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Failure assessment of seam-welded pipe under fatigue and thermal loading

2024· article· en· W4403261537 on OpenAlexaff
Mohamed S. Gadala, Ibrahim M. Gadala, Abdulrahman Gomaa

Bibliographic record

VenueEngineering Failure Analysis · 2024
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsPowertech Labs (Canada)University of British Columbia
FundersDepartment of Education and Knowledge
KeywordsWeldingThermal fatigueStructural engineeringFatigue testingMaterials scienceForensic engineeringEngineeringThermalComposite material

Abstract

fetched live from OpenAlex

• This paper presents an assessment of the failure of a seam-welded pipeline under fatigue and thermal loading. • Both experimental analysis and numerical simulations utilizing the finite element method (FEM) were conducted to understand potential failure modes of a seam-welded SS304L pipeline. • The experimental analysis explored different aspects of failure, such as stress corrosion cracking, welding defects, and the impacts of thermal and fatigue loading, as well as overloads. • The numerical simulations investigated weld defects, residual stresses, fatigue and thermal loading, and the propagation of fatigue cracks. • It is concluded that the primary cause of failure was linked to suboptimal welding procedures and post-welding treatment, along with the presence of high levels of residual stresses in the heat-affected zone (HAZ) of the weld. This paper presents an assessment of the failure of a seam-welded pipeline under fatigue and thermal loading. The investigation concentrated on analyzing several factors potentially influencing pipeline failure. Both experimental analysis and numerical simulations utilizing the finite element method (FEM) were conducted to understand potential failure modes of a seam-welded SS304L pipeline. The experimental and FEA analyses investigated various failure factors including weld defects, fatigue and thermal loading, residual stresses, and overloads. The study concludes that substandard welding procedures and inadequate post-weld treatment, coupled with elevated residual stresses in the heat-affected zone (HAZ) of the weld, were the primary contributors to failure. The paper systematically outlines the utilization of experimental and FE analyses in examining practical engineering failure instances. This approach strengthens the ability to avert failures and navigate risks with precision. Furthermore, the results highlight the critical significance of adhering to code and standard guidelines concerning pipe seam welds and post-weld treatment practices.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.229
Teacher spread0.222 · 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 designBench or experimental
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
Published2024
Admission routes1
Has abstractyes

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