Profile Matching for Performance Assessment of Dented Pipe
Bibliographic record
Abstract
Abstract In the finite element analysis of dented pipes, an indenter is numerically pushed against the pipe wall to produce a dent profile measured from in-line inspection (ILI). The analysis is repeated until the differences between the calculated and measured profiles (i.e., depths) are less than a tolerance limit. If the differences between the depths are less than the typical ILI measurement tolerance, then the FE profile is assumed to represent the actual profile. ILI tools were reported to measure dent depths with a tolerance of 0.5% and 0.77% of the pipe’s outer diameter. However, many different dent profiles can be developed with depths within the tolerance limit, resulting in different stress and strain levels, that can affect the assessment results. This paper presents an investigation of the effects of different profile shapes on the results of performance assessment of dented pipes. Three-dimensional FE analysis was conducted to simulate the dent on a pipe using the different sizes and shapes of the indenter. The stresses and strains on the pipes with the dent depths within the tolerance are compared. The study reveals that the dent profile created using the depth tolerance criteria may lead to misleading information of the pipe performance.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".