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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it