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基于神经网络的核电厂设备易损性分析

刘鸿泉 陈少林 孙晓颖 吴绍恒

刘鸿泉, 陈少林, 孙晓颖, 吴绍恒. 基于神经网络的核电厂设备易损性分析. 力学学报, 待出版 doi: 10.6052/0459-1879-21-466
引用本文: 刘鸿泉, 陈少林, 孙晓颖, 吴绍恒. 基于神经网络的核电厂设备易损性分析. 力学学报, 待出版 doi: 10.6052/0459-1879-21-466
Liu Hongquan, Chen Shaolin, Sun Xiaoying, Wu Shaoheng. Vulnerability analysis of npp equipment based on neural network. Chinese Journal of Theoretical and Applied Mechanics, in press doi: 10.6052/0459-1879-21-466
Citation: Liu Hongquan, Chen Shaolin, Sun Xiaoying, Wu Shaoheng. Vulnerability analysis of npp equipment based on neural network. Chinese Journal of Theoretical and Applied Mechanics, in press doi: 10.6052/0459-1879-21-466

基于神经网络的核电厂设备易损性分析

doi: 10.6052/0459-1879-21-466
基金项目: 华龙一号及在役核电机组关键技术装备攻关工程项目—核电厂结构分析软件”项目资助, 国家自然科学基金资助(51978337, U2039209)
详细信息
    作者简介:

    陈少林, 教授, 主要研究方向: 地震工程. E-mail: iemcsl@nuaa.edu.cn

  • 中图分类号: TU271.5

VULNERABILITY ANALYSIS OF NPP EQUIPMENT BASED ON NEURAL NETWORK

  • 摘要: 核电结构的易损性分析是核电厂地震安全评估中至关重要的一环, 但是由于核电结构的复杂性以及考虑土−结相互作用(SSI)时较大的计算规模, 使得计算核电厂设备易损性曲线十分耗时. 为发展高效的核电厂设备易损性分析方法, 本文采用核电结构土−结相互作用分析的分区计算方法, 并利用有限的SSI分析结果建立神经元模型(ANN)代替有限元模型(FEM), 分别基于对数正态假定的回归法和蒙特卡洛法进行了设备易损性分析. ANN数值模拟包括了以下内容: (1)基于半偏相关系数选择最相关的地震动参数作为ANN输入, 并通过交叉检验建立神经元模型; (2)量化研究ANN数值模拟的预测不确定性, 其中包含了由于简化地震动输入引起的随机不确定性和训练样本缺失引起的认知不确定性; (3)基于ANN模型预测结果分别采用蒙特卡洛法和基于对数正态假定的回归法进行设备的易损性分析. 本文探讨了不同的地震强度指标以及土层材料的不确定性对易损性曲线的影响, 同时验证了回归法中对数正态模型假定的基本合理性, 为核电厂设备易损性分析提供了一种可能方向.

     

  • 图  1  (a)工作流程图; (b) SSI分析方法

    Figure  1.  (a)Work flow of FEM; (b) SSI analysis method

    图  2  三层BP网络示意图

    Figure  2.  Schematic diagram of three-layer BP network

    图  3  土-结有限元模型

    Figure  3.  FEM of Soil-structure

    图  4  URS及地震反应谱

    Figure  4.  URS and the spectra of strong motions

    图  5  交叉验证

    Figure  5.  Cross validation

    图  6  Case2工况下ANN训练过程: (a) ANN训练结果; (b) ANN残差分布; (c)认知不确定性

    Figure  6.  ANN training process under Case2 conditions: (a) ANN training results; (b) ANN residual distribution; (c) epistemic uncertainty

    图  7  回归法计算易损性曲线

    Figure  7.  Fragility curves by Reg

    图  8  不同输入下基于ANN的易损性曲线

    Figure  8.  Fragility curves based on ANN under different inputs

    图  9  不考虑土层材料不确定性(Case3)和考虑土层材料不确定性(Case5)的易损性曲线

    Figure  9.  Fragility curves without considering soil material uncertainty (Case3) and considering soil material uncertainty (Case5)

    表  1  核电材料不确定性

    Table  1.   Uncertainties in material parameters of NPP

    TypeDistributionE/GPaC.V
    NABLog-norm24.70.2
    NSBLog-norm32.90.2
    SCVLog-norm210.00.2
    下载: 导出CSV

    表  2  土层材料不确定性

    Table  2.   Uncertainties in material parameters of soil

    LayerH/mDistributionVs(m/s2)C.V
    L120Log-norm5600.2
    L220Log-norm6730.2
    L320Log-norm7940.2
    下载: 导出CSV

    表  3  地震动特征值

    Table  3.   Seismic intensity measures

    IMsDefinitionRRsp
    PGA$ \max \left| {a(t)} \right| $0.730.18
    PGV$ \max \left| {v(t)} \right| $0.29−0.13
    PGD$ \max \left| {d(t)} \right| $0.360.07
    IA$\dfrac{ {\text{π} } }{ {2 g} }\displaystyle\int_0^{ {t_{ {\text{total} } } }} {a{ {(t)}^2}{\rm{d}}t}$0.49−0.03
    CAV$\displaystyle\int_0^{ {t_{ {\text{total} } } }} {\left| {a(t)} \right|{\rm{d}}t}$0.380.03
    PSamax$ \max (PSa(T)) $0.58−0.03
    Tp$ \arg \max (PSa(T)) $−0.260.16
    ASA$\displaystyle\int_5^{33} {PSa(f){\rm{d}}f}$0.850.33
    注: R为相关系数, RSP为半偏相关系数
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-09-12
  • 录用日期:  2022-05-10
  • 网络出版日期:  2022-05-06

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