Application of the neural network equation of state in numerical simulation of intense blast wave

LI Qinchao; YAO Chengbao; CHENG Shuai; ZHANG Dezhi; LIU Wenxiang

doi:10.11883/bzycj-2022-0222

Volume 43 Issue 4

Apr. 2023

Turn off MathJax

Article Contents

Abstract

References

Article Navigation > Explosion And Shock Waves > 2023 > 43(4): 044202

LI Qinchao, YAO Chengbao, CHENG Shuai, ZHANG Dezhi, LIU Wenxiang. Application of the neural network equation of state in numerical simulation of intense blast wave[J]. Explosion And Shock Waves, 2023, 43(4): 044202. doi: 10.11883/bzycj-2022-0222

Citation:

LI Qinchao, YAO Chengbao, CHENG Shuai, ZHANG Dezhi, LIU Wenxiang. Application of the neural network equation of state in numerical simulation of intense blast wave[J]. Explosion And Shock Waves, 2023, 43(4): 044202. doi: 10.11883/bzycj-2022-0222

Citation:

PDF( 4261 KB)

Application of the neural network equation of state in numerical simulation of intense blast wave

doi: 10.11883/bzycj-2022-0222

Northwest Institute of Nuclear Technology, Xi’an 710024, Shaanxi, China

Received Date: 2022-05-24
Rev Recd Date: 2022-10-24

Available Online: 2022-10-25

Publish Date: 2023-04-05

Abstract

Abstract

The main challenge of numerical simulation of intense explosion is how to accurately determine the equations of state for the explosive products. The traditional equations of state are mostly empirical or semi-empirical formulas, which can just deal with ordinary explosions, but the treatment of intense explosions is of great limitation. The parameters of intense explosive products span an extremely wide range, which often exceeds the scope of empirical formula. Neural network has an excellent nonlinear fitting function and can realize the function of the equations of state. At the same time, there are a lot of state parameters of material in the sesame library, and the material parameters suitable for intense explosive products were selected as training data of neural network. The tabulated data of intensive explosive product samples were pretreated to make them better used in neural networks, then the data was adopted as training set to train the BP neural network and a one-dimensional spherical numerical code embedded with neural network equation of state was used to calculate the blast wave parameters of the explosion of fission device. In the process of neural network construction, the structure of neural network was optimized by enumeration experiment, and the structure of multi-layer neural network with a simple structure and good precision was obtained. In the process of numerical calculation, the code called the embedded neural network equations of state module, calculated the pressure of the explosive product through the density and the specific internal energy, and the flow field parameters of the whole explosive blast wave were finally obtained. The numerical results show that the calculated peak overpressure, arrival time and positive pressure duration coincide with the standard values, which proves the feasibility of the application of the neural network equation of states in the intense blast wave calculations. The results are of great significance to the numerical simulation of intense explosion.
- blast wave,
- neural network,
- equation of state,
- numerical simulation,
- sesame EOS data base

FullText(HTML)

References(16)

References

[1]	BLAZEK J. Computational fluid dynamics: principles and applications [M]. 3rd ed. San Diego: Butterworth-Heineman, 2015. DOI: 10.1016/C2013-0-19038-1.
[2]	奥尔连科Л Π. 爆炸物理学 [M]. 孙承纬, 译. 北京: 科学出版社, 2011. ОРЛЕНΚО Л Π. Explosion physics [M]. SUN C W, trans. Beijing: Science Press, 2011.
[3]	GHABOUSSI J, GARRETT JR J H, WU X. Knowledge-based modeling of material behavior with neural networks [J]. Journal of Engineering Mechanics, 1991, 117(1): 132–153. DOI: 10.1061/(ASCE)0733-9399(1991)117:1(132.
[4]	JUNG S, GHABOUSSI J. Neural network constitutive model for rate-dependent materials [J]. Computers & Structures, 2006, 84(15/16): 955–963. DOI: 10.1016/j.compstruc.2006.02.015.
[5]	曹吉星. 钢纤维混凝土的动态本构模型及其有限元方法 [D]. 成都: 西南交通大学, 2011. CAO J X. Dynamic constitutive model of steel fiber reinforced concrete and its finite element method [D]. Chengdu: Southwest Jiaotong University, 2011.
[6]	何龙, 张冉阳, 赵刚要, 等. 基于BP神经网络的GH5188高温合金本构模型 [J]. 特种铸造及有色合金, 2021, 41(2): 223–226. DOI: 10.15980/j.tzzz.2021.02.020. HE L, ZHANG R Y, ZHAO G Y, et al. Constitutive model of GH5188 superalloy based on BP neural network [J]. Special Casting & Nonferrous Alloys, 2021, 41(2): 223–226. DOI: 10.15980/j.tzzz.2021.02.020.
[7]	崔浩, 郭锐, 宋浦, 等. BP-GA算法确定未反应炸药的JWL状态方程参数 [J]. 含能材料, 2022, 30(1): 43–49. DOI: 10.11943/CJEM2021133. CUI H, GUO R, SONG P, et al. Determination of parameters of JWL equation of state for unreacted explosives based on BP-GA algorithm [J]. Chinese Journal of Energetic Materials, 2022, 30(1): 43–49. DOI: 10.11943/CJEM2021133.
[8]	乔登江. 地下核爆炸现象学概论(上册) [M]. 北京: 国防工业出版社, 2002: 46-47.
[9]	夏先贵. SESAME库的引进和开发 [J]. 爆轰波与冲击波, 1992(2): 26–29. XIA X G. Introduction and development of SESAME data [J]. Detonation Wave & Shock Wave, 1992(2): 26–29.
[10]	夏先贵. 开发应用SESAME EOS数据库在爆轰物理实验中应用 [J]. 爆轰波与冲击波, 1993(3): 19–28.
[11]	周鼎涛. BP神经网络是不是隐含层节点越多越好, 还是只要最优就行? [EB/OL]. (2022-03-22)[2022-04-01]. https://m.jingyanlib.com/resultpage?id=J7SO2K-JQlcCGxGpLzDIEw.
[12]	姚成宝, 付梅艳, 韩峰, 等. 欧拉坐标系下具有锐利相界面的可压缩多介质流动数值方法研究 [J]. 力学学报, 2020, 52(4): 1063–1079. DOI: 10.6052/0459-1879-20-054. YAO C B, FU M Y, HAN F, et al. Numerical scheme of multi-material compressible flow with sharp interface on eulerian grids [J]. Chinese Journal of Theoretical and Applied Mechanics, 2020, 52(4): 1063–1079. DOI: 10.6052/0459-1879-20-054.
[13]	KHAN F A. On Tsar Bomba—the most powerful nuclear weapon ever tested [J]. Physics Education, 2021, 56(1): 013002. DOI: 10.1088/1361-6552/abbcbc.
[14]	FETTER S, FROLOV V A, MILLER M, et al. Detecting nuclear warheads [J]. Science & Global Security, 1990, 1(3/4): 225–253. DOI: 10.1080/08929889008426333.
[15]	TAYLOR T B. Verified elimination of nuclear warheads [J]. Science & Global Security, 1989, 1(1/2): 1–26. DOI: 10.1080/08929888908426321.
[16]	GLASSTONE S, DOLAN P J. The effects of nuclear weapons [M]. Washington: United States Department of Defense and the United States Department of Energy, 1977.

Relative Articles

[1]	GUO Tongtong, GUO Yu, YU Jun, CHEN Juan, WANG Haikun, ZHANG Lunping. Rapid prediction and optimization method for protective effectiveness of flexibly supported plate structure under underwater explosive[J]. Explosion And Shock Waves, 2024, 44(10): 105101. doi: 10.11883/bzycj-2024-0068
[2]	QIN Shuai, LIU Hao, CHEN Li, ZHANG Lei. Outlier detection algorithms for penetration depth data of concrete targets combined with prior knowledge[J]. Explosion And Shock Waves, 2024, 44(3): 031406. doi: 10.11883/bzycj-2023-0287
[3]	LIU Di, CHEN Jing, ZHANG Anqiang, ZHAO Xiaodong, ZHANG Shuangbo, KANG Jianyi, LI Chaolong, ZENG Ling. Numerical simulation study on the protective effects of polyurea materials against lung blast injuries under blast wave loading[J]. Explosion And Shock Waves, 2024, 44(12): 121423. doi: 10.11883/bzycj-2024-0205
[4]	ZHANG Shizhong, LI Jinping, KANG Yue, HU Jianqiao, CHEN Hong. Generation of near-field blast wave by means of shock tube[J]. Explosion And Shock Waves, 2024, 44(12): 121434. doi: 10.11883/bzycj-2024-0204
[5]	JIAO Junjie, SHAN Feng, WANG Hancheng, QI Yanjie, PAN Xuchao, FANG Zhong, CHENG Yubo, HE Xiaolan, CI Shengjie, HE Yong. Determination of JWL equation of state based on the detonation product from underwater explosion[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2024-0203
[6]	ZHANG Yuzhuo, ZHAO Zheng. Parameter inversion of the polymethyl methacrylate constitutive model based on explosive cutting experiment[J]. Explosion And Shock Waves, 2023, 43(5): 054201. doi: 10.11883/bzycj-2023-0006
[7]	WANG Zhi, CHANG Lijun, HUANG Xingyuan, CAI Zhihua. Simulation on the defending effect of composite structure of body armor under the combined action of blast wave and fragments[J]. Explosion And Shock Waves, 2023, 43(6): 063202. doi: 10.11883/bzycj-2022-0515
[8]	ZHANG Wenchao, WANG Shu, LIANG Zengyou, QIN Bin, LU Haitao, CHEN Xinyuan, LU Wenjie. A study of blast wave protection efficiency of helmet based on air flow field pressure analysis[J]. Explosion And Shock Waves, 2022, 42(11): 113201. doi: 10.11883/bzycj-2021-0411
[9]	WANG Qinghua, GUO Weiguo, XU Feng, GAO Meng, WANG Zhihao. Synchronous and decoupling calibration of tri-axial impact force transducers based on a Hopkinson bar and an artificial neural network[J]. Explosion And Shock Waves, 2022, 42(10): 104101. doi: 10.11883/bzycj-2022-0015
[10]	ZHANG Lei, WU Hao, ZHAO Qiang, WANG Xing, REN Xinjian, WANG Jimin, KONG Defeng. Calculation method of damage effects of underground engineering objectives based on data mining technology[J]. Explosion And Shock Waves, 2021, 41(3): 031101. doi: 10.11883/bzycj-2020-0114
[11]	LI Zhijie, YOU Xiaochuan, LIU Zhanli, DU Zhibo, ZHANG Yi, YANG Ce, ZHUANG Zhuo. Numerical simulation of the mechanism of traumatic brain injury induced by blast shock waves[J]. Explosion And Shock Waves, 2020, 40(1): 015901. doi: 10.11883/bzycj-2018-0348
[12]	SUN Yuxiang, WANG Jie, WU Haijun, ZHOU Jiequn, LI Jinzhu, PI Aiguo, HUANG Fenglei. Experiment and simulation on high-pressure equation of state for concrete[J]. Explosion And Shock Waves, 2020, 40(12): 121401. doi: 10.11883/bzycj-2020-0002
[13]	LI Mingxing, WANG Xianhui, ZHOU Yunbo, SUN Xiaowang, ZENG Bin, HU Wenhai. Research on optimization of vehicle anti-shock protection components based on neural network[J]. Explosion And Shock Waves, 2020, 40(2): 024203. doi: 10.11883/bzycj-2019-0055
[14]	Sun Huixiang, Lu Feng, Chi Weisheng, Kang Ting, Liu Yuanfei. Dynamic interaction between surrounding rock and initial supporting structure subjected to explosion shock wave[J]. Explosion And Shock Waves, 2017, 37(4): 670-676. doi: 10.11883/1001-1455(2017)04-0670-07
[15]	Li Li-sha, Du Jian-guo, Zhang Hong-hai, Xie Qing-liang. Numerical simulation of damage of brick wall subjected to blast shock vibration[J]. Explosion And Shock Waves, 2015, 35(4): 459-466. doi: 10.11883/1001-1455(2015)04-0459-08
[16]	Yuan Shuai, Wen Shang-gang, Li Ping, Dong Yu-bin. Simulation of free surface particle velocity of flyer under the strong detonation drive[J]. Explosion And Shock Waves, 2015, 35(2): 197-202. doi: 10.11883/1001-1455(2015)02-0197-06
[17]	Wei Xian-feng, Long Xin-ping, Han Yong. Studies on the state equation of the underwater detonation products for PBX-01 explosive[J]. Explosion And Shock Waves, 2015, 35(4): 599-602. doi: 10.11883/1001-1455(2015)04-0599-04
[18]	CHEN Jun, ZENG Dai-peng, SUN Cheng-wei, ZHANG Zhen-yu, TAND uo-wang. Equationsofstateforoverdriven-detonationproducts ofJB-9014explosive[J]. Explosion And Shock Waves, 2010, 30(6): 583-587. doi: 10.11883/1001-1455(2010)06-0583-05
[19]	ZHAO Yan-hong, LIU Hai-feng, ZHANG Guang-cai. EquationofstateofdetonationproductsforPBX9502explosive[J]. Explosion And Shock Waves, 2010, 30(6): 647-651. doi: 10.11883/1001-1455(2010)06-0647-05
[20]	LI De-hua1, CHENG Xin-lu, YANG Xiang-dong, WU Guo-dong. Numerical simulation of detonation parameters for PETN, RDX and HMX explosives[J]. Explosion And Shock Waves, 2005, 25(4): 325-329. doi: 10.11883/1001-1455(2005)04-0325-05

Supplements(0)

Cited By

Cited by

Periodical cited type(4)

1.	李珩，马国锐，刘宇迪，张海明. 基于遥感影像的大当量爆炸建筑物毁伤评估模型. 爆炸与冲击. 2024(03): 80-89 . 本站查看
2.	秦帅，刘浩，陈力，张磊. 融合先验知识的混凝土侵彻深度试验数据异常点检测算法. 爆炸与冲击. 2024(03): 70-79 . 本站查看
3.	马天宝，龙俊文，刘玥. 基于BP神经网络的水中双爆源爆炸冲击波峰值压力预测模型研究. 北京理工大学学报. 2024(03): 260-269 .
4.	韩小祥，李君，张欣，原林，刘洋，王博宇. 核爆炸光辐射能量分布的模拟仿真研究. 强激光与粒子束. 2024(07): 119-130 .