Ovalization Defect in Energy Pipes Caused by Concentrated Load
Bibliographic record
Abstract
Steel pipes are used to build pipelines that carry gas and oil across a country or a continent. The majority of onshore pipelines run underground; hence, they are called buried pipelines. These buried pipelines must endure external interferences and complex loading that result from geotechnical causes, aggressive environments, and operational requirements. Many segments of an underground pipeline may rest on rock tips and other localized hard surfaces, resulting in concentrated reaction load acting on small area of the outer wall of the operating pipeline. As a result, permanent inward deformations in the pipe wall, known as dent defect, can form. In addition, a resulting cross-sectional irregularity, known as an ovalization defect, can also occur. Pipe ovalization defects are a concern of pipeline operating companies, as the defect may challenge a pipeline's operation and/or structural integrity and safety. This research was completed by the Centre of Engineering Research in Pipelines located at the University of Windsor to examine the effects that rock tip shape, operating (internal) pressure, and a pipe's diameter-to-thickness ratio (D/t) have on an NPS30 X70-grade pipe's ovalization defect when it is subjected to such a concentrated load. This article discusses the lab-based full-scale examinations, finite element analysis (FEA) simulations, results, and discussions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".