Statistical Estimation of Residual Strength and Reliability of Corroded Pipeline Elbow Part Based on a Direct Fe-Simulations

Larin, Oleksiy; Potopalska, Kseniia; Mygushchenko, R. P.

doi:10.24874/jsscm.2018.12.01.06

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…Miller et al (1989) stated that for an axial crack at the top of the elbows of a pipe, boundary pressures are not affected by the curvature of the elbow. An evaluation the residual strength of the damaged elbow of the pipeline is carried out on the basis of statistical estimation of the concentration of the stresses and plastic strains of the design with a corrosion defect, given with random dimensions, depending on the time of its occurrence, with a development of a parametric FE model (Larin et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Linear and Non-Linear Analyses of Pressure Pipe Elbows

Ali,

Rachid,

Abdelmadjid

et al. 2023

JSSCM

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The introduction of pressure piping increasingly requires the use of efficient materials which must withstand both high hydrostatic pressures, high mechanical loads and high thermal gradients. At nuclear power plants, piping systems are subjected to high pressures (up to 60 MPa). The occurrence of cracks and their possible spread is one of the serious risks in the industry. For this reason, elbows are considered critical components of a piping system. Therefore, in order to design and/or qualify a pipe structurally, it is useful to have a reliable evaluation of their structural behavior under different combined loads. The objective of this study is to determine analytical functions for the evaluation of the J integral as a function of crack size, elbow size, and the nature and intensity of loading. This assessment is derived from the numerical results obtained by the WARP3D calculation code exploiting the finite element method (FEM).

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Section: Introductionmentioning

confidence: 99%

Linear and Non-Linear Analyses of Pressure Pipe Elbows

Ali,

Rachid,

Abdelmadjid

et al. 2023

JSSCM

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show abstract

“…Khalajestani K.M [4] used FEM to analyze the residual strength of pressure pipelines with corrosion defects, and the research results proved that the depth and length of defects and the spacing between adjacent defects had a greater impact on the residual strength of pipelines. Larin [5] used FEM to analyze and determine the local area of the maximum stress, and found that the stress state was affected by the internal pressure and time of the corroded defective pipelines.…”

Section: Introductionmentioning

confidence: 99%

Magnetic Flux Leakage Testing Method for Pipelines with Stress Corrosion Defects Based on Improved Kernel Extreme Learning Machine

Li,

Sun,

Liu

2023

Electronics

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This study aims to study the safety of oil and gas pipelines under stress corrosion conditions and grasp the corrosion damage situation timely and accurately. Consequently, a non-destructive testing method combining magnetic flux leakage testing technology and a kernel function extreme learning machine improved by genetic algorithm (GA-KELM) is proposed. Firstly, the variation of the corrosion defect dimension and profile with time is obtained by numerical simulation. At the same time, the distribution of the magnetic flux leakage signal under different defect conditions is analyzed and studied. Finally, feature selection is carried out on the magnetic flux leakage signal distribution curve, and GA-KELM is used to predict the depth and length of corrosion defects so as to realize the non-destructive testing of the pipeline defects. The results show that different geometric features result in different magnetic flux leakage signal distributions. There is a corresponding relationship between the defect dimension and extreme value, area, and peak width of the magnetic flux leakage signal distribution curve. The GA-KELM prediction model can effectively predict the depth and length of corrosion defects, and the prediction accuracy is better than the traditional extreme learning machine prediction model.

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A Data-Driven Approach to the Prediction of Spheroidal Graphite Cast Iron Yield Surface Probability Characteristics

Shapovalova

Vodka

2021

Lecture Notes in Networks and Systems

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Statistical Estimation of Residual Strength and Reliability of Corroded Pipeline Elbow Part Based on a Direct Fe-Simulations

Cited by 6 publications

References 17 publications

Linear and Non-Linear Analyses of Pressure Pipe Elbows

Linear and Non-Linear Analyses of Pressure Pipe Elbows

Magnetic Flux Leakage Testing Method for Pipelines with Stress Corrosion Defects Based on Improved Kernel Extreme Learning Machine

A Data-Driven Approach to the Prediction of Spheroidal Graphite Cast Iron Yield Surface Probability Characteristics

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