Behavior of NPS30 Pipe Subject to Denting Load
Bibliographic record
Abstract
Pipeline is the common mode for transporting oil, gas, and various petroleum products. Structural integrity of oil and gas transmission pipelines is often threatened by external interferences such as concentrated lateral loads and as a result, a failure of the pipeline may occur due to “mechanical damages”. Sometime, this load may not cause immediate rupture of pipes; rather form a dent which can reduce the pressure capacity of the pipeline. A dent is a localized defect in the pipe wall in the form of a permanent inward plastic deformation. This kind of defect is a matter of serious concern for the pipeline operator since a rupture or a leak may occur. Accordingly, an extensive experimental study is currently underway at the Centre for Engineering Research in Pipelines (CERP), University of Windsor on 30 inch (762 mm) diameter and X70 grade pipes with D/t of 90. The aim of this research is to examine the influence of various parameters such as dent shape and service pressure on strain distributions of dented pipe. Also, three-dimensional finite element models were developed and validated for determining strains underneath the indenter. The load-deformation behavior of pipes subject to this type of lateral denting load obtained from experimental study and finite element analysis is discussed in this paper. In addition, distributions of important strains in and around the dent obtained from the study are also discussed.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".