Application of genetic algorithm to calculation of detonation parameters
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摘要: 为解决爆轰参数计算中自变量取值范围要求严格以及收敛性差等问题，在最小自由能法的基础上，引入遗传算法建立了炸药爆轰参数的计算方法，并利用典型单质炸药和混合炸药的实验结果对计算结果进行了验证。结果表明：采用该方法计算得到的单质和混合炸药的爆速、爆压与实验测试结果基本一致，误差在5%以内，满足炸药性能预估的要求；该方法人工干预弱，只需一次性确定少数主要组分物质的量的变化范围，可适应于多配方优化计算。Abstract: To solve the problems of bad convergence and to satisfy the strict requirement to initial conditions in the calculation of detonation parameters, a new calculation method was proposed based on the principles of chemical equilibrium and the method of minimum free energy by introducing the genetic algorithm, and the calculated results of typical explosives were compared with the experimental data to verify the validity and the accuracy of the new method. It is demonstrated that, by the new method, the calculated results of detonation pressure and velocity of the explosives correspond well with the experimental data with the errors under 5%, which can be applied to predict explosive properties. Also, the computational process is convenient with little manual intervention and entails only the verification of the moles variation form of the few major ingredients, which may be used for multi-formula optimization design.
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Key words:
- mechanics of explosion /
- genetic algorithm /
- minimum free energy /
- detonation parameters
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表 1 不同炸药爆轰产物各组分的物质的量的计算结果
Table 1. Amount of substance calculted by different methods for different detonation products of different explosives
炸药 计算方法 x(Cs)/mol x(CO)/mol x(CO2)/mol x(H2O)/mol x(NH3)/mol x(H2)/mol x(N2)/mol TNT 遗传算法 22.651 0.920 7.252 10.993 0.006 0.004 6.601 平衡常数法 23.084 0.009 7.665 10.970 0.000 0.000 6.608 RDX 遗传算法 6.649 0.219 6.646 13.513 0.000 0.000 13.512 平衡常数法 6.564 0.000 6.564 13.216 0.000 0.000 13.216 HMX 遗传算法 6.721 0.059 6.724 13.502 0.001 0.000 13.504 平衡常数法 6.757 0.000 6.757 13.514 0.000 0.000 13.514 TATB 遗传算法 17.323 0.239 5.694 11.626 0.001 0.000 11.626 平衡常数法 17.428 0.000 5.809 11.619 0.000 0.000 11.619 TNT36/RDX64 遗传算法 12.474 0.346 6.925 12.610 0.001 0.001 11.025 平衡常数法 12.647 0.000 7.098 12.612 0.000 0.000 11.027 PETN50/
TNT50遗传算法 12.653 0.972 9.694 11.823 0.002 0.002 6.463 平衡常数法 13.140 0.000 10.178 11.829 0.000 0.000 6.465 HMX76.3/
TNT23.7遗传算法 10.524 0.214 6.871 12.912 0.000 0.000 11.868 平衡常数法 10.631 0.000 6.978 12.913 0.000 0.000 11.869 
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表 2 不同方法得到的不同炸药的爆轰速度和爆轰压力
Table 2. Detonation velocity and pressure obtained by different methods for different explosives
炸药 Dexp/(km·s-1) pexp/GPa 计算方法 Dcal/(m·s-1) $\frac{{{D_{{\rm{ cal }}}} - {D_{{\rm{ exp }}}}}}{{{D_{{\rm{ cal }}}}}}/\% $ pcal/GPa $\frac{{{p_{{\rm{ cal }}}} - {p_{{\rm{ exp }}}}}}{{{p_{{\rm{ cal }}}}}}/\% $ TNT 6.950 19.5 遗传算法 7.064 1.6 20.2 3.6 平衡常数法 6.762 2.7 18.9 -3.1 RDX 8.754 34.7 遗传算法 8.875 1.4 35.0 0.8 平衡常数法 8.777 0.3 34.1 -1.5 HMX 9.100 39.3 遗传算法 9.287 2.1 40.6 3.3 平衡常数法 9.260 1.8 38.8 -1.3 TATB 7.860 31.5 遗传算法 8.234 4.7 32.0 1.6 平衡常数法 7.851 -0.1 28.9 -8.2 TNT36/
RDX648.030 29.4 遗传算法 8.186 1.9 29.0 -1.4 平衡常数法 8.046 0.2 27.4 -6.8 PETN50/
TNT507.465 24.8 遗传算法 7.718 3.4 24.5 -1.2 平衡常数法 7.532 0.9 23.4 -5.6 HMX76.3/
TNT23.78.476 34.3 遗传算法 8.695 2.5 34.3 0.0 平衡常数法 8.571 1.1 32.3 -5.8
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