Finite element model driven rapid localization method for multi-damages on pressure vessels
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摘要: 面向大型压力容器特种设备的球形封底结构，传统的无损检测较难完成在役大型储底部的在线损伤检测。压电陶瓷传感器（PZT）能够同时激发和接收Lamb波实现大面积区域的损伤检测。文章利用压电陶瓷传感器构造了均匀圆心阵列，并提出了一种仿真数据驱动的压力容器球形封底多损伤快速定位方法。首先，对在半球形封底结构模型上建立阵列Lamb波信号的传播模型，利用阵列导向矢量构造圆心阵列稀疏特征；其次，在ABAQUS有限元软件中建立压力容器封底有限元分析模型，获取虚拟阵列稀疏特征库；最后，将损伤信号的阵列转向向量与虚拟阵列稀疏特征库进行相似性比较，通过损伤成像快速确定损伤信号的位置。数值和实验结果都验证了基于虚拟阵列稀疏特征建模的多损伤定位方法能够有效地监测轴对称结构，且精度较高。Abstract: Torispherical structures are one of the main head or bottom cover parts of special equipment such as pressure vessels. Conventional nondestructive testing methods are not quite applicable for online damages inspection at the head or bottom part of large storage tanks in-service. Piezoelectric transducers (PZT) are capable for both exciting and receiving Lamb waves to scan large surface areas and detect damages. This paper constructs a compact uniform circular array using PZTs and presents a virtual array sparse feature based multi-damage localization method for hemispherical head. Firstly, array Lamb wave signal propagating on hemispherical structures is modeling for constructing array sparse features using the array steering vector. Secondly, a finite element analysis model of pressure vessel head was created in ABAQUS to construct virtual array sparse feature library. Finally, their locations can be quickly determined by damages imaging using similarity compared the array steering vector of damage signals with the virtual array sparse feature library. Both the numerical and experimental results verify the virtual array sparse feature modeling based multiple damages localization method can effectively monitor axisymmetric structures with high accuracy.
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Key words:
- pressure vessel /
- damage detection /
- simulation driven /
- rapid localization
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表 1 定位结果与误差统计
序号 模拟损伤位置 预测位置及误差 $ {r_1} $/
mm$ {\theta _2} $/
（°）$ {r_2} $/
mm$ {\theta _2} $/
（°）$ {\hat r_1} $/
mm$ E_1^r $ $ {\hat \theta _1} $/
（°）$ E_1^\theta $ $ {\hat r_2} $/
mm$ E_2^r $ $ {\hat \theta _2} $/
（°）$ E_2^\theta $ 1 130 −45 - - 138 8 −44 1 - - - - 2 110 150 - - 116 6 148 2 - - - - 3 63 180 163 90 65 2 178 2 170 7 95 5 4 110 225 230 90 115 5 230 5 243 13 85 5 
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