Dent strain and stress analyses and implications concerning API RP 1183 - Part II: Examples of dent geometry and strain analyses during contact and re-rounding
Bibliographic record
Abstract
API Recommended Practice (RP) 1183 considers three levels of assessment. Its Level 1 and Level 2 processes were considered viable for single peak dents with smooth profiles. The RP deals with more complex dents by way of a Level 3 approach that was reliant on finite element analysis. Part II of this four-part series of papers has identified the assumptions central to the practices of the RP, and evaluated them in regard to fully symmetric dents whose geometry is broadly aligned with those assumptions. Thereafter, it has examined the effects of asymmetry and skew angle benchmarked relative to the symmetric dents. It becomes apparent that even for symmetric dents significant errors emerge in the RPs practices based on its reliance on dent profiles characterized along their axial and transverse axes cut through the apex, and the effects of the plastic deformation history developed in forming the dent. As for Part I, it was found that the practices of RP 1183 can 1) incorrectly categorize dents, and 2) grossly underestimate dent severity due to asymmetry and skew angles considered acceptable for Level 2 assessment. Error analyses and trending indicated conservative as well as nonconservative errors, with some more than 300%. As noted in Part I, Part III will consider cyclic loading of dents, and the viability of the dent stress and fatigue analyses that underlie the API-RP 1183 Level 1 and Level 2 assessment practices, whereas Part IV considers the viability of the numerical formulations and modeling that underlie its practices.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".