Multi-Factors Analysis of Annular Shaped Charge Capability of Penetrating Target Board
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摘要: 环型聚能装药结构参数与其侵彻靶板能力间的关系难以用精确的数学函数表达,因此利用灰色关联理论建立描述该关系的模型是有意义的。首先采用灰色关联度理论对正交试验数据进行初步处理分析,将多目标问题转化为单目标问题,得到各结构参数与侵彻靶板能力的灰色关联度;然后应用基于支持向量机回归、粒子群优化、遗传算法等参数寻优算法的支持向量机(SVM)网络回归模型对灰色关联度进行预测,从而实现对环型聚能装药侵彻靶板能力的计算。结果表明,使用基于遗传算法参数寻优的SVM网络回归模型拟合精度最高,该模型可以很好地描述正交试验中环型聚能装药结构参数与侵彻靶板能力间的关系。最后选用正交试验外的一组数据,应用LS-DYNA对该结构参数下的环型聚能装药侵彻靶板过程进行仿真,并将仿真试验数据与SVM网络回归模型的预测值作比较,验证了该模型的可靠性。Abstract: The relationship between the structural parameters and penetration capacity of annular shaped charge is hard to express by analytical mathematical formulas, so it is meaningful to establish a model to describe this relationship using the grey relational analysis theory.In our study, the orthogonal test data were at first processed by the grey correlation theory, transforming the multi-objective problem into a single objective problem and obtaining the grey correlation degree between the structural parameters and penetration ability.The support vector machine (SVM) network regression models based on the parameter optimization algorithm methods of support vector regression, particle swarm optimazition, and genetic algorithm were then used to forecast the grey correlation degree and predict annular shaped charge capability.The results show that the fitted curve of the SVM regression model based on the GA algorithm is the most accurate, and can successfully describe the relationship between the structural parameters and penetrating ability of the annular shaped charge in the orthogonal test.Finally, using a set of data out of the orthogonal test, the SVM prediction model is verified by comparing the predicted results with the results obtained by LS-DYNA software.
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No. 2α/(°) δ/(mm) h/(mm) H/(mm) 1 55 4.0 115 70 2 60 4.5 120 75 3 65 5.0 125 80 4 70 5.5 130 85 No. 2α/
(°)δ/
(mm)h/
(mm)H/
(mm)t/
(μs)v/
(m/s)1 55 4.0 115 70 78.01 1033 2 55 4.5 120 75 66.00 1125 3 55 5.0 125 80 56.02 1139 4 55 5.5 130 85 57.98 1152 5 60 4.0 120 80 54.03 1331 6 60 4.5 115 85 56.01 1274 7 60 5.0 130 70 50.01 1303 8 60 5.5 125 75 51.99 1328 9 65 4.0 125 85 45.99 1542 10 65 4.5 130 80 44.00 1477 11 65 5.0 115 75 50.01 1265 12 65 5.5 120 70 52.03 1264 13 70 4.0 130 75 42.00 1506 14 70 4.5 125 70 46.00 1425 15 70 5.0 120 85 48.04 1421 16 70 5.5 115 80 50.02 1308 表 3 原始数据归一化后的结果
Table 3. Nomalized results of original data
No. A B C D t* v* 1 1.00 1.00 1.00 1.00 1.00 1.00 2 1.00 0.67 0.67 0.67 0.82 0.33 3 1.00 0.33 0.33 0.33 0.79 0.61 4 1.00 0 0 0 0.77 0.56 5 0.67 1.00 0.67 0.33 0.41 0.67 6 0.67 0.67 1.00 0 0.53 0.61 7 0.67 0.33 0 1.00 0.47 0.78 8 0.67 0 0.33 0.67 0.42 0.72 9 0.33 1.00 0.33 0 0 0.89 10 0.33 0.67 0 0.33 0.13 0.94 11 0.33 0.33 1.00 0.67 0.54 0.78 12 0.33 0 0.67 1.00 0.55 0.72 13 0 1.00 0 0.67 0.07 1.00 14 0 0.67 0.33 1.00 0.23 0.89 15 0 0.33 0.67 0 0.24 0.83 16 0 0 1.00 0.33 0.46 0.78 表 4 穿靶时间与比较序列因素灰色关联度计算结果
Table 4. Grey correlation calculation results based on penetrated target time and the comparison sequence factors
Factor Grey correlation coefficient Connectivity A 0.33 0.43 0.56 0.53 1.00 0.90 0.82 0.90 0.47 0.45 0.53 0.56 0.33 0.36 0.38 0.39 0.52 B 0.33 0.60 0.64 0.47 0.60 0.90 0.53 0.41 0.82 0.64 0.53 0.41 1.00 0.69 0.50 0.39 0.59 C 0.33 0.60 0.64 0.47 1.00 0.56 0.39 0.56 0.47 0.35 0.69 0.90 0.33 0.47 0.75 0.69 0.55 D 0.33 0.60 0.64 0.47 0.60 0.45 0.69 0.90 0.36 0.45 0.82 0.64 0.60 0.82 0.38 0.53 0.55 表 5 剩余速度与比较序列因素灰色关联度计算结果
Table 5. Grey correlation calculation results based on penetrated residual velocity and the comparison sequence factors
Factor Grey correlation coefficient Connectivity A 1.00 0.73 0.71 0.68 0.66 0.78 0.72 0.67 0.60 0.71 0.70 0.70 0.88 0.69 0.68 0.52 0.69 B 1.00 0.77 0.52 0.39 0.46 0.78 0.79 0.54 0.33 0.48 0.70 0.48 0.35 0.53 0.84 0.52 0.53 C 1.00 0.77 0.52 0.39 0.66 0.51 0.52 0.85 0.60 0.80 0.52 0.81 0.88 0.83 0.54 0.48 0.60 D 1.00 0.77 0.52 0.39 0.86 0.49 0.49 0.67 1.00 0.71 0.80 0.52 0.46 0.39 0.68 0.80 0.59 表 6 穿靶时间与每次试验的灰色关联度计算结果
Table 6. Results of grey correlation degree on penetrated target time with each test
No. Grey correlation coefficent Connectivity 1 0.33 0.33 0.33 0.33 0.3333 2 0.43 0.60 0.60 0.60 0.5873 3 0.56 0.64 0.64 0.64 0.6371 4 0.53 0.47 0.47 0.47 0.4776 5 1.00 0.60 1.00 0.60 0.7528 6 0.90 0.90 0.56 0.45 0.6571 7 0.82 0.53 0.39 0.69 0.5588 8 0.90 0.41 0.56 0.90 0.6443 9 0.47 0.82 0.47 0.36 0.5449 10 0.45 0.64 0.35 0.45 0.4774 11 0.53 0.53 0.69 0.82 0.6687 12 0.56 0.41 0.90 0.64 0.6443 13 0.33 1.00 0.33 0.60 0.6211 14 0.36 0.69 0.47 0.82 0.6388 15 0.37 0.50 0.75 0.38 0.5298 16 0.39 0.39 0.69 0.53 0.5267 表 7 参考序列因素与每次试验灰色关联度计算结果
Table 7. Grey correlation calculation results based on the reference sequence factors and each test
No. Grey correlation coefficient Connectivity 1 0.3333 1.0000 0.6605 2 0.5873 0.7611 0.6726 3 0.6371 0.5510 0.5948 4 0.4776 0.4404 0.4593 5 0.7528 0.6624 0.7084 6 0.6571 0.6237 0.6407 7 0.5588 0.6149 0.5863 8 0.6443 0.6854 0.6645 9 0.5449 0.6374 0.5903 10 0.4774 0.6695 0.5717 11 0.6687 0.6808 0.6746 12 0.6443 0.6183 0.6315 13 0.6211 0.6108 0.6160 14 0.6388 0.6012 0.6203 15 0.5298 0.6840 0.6055 16 0.5267 0.5874 0.5565 -
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