Critical Buckling Strain in High Strength Steel Pipes Using Isotropic-Kinematic Hardening
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".