Prediction of peak pressure in the explosion<sub>-</sub>vented vessel with a venting duct based on support vector machine

Zhang Qing-wu; Jiang Jun-cheng; Yu Yuan; Cui Yi-hu

doi:10.11883/1001-1455(2014)06-0748-06

Volume 34 Issue 6

Dec. 2014

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Zhang Qing-wu, Jiang Jun-cheng, Yu Yuan, Cui Yi-hu. Prediction of peak pressure in the explosion-vented vessel with a venting duct based on support vector machine[J]. Explosion And Shock Waves, 2014, 34(6): 748-753. doi: 10.11883/1001-1455(2014)06-0748-06

Citation:

Zhang Qing-wu, Jiang Jun-cheng, Yu Yuan, Cui Yi-hu. Prediction of peak pressure in the explosion_-vented vessel with a venting duct based on support vector machine[J]. Explosion And Shock Waves, 2014, 34(6): 748-753. doi: 10.11883/1001-1455(2014)06-0748-06

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Prediction of peak pressure in the explosion_-vented vessel with a venting duct based on support vector machine

doi: 10.11883/1001-1455(2014)06-0748-06

School of Urban Construction and Safety Engineering, Nanjing University of Technology, Nanjing210009, Jiangsu, China

Funds: Supported bythe National Natural Science Foundation of China (20976081, 50904037)

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Corresponding author: Jiang Jun-cheng, jcjiang@njut.edu.cn
Received Date: 2013-04-12
Rev Recd Date: 2013-06-08
Publish Date: 2014-11-25

Abstract

Abstract

To predict the peak pressure in the explosion－vented vessel with a venting duct, the influencing factors on the peak pressure were abstracted from the experimental data in literatures.The abstracted factors were deployed as the inputs to the support vector machine(SVM), and the corresponding peak pressures were used as the outputs.Thereby, the SVM model was developed.The validity of the SVM model was checked by comparing the predictive capacities between the SVM model and the empirical formula.The results show that the SVM model has a better predictive capacity than the empirical formula.
- mechanics of explosion,
- peak pressure,
- support vector machine,
- explosion-vented vessel,
- venting duct,

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References(16)

References

[1]	American National Standards Institute. NFPA 68: Standard on explosion protection by deflagration venting[S]. Quincy: National Fire Protection Association, 2007.
[2]	Verein Deutscher Ingenieure. VDI 3673, PartⅠ: Pressure venting of dust explosions[S]. Dusseldorf: Verien Deutscher Ingenieure, 1995.
[3]	师喜林, 王志荣, 蒋军成.球形容器内气体的泄爆过程[J].爆炸与冲击, 2009, 29(4): 390-394. Shi Xi-lin, Wang Zhi-rong, Jiang Jun-cheng. Explosion-vented processes for methane-air premixed gas in spherical vessels with venting pipes[J]. Explosion and Shock Waves, 2009, 29(4): 390-394.
[4]	Ponizy B, Leyer J C. Flame dynamics in a vented vessel connected to a duct: 1. Mechanism of vessel-duct interaction[J]. Combustion and Flame, 1999, 116: 259-271.
[5]	Ponizy B, Leyer J C. Flame dynamics in a vented vessel connected to a duct: 2. Influence of ignition site, membrane rupture, and turbulence[J]. Combustion and Flame, 1999, 116: 272-281.
[6]	Ferrara G, Willacy S K, Phylaktou H N, et al. Venting of gas explosion through relief ducts: Interaction between internal and external explosions[J]. Journal of Hazardous Materials, 2008, 155: 358-368.
[7]	Benedetto A D, Russo P, Salzano E. The design of duct venting of gas explosions[J]. Process Safety Progress, 2008, 27(2): 164-172.
[8]	Bartknecht W, Zwahlen G. Explosionsschutz[J]. Grundlagen und Anwendung, 1993: 470-535.
[9]	Vapnik V N. The nature of statistical learning theory[M]. Springer, 1995.
[10]	钱渊, 宋军, 傅柯.基于支持向量机补偿的灰色模型网络流量预测[J].探测与控制学报, 2012, 34(1): 69-72. Qian Yuan, Song Jun, Fu Ke. Grey model of network traffic prediction based on support vector machines[J]. Journal of Detection and Control, 2012, 34(1): 69-72.
[11]	杜颖, 卢继平, 李青, 等.基于最小二乘支持向量机的风电场短期风速预测[J].电网技术, 2008, 32(15): 62-66. Du Ying, Lu Ji-ping, Li Qing, et al. Short-term wind speed forecasting of wind farm based on least square-support vector machine[J]. Power System Technology, 2008, 32(15): 62-66.
[12]	Esen H, Inalli M, Sengur A, et al. Modeling aground-coupled heat pump system by a support vector machine[J]. Renewable Energy, 2008, 33(8): 1814-1823.
[13]	Pan Yong, Jiang Jiang-jun, Wang Rui, et al. A novel QSPR model for prediction of lower flammability limits of organic compounds based on support vector machine[J]. Journal of Hazardous Materials, 2009, 168: 962-969.
[14]	Cai Jie-jin. Applying support vector machine to predict the critical heat flux in concentric-tube open thermosiphon[J]. Annals of Nuclear Energy, 2012, 43: 114-122.
[15]	孟倩, 王洪权, 王永胜, 等.煤自燃极限参数的支持向量机预测模型[J].煤炭学报, 2009, 34(11): 1489-1493. Meng Qian, Wang Hong-quan, Wang Yong-sheng, et al. Predicting limit parameters of coal self-ignition based on support vector machine[J]. Journal of China Coal Society, 2009, 34(11): 1489-1493.
[16]	杨斌, 匡立春, 孙中春.一种用于测井油气层综合识别的支持向量机方法[J].测井技术, 2005, 29(6): 511-514. Yang Bin, Kuang Li-chun, Sun Zhong-chun. On support vector machines method to identify oil and gas zone with logging and mudlog information[J]. Well Logging Technology, 2005, 29(6): 511-514.