[an error occurred while processing this directive] [an error occurred while processing this directive] [an error occurred while processing this directive]
[an error occurred while processing this directive]

Journal of Projectiles, Rockets, Missiles and Guidance >

2025 , Vol. 45 >Issue 5: 847 - 856

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2025.05.029

Academic article

The Equivalent Research of Light Radiation and Laser Damage on Special Protective Materials

  • Gu Xinghan ,
  • CAI Xinghui , * ,
  • WU Yuji ,
  • WANG Tao , **
Expand
  • Rocket Force University of Engineering, Xi’an 710025, Shaanxi, China

Received date: 2025-03-17

  Online published: 2025-11-28

Fold

Abstract

Light radiation is an important component of nuclear explosion energy, and studying its impact on special protective materials is of significant importance. To facilitate the assessment of the damage to special protective materials under the light radiation from an airborne nuclear explosion, a hybrid optimization approach based on Bootstrap-TRF segmentation fitting method is proposed. This method is derived from experimental data on laser damage for various materials and real-world damage data from nuclear explosions, combined with the Bootstrap theory commonly used in small sample problems. The algorithm achieves a fitting goodness of R2 = 0.458 8, with an average relative error of only 26.45% in the test set. In the high-energy segment (x= 5.49 × 106 J/m2), the prediction error is as low as 1.92%, with a computation time of 18.69 seconds. Compared to existing studies on small sample problems, such as the 3rd-order B-spline interpolation method based on Bootstrap theory and the the LS-SVM (least squares xxxxxxx) method, the proposed algorithm demonstrates strong performance in fitting goodness, error reduction, extrapolation ability, and computational efficiency. It shows significant effectiveness and applicability in fitting the equivalent relationship function of material damage between light radiation and laser.

Key words： light radiation; nuclear explosion; laser; small sample; equivalent relationship function; data fitting

Cite this article

Gu Xinghan , CAI Xinghui , WU Yuji , WANG Tao . The Equivalent Research of Light Radiation and Laser Damage on Special Protective Materials[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2025 , 45(5) : 847 -856 . DOI: 10.15892/j.cnki.djzdxb.2025.05.029

[an error occurred while processing this directive]

References

Publishing order | Descend order by publishing year | Descend order by cited within
[1]
蔡星会, 吴宇际, 王涛, 卢江仁. 核爆炸效应及防护[M]. 西安: 西安电子科技大学出版社, 2022.

CAI X H, WU Y J, WANG T, LU J R. Effects of Nuclear Explosions and Protection[M]. Xi’an: Xi’an University of Electronic Science and Technology Press, 2022.

[2]
崔春鹏. 激光原理及应用[J]. 科学中国人, 2015(36): 9.

CUN C P. Principles and Applications of Lasers[J]. Science China, 2015(36): 9.

[3]
M.R.斯皮格尔,L.J. 斯蒂芬斯. 统计学[M]. 北京: 科学出版社, 2002.

M.R. Spiegel, L.J. Stephens. Statistics[M]. Beijing: Science Press, 2002.

[4]
Burggraf J, Zylstra A. Lasers for the observation of multiple order nuclear reactions[J]. Frontiers in Physics, 2022, 10: 993632.

DOI

[5]
Negoita F, Roth M, Thirolf P G, et al. Laser driven nuclear physics at ELINP[J]. arxiv preprint arxiv: 2201. 01068, 2022.

[6]
韩小祥, 李君, 张欣, 等. 核爆炸光辐射能量分布的模拟仿真研究[J]. 强激光与粒子束, 2024, 36: 076003.

HAN X X, LI J, ZHANG X, et al. Simulation research on energy distribution of light radiation from nuclear explosion[J]. High Power Laser and Particle Beams, 2024, 36: 076003.

[7]
李晓菲, 李帆, 尹禄高, 等. 低空核爆炸环境效应模拟研究[J]. 强度与环境, 2022, 49(5): 48-55.

LI X F, LI F, YIN L G, et al. Simulation Study on Low-altitude Nuclear Explosion Environment Effect[J]. STRUCTURE & ENVIRONMENT ENGINEERING, 2022, 49(5): 48-55.

[8]
Yuan L., Li J., Wang B, et al. Temperature dynamics and mechanical properties analysis of carbon fiber epoxy composites radiated by nuclear explosion simulated light source. Sci Rep 15, 1799 (2025).

DOI

[9]
王锋, 范江兵, 郑毅, 等. 光辐射模拟在轨测试系统激光出射能力试验研究[J]. 核电子学与探测技术, 2009, 29(6): 1475-1478,1497.

WANG F, FAN J B, ZHENG Y, et al. Test on Laser Eradiation Performance of Optical Radiations Simulating On-orbit Testing System[J]. Nuclear Electronic & Detection Technology, 2009, 29(6): 1475-1478,1497.

[10]
张大威. 高空核爆软X射线辐射特性及其实验室模拟研究[D]. 吉林: 中国科学院长春光学精密机械与物理研究所, 2006.

ZHANG D W. High-altitude nuclear explosion soft X-ray radiation characteristics and their laboratory simulation study[D]. Jilin: Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, 2006

[11]
Bradley E. Bootstrap methods: Another look at the jackknife[J]. The annals of Statistics, 1979, 7(1): 1-26.

[12]
Tibshirani R J, Efron B. An introduction to the bootstrap[J]. Monographs on statistics and applied probability, 1993, 57(1): 1-436.

[13]
Efron B. Nonparametric estimates of standard error: the jackknife, the bootstrap and other methods[J]. Biometrika, 1981, 68(3): 589-599.

DOI

[14]
梁武. 小子样下数据处理的若干问题研究[D]. 兰州大学, 2008.

LIANG W. Study on Several Issues in Data Processing for Small Samples[D]. Lanzhou University, 2008.

[15]
赵远, 杨琳. 基于 Bootstrap 理论的小子样寿命评估模型[J]. 北京航空航天大学学报, 2022, 48(1): 106-112.

ZHAO Y, YANG L. Small Sample Lifetime Assessment Model Based on Bootstrap Theory[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(1): 106-112.

[16]
Hall P. The bootstrap and Edgeworth expansion[M]. New York, NY: Springer Science & Business Media, 1992.

[17]
Lahiri S N. Resampling methods for dependent data[M]. New York, NY: Springer Science & Business Media, 2003.

[18]
Coleman T F, Li Y. On the convergence of reflective Newton methods for large-scale nonlinear minimization subject to bounds[J]. Mathematical programming, 1994, 67(1): 189-224.

DOI

[19]
Coleman T F, Li Y. An interior trust region approach for nonlinear minimization subject to bounds[J]. SIAM Journal on optimization, 1996, 6(2): 418-445.

DOI

[20]
Nocedal J, Wright S J. Numerical optimization[M]. 2nd ed. New York, NY: Springer Science & Business Media, 2006.

[21]
Bishop C M, Nasrabadi N M. Pattern recognition and machine learning[M]. New York: springer, 2006.

[22]
Hong Z, Luo G. An enhanced approach for small sample learning with weighted data distribution estimation[J]. Knowledge-Based Systems, 2020, 195: 105712.

[23]
Kumar A, Kim J. A comparative study on data splitting methods for predictive modeling with small-sample datasets. Journal of Statistical Computation and Simulation, 2021, 91(4): 736-753.

[24]
Brownlee J. Machine Learning Mastery: How to split data for small-sample machine learning. 2019.[Online]. Available: https://machinelearningmastery.com/how-to-split-data-for-small-sample-machine-learning/.

[25]
Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: Machine Learning in Python[J]. Journal of Machine Learning Research, 2011, 12: 2825-2830.

[26]
Kuhn M, Johnson K. Applied Predictive Modeling[M]. New York: Springer, 2013.

Options
Outlines

/

〈 〉

[an error occurred while processing this directive]