NDLI: Reliability-based structural integrity assessment of Liquefied Natural Gas tank with hydrogen blistering defects by MCS method

Content Provider	IEEE Xplore Digital Library
Author	Jun Hu Li-wei Yan Fei Liu Quan Duan Zao-xiao Zhang
Copyright Year	2010
Description	Author affiliation: School of Energy and Power Engineering, Xi'an Jiaotong University, 710049, China (Jun Hu; Li-wei Yan; Fei Liu; Quan Duan; Zao-xiao Zhang)
Abstract	Hydrogen blistering is one of the serious threats to safe operation of a Liquefied Natural Gas (LNG) tank, therefore safety analysis of hydrogen blistering defects is very important. In order to assess the reliability-based structural integrity of the LNG tank with defects of hydrogen blistering, the following steps were carried out. Firstly, Abaqus code, one of the Finite Element Method (FEM) software, was utilized to calculate 100 J-integral values of crack tip by defining directly. Secondly, the 100 J-integral values of crack tip were used as training data and testing data by Optimized Least Squares Support Vector Machine (OLS-SVM), Least Squares Support Vector Machine (LS-SVM) and Artificial Neural Networks (ANN) to get other 20000 J-integral values of crack tip. Finally, Monte-Carlo Simulation (MCS) was used to assess the reliability-based structural integrity analysis. The results showed that the hydrogen blistering defect with crack will propagate with about 14 percent chance in such a case. It also proved that MCS combined with FEM and SVM was an effective and prospective method for research and application of integrity assessment, which could overcome the data source problem.
Starting Page	4194
Ending Page	4198
File Size	343170
Page Count	5
File Format	PDF
ISBN	9781424459582
e-ISBN	9781424459612
DOI	10.1109/ICNC.2010.5583691
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Structural integrity J-integral Artificial neural networks Materials Reliability theory Liquefied natural gas Support Vector Machine Finite element methods MCS
Content Type	Text
Resource Type	Article

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