Reliability on Underwater Target Structure Subjected to Underwater Explosion
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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。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.
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Key words:
- underwater explosion load /
- reliability /
- failure probability /
- neural network /
- Monte-Carlo method
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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 MExperimental
result1 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. 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
valuePolynomial regression BP neural network 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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