Analytical study of subsea pipeline behaviour subjected to axial load in free-span location

Seyfipour, I.; Bahaari, Mohammad Reza

doi:10.1080/20464177.2018.1532027

Cited by 17 publications

(3 citation statements)

References 16 publications

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“…Premature failure of the pipe occurs when the stress at the critical point exceeds the material’s true ultimate tensile strength. As such, to maintain a safe and efficient operation, the integrity of a pipeline must be evaluated from time to time [ 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network-Based Failure Pressure Prediction of API 5L X80 Pipeline with Circumferentially Aligned Interacting Corrosion Defects Subjected to Combined Loadings

2022

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Conventional pipeline corrosion assessment methods produce conservative failure pressure predictions for pipes under the influence of both internal pressure and longitudinal compressive stress. Numerical approaches, on the other hand, are computationally expensive. This work provides an assessment method (empirical) for the failure pressure prediction of a high toughness corroded pipe subjected to combined loading, which is currently unavailable in the industry. Additionally, a correlation between the corrosion defect geometry, as well as longitudinal compressive stress and the failure pressure of a pipe based on the developed method, is established. An artificial neural network (ANN) trained with failure pressure from FEA of an API 5L X80 pipe for varied defect spacings, depths, defect lengths, and longitudinal compressive loads were used to develop the equation. With a coefficient of determination (R2) of 0.99, the proposed model was proven to be capable of producing accurate predictions when tested against arbitrary finite element models. The effects of defect spacing, length, and depth, and longitudinal compressive stress on the failure pressure of a corroded pipe with circumferentially interacting defects, were then investigated using the suggested model in a parametric analysis.

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Section: Introductionmentioning

confidence: 99%

Artificial Neural Network-Based Failure Pressure Prediction of API 5L X80 Pipeline with Circumferentially Aligned Interacting Corrosion Defects Subjected to Combined Loadings

2022

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“…External loadings such as longitudinal stresses are often caused by geological movement and thermal stress [4]. Longitudinal compressive stress often causes buckling and wrinkling failure, thus reducing the remaining strength of corroded pipelines when they are subjected to combined loadings [5][6][7]. A corroded pipeline will fail below the remaining strength predicted by DNV when the combined stresses due to internal pressure and external loads exceed ultimate tensile strength of the pipeline.…”

Section: Introductionmentioning

confidence: 99%

An Artificial Neural Network-Based Equation for Predicting the Remaining Strength of Mid-to-High Strength Pipelines with a Single Corrosion Defect

Kumar

Karuppanan

et al. 2022

Applied Sciences

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Numerical methods such as finite element analysis (FEA) can accurately predict remaining strength, with strong correlation with actual burst tests. However, parametric studies with FEA are time and computationally intensive. Alternatively, an artificial neural network-based equation can be used. In this work, an equation for predicting the remaining strength of mid-to-high strength pipelines (API 5L X52, X65, and X80) with a single corrosion defect subjected to combined loadings of internal pressure and longitudinal compressive stress was derived from an ANN model trained based on FEA results. For FEA, the pipe was assumed to be isotropic and homogenous, and the effects of temperature on the pipe failure pressure were not considered. The error of remaining strength predictions, based on the equation, ranged from −6.33% to 2.39% when compared to an unseen FEA dataset, with a correlation value (R2) of 0.9975. A parametric study was subsequently performed using the equation to determine the effects of material property, defect depth, defect length, and longitudinal compressive stress on the remaining strength of pipelines with a single corrosion defect. Defect depth reduced the failure pressure by more than 65% on average, longitudinal compressive stress by more than 20% on average, and defect length by more than 21% on average.

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“…When the stress exceeds the true ultimate tensile strength of the pipe, the pipe fails prematurely. The failure pressure of a pipeline is significantly influenced by the length and depth of the corrosion defect when subjected to axial compressive stress [10][11][12], especially in the presence of interacting defects. It is therefore crucial that the integrity of a pipeline is assessed periodically to ensure a safe and efficient operation.…”

Section: Introduction 1overview Of Pipelines In the Oil And Gas Industrymentioning

confidence: 99%

An Empirical Equation for Failure Pressure Prediction of High Toughness Pipeline with Interacting Corrosion Defects Subjected to Combined Loadings Based on Artificial Neural Network

2021

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Conventional pipeline corrosion assessment methods for failure pressure prediction do not account for interacting defects subjected to internal pressure and axial compressive stress. In any case, the failure pressure predictions are conservative. As such, numerical methods are required. This paper proposes an alternative to the computationally expensive numerical methods, specifically an empirical equation based on Finite Element Analysis (FEA). FEA was conducted to generate training data for an ANN after validating the method against full scale burst test results from past research. An ANN with four inputs and one output was developed. The equation was developed based on the weights and biases of an ANN model trained with failure pressure from the FEA of a high toughness pipeline for various defect spacings, defect depths, defect lengths, and axial compressive stresses. The proposed model was validated against actual burst test results for high toughness materials, with a R2 value of 0.99. Extensive parametric study was subsequently conducted to determine the effects of defect spacing, defect length, defect depth, and axial compressive stress on the failure pressure of the pipe. The results of the empirical equation are comparable to the results from numerical methods for the pipes and loadings considered in this study.

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Analytical study of subsea pipeline behaviour subjected to axial load in free-span location

Cited by 17 publications

References 16 publications

Artificial Neural Network-Based Failure Pressure Prediction of API 5L X80 Pipeline with Circumferentially Aligned Interacting Corrosion Defects Subjected to Combined Loadings

Artificial Neural Network-Based Failure Pressure Prediction of API 5L X80 Pipeline with Circumferentially Aligned Interacting Corrosion Defects Subjected to Combined Loadings

An Artificial Neural Network-Based Equation for Predicting the Remaining Strength of Mid-to-High Strength Pipelines with a Single Corrosion Defect

An Empirical Equation for Failure Pressure Prediction of High Toughness Pipeline with Interacting Corrosion Defects Subjected to Combined Loadings Based on Artificial Neural Network

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