Dent strain and stress analyses and implications concerning API RP 1183 - Part I: Background for dent geometry and strain analyses during contact and re-rounding
Bibliographic record
Abstract
API RP 1183 was developed through industry collaboration to manage the threat posed by dents. It provides screening and more detailed techniques designed to manage single peak smooth dents. For more complex scenarios such as kinked and skewed dents its practices rely on numerical analysis. This paper is the first of four that considers issues that arise when, consistent with API RP 1183, the axial and transverse profiles of dents are used as the basis for dent geometry and strain analyses. Part I presents background concepts and discusses the numerical details and other modeling that underpin API RP 1183. Part II presents a series of examples that amplify the concerns foreshadowed in Part I. Part III considers the cyclic loading of dents, and the viability of the dent stress and fatigue analyses that underlie those practices of API RP 1183, while Part IV focusses on the numerical and modeling aspects. It becomes apparent from Part I that the benefits of the shell-element formulation adopted to simulate tens of thousands of dents has glossed over some key aspects that lead to significant unconservative errors, or lead to gaps in its dent management. Likewise, the broad utility of its global regression equations was found prone to significant err. The analysis of single peak dents with smooth profiles based on their axial and transverse profiles as outlined in API RP 1183 was found to incorrectly categorize dents, mis-predict their severity. Finally, a path toward resolution was noted.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".