水下爆炸载荷作用下水下目标结构的可靠性研究

李万; 张志华; 李华; 李大伟

doi:10.11858/gywlxb.2014.03.010

水下爆炸载荷作用下水下目标结构的可靠性研究

doi: 10.11858/gywlxb.2014.03.010

1.
海军驻九江地区军事代表室，江西九江 332007
2.
海军工程大学兵器工程系，湖北武汉 430033

基金项目: 国家自然科学基金(50775218);海军工程大学博士生创新基金(HGBSJJ2011009)

详细信息

作者简介:
李万(1986—)，男，博士研究生，主要从事武器系统总体技术、高维数据处理、水下爆炸等研究.E-mail:liwan3525083@163.com

中图分类号: O383.1
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出版历程
- 收稿日期: 2012-05-21
- 修回日期: 2012-08-23

Reliability on Underwater Target Structure Subjected to Underwater Explosion

1.
Military Representative Office of Navy in Jiujiang Area, Jiujiang 332007, China
2.
Weapon Engineering Department, Naval University of Engineering, Wuhan 430033, China

摘要

摘要: 为研究水下爆炸载荷作用下某水下目标结构的可靠性，通过开展多组试验获得了真实可靠的样本数据，进而分别利用多项式逐步回归和神经网络对样本数据进行拟合分析，获得结构响应变量和输入变量之间的近似解析表达式。然后利用蒙特卡洛模拟法得到结构响应的统计参数和分布函数，并对结构进行可靠性分析和计算，最后得到了爆炸点在平面内变化时结构的失效概率曲线，为工程结构的防护提供了参考。
- 水下爆炸载荷 /
- 可靠性 /
- 失效概率 /
- 神经网络 /
- 蒙特卡洛模拟法
Abstract: In order to study the reliability of underwater target structure subjected to underwater explosion, reliable sample data were obtained through many groups of experiments.Two methods including the stepwise polynomial regression method and the BP neural network method were presented to fit to these samples, and the approximate analytic expressions were achieved between the structural response variables and the input variables.Then Monte-Carlo method was used to obtain the statistical characteristics and distribution function of the structural response variables, through which the structure reliability could be calculated.Results show that the invalidation probability of target with explosion centre changing can be displayed, and this method provides reference for the defensive structure.
- underwater explosion load /
- reliability /
- failure probability /
- neural network /
- Monte-Carlo method

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图 1 3层BP网络结构

Figure 1. Structure of BP network with three layers

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图 2 网络训练结果

Figure 2. Network training results

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图 3 毁伤等效值分布直方图

Figure 3. Damage distribution histogram of damage equivalent value

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图 4 毁伤等效值的正态性检验

Figure 4. Normality test of damage equivalent value

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图 5 基于多项式逐步回归模型的失效概率

Figure 5. Failure probability based on stepwise polynomial regression model

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图 6 基于BP神经网络模型的失效概率

Figure 6. Failure probability based on BP neural network model

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表 1 不同方向角和爆炸距离的结构毁伤效果

Table 1. Damage results of the target at different direction angles and explosion distances

Experiment No.	α	R/(m)	M	Discrimination result by M	Experimental result
1	π/4	3.70	6.312 0	Ⅰ	Ⅰ
2	π/4	2.70	-3.803 6	Ⅱ	Ⅱ
3^﹟	π/4	3.20	3.652 5	Ⅰ	Ⅰ
4	π/4	2.55	0.659 4	Ⅰ	Ⅰ
5	0	2.70	-4.112 9	Ⅱ	Ⅱ
6	3π/4	2.70	1.946 8	Ⅰ	Ⅰ
7	π/2	2.70	0.244 0	Ⅰ	Ⅰ
8^﹟	3π/4	2.40	-6.903 1	Ⅱ	Ⅱ
9	3π/4	2.20	-5.045 9	Ⅱ	Ⅱ
10	3π/4	2.20	2.634 0	Ⅰ	Ⅰ
11	π/2	2.20	-3.932 6	Ⅱ	Ⅱ
12	π/4	3.20	1.170 3	Ⅰ	Ⅰ
13^﹟	π/4	2.90	-1.238 4	Ⅱ	Ⅱ
14	π/4	3.05	2.614 0	Ⅰ	Ⅰ
15	0	2.90	2.866 4	Ⅰ^*	Ⅱ^*
16	3π/4	2.90	4.361 8	Ⅰ	Ⅰ
17	π/2	2.90	3.529 8	Ⅰ	Ⅰ
18	π/2	2.70	4.482 3	Ⅰ	Ⅰ
19	3π/4	2.40	-0.816 8	Ⅱ	Ⅱ
Note: The data with * show that discrimination results are different from experimental results; The data with ﹟ are verification data.

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表 2 两种模型的预测结果分析

Table 2. Prediction results analysis for two kinds of model

Verification Exp.No.	Verification value	Polynomial regression		BP neural network
Verification Exp.No.	Verification value	Predictive value	Relative error/(%)	Predictive value	Relative error/(%)
3	3.652 5	3.566 7	2.35	3.589 1	1.74
8	-6.903 1	-4.675 1	32.28	-5.986 3	13.28
13	-1.238 4	-0.935 5	24.46	-1.268 7	2.45

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参考文献(8)

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