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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.

基于图像处理的超高速撞击碎片云的动态建模与损伤估计

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