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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

陈媛,周志杰,王杰,等. 考虑环境干扰的大型贮液箱结构安全评估模型[J]. 北京航空航天大学学报,2023,49(4):981-989 doi: 10.13700/j.bh.1001-5965.2021.0350
引用本文: 陈媛,周志杰,王杰,等. 考虑环境干扰的大型贮液箱结构安全评估模型[J]. 北京航空航天大学学报,2023,49(4):981-989 doi: 10.13700/j.bh.1001-5965.2021.0350
CHEN Y,ZHOU Z J,WANG J,et al. Structural safety assessment model of large liquid tanks considering environmental disturbance[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(4):981-989 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0350
Citation: CHEN Y,ZHOU Z J,WANG J,et al. Structural safety assessment model of large liquid tanks considering environmental disturbance[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(4):981-989 (in Chinese) 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
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

Structural safety assessment model of large liquid tanks considering environmental disturbance

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
  • 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。

     

  • 图 1  FEM-BRB-c评估模型框架

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

    图 2  基于FEM的BRB建立流程

    Figure 2.  Establishment of BRB based on FEM

    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

    图 4  石油储罐有限元模型

    Figure 4.  Finite element model of oil tank

    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

    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

    图 7  21种工况对应的归一化变形量

    Figure 7.  Normalized deformation amount corresponding to 21 cases

    图 8  石油储罐的观测数据

    Figure 8.  Observation data of oil tank

    图 9  交叉验证评估结果

    Figure 9.  Assessment results generated by cross-validation

    表  1  结构变形程度的参考值

    Table  1.   Referential values of structural deformation states

    参考等级VSMHVH
    效用值00.30.71
    下载: 导出CSV

    表  2  不均匀沉降载荷的参考值

    Table  2.   Referential values of uneven settlement

    参考等级NLNMNSZSML
    参考值/mm−41.89−20.95−1001020.9541.89
    下载: 导出CSV

    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/m011.422.8
    下载: 导出CSV

    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
    $ x_{1} $$x_{2}$$ x_{1} $$ x_{k} $
    11NL空罐0,0,0.000 7,0.999 3121Z满罐0,0.721 3,0.278 7,0
    21NL半罐0,0,0,1131S空罐0.331 3,0.668 7,0,0
    31NL满罐0,0,0.015, 0.985 0141S半罐0.336 7,0.663 3,0,0
    41NM空罐0,0.502 5,0.497 5,0151S满罐0,0.511 5,0.488 5,0
    51NM半罐0,0.502 0,0.498 0,0161M空罐0,0.693 3,0.306 7,0
    61NM满罐0,0.388 0,0.612 0,0171M半罐0,0.697 5,0.302 5,0
    71NS空罐0.209 0,0.791 0,0,0181M满罐0,0.167 3,0.832 7,0
    81NS半罐0.208 3,0.791 7,0,0191L空罐0,0,0.508 0,0.492 0
    91NS满罐0,0.618 0,0.382 0,0201L半罐0,0,0.513 7,0.486 3
    10 1Z空罐1,0,0,0211L满罐0,0,0.256 3,0.743 7
    11 1Z半罐0.623 7,0.376 3,0,0
    下载: 导出CSV

    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 }} $
    规则的隶属度$ {\alpha _k} = \alpha _1^k \wedge \alpha _2^k $
     注:“$ \wedge $”表示取小运算
    下载: 导出CSV

    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
    最大值最小值平均值
    FEM-BRB-c2.54×10−36.79×10−46.45×10−4
    FEM-BRB6.89×10−33.68×10−41.31×10−3
    BP1.38×10−21.97×10−63.09×10−3
    模糊推理8.52×10−26.03×10−23.97×10−2
    下载: 导出CSV
  • [1] YANG L C, CHEN Z P, CAO G W, et al. An analytical formula for elastic-plastic instability of large oil storage tanks[J]. International Journal of Pressure Vessels and Piping, 2013, 101: 72-80. doi: 10.1016/j.ijpvp.2012.10.006
    [2] LIU W Y, CHEN C H, CHEN W T, et al. A study of caprolactam storage tank accident through root cause analysis with a computational approach[J]. Journal of Loss Prevention in the Process Industries, 2017, 50: 80-90. doi: 10.1016/j.jlp.2017.09.004
    [3] PASMAN H J, ROGERS W J. Risk assessment by means of Bayesian networks: A comparative study of compressed and liquefied H2 transportation and tank station risks[J]. International Journal of Hydrogen Energy, 2012, 37(22): 17415-17425. doi: 10.1016/j.ijhydene.2012.04.051
    [4] HYUN K C, MIN S, CHOI H, et al. Risk analysis using fault-tree analysis (FTA) and analytic hierarchy process (AHP) applicable to shield TBM tunnels[J]. Tunnelling and Underground Space Technology, 2015, 49: 121-129. doi: 10.1016/j.tust.2015.04.007
    [5] YANG Y F, CHEN G H, RENIERS G. Vulnerability assessment of atmospheric storage tanks to floods based on logistic regression[J]. Reliability Engineering & System Safety, 2020, 196: 106721.
    [6] 杨继星, 佘笑梅, 黄玉钏, 等. 基于BP神经网络的苯储罐泄漏事故风险评价模型研究[J]. 中国安全生产科学技术, 2019, 15(1): 157-162.

    YANG J X, SHE X M, HUANG Y C, et al. Research on risk assessment model for leakage accident of benzene tank based on BP neural network[J]. Journal of Safety Science and Technology, 2019, 15(1): 157-162(in Chinese).
    [7] ŽARKOVIĆ M, STOJKOVIĆ Z. Analysis of artificial intelligence expert systems for power transformer condition monitoring and diagnostics[J]. Electric Power Systems Research, 2017, 149: 125-136. doi: 10.1016/j.jpgr.2017.04.025
    [8] VEMA V, SUDHEER K P, CHAUBEY I. Fuzzy inference system for site suitability evaluation of water harvesting structures in rainfed regions[J]. Agricultural Water Management, 2019, 218: 82-93. doi: 10.1016/j.agwat.2019.03.028
    [9] YANG J B, LIU J, WANG J, et al. Belief rule-base inference methodology using the evidential reasoning approach-RIMER[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 2006, 36(2): 266-285. doi: 10.1109/TSMCA.2005.851270
    [10] ZHOU Z J, HU G Y, HU C H, et al. A survey of belief rule-base expert system[J]. IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2021, 51(8): 4944-4958. doi: 10.1109/TSMC.2019.2944893
    [11] 鱼蒙, 黄健, 孔江涛. 输入信息不完整的置信规则库推理方法[J]. 哈尔滨工业大学学报, 2019, 51(4): 51-59.

    YU M, HUANG J, KONG J T. Belief rule-base inference methodology with incomplete input[J]. Journal of Harbin Institute of Technology, 2019, 51(4): 51-59(in Chinese).
    [12] CHEN Y, ZHOU Z J, YANG L H, et al. A novel structural safety assessment method of large liquid tank based on the belief rule base and finite element method[J]. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 2022, 236(3): 458-476. doi: 10.1177/1748006X211021690
    [13] WANG X J, LI X L, ZHAO Y L, et al. Credibility analysis of air quality data based on improved measurement method[C]//2019 Chinese Control and Decision Conference (CCDC). Piscataway: IEEE Press, 2019: 4421-4425.
    [14] 周志杰, 刘涛源, 胡冠宇, 等. 一种基于数据可靠性和区间证据推理的故障检测方法[J]. 自动化学报, 2020, 46(12): 2628-2637.

    ZHOU Z J, LIU T Y, HU G Y, et al. A fault detection method based on data reliability and interval evidence reasoning[J]. Acta Automatica Sinica, 2020, 46(12): 2628-2637(in Chinese).
    [15] 中国国家标准化管理委员会, 中华人民共和国国家质量监督检验检疫总局. 锅炉和压力容器用钢板: GB 713-2014[S]. 北京: 中国标准出版社, 2014.

    Standardization administration of the P.R.C., General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China. Steel plates for boilers and pressure vessels: GB 713-2014[S]. Beijing: the Standards Press of China, 2014.
    [16] American Petroleum Institute. Tank inspection, repair, alteration, and reconstruction: API 653-2009[S]. Washington, D.C.: American Petroleum Institute.
    [17] CLOUGH R W. The finite element method in plane stress analysis[C]// Proceedings of 2nd ASCE Conference on Electronic Computation. Pittsburgh: ASCE Press, 1960: 345-78.
    [18] PAVOL L, MIROSLAV P, JOZEF B. Static structural analysis of water tank[J]. American Journal of Mechanical Engineering, 2015, 3(6): 230-234.
    [19] AKSU G, GÜZELLER C O, ESER M T. The effect of the normalization method used in different sample sizes on the success of artificial neural network model[J]. International Journal of Assessment Tools in Education, 2019, 6(2): 170-192.
    [20] YANG J B. Rule and utility based evidential reasoning approach for multiattribute decision analysis under uncertainties[J]. European Journal of Operational Research, 2001, 131(1): 31-61. doi: 10.1016/S0377-2217(99)00441-5
    [21] YOU Y Q, SUN J B, JIANG J, et al. A new modeling and inference approach for the belief rule base with attribute reliability[J]. Applied Intelligence, 2020, 50(3): 976-992. doi: 10.1007/s10489-019-01586-2
    [22] ZHOU Z J, HU G Y, ZHANG B C, et al. A model for hidden behavior prediction of complex systems based on belief rule base and power set[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018, 48(9): 1649-1655. doi: 10.1109/TSMC.2017.2665880
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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

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