强激光加载下铝、锡微喷特性实验研究
Experimental investigation on micro-jetting characteristics of aluminum and tin under intense laser loading
罗笔瀚 1王洪建 2冯永祯1
作者信息
- 1. 重庆工商大学机器人与激光智能制造研究所,重庆 400067
- 2. 重庆工商大学机器人与激光智能制造研究所,重庆 400067;重庆工商大学制造装备机构设计与控制重庆市重点实验室,重庆 400067
- 折叠
摘要
基于皮秒激光驱动的高能X射线源具有微焦点、高亮度和高分辨的优良特性,已成为研究金属材料微层裂和微喷射现象的热门诊断方法之一.锡因其熔点较低而成为微喷研究熔化影响的首选材料,铝由于熔点相对较高,不易发生冲击或卸载熔化形成液滴喷射,因此,铝微喷实验研究结果相对较少.基于"星光-Ⅲ"激光装置的纳秒加载源和皮秒束诊断源,发展了皮秒激光驱动高能X射线背光照相技术,研究了表面含V型几何缺陷的金属锡、铝微喷特性,通过延时控制,获取了两种典型金属微喷射过程的高分辨物理图像,得到了喷射物质的形态分布、平均速度和面密度分布等重要信息.采用SPH方法对实验结果进行了数值模拟分析,揭示了实验中喷射物的形成机理以及高速喷射物质的来源.
Abstract
Picosecond laser-driven high-energy X-ray sources with excellent characteristics of micro-focus,high brightness and high resolution,have become one of the popular diagnostic methods for studying micro-crack and micro-jetting phenomena of metal materials.Tin has become the preferred material for ejection research on the melting effect because of its low melting point,and aluminum is relatively high due to its relatively high melting point,which is not easy to impact or unload melting to form droplet injection,so the results of aluminum micro-jetting experimental re-search are relatively few.Based on the nanosecond loading source and picosecond beam diagnostic source of the XGⅢlaser device,the influence of surface geometric defect configuration on the metal micro-injection process was studied,and high-resolution physical images of two typical metal micro-injection processes were obtained through delay con-trol,and important information such as morphological distribution,average velocity and areal density distribution of the ejected substances was obtained.The SPH method was used to simulate the experimental results,and the formation mechanism of the ejecta in the experiment and the source of the high-speed ejected material were revealed.
关键词
强激光/金属微喷/表面缺陷/X射线背光照相Key words
intense laser/metallic micro-jetting/surface defects/x-ray backlight radiography引用本文复制引用
基金项目
国家自然科学基金委员会-中国工程物理研究院联合基金(U2030120)
重庆工商大学研究生科研创新项目(yjscxx2022-112-56)
出版年
2024
NETL
NSTL
万方数据