A method for predicting peak pressure in an explosion shock tube based on BP neural network

CHEN Ziwei; WANG Zhongqi; ZENG Linghui

doi:10.11883/bzycj-2023-0187

Volume 44 Issue 5

May 2024

Turn off MathJax

Article Contents

Abstract

References

Article Navigation > Explosion And Shock Waves > 2024 > 44(5): 054101

Zhang Wei, Liu Yunfeng, Teng Honghui, Jiang Zonglin. Auto-ignition effect in gaseous detonation propagation[J]. Explosion And Shock Waves, 2017, 37(2): 274-282. doi: 10.11883/1001-1455(2017)02-0274-09

Citation:

CHEN Ziwei, WANG Zhongqi, ZENG Linghui. A method for predicting peak pressure in an explosion shock tube based on BP neural network[J]. Explosion And Shock Waves, 2024, 44(5): 054101. doi: 10.11883/bzycj-2023-0187

Citation:

PDF( 2352 KB)

A method for predicting peak pressure in an explosion shock tube based on BP neural network

doi: 10.11883/bzycj-2023-0187

State Key Laboratory of Explosive Science and Technology, Beijing Institute of Technology, Beijing 100081, China

Received Date: 2023-05-18
Rev Recd Date: 2024-03-15

Available Online: 2024-03-26

Publish Date: 2024-05-08

Abstract

Abstract

In response to the problems of the lack of corresponding empirical formulas and the poor timeliness of simulation for the explosive shock tube, and to quickly obtain the peak pressure of the shock tube used in explosions, a four-layer back propagation (BP) neural network was established to predict the peak pressure in the experimental section of the shock tube. After verifying the grid independence, numerical simulation was used to calculate the peak pressure of the test section of the shock tube, and the simulation data were compared with the experimental data of the shock tube explosion, and the average relative error is 2.49%. After proving the accuracy of the numerical simulation values, the 195 sets of peak pressure obtained from the numerical simulation in the shock tube test section were used as the output layer, and the TNT dosage in the shock tube driving section, aspect ratio of the charge column, and explosion proportional distance were used as the input layer for BP neural network training. To speed up the neural network iterations and increase the prediction accuracy, Adam's algorithm was used as an optimization algorithm for neural network error gradient descent. The results show that the predicted results obtained through the trained neural network are basically consistent with the simulated values, and the average relative error between the predicted results and the numerical values is 3.26%. In contrast to the evaluation metrics obtained using multiple regression analysis (mean absolute error (MAE) of 480 and coefficient of determination (R²) of 0.58), the four-layer BP neural network obtains a MAE of 25.4 and an R² of 0.99 for the validation set. The BP neural network model can reflect the mapping relationship between the peak pressure of the shock tube explosion and the influencing factors, and improve several times compared with the time required for numerical simulation, so it has the value of practical engineering applications.
- BP neural network,
- shock tube,
- peak pressure,
- adaptive moment estimation

FullText(HTML)

References(18)

References

[1]	徐春光, 白晓征, 刘瑜, 等. 爆炸激波管管口稀疏波对试验段的影响 [J]. 国防科技大学学报, 2011(4): 1–5. DOI: 10.3969/j.issn.1001-2486.2011.04.001. DOI: 10.3969/j.issn.1001-2486.2011.04.001. XU C G, BAI X Z, LIU Y, et al. Research on the influence of rarefaction wave to the experimental section in blast shock tube [J]. Journal of National University of Defense Technology, 2011(4): 1–5. DOI: 10.3969/j.issn.1001-2486.2011.04.001. DOI: 10.3969/j.issn.1001-2486.2011.04.001.
[2]	张军, 黄含军, 王军评, 等. 炸药驱动式爆炸管的载荷计算 [J]. 装备环境工程, 2021, 18(5): 21–27. DOI: 10.7643/issn.1672-9242.2021.05.004. ZHANG J, HUANG H J, WANG J P, et al. Simulation on the blast load inside the explosively drived shock tube [J]. Equipment Environment Engineering, 2021, 18(5): 21–27. DOI: 10.7643/issn.1672-9242.2021.05.004.
[3]	崔云霄, 王万鹏, 王雷元, 等. 压缩气体驱动大型激波管内部流场的数值模拟 [C]// 2014年中国计算力学大会论文集. 2014: 528–534. CUI Y X, WANG W P, WANG L Y, et al. Numerical simulation of Flow-Field in shockwave tube driven by condensed gas [C]//Proceedings of the 2014 Chinese Conference on Computational Mechanics, 2014: 528–534.
[4]	ISMAIL A, EZZELDIN M, EL-DAKHAKHNI W, et al. Blast load simulation using conical shock tube systems [J]. International Journal of Protective Structures, 2020, 11(2): 135–158. DOI: 10.1177/2041419619858098.
[5]	刘瑞朝, 任辉启, 徐翔云. 大型爆炸激波管数值模拟 [J]. 防护工程, 2009, 31(1): 31–35. LIU R C, REN H Q, XU X Y. Numerical simulation of large blast simulators [J]. Protective Engineering, 2009, 31(1): 31–35.
[6]	谢全民. 爆破振动信号分形维数相关性研究 [J]. 振动与冲击, 2021, 40(22): 48–51, 59. DOI: 10.13465/j.cnki.jvs.2021.22.007. XIE Q M. Correlation study on the fractal dimension of blasting vibration signals [J]. Journal of Vibration and Shock, 2021, 40(22): 48–51, 59. DOI: 10.13465/j.cnki.jvs.2021.22.007.
[7]	XU Y, HUANG Y M, MA G W. A beetle antennae search improved BP neural network model for predicting multi-factor-based gas explosion pressures [J]. Journal of Loss Prevention in the Process Industries, 2020, 65: 104117. DOI: 10.1016/j.jlp.2020.104117.
[8]	袁格侠, 刘宏昭, 钱学梅, 等. 求解超高压筒形容器爆破压力的神经网络方法 [J]. 兵器材料科学与工程, 2010, 33(2): 31–34. DOI: 10.3969/j.issn.1004-244X.2010.02.009. YUAN G X, LIU H Z, QIAN X M, et al. ANN-based prediction of bursting pressure under ultra-high pressure for cylindrical vessel [J]. Ordnance Material Science and Engineering, 2010, 33(2): 31–34. DOI: 10.3969/j.issn.1004-244X.2010.02.009.
[9]	唐泽斯, 郭进, 王金贵, 等. 基于人工神经网络的气体泄爆最大超压预测研究 [J]. 中国安全生产科学技术, 2020, 16(4): 56–62. DOI: 10.11731/j.issn.1673-193x.2020.04.009. TANG Z S, GUO J, WANG J G, et al. Study on prediction of maximum overpressure in gas explosion venting based on artificial neural network [J]. Journal of Safety Science and Technology, 2020, 16(4): 56–62. DOI: 10.11731/j.issn.1673-193x.2020.04.009.
[10]	李江涛. 矿井瓦斯爆炸超压值分布的神经网络预测方法 [J]. 煤矿安全, 2013, 44(2): 157–160. DOI: 10.13347/j.cnki.mkaq.2013.02.012. LI J T. Overpressure distribution prediction of mine gas explosion based on artificial neural networks [J]. Safety in Coal Mines, 2013, 44(2): 157–160. DOI: 10.13347/j.cnki.mkaq.2013.02.012.
[11]	严国建, 周明安, 余轮, 等. 空气中爆炸冲击波超压峰值的预测 [J]. 采矿技术, 2011, 11(5): 89–90, 112. DOI: 10.3969/j.issn.1671-2900.2011.05.035. YAN G J, ZHOU M A, YU L, et al. Prediction of the peak overpressure of explosive shock waves in air [J]. Mining Technology, 2011, 11(5): 89–90, 112. DOI: 10.3969/j.issn.1671-2900.2011.05.035.
[12]	施建俊, 李庆亚, 张琪, 等. 基于Matlab和BP神经网络的爆破振动预测系统 [J]. 爆炸与冲击, 2017, 37(6): 1087–1092. DOI: 10.11883/1001-1455(2017)06-1087-06. SHI J J, LI Q Y, ZHANG Q, et al. Forecast system for blasting vibration velocity peak based on Matlab and BP neural network [J]. Explosion and Shock Waves, 2017, 37(6): 1087–1092. DOI: 10.11883/1001-1455(2017)06-1087-06.
[13]	GUO Q P, YANG S J, WANG Y C, et al. Prediction research for blasting peak particle velocity based on random GA-BP network group [J]. Arabian Journal of Geosciences, 2022, 15(15): 1351. DOI: 10.1007/s12517-022-10615-3.
[14]	郭璇, 马思远, 郭一帆, 等. 基于BP神经网络的围岩介质爆炸峰值压力预测 [J]. 振动与冲击, 2019, 38(3): 199–206. DOI: 10.13465/j.cnki.jvs.2019.03.028. GUO X, MA S Y, GUO Y F, et al. Blast peak pressure prediction for surrounding rock medium based on BP neural network method [J]. Journal of Vibration and Shock, 2019, 38(3): 199–206. DOI: 10.13465/j.cnki.jvs.2019.03.028.
[15]	陈俊霖, 侯麒麟, 宁文学, 等. 基于BP神经网络的火箭弹射击效率计算方法 [J]. 火力与指挥控制, 2022, 47(6): 87–92. DOI: 10.3969/j.issn.1002-0640.2022.06.014. CHEN J L, HOU Q L, NING W X, et al. Calculation method of rocket firing efficiency based on BP neural network [J]. Fire Control & Command Control, 2022, 47(6): 87–92. DOI: 10.3969/j.issn.1002-0640.2022.06.014.
[16]	焦李成, 杨淑媛, 刘芳, 等. 神经网络七十年: 回顾与展望 [J]. 计算机学报, 2016, 39(8): 1697–1716. DOI: 10.11897/SP.J.1016.2016.01697. JIAO L C, YANG S Y, LIU F, et al. Seventy years beyond neural network: retrospect and prospect [J]. Chinese Journal of Computers, 2016, 39(8): 1697–1716. DOI: 10.11897/SP.J.1016.2016.01697.
[17]	段宝福, 张猛, 李俊猛. 逐孔起爆震动参数预报的BP神经网络模型 [J]. 爆炸与冲击, 2010, 30(4): 401–406. DOI: 10.11883/1001-1455(2010)04-0401-06. DUAN B F, ZHANG M, LI J M. A BP neural network model for forecasting of vibration parameters from hole by hole detonation [J]. Explosion and Shock Waves, 2010, 30(4): 401–406. DOI: 10.11883/1001-1455(2010)04-0401-06.
[18]	范勇, 裴勇, 杨广栋, 等. 基于改进PSO-BP神经网络的爆破振动速度峰值预测 [J]. 振动与冲击, 2022, 41(16): 194–203, 302. DOI: 10.13465/j.cnki.jvs.2022.16.025. FAN Y, PEI Y, YANG G D, et al. Prediction of blasting vibration velocity peak based on an improved PSO-BP neural network [J]. Journal of Vibration and Shock, 2022, 41(16): 194–203, 302. DOI: 10.13465/j.cnki.jvs.2022.16.025.

Relative Articles

[1]	XI Shangbin, SU Yu. Phase-field simulation of microstructural dynamics in NiTi shape memory alloys and their intrinsic strain rate sensitivities[J]. Explosion And Shock Waves, 2022, 42(9): 091403. doi: 10.11883/bzycj-2021-0461
[2]	SHI Tongya, LIU Dongsheng, CHEN Wei, XIE Puchu, WANG Xiaofeng, WANG Yonggang. Dynamic tensile behavior and spall fracture of GP1 stainless steel processed by selective laser melting[J]. Explosion And Shock Waves, 2019, 39(7): 073101. doi: 10.11883/bzycj-2019-0015
[3]	XU Lizhi, GAO Guangfa, ZHAO Zhen, WANG Jiangbo, CHENG Chun, DU Zhonghua. Compressive mechanical properties of polyethylene at different strain rates[J]. Explosion And Shock Waves, 2019, 39(1): 013301. doi: 10.11883/bzycj-2017-0266
[4]	Li Xianglong, Wang Jianguo, Zhang Zhiyu, Huang Yonghui. Experimental study for effects of strain rates and joint angles on dynamic responses of simulated rock materials[J]. Explosion And Shock Waves, 2016, 36(4): 483-490. doi: 10.11883/1001-1455(2016)04-0483-08
[5]	Hou Hai-zhou, Hu Yi-ting, Peng Jin-hua, Jin Jian-wei. Dynamic behavior and constitutive model of phenolic cotton fabric material under impact loading[J]. Explosion And Shock Waves, 2015, 35(6): 858-863. doi: 10.11883/1001-1455(2015)06-0858-06
[6]	Shi Fei-fei, Suo Tao, Hou Bing, Li Yu-long. Strain rate and temperature sensitivity and constitutive model of YB-2 of aeronautical acrylic polymer[J]. Explosion And Shock Waves, 2015, 35(6): 769-776. doi: 10.11883/1001-1455(2015)06-0769-08
[7]	Wang Peng-fei, Xu Song-lin, Li Zhi-bin, Hu Shi-sheng. Effect of micro-structure on the strain rate of cellular materials[J]. Explosion And Shock Waves, 2014, 34(3): 285-291. doi: 10.11883/1001-1455(2014)03-0285-07
[8]	Hong Liang, Jin Zhi-ren, Deng Zong-wei. Bar diameter effect of minimum loading strain rate in granite impacting tests by SHPB[J]. Explosion And Shock Waves, 2014, 34(3): 328-333. doi: 10.11883/1001-1455(2014)03-0328-06
[9]	WANG Peng-fei, HU Shi-sheng. Mechanicalpropertiesoffoamaluminum withdifferentsizes[J]. Explosion And Shock Waves, 2012, 32(4): 393-398. doi: 10.11883/1001-1455(2012)04-0393-06
[10]	WEI Yan-peng, YU Gang, DUAN Zhu-ping. Mechanicalpropertiesoflaser-weldeddissimilarstainlesssteelsstructure atelevatedtemperatureandhighstrainrates[J]. Explosion And Shock Waves, 2011, 31(5): 504-509. doi: 10.11883/1001-1455(2011)05-0504-06
[11]	FAN Fei-lin, XU Jin-yu, LI Wei-min, YANG Jin-yong, ZHAI Yi. Impact mechanical properties of slag- and fly ash-based geopolymer concrete[J]. Explosion And Shock Waves, 2009, 29(5): 516-522. doi: 10.11883/1001-1455(2009)05-0516-07
[12]	RONG Zhi-dan, SUN Wei. Influences of coarse aggregate on dynamic mechanical behaviors of ultrahigh-performance cementitious composites[J]. Explosion And Shock Waves, 2009, 29(4): 361-366. doi: 10.11883/1001-1455(2009)04-0361-06
[13]	XU Wei-fang, ZHONG Wei-zhou, CHEN Gang, LI Si-zhong, CHEN Zhong-fu, HUANG Xi-cheng. On geometric shape of the specimen in impact tensile test[J]. Explosion And Shock Waves, 2008, 28(2): 149-153. doi: 10.11883/1001-1455(2008)02-0149-05
[14]	LAN Sheng-wei, ZENG Xin-wu. Effect of grain size on dynamic mechanical properties of pure aluminum[J]. Explosion And Shock Waves, 2008, 28(5): 462-466. doi: 10.11883/1001-1455(2008)05-0462-05
[15]	GUO Wei-guo, LI Yu-long, HUANG Fu-zeng. Deformation and mechanical property of aluminium foam at different strain rates[J]. Explosion And Shock Waves, 2008, 28(4): 289-292. doi: 10.11883/1001-1455(2008)04-0289-04
[16]	LI Yu-long, GUO Wei-guo. Miniature-Hopkinson bar technique[J]. Explosion And Shock Waves, 2006, 26(4): 303-308. doi: 10.11883/1001-1455(2006)04-0303-06
[17]	JIANG Bang-hai, ZHANG Ruo-qi. Strain rate-dependent Tsai-Hill strength criteria for a carbon fiber woven reinforced composite[J]. Explosion And Shock Waves, 2006, 26(4): 333-338. doi: 10.11883/1001-1455(2006)04-0333-06
[18]	SUN Zi-jian, WANG Li-li. The constitutive behavior of PP/PA polymer blends taking account of damage evolution at high strain rate and large deformation[J]. Explosion And Shock Waves, 2006, 26(6): 492-497. doi: 10.11883/1001-1455(2006)06-0492-06
[19]	WU Xu-tao, HU Shi-sheng, CHEN De-xing, YU Ze-qing. Impact compression experiment of steel fiber reinforced high strength concrete[J]. Explosion And Shock Waves, 2005, 25(2): 125-131. doi: 10.11883/1001-1455(2005)02-0125-07
[20]	GUO Wei-guo. Flow stress and constitutive model of OFHC Cu for large deformation, different temperatures and different strain rates[J]. Explosion And Shock Waves, 2005, 25(3): 244-250. doi: 10.11883/1001-1455(2005)03-0244-07

Supplements(0)

Cited By

Cited by

Periodical cited type(4)

1.	赵焕娟，汪乐溪，张英华，林敏，周正青，蒋慧灵. 孔隙比对氢气爆轰波结构的定量影响. 工程热物理学报. 2024(01): 303-309 .
2.	刘思远，汪洋，李真珍，滕宏辉，田诚. 中心锥喷管喉道比参数对旋转爆轰燃烧影响的数值研究. 推进技术. 2023(09): 120-131 .
3.	王硕，王中，余杰皓. 喷射气流灭弧防雷器的爆轰弹丸设计研究. 电瓷避雷器. 2020(02): 20-25 .
4.	武郁文，褚驰，翁春生，郑权. 孔板扰动对爆轰波胞格结构特性影响的实验研究. 爆炸与冲击. 2019(11): 15-23 . 本站查看