基于动态预测的深水爆炸试验容器可靠性分析

李琳娜; 钟东望; 黄小武; 何理; 司剑峰; 涂圣武

doi:10.11883/bzycj-2020-0078

基于动态预测的深水爆炸试验容器可靠性分析

doi: 10.11883/bzycj-2020-0078

李琳娜^{1, 2,},
钟东望^{1, 2, ,},
黄小武^{3, 4},
何理^{1, 2},
司剑峰^{1, 2},
涂圣武¹

1.
武汉科技大学理学院，湖北武汉 430065
2.
武汉科技大学冶金工业过程系统科学湖北省重点实验室，湖北武汉 430065
3.
江汉大学爆破工程湖北省重点实验室，湖北武汉 430056
4.
武汉爆破有限公司，湖北武汉 430030

基金项目: 国家自然科学基金（51904210，51404175）；冶金工业过程系统科学湖北省重点实验室开放基金（Y201712）；爆破工程湖北省重点实验室开放基金（HKLBEF202009）

详细信息

作者简介:
李琳娜（1978－　），女，博士，教授，lilinna@wust.edu.cn

通讯作者:
钟东望（1963－　），男，博士，教授，12276086@qq.com

中图分类号: O389; TB114.3
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- 文章访问数: 800
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- 被引次数: 5
出版历程
- 收稿日期: 2020-03-23
- 修回日期: 2020-07-16
- 刊出日期: 2021-01-05

Reliability analysis of deepwater explosion test vessel based on dynamic prediction

LI Linna^{1, 2
,},
ZHONG Dongwang^{1, 2
, ,},
HUANG Xiaowu^{3, 4},
HE Li^{1, 2},
SI Jianfeng^{1, 2},
TU Shengwu¹

1.
College of Science, Wuhan University of Science and Technology, Wuhan 430065, Hubei, China
2.
Hubei Province Key Laboratory of Systems Science in Metallurgical Process, Wuhan University ofScience and Technology, Wuhan 430065, Hubei, China
3.
Hubei Key Laboratory of Blasting Engineering, Jianghan University, Wuhan 430056, Hubei, China
4.
Wuhan Blasting Co., Ltd., Wuhan 430030, Hubei, China

摘要

摘要: 为了确保深水爆炸试验容器在服役期间的安全性，提出了一种基于智能预测的随机-区间动态可靠性模型，通过动态测试数据建立了容器响应的广义回归神经网络（general regression neural network，GRNN）预测模型，获得了容器的最大应变区间变量，同时考虑容器结构的随机特性，开展了现役深水爆炸试验容器的可靠性分析，并分别采用3种方法进行了可靠性指标计算。分析结果表明，对于深水爆炸试验容器这类高可靠性且缺乏样本数据的结构，建立基于动态预测的混合可靠性模型，并通过区间计算可靠性指标的方法简便、可行；模型的区间变量随着结构动态测试数据的变化而变化，且对结构的不确定性分析也是动态的，因此得到的容器可靠性也随着其服役过程不断推进，具有动态特性，可以更好地反映容器在服役期间的性能变化，为容器的使用维护提供决策依据。
- 动态预测 /
- 区间变量 /
- 随机变量 /
- 混合可靠性 /
- 深水爆炸容器
Abstract: A deep-water explosion test vessel is an important test equipment which is filled with water to simulate different water depth environment by loading different hydrostatic pressure, and it can be used to study deep water explosion theory and engineering technology based on the similarity principle. In order to ensure the safety of vessels used in deep-water explosion test, a random-interval dynamic reliability model based on intelligent prediction is proposed in this paper. A GRNN prediction network of vessel response is established through dynamic test data, and the maximum strain interval variable of the vessel is obtained. Considering the random characteristics of the vessel structure, the reliability analysis of the in-service deep-water explosion test vessel is carried out. During the period, three methods are used to calculate the reliability index, and the analysis shows that for the vessel structure with high reliability and lack of sample data, the hybrid reliability model based on dynamic prediction is established by the calculation of interval reliability index. The method is simple and feasible. At the same time, the interval variables of the model change with the structural dynamic test data, and the uncertainty analysis of the structure is also dynamic. Therefore, the reliability of the container obtained is also changing with the service process, and has dynamic characteristics, which can better reflect the performance changes of the container during the service period and provide the basis for the use and maintenance decision of the container.
- dynamic prediction /
- interval variable /
- random variable /
- hybrid reliability /
- deep-water explosion vessels

HTML全文

图 1 深水爆炸试验容器结构（单位: mm）

Figure 1. Structure drawing of deep-water explosion test vessel (unit: mm)

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图 2 测点位置示意图

Figure 2. Schematic diagram of measuring points location

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图 3 动态应变GRNN预测过程

Figure 3. GRNN prediction process of dynamic strain

下载: 全尺寸图片幻灯片

表 1 测试应变数据

Table 1. Test stain data

试验编号	药量/ g	加载静水压/ MPa	容器应变/10⁻⁴		试验编号	药量/ g	加载静水压/ MPa	容器应变/10⁻⁴
试验编号	药量/ g	加载静水压/ MPa	测点1	测点2	试验编号	药量/ g	加载静水压/ MPa	测点1	测点2
1	5.0	0	4.90	6.00	17	0.8	1.0	0.85	1.29
2	5.0	2.0	4.19	5.27	18	2.4	1.0	1.42	2.71
3	10.0	0	6.14	6.07	19	0.8	1.5	0.98	2.13
4	10.0	2.0	5.60	5.85	20	2.4	1.5	1.70	1.14
5	0.8	0	1.02	1.92	21	0.8	2.0	0.99	1.20
6	0.8	0.5	1.38	3.66	22	2.4	2.0	1.94	1.70
7	0.8	1.0	2.43	2.87	23	0.8	0.3	0.99	1.49
8	0.8	1.5	1.51	2.57	24	2.4	0.3	1.11	1.71
9	0.8	2.0	1.14	3.45	25	0.8	1.3	0.65	1.11
10	2.4	0	1.82	3.83	26	0.8	0.8	0.71	1.07
11	2.4	1.0	2.67	5.60	27	2.4	0.8	1.32	1.90
12	2.4	2.0	1.97	4.54	28	2.4	1.5	1.11	2.27
13	0.8	0	0.54	1.25	29	2.4	1.3	1.78	1.79
14	2.4	0	1.89	2.85	30	0.8	1.8	0.68	1.12
15	0.8	0.5	0.87	2.09	31	2.4	1.8	1.82	2.05
16	2.4	0.5	1.37	2.53

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表 2 随机变量分布

Table 2. Distribution of random variables

随机变量	均值	标准差	变异系数	分布类型
屈服强度	345	10.35	0.03	GASS
弹性模量	209	6.27	0.03	GASS

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表 3 第32次试验预测结果

Table 3. Prediction results of the 32nd test

试验次数	药量/g	加载静水压/MPa	位置	容器应变/10⁻⁴	预测绝对误差
32	10	2.0	测点1	5.54	9.357 5
32	10	2.0	测点2	5.27	11.938 2

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表 4 容器可靠性计算结果

Table 4. Calculation results of vessel reliability

计算方法	失效概率	区间可靠性指标	计算时间/s
区间随机化功能函数	0		0.277 5
随机区间化功能函数		3.375 1	0.001 0
二级功能函数	0		0.109 9

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