilitate the subsequent feature analysis. Then, using image processing techniques, such as denoising and segmentation techniques, special fragment features are extracted from successive images. Based on the extracted information, image matching of debris is conducted and the trajectory of debris clouds is modeled according to the matched debris. A comparison of the results obtained using our method and traditional numerical methods shows that the method of obtaining hypervelocity impact experimental data through image processing will provide critical information for improving numerical simulations. Finally, an improved estimation of damage to the rear wall is presented based on the constructed model. The proposed model is validated by comparing the estimated damage to the actual damage to the rear wall.

摘要

由于难以从实验中获得高质量碎片云图像, 对薄板上超高速撞击产生的碎片云进行轨迹建模和有效损伤估计一直是一项具有挑战性的任务。为提高超高速撞击对典型双层板防护结构损伤的估计精度, 本文结合传统数值分析结果, 利用图像处理技术, 研究了连续阴影图中碎片云的分布特征。本文的目标是从图像处理获取的阴影图中提取碎片云的目标运动参数, 并构建轨迹模型用来估计损伤。在超高速撞击实验中, 我们从超高速序列激光阴影成像设备中获得8个连续阴影图片帧, 从中选择4个具有代表性的帧用于后续特征分析。然后, 利用去噪和分割等图像处理技术, 从连续图像帧中提取特殊碎片特征。在提取的信息基础上, 进行碎片图像匹配, 并根据匹配的碎片对碎片云的轨迹进行建模。本文方法获得的结果与传统数值推导结果的对比表明, 从图像处理中获取超高速撞击实验数据的方法可以为改进数值模拟方法提供关键信息。最后, 基于所构建的模型, 提出一种改进的后壁损伤估计方法。估计的损坏与后墙实际损坏情况的对比证明了所提模型的有效性。