Response of a cylindrical steel container to internal explosive loading with variation in the degree of water filling

Ryzhanskii, V. A.; Ivanov, A. G.; Kovalev, N. P.; Simonov, G. P.; Chernyshov, Yu. D.; Минеев, В. П.; Zhukov, V. V.

doi:10.1007/bf02699484

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“…Furthermore, Refs. [4][5][6][7][8] deduced a relationship with the planestrain ring theory between the explosion weight and the maximum plastic deformation. For air-filled ECVs, by using the plane-strain ring theory, Duffey [9,10] obtained the final radial displacement in the explosion section and the displacement along the axial direction, which were both in good agreement with experimental results.…”

Section: Introductionmentioning

confidence: 99%

Dynamic responses of discrete multi-layered explosion containment vessels with the consideration of strain-hardening and strain-rate effects

Chen

Zheng

et al. 2010

International Journal of Impact Engineering

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Section: Introductionmentioning

confidence: 99%

Dynamic responses of discrete multi-layered explosion containment vessels with the consideration of strain-hardening and strain-rate effects

Chen

Zheng

et al. 2010

International Journal of Impact Engineering

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Explosion resistance of a steel cylindrical shell

Ryzhanskii

Syrunin

2011

Combust Explos Shock Waves

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Dynamic response prediction of water medium explosion container based on convolutional neural network

Fang

Zhong

et al. 2021

JCM

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The water medium explosion container is an experimental device that simulates explosion in different water depth environments by loading different hydrostatic pressures and different doses of explosive. To ensure its safety during service, it is necessary to study the dynamic response of water medium explosion container. Because the dynamic response is complicated and the correlation between the response and the load of the container is nonlinear, it is difficult to calculate the dynamic response by analytical and numerical methods. In this paper, a model is built based on convolutional neural network (CNN) to predict the dynamic response of water medium explosion container. The accuracy and usability of the CNN prediction model are verified by comparison with the prediction results of the BP neural network model. The results show that CNN can be effectively used to predict the strain response of the dynamic response of water medium explosion container. and this method will play an important role in the later study of the overall feature analysis of the dynamic response of the water medium explosion vessel.

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Response of a cylindrical steel container to internal explosive loading with variation in the degree of water filling

Cited by 4 publications

References 1 publication

Dynamic responses of discrete multi-layered explosion containment vessels with the consideration of strain-hardening and strain-rate effects

Dynamic responses of discrete multi-layered explosion containment vessels with the consideration of strain-hardening and strain-rate effects

Explosion resistance of a steel cylindrical shell

Dynamic response prediction of water medium explosion container based on convolutional neural network

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