Revisiting burst pressure models for corroded pipelines

Phan, Hieu Chi; Dhar, Ashutosh Sutra; Mondal, Bipul Chandra

doi:10.1139/cjce-2016-0519

Cited by 25 publications

(5 citation statements)

References 16 publications

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“…It is because they are important in predicting the failure pressure in corroded pipelines with greater accuracy and reliability. Therefore, FEA is recommended in cases where the corrosion defect shows complex geometry and when the risk of a pipeline failure is very high (Phan et al, 2017).…”

Section: Resultsmentioning

confidence: 99%

Effects of depth in external and internal corrosion defects on failure pressure predictions of oil and gas pipelines using finite element models

Colindres

Méndez

Velázquez

et al. 2020

Advances in Structural Engineering

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This study presents, for the first time, the mechanical behavior of API 5L pipeline steels X42, X52, X60, X70, X80, and X100 with external and internal corrosion defects as well as a combination of both defects that has been named external–internal corrosion defects. The conventional methods to predict failure pressure in corroded pipes, such as B31G, RSTRENG-1, SHELL, DNV-99, PCORRC, and FITNET FFS, have also been discussed in this article. In addition, pipeline failure pressure has been estimated using the finite element method, considering that it is the best approach to calculate actual failure pressure. The external and internal corrosion defect investigated in this research manifests as a rectangular shape with spherical ends at the edges. When the external–internal corrosion defect appears, failure pressure data decrease dramatically because of severe damage. This is due to the decrease in the ligament (effective area) caused by the corrosion defect. To have a good estimation of the pipeline failure pressure with an external–internal corrosion defect, DNV-99 method can be used with acceptable certainty.

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Section: Resultsmentioning

confidence: 99%

Effects of depth in external and internal corrosion defects on failure pressure predictions of oil and gas pipelines using finite element models

Colindres

Méndez

Velázquez

et al. 2020

Advances in Structural Engineering

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“…For this purpose, they implemented a differential evolution algorithm to minimize the prediction errors. The expressions achieved are the following [75]:…”

Section: Approaches Proposed By Phan Et Al [75]mentioning

confidence: 99%

“…These patterns can be explained considering the defect idealization and the fact that some models like Phan1-3 were obtained based on methods that minimized the error predictions from a set of experimental tests. Actually, Phan and co-workers have pointed out possible conservative results with their burst models [75].…”

Section: Mean Time To Failure Comparisonmentioning

confidence: 99%

Reliability assessments of corroded pipelines based on internal pressure – A review

Amaya-Gómez

Sánchez-Silva

Bastidas‐Arteaga

et al. 2019

Engineering Failure Analysis

125

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Corrosion is one of the most significant threats for onshore pipelines that may lead to a Loss of Containment (LOC). A LOC poses significant consequences over the surrounding people and environment because of the hazardousness of the transporting fluids, so different efforts have been raised to predict pipe failures, which are commonly based on reliability assessments with limit state functions. These functions are gathered in serviceability, leakage, and ultimate conditions, out of which the last two approaches contemplate a LOC. This paper reviews recognized limit state functions for corroded pipelines, and it discusses their assumptions and applicability. Specifically, this paper focuses on burst limit pressures considering the relevance in the academic literature and Oil & Gas standards. Therefore, a thorough comparison is presented based on failure criteria, acceptable defect dimensions, failure probability, and error prediction based on experimental and numerical burst tests. The objective is to evaluate the level of conservatism of each simplified model depending on the material toughness and the corrosion rate. This review aims to support a reliability model selection in corroded pipelines for future intervention strategies.

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“…Many research projects have been published, with a view to developing a semi-data driven and semianalytical model; the aim is for the final result to be a solid equation for predicting the variable of interest with optimized factors. [31][32][33] These models have the obvious advantages of being explicit and requiring least computational effort. Phan et al 31 use pre-fixed equations with changeable factors, and optimize them by minimizing the error between simulated and predicted outputs.…”

Section: Introductionmentioning

confidence: 99%

“…[31][32][33] These models have the obvious advantages of being explicit and requiring least computational effort. Phan et al 31 use pre-fixed equations with changeable factors, and optimize them by minimizing the error between simulated and predicted outputs. The authors later developed their study with Phan et al, 32 developing an empirical model to predict defective pipe moment capacity, with 49 factors.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Neural Network architecture and derivation of closed-form equation to predict ultimate load of functionally graded material plate

Duong

Phan

2023

Advances in Mechanical Engineering

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Functionally Graded Material (FGM) plate is a complicated structure with complex allocation of spatially changing proportions of ceramic and metal within the matter. Various analytical and numerical methods have been applied with a view to evaluating the critical load of FGM plate. However, these conventional methods struggle when the computational complexity is significant, which represents an obstacle to incorporation with other advanced techniques where computational power is required (e.g. optimization or random simulations). The Neural Network (NNet) model has been successfully applied to resolve this issue. However, the conventional NNet requires proper configuration to take advantage of the model, and thus, careful parameter tuning is required. Furthermore, the NNet is typically a “black box,” where the prediction mechanism is hidden. This paper establishes an optimized architecture for NNet, with parametric study of the model’s hyperparameters. Variance propagation is also applied to observe the variation of the model’s performance on random sub-databases splintered from the database. To this end, the explicit expression of the trained NNet model is provided after mathematically deploying the hidden algebra behind an NNet prediction. The developed model has very promising evaluation metrics: R2, MAE, and RMSE on the test set are 0.999925, 0.067516, and 0.146438, respectively.

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Revisiting burst pressure models for corroded pipelines

Cited by 25 publications

References 16 publications

Effects of depth in external and internal corrosion defects on failure pressure predictions of oil and gas pipelines using finite element models

Effects of depth in external and internal corrosion defects on failure pressure predictions of oil and gas pipelines using finite element models

Reliability assessments of corroded pipelines based on internal pressure – A review

Optimization of Neural Network architecture and derivation of closed-form equation to predict ultimate load of functionally graded material plate

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