电磁炮纤维缠绕约束结构多目标优化

doi:10.12382/bgxb.2023.1144

摘要/Abstract

摘要：

通过复合材料的经典层合板理论与坐标变换,简化材料模型,避免了复杂的复合材料铺层建模。为了解决电磁炮纤维缠绕约束结构的多目标优化问题,提出一种改进型免疫克隆布谷鸟多目标优化算法(Multi-objective Improved Immune Clonal Cuckoo Algorithm,MOIICCA)。通过对ZDT1~ZDT3测试函数的100组仿真计算,验证了MOIICCA的准确性,并利用反世代距离(Inverted Generational Distance,IGD)评价指标来度量MOIICCA的性能。通过引入深度神经网络(Deep Neural Network,DNN),以646组电磁炮有限元计算结果为训练集,训练出满足工程使用要求的DNN代理模型来代替有限元仿真,提高了多目标优化的计算效率。最后利用MOIICCA对电磁炮纤维缠绕约束结构进行多目标优化,得到符合多目标优化要求的Pareto解集。IGD结果表明:MOIICCA相比于多目标粒子群优化算法和非支配排序遗传算法具有更高的计算精度和计算效率且在高维问题求解时更具优势,测试的时间结果也表明MOIICCA可以在更短的时间内求解得到质量更优的Pareto解集。前10组Pareto解的结果表明,电磁炮纤维缠绕约束结构的碳纤维层1主要以提高环向强度为主、碳纤维层2主要以平衡环向强度与轴向刚度为主。

关键词: 电磁炮, 复合材料, 多目标优化, 纤维缠绕约束结构, 克隆选择算法, 布谷鸟搜索算法

Abstract:

Through the classical lamination theory and coordinate transformation, the material model is simplified, and the modeling of complex laminated composite is avoided. A multi-objective improved immune clonal cuckoo algorithm (MOIICCA) is proposed for the multi-objective optimization of fiber winding constrained structure of electromagnetic gun. The accuracy of MOIICCA algorithm is verified by 100 simulation calculations of ZDT1-ZDT3 test function, and the performance of MOIICCA algorithm is measured by inverted generational distance (IGD) evaluation index. By introducing the learning method of deep neural network and taking 646 groups of electromagnetic gun finite element calculation results as the training set, the deep neural network agent model which meets the engineering application requirements is trained to replace the finite element simulation, thus improving the computational efficiency of multi-objective optimization. Finally, MOIICCA algorithm is used to optimize the constrained structure of electromagnetic gun fiber winding, and the Pareto solution set is obtained. IGD results show that MOIICCA algorithm has higher computational accuracy and efficiency than the multiple objective particle swarm optimization algorithm and the non-dominated sorting genetic algorithm II, and has more advantages in solving the high-dimensional problems, and t. The test results also show that MOIICCA algorithm can get better Pareto set in a shorter time. The results of the first 10 sets of Pareto solutions show that the fiber layer 1 of the winding structure mainly improves the circumferential strength, and the fiber layer 2 mainly balances the circumferential strength and axial stiffness.

Key words: electromagnetic gun, composite material, multi-objective optimization, fiber winding constrained structure, clonal selection algorithm, cuckoo search algorithm

中图分类号:

TJ04

赵伟, 侯保林. 电磁炮纤维缠绕约束结构多目标优化[J]. 兵工学报, 2024, 45(11): 3820-3832.

ZHAO Wei, HOU Baolin. Multi-objective Optimization of Filament Winding Constrained Structure of Electromagnetic Gun[J]. Acta Armamentarii, 2024, 45(11): 3820-3832.

图/表 20

参考文献 23

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材料	密度ρ/(kg·m^-3)	弹性模量E/GPa	泊松比ν
铜导轨	8300	110.00	0.340
陶瓷绝缘材料	3896	293.15	0.218
钢套	7850	206.00	0.300

材料	密度ρ/(kg·m^-3)	弹性模量E/GPa	泊松比ν
铜导轨	8300	110.00	0.340
陶瓷绝缘材料	3896	293.15	0.218
钢套	7850	206.00	0.300

参数	数值	参数	数值
密度ρ/(kg·m^-3)	2000	环-径向泊松比ν_yz	0.4
轴向弹性模量E_xx/GPa	45.0	轴-径向泊松比ν_xz	0.2
环向弹性模量E_yy/GPa	10.0	轴-环向剪切模量G_xy/GPa	5.0
径向弹性模量E_zz/GPa	10.0	轴-径向剪切模量G_xz/GPa	3.8
轴-环向泊松比v_xy	0.3	环-径向剪切模量G_yz/GPa	5.0

参数	数值	参数	数值
密度ρ/(kg·m^-3)	2000	环-径向泊松比ν_yz	0.4
轴向弹性模量E_xx/GPa	45.0	轴-径向泊松比ν_xz	0.2
环向弹性模量E_yy/GPa	10.0	轴-环向剪切模量G_xy/GPa	5.0
径向弹性模量E_zz/GPa	10.0	轴-径向剪切模量G_xz/GPa	3.8
轴-环向泊松比v_xy	0.3	环-径向剪切模量G_yz/GPa	5.0

参数	数值	参数	数值
密度ρ/(kg·m^-3)	1680	轴向泊松比ν₁₂	0.259
轴向弹性模量E₁₁/GPa	143.8	轴向剪切模量G₁₂/GPa	4.82
横向弹性模量E₂₂/GPa	4.82	横向剪切模量G₂₃/GPa	3.38