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Record W2076356466 · doi:10.1115/ipc2010-31149

Critical Buckling Strain in High Strength Steel Pipes Using Isotropic-Kinematic Hardening

2010· article· en· W2076356466 on OpenAlexaff
Ali Fathi, J. J. Roger Cheng, Samer Adeeb, Joe Zhou

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsTransCanada (Canada)University of Alberta
Fundersnot available
KeywordsIsotropyMaterials scienceAnisotropyStrain hardening exponentHardening (computing)Structural engineeringTransverse planeBucklingFinite element methodComposite materialMaterial propertiesMechanicsPhysicsEngineeringOptics

Abstract

fetched live from OpenAlex

High strength steel pipes (HSSP) have become more popular recently for highly pressurized pipelines built to transport natural gas from remote fields to energy markets. Material tests on HSSP showed significant material anisotropy caused by the pipe making process, UOE. A combined isotropic-kinematic hardening material model is developed based on observations made on longitudinal and transverse stress strain data of HSSP. This material model combines linear isotropic hardening with Armstrong-Fredrick kinematic hardening and can be easily calibrated by longitudinal and transverse tension coupon test results. The proposed material model is used to show how considering material anisotropy affects the critical buckling strain of HSSP in the longitudinal direction. Finite element (FE) models are developed to simulate one pressurized and one unpressurised HSSP tested under monotonic displacement-controlled bending. Isotropic and anisotropic material modeling methods are used for each HSSP models. In the isotropic material model, longitudinal stress-strain data of HSSP material is used to define the stress-strain relationship. In the anisotropic model combined hardening material model, calibrated by longitudinal and transverse HSSP stress-strain data, is used. Critical buckling strain predictions by isotropic and anisotropic models of these pipes are compared with test results and also with some available criteria in standards and literatures. These comparisons show that anisotropic models give predictions closer to test results.

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)0.001
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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.242
Teacher spread0.231 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

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