考虑环境干扰的大型贮液箱结构安全评估模型

陈媛; 周志杰; 王杰; 明志超; 李改灵; 李亚鹏

doi:10.13700/j.bh.1001-5965.2021.0350

考虑环境干扰的大型贮液箱结构安全评估模型

doi: 10.13700/j.bh.1001-5965.2021.0350

1.
火箭军工程大学导弹工程学院，西安 710025
2.
陆军军事交通学院运输装备保障部，天津 300161
3.
火箭军装备部驻宝鸡地区军事代表室，宝鸡 721000

基金项目: 国家自然科学基金(61833016)；陕西省杰出青年科学基金(2020JC-34)；海南省重点研究开发计划(ZDYF2019007)

详细信息

通讯作者:
E-mail：zhouzj04@tsinghua.org.cn

中图分类号: N945.17
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- 被引次数: 0
出版历程
- 收稿日期: 2021-06-25
- 录用日期: 2021-09-17
- 网络出版日期: 2021-10-11
- 整期出版日期: 2023-04-30

Structural safety assessment model of large liquid tanks considering environmental disturbance

1.
Missile Engineering Institute，Rocket Force University of Engineering，Xi’an 710025，China
2.
School of Transportation Equipment Support Department，Army Military Transportation University，Tianjin 300161，China
3.
Military Representative Office in Baoji，Equipment Department of Rocket Army，Baoji 721000，China

Funds: National Natural Science Foundation of China (61833016); Shaanxi Provincial Seience Foundation for Outstanding Young Scholars (2020JC-34); Key Research and Development Project of Hainan Province (ZDYF2019007)

More Information

Corresponding author: E-mail：zhouzj04@tsinghua.org.cn

摘要

摘要:
针对大型贮液箱（LLT）结构安全评估面临的先验信息缺失、监测信息不完全可靠等问题，基于置信规则库（BRB）和有限元方法（FEM），提出了一种考虑环境干扰的大型贮液箱结构安全评估模型。基于行业标准和专家知识，借助有限元方法进行评估模型初始参数的估计；基于信息一致性方法计算指标可信度，以反映实际工程中扰动因素对监测数据的影响；提出一种新的融合方法，将指标可信度合理融合到模型推理过程中，完成评估模型的构建；选用10⁵ m³石油储罐作为研究对象，对所提模型的有效性进行验证。研究结果表明：所提模型不仅能有效处理监测数据不可靠问题，也能够将大型贮液箱复杂系统内部结构机理考虑在内，有效克服先验信息不足给评估精度带来的影响。
- 大型贮液箱 /
- 结构安全评估 /
- 置信规则库 /
- 指标可信度 /
- 有限元
Abstract:
To solve the problems of lack of prior information and incomplete reliability of monitoring data in structural safety assessment of large liquid tanks (LLT), a structural safety assessment model of large liquid tanks considering environmental disturbance is proposed based on the belief rule base (BRB) and finite element method (FEM). First, the FEM estimates the basic parameters of the proposed model using industry standards and subject-matter knowledge. Secondly, the index credibility is calculated based on the information consistency method to reflect the influence of disturbance factors on the monitoring data in actual engineering. Then, a new fusion method can be used to integrate the index credibility into the model reasoning process to complete the construction of the proposed model. Finally, a 100,000 m3 oil storage tank is selected as the research object to verify the effectiveness of the proposed model. The results show that the proposed model can not only effectively deal with the problem of unreliable monitoring data, but also take into account the internal structure mechanism of large liquid tanks, thereby minimizing the negative effects of a lack of prior knowledge on the accuracy of the assessment.
- large liquid tanks /
- structural safety assessment /
- belief rule base /
- index credibility /
- finite element

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图 1 FEM-BRB-c评估模型框架

Figure 1. Framework of FEM-BRB-c assessment model

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图 2 基于FEM的BRB建立流程

Figure 2. Establishment of BRB based on FEM

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图 3 石油储罐物理实体和简化模型

Figure 3. Physical entity and simplified models of oil tank

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图 4 石油储罐有限元模型

Figure 4. Finite element model of oil tank

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图 5 加强环的结构参数及与罐壁间的连接关系

Figure 5. Structural parameters of reinforcement equipment and its connection with oil tank wall

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图 6 工况1下的石油储罐结构变形云图

Figure 6. Structural deformation nephogram of oil tank under case 1

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图 7 21种工况对应的归一化变形量

Figure 7. Normalized deformation amount corresponding to 21 cases

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图 8 石油储罐的观测数据

Figure 8. Observation data of oil tank

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图 9 交叉验证评估结果

Figure 9. Assessment results generated by cross-validation

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表 1 结构变形程度的参考值

Table 1. Referential values of structural deformation states

参考等级	VS	M	H	VH
效用值	0	0.3	0.7	1

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表 2 不均匀沉降载荷的参考值

Table 2. Referential values of uneven settlement

参考等级	NL	NM	NS	Z	S	M	L
参考值/mm	−41.89	−20.95	−10	0	10	20.95	41.89

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表 3 液面高度载荷的参考值

Table 3. Referential values of liquid level load

参考等级	空罐	半罐	满罐
参考值/m	0	11.4	22.8

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表 4 初始置信规则库

Table 4. Initial belief rule base

编号	规则权重	指标		结构变形尺寸置信分布	编号	规则权重	指标		结构变形尺寸置信分布
编号	规则权重	$ x_{1} $	$x_{2}$	结构变形尺寸置信分布	编号	规则权重	$ x_{1} $	$ x_{k} $	结构变形尺寸置信分布
1	1	NL	空罐	0,0,0.000 7,0.999 3	12	1	Z	满罐	0,0.721 3,0.278 7,0
2	1	NL	半罐	0,0,0,1	13	1	S	空罐	0.331 3,0.668 7,0,0
3	1	NL	满罐	0,0,0.015, 0.985 0	14	1	S	半罐	0.336 7,0.663 3,0,0
4	1	NM	空罐	0,0.502 5,0.497 5,0	15	1	S	满罐	0,0.511 5,0.488 5,0
5	1	NM	半罐	0,0.502 0,0.498 0,0	16	1	M	空罐	0,0.693 3,0.306 7,0
6	1	NM	满罐	0,0.388 0,0.612 0,0	17	1	M	半罐	0,0.697 5,0.302 5,0
7	1	NS	空罐	0.209 0,0.791 0,0,0	18	1	M	满罐	0,0.167 3,0.832 7,0
8	1	NS	半罐	0.208 3,0.791 7,0,0	19	1	L	空罐	0,0,0.508 0,0.492 0
9	1	NS	满罐	0,0.618 0,0.382 0,0	20	1	L	半罐	0,0,0.513 7,0.486 3
10	1	Z	空罐	1,0,0,0	21	1	L	满罐	0,0,0.256 3,0.743 7
11	1	Z	半罐	0.623 7,0.376 3,0,0

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表 5 BP和模糊推理算法的参数设置细节

Table 5. Parameter setting details of BP and fuzzy inference method

方法	模型参数	参数设置
BP	训练次数	100
	训练目标最小误差	1×10⁻³
	学习速率	0.01
	隐含层神经元节点个数	9
模糊推理	模糊矩阵	$ {\mathbf{\beta }} $
模糊推理	规则的隶属度	$ {\alpha _k} = \alpha _1^k \wedge \alpha _2^k $
注：“$ \wedge $”表示取小运算

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表 6 不同模型交叉验证后产生的评估精度

Table 6. Different models evaluation accuracy resulting from cross-validation

方法	MSE
方法	最大值	最小值	平均值
FEM-BRB-c	2.54×10⁻³	6.79×10⁻⁴	6.45×10⁻⁴
FEM-BRB	6.89×10⁻³	3.68×10⁻⁴	1.31×10⁻³
BP	1.38×10⁻²	1.97×10⁻⁶	3.09×10⁻³
模糊推理	8.52×10⁻²	6.03×10⁻²	3.97×10⁻²

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