核爆炸光辐射能量分布的模拟仿真研究

韩小祥; 李君; 张欣; 原林; 刘洋; 王博宇

doi:10.11884/HPLPB202436.230406

核爆炸光辐射能量分布的模拟仿真研究

doi: 10.11884/HPLPB202436.230406

韩小祥^1,,
李君¹,
张欣¹,
原林¹,
刘洋¹,
王博宇^{1, 2, ,}

1.
西安工程大学理学院，西安 710048
2.
西安工业大学核科学与技术研究院，西安 710021

基金项目: 国家自然科学基金项目(U2330109、61805212); 陕西省自然科学基金项目（2022JQ-660、2020JQ-830）

详细信息

作者简介:
韩小祥，hanxiaoxiang@xpu.edu.cn

通讯作者:
王博宇，wangby2008@foxmail.com。

中图分类号: O381
计量
- 文章访问数: 60
- HTML全文浏览量: 16
- PDF下载量: 11
- 被引次数: 0
出版历程
- 收稿日期: 2023-11-16
- 修回日期: 2024-05-09
- 录用日期: 2024-05-09
- 网络出版日期: 2024-05-20
- 刊出日期: 2024-05-31

Simulation research on energy distribution of light radiation from nuclear explosion

Han Xiaoxiang^1
,,
Li Jun¹,
Zhang Xin¹,
Yuan Lin¹,
Liu Yang¹,
Wang Boyu^{1, 2
, ,}

1.
School of Science, Xi’an Polytechnic University, Xi’an 710048, China
2.
Institute of Nuclear Science and Technology, Xi’an Technological University, Xi’an 710021, China

摘要

摘要: 光辐射是核爆炸中能量的重要组成部分，因此研究其在空间中的热能分布规律具有重要的意义。根据核爆炸理论中火球的发展规律和光辐射的瞬时能量特征得到核爆炸光辐射的热能计算公式，该公式主要与爆炸高度、爆炸当量、大气衰减系数、火球半径和火球温度有关。首先，通过设计不同地图、改变核爆的相关参数进行模拟计算，分析了核爆炸光辐射热能的分布特点。然后，结合光辐射烧伤的伤情分级标准，在模拟程序中添加搜寻功能，实现自动对虚拟地图的伤情分级半径区域进行划分。最后，采用神经网络训练模拟数据，得到核爆的相关参数与地图伤情分级半径的映射关系，从而能够由核爆炸参数直接预测目标地图上的伤情分级半径，可大大缩短计算时间。
- 核爆炸 /
- 光热辐射 /
- 神经网络 /
- 数值模拟 /
- 烧伤分级
Abstract: Light radiation is a crucial component of the energy produced in nuclear explosion, making the study of its space distribution highly significant. This paper presents the derivation of a formula for computing thermal energy induced by the light radiation of a nuclear explosion. The derivation integrates the fireball development laws with the transient energy dynamics of light radiation. The resultant formula exhibits a dependency on several factors, including the height of the explosion, the yield of the explosion, atmospheric attenuation coefficients, as well as the radius and temperature of the fireball. By creating diverse maps and adjusting pertinent parameters, simulating calculations are conducted to elucidate the distribution patterns of the transient thermal energy from nuclear explosion light radiation. Furthermore, the burn injury grading standards are incorporated into the simulation by introducing a search function that autonomously categorizes the injury grading zones on the virtual map. What’s more, neural networks are employed to train the numerical models, aiming to discern the correlation between the parameters associated with nuclear explosions and the injury grading radius on the map. This innovative approach enables direct prediction of the injury grading radius based on nuclear explosion parameters, thus significantly shortens the calculation process.
- nuclear explosion /
- photothermal radiation /
- neural network /
- numerical modeling /
- burn grading

HTML全文

图 1 点源爆炸的热辐射模型

Figure 1. Thermal radiation model of point source explosion

下载: 全尺寸图片幻灯片

图 2 人工神经元结构图

Figure 2. Structure diagram of an artificial neuron

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图 3 核爆炸光辐射的模拟仿真设计图

Figure 3. Design diagram for numerical simulation of nuclear explosion light radiation

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图 4 虚拟山地的地图下不同时刻核爆炸光辐射瞬时热能分布图

Figure 4. Instantaneous thermal energy distribution of nuclear explosion light radiation at different time under virtual mountain map

下载: 全尺寸图片幻灯片

图 5 虚拟城市地图下不同时刻核爆炸光辐射瞬时热能分布图

Figure 5. Instantaneous thermal energy distribution of nuclear explosion light radiation at different time under virtual city map

下载: 全尺寸图片幻灯片

图 6 不同参数对应的核爆炸光辐射热能图

Figure 6. Thermal energy maps of optical radiation from nuclear explosions corresponding to different parameter

下载: 全尺寸图片幻灯片

图 7 湿度与核爆炸光辐射热能之间关系图

Figure 7. Relationship between humidity and thermal energy of nuclear explosion

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图 8 虚拟山地的烧伤区域划分图

Figure 8. Nuclear explosion burn area division on virtual mountain map

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图 9 人工神经网络的预测模型图

Figure 9. Artificial neural network prediction model

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图 10 神经网络模型预测结果与误差图

Figure 10. Artificial prediction results and error diagram of neural network mode

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表 1 光辐射对生物的烧伤伤情分级标准

Table 1. Grades for classification of biological burns caused by light radiation

grading rules of injury severity in light radiation burns	ratio of burn area	thermal energy value of light radiation/(cal·cm⁻²)	condition
minor burn	＜10%	5.01～14.95	Systemic symptoms are generally not obvious and usually do not result in loss of combat effectiveness.
moderate burn	10%～20%	14.95～30.14	Systemic symptoms are obvious, and some may experience shock, but the injuries are usually not very serious.
severe burn	20%～50%	30.14～50.17	Systemic symptoms are generally severe, with shock occurring in the early stages and soon entering an infection phase that lasts for several days to weeks. If actively treated, the vast majority of these injuries can be cured, and they will quickly lose their combat effectiveness after injury.
extremely severe burn	＞50%	＞50.17	In the early stages, there is often severe shock, and infections appear early and severe, which brings great difficulties to treatment. The injured will immediately lose their combat effectiveness after injury.

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参考文献(31)

[1]	Brode H L. Fireball phenomenology[R]. The RAND Corporation, 1964.
[2]	Brode H L, Hillendahl R W, Landshoff R K. Thermal radiation phenomena. Volume v. Radiation hydrodynamics of high temperature air. Final report[R]. Sunnyvale: The Lockheed Missiles and Space Company, 1967.
[3]	Glasstone S. The effects of nuclear weapons[M]. Atomic Energy Commission, 1962.
[4]	Hoerlin H. United States high-altitude test experiences: a review emphasizing the impact on the environment[R]. Los Alamos: Los Alamos Scientific Laboratory, 1976.
[5]	Hoerlin H. Artificial aurora and upper atmospheric shock produced by Teak[R]. Los Alamos: Los Alamos Scientific Laboratory, 1961.
[6]	田宙, 乔登江, 郭永辉. 不同高度强爆炸早期火球数值研究[J]. 兵工学报, 2009, 30(8):1078-1083 doi: 10.3321/j.issn:1000-1093.2009.08.014 Tian Zhou, Qiao Dengjiang, Guo Yonghui. Numerical investigation of early fireball of strong explosion for different altitudes[J]. Acta Armamentarii, 2009, 30(8): 1078-1083 doi: 10.3321/j.issn:1000-1093.2009.08.014
[7]	中国人民解放军总装备部军事训练教材编辑工作委员会. 核爆炸物理概论[M]. 北京: 国防工业出版社, 2003 The Military Training Textbook Editing Committee of the General Equipment Department of the People's Liberation Army of China. Introduction to the physics of nuclear explosions[M]. Beijing: National Defense Industry Press, 2003
[8]	王心正, 隋卫星. 高空核爆炸火球的二维辐射流体力学计算[J]. 计算物理, 1987, 4(2):159-168 Wang Xinzheng, Sui Weixing. Two-dimension radiation hydrodynamics calculation of the high-altitude fireball[J]. Chinese Journal of Computational Physics, 1987, 4(2): 159-168
[9]	闫凯. 辐射流体力学数值模拟中的隐式蒙特卡罗方法[J]. 原子能科学技术, 2021, 55(3):397-404 doi: 10.7538/yzk.2020.youxian.0139 Yan Kai. Implicit Monte Carlo method in radiation hydrodynamics[J]. Atomic Energy Science and Technology, 2021, 55(3): 397-404 doi: 10.7538/yzk.2020.youxian.0139
[10]	闫凯, 刘钰, 田宙. 低空强爆炸中火球的一维数值模拟研究[J]. 原子能科学技术, 2015, 49(8):1345-1353 doi: 10.7538/yzk.2015.49.08.1345 Yan Kai, Liu Yu, Tian Zhou. Numerical simulation of fireball in strong explosion at low altitude[J]. Atomic Energy Science and Technology, 2015, 49(8): 1345-1353 doi: 10.7538/yzk.2015.49.08.1345
[11]	王学栋, 朱金辉, 左应红, 等. 复杂地形对核爆炸瞬发中子辐射场的影响[J]. 现代应用物理, 2023, 14:030202 Wang Xuedong, Zhu Jinhui, Zuo Yinghong, et al. Influence of complex terrain on prompt neutron radiation field of nuclear detonation[J]. Modern Applied Physics, 2023, 14: 030202
[12]	杨宏, 贾维敏. 基于神经网络的综合评判在核爆模式识别中的应用[J]. 核电子学与探测技术, 2000, 20(4):279-283 doi: 10.3969/j.issn.0258-0934.2000.04.009 Yang Hong, Jia Weimin. Recognition of underground nuclear explosion and natural earthquake based on neural network[J]. Nuclear Electronics & Detection Technology, 2000, 20(4): 279-283 doi: 10.3969/j.issn.0258-0934.2000.04.009
[13]	Liu Daizhi, Li Xihai, Zhang Bin. Feature selection and identification of underground nuclear explosion and natural earthquake based on gamma test and BP neural network[C]//Proceedings of the Second International Symposium on Neural Networks. 2005: 393.
[14]	李鹏, 宋立军, 韩超, 等. 基于AR模型与神经网络的核爆与闪电电磁脉冲信号识别[J]. 强激光与粒子束, 2010, 22(12):3052-3056 doi: 10.3788/HPLPB20102212.3052 Li Peng, Song Lijun, Han Chao, et al. Recognition of NEMP and LEMP signals based on auto-regression model and artificial neutral network[J]. High Power Laser and Particle Beams, 2010, 22(12): 3052-3056 doi: 10.3788/HPLPB20102212.3052
[15]	Wu Yunhui, Zhang Jiemin, Che Xingmin, et al. Research on the recognition of infrasound signal of nuclear explosion by SVM and CNN[J]. IOP Conference Series: Earth and Environmental Science, 2020, 610: 012010. doi: 10.1088/1755-1315/610/1/012010
[16]	绪梅, 冯地清. 利用多层神经网络实现核爆/非核爆的模糊综合评判方法[C]//第7届全国核电子学与核探测技术学术年会论文集(三). 2021: 1062-1067 Xu Mei, Feng Diqing. Fuzzy comprehensive evaluation method for nuclear/non nuclear explosions using multi-layer neural networks[C]//Proceedings of the 7th National Academic Annual Conference on Nuclear Electronics and Nuclear Detection Technology. 2021: 1062-1067
[17]	Zhou Wen, Sun Guomin, Yang Zihui, et al. BP neural network based reconstruction method for radiation field applications[J]. Nuclear Engineering and Design, 2021, 380: 111228. doi: 10.1016/j.nucengdes.2021.111228
[18]	李秦超, 姚成宝, 程帅, 等. 神经网络状态方程在强爆炸冲击波数值模拟中的应用[J]. 爆炸与冲击, 2023, 43:054202 doi: 10.11883/bzycj-2022-0222 Li Qinchao, Yao Chengbao, Cheng Shuai, et al. Application of the neural network equation of state in numerical simulation of intense blast wave[J]. Explosion and Shock Waves, 2023, 43: 054202 doi: 10.11883/bzycj-2022-0222
[19]	Barama L, Williams J, Newman A V, et al. Global nuclear explosion discrimination using a convolutional neural network[J]. Geophysical Research Letters, 2023, 50: e2022GL101528. doi: 10.1029/2022GL101528
[20]	王坚, 李路翔. 核武器效应及防护[M]. 北京: 北京理工大学出版社, 1993 Wang Jian, Li Luxiang. Nuclear weapons effects and protection[M]. Beijing: Beijing Institute of Technology Press, 1993
[21]	徐恒. 高空核爆炸X射线能量沉积及热辐射特性研究[D]. 长沙: 国防科技大学, 2020: 22-41 Xu Heng. Research on the energy deposition of X-rays and characteristic of thermal radiation in the high altitude nuclear detonation[D]. Changsha: National University of Defense Technology, 2020: 22-41
[22]	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]. 长春: 中国科学院研究生院(长春光学精密机械与物理研究所), 2006: 23-36 Zhang Dawei. Theoretical and laboratorial studies of radiative characteristics of soft X-rays from high-altitude nuclear explosions[D]. Changchun: Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, 2006: 23-36
[23]	Jung H, Shim W. Calculation of thermal fluence from extremely high-energy emission in air[J]. IEEE Transactions on Nuclear Science, 2015, 62(3): 1395-1398. doi: 10.1109/TNS.2015.2421891
[24]	王建国, 牛胜利, 张殿辉, 等. 高空核爆炸效应参数手册[M]. 北京: 原子能出版社, 2010 Wang Jianguo, Niu Shengli, Zhang Dianhui, et al. Manual of parameters for high-altitude nuclear explosion effects[M]. Beijing: Atomic Energy Press, 2010
[25]	吴健辉. 核爆炸光辐射特性及探测技术的理论与实验研究[D]. 武汉: 华中科技大学, 2009: 24-59 Wu Jianhui. Study on theory and experiment of the characteristics and detection technology of nuclear explosion radiation[D]. Wuhan: Huazhong University of Science & Technology, 2009: 24-59
[26]	Marrs R E, Moss W C, Whitlock B. Thermal radiation from nuclear detonations in urban environments[R]. UCRL-TR-231593, 2007.
[27]	Zhao Chengcheng, Yin Junqing, Chen Yongdang, et al. Research on buckling load prediction of composite stiffened plates based on BP neural network[J]. Journal of Physics: Conference Series, 2020, 1576: 012032. doi: 10.1088/1742-6596/1576/1/012032
[28]	Li Xiang, Chen Haolong, Liu Zhaoli, et al. Identifying varying thermal diffusivity of inhomogeneous materials based on a hybrid physics-informed neural network[J]. International Journal of Applied Mechanics, 2022, 14: 2250027. doi: 10.1142/S1758825122500272
[29]	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 Xiaofei, Li Fan, Yin Lugao, et al. Simulation study on low-altitude nuclear explosion environment effect[J]. Structure & Environment Engineering, 2022, 49(5): 48-55
[30]	吴健辉. 核爆炸光辐射特性及探测技术的理论与实验研究[D]. 武汉: 华中科技大学, 2009: 44-45 Wu Jianhui. Study on theory and experimental of the characteristics and detection technology of nuclear explosion radiation[D]. Wuhan: Huazhong University of Science and Technology, 2009: 44-45
[31]	姜巍巍, 李奇, 李俊杰, 等. BLEVE火球热辐射及其影响评价模型介绍[J]. 工业安全与环保, 2007, 33(5):23-24 doi: 10.3969/j.issn.1001-425X.2007.05.010 Jiang Weiwei, Li Qi, Li Junjie, et al. The introduction of the BLEVE fireball thermal radiation and its impact assessment model[J]. Industrial Safety and Environmental Protection, 2007, 33(5): 23-24 doi: 10.3969/j.issn.1001-425X.2007.05.010