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Multi-physics microstructural modelling of a carbon steel pipe failure in sour gas service

2025· article· en· W4408520871 on OpenAlexaff
Mohamed Elkhodbia, Ibrahim M. Gadala, Imad Barsoum, Akram Alfantazi, Magd Abdel Wahab

Bibliographic record

VenueEngineering Failure Analysis · 2025
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Properties and Failure Mechanisms
Canadian institutionsUniversity of British Columbia
FundersAbu Dhabi National Oil Company
KeywordsSour gasCarbon steelMaterials scienceEngineeringMetallurgyForensic engineeringMechanical engineeringNatural gasWaste managementCorrosion

Abstract

fetched live from OpenAlex

This study presents a comprehensive failure analysis of an ASTM A106B steel pipe exposed to sour natural gas, focusing on degradation and cracking mechanisms. A range of experimental methodologies, including visual inspection, chemical spot tests, XRD analysis, SEM-EDS examination, metallographic analysis, and hardness testing, were employed to identify critical material deficiencies. The findings indicate that environmentally assisted cracking (EAC) initiated at the pipe’s outer diameter (OD) and propagated inward. The experiments also revealed a hardness gradient across the pipe’s thickness and a non-uniform distribution of microstructural inclusions. Additionally, a coupled chemo-mechano-damage finite element analysis (FEA) was conducted to simulate crack propagation driven by hydrogen embrittlement. The FEA used a phase-field approach to model interactions between hydrogen diffusion, mechanical stresses, and microstructural features such as non-uniform inclusion distribution and varying hardness across the pipe wall. The simulations successfully mimicked the crack growth path under sulphide stress cracking (SSC) conditions, demonstrating the influence of material inhomogeneity. The results confirmed that failure initiated at the OD and propagated inward due to hydrogen accumulation at inclusions. These inclusions caused higher gradients of hydrostatic stress, accelerating hydrogen accumulation and crack initiation in regions with a higher inclusion density. Regions of higher hardness were particularly susceptible to failure, as they exhibit lower fracture toughness, which is further degraded by hydrogen diffusion, accelerating the failure process. This study highlights the critical role of microstructural heterogeneities and hydrogen embrittlement in pipeline failure and suggests that the methods presented can be applied to pipelines in hydrogen blending or pure hydrogen transmission, offering key insights for improving material selection and design for pipelines in sour gas and hydrogen environments. • Conducted experimental failure analysis of ASTM A106B steel pipe exposed to sour gas. • Identified EAC initiation at the outer diameter due to hardness gradients and inclusions. • Simulated failure by integrating phase-field modeling and hydrogen diffusion. • Provided insights for pipeline integrity in sour and hydrogen environments.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0020.000
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.196
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2025
Admission routes1
Has abstractyes

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