Automatic Optimization of JWL‐Miller parameters of HMX‐based aluminized explosive based on genetic algorithm,Propellants, Explosives, Pyrotechnics

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Automatic Optimization of JWL‐Miller parameters of HMX‐based aluminized explosive based on genetic algorithm
Propellants, Explosives, Pyrotechnics ( IF 1.7 ) Pub Date : 2024-03-15 , DOI: 10.1002/prep.202300195
Xing‐Long Li _{1,

2} , Ke‐Quan Chen _{1,

2} , Heng‐Jian Huang _{1,

2} , Sha Yang _{1,

2} , Qing‐Guan Song _{1,

2} , Wei Cao _{1,

2} , Zhong‐Hua Lu _{1,

2} , Chao Tian _{1,

2} , Cheng Hua _{1,

2}

Affiliation

The calibration of JWL‐Miller equation of state (EOS) parameters for aluminized explosive is a cumbersome but important work in explosive evaluation. Manual calibration is usually adopted while the work may be tedious and the optimal results may be unachievable. An automatic calibrating method was established to optimize this procedure based on genetic algorithm program and finite element software. Optimal JWL‐Miller EOS parameters were achieved by iterative calculation calibrating with cylinder test results and underwater‐explosion experiment results. Cylinder test results were adopted to illustrate the initial phase of explosion, and underwater explosion experiments were conducted to calibrating the Miller term of the equation of state. The results showed that the error between cylinder test and simulation result was less than 1 %, the error of underwater explosion impulse between test and simulation results was less than 3.73 %. The optimized parameters of JWL‐Miller EOS will be useful in the numerical simulation research of aluminized explosives.

中文翻译：

基于遗传算法的HMX含铝炸药JWL-Miller参数自动优化

含铝炸药JWL-Miller状态方程（EOS）参数的标定是炸药评价中一项繁琐但重要的工作。通常采用手动校准，工作繁琐且达不到最佳结果。基于遗传算法程序和有限元软件，建立了自动标定方法来优化该过程。通过利用气缸试验结果和水下爆炸实验结果进行迭代计算标定，获得了最优的 JWL-Miller EOS 参数。采用圆筒试验结果来说明爆炸初始阶段，并进行水下爆炸实验来标定状态方程的米勒项。结果表明:气瓶试验与模拟结果误差小于1%,水下爆炸冲量试验与模拟结果误差小于3.73%。JWL-Miller EOS的优化参数将有助于含铝炸药的数值模拟研究。

更新日期：2024-03-15

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