Prediction of Shock Sensitivity of RDX-based Composite Explosive by Particle Swarm Neural Network in Large-scale Gap Test

doi:10.3969/j.issn.1000-1093.2014.02.008

Abstract

Abstract: The large-scale gap thickness value of RDX-based composite explosive shock sensitivity is predicted by particle swarm neural network for reducing the number of tests and saving the test cost. 41 groups of RDX-based composite explosives with different densities, void ratios, charge structures and RDX contents are selected for test. The practical density of explosive, void ratio, RDX and additives content are taken into account as main influence factors. The nonlinear relationship among three influence factors and large scale gap thickness value is analyzed. The neural network model optimized by particle swarm algorithm is established for the above four variables and gap thickness value. The calculation results show that there is a good mapping model between the four variables and large scale gap thickness value; the predicted values are in good agreement with the experimental results, and the relative error is within 10%. The predicted value of the particle sarwm neural network can provide reference for the large-scale gap test of RDX-based composite explosive.

Key words: ordnance science and technology, composite explosive, shock sensitivity, large-scale gap test, neural network

CLC Number:

TJ55

YUAN Jun-ming, LIU Yu-cun, WANG Jian-hua, CHANG Shuang-jun, YU Yan-wu. Prediction of Shock Sensitivity of RDX-based Composite Explosive by Particle Swarm Neural Network in Large-scale Gap Test[J]. Acta Armamentarii, 2014, 35(2): 188-193.

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