Predictive modeling techniques for nanosecond-laser damage growth in fused silica optics

被引：32
作者：
Liao, Zhi M. [1 ]
Abdulla, Ghaleb M. [1 ]
Negres, Raluca A. [1 ]
Cross, David A. [1 ]
Carr, Christopher W. [1 ]
机构：
[1] Lawrence Livermore Natl Lab, Livermore, CA 94550 USA
来源：
OPTICS EXPRESS | 2012年 / 20卷 / 14期
关键词：
SITES;
D O I：
10.1364/OE.20.015569
中图分类号：
O43 [光学];
学科分类号：
070207 ; 0803 ;
摘要：
Empirical numerical descriptions of the growth of laser-induced damage have been previously developed. In this work, Monte-Carlo techniques use these descriptions to model the evolution of a population of damage sites. The accuracy of the model is compared against laser damage growth observations. In addition, a machine learning (classification) technique independently predicts site evolution from patterns extracted directly from the data. The results show that both the Monte-Carlo simulation and machine learning classification algorithm can accurately reproduce the growth of a population of damage sites for at least 10 shots, which is extremely valuable for modeling optics lifetime in operating high-energy laser systems. Furthermore, we have also found that machine learning can be used as an important tool to explore and increase our understanding of the growth process. (C) 2012 Optical Society of America
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收藏
页码：15569 / 15579
页数：11
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