Estimating J-R Curve From CVN Upper Shelf Energy and its Application
Bibliographic record
Abstract
Accuracy in predictions of burst pressures for cracks in pipelines has significant impact on the pipeline integrity management decisions. One of the fracture mechanics models used for failure pressure prediction is API 579 Level 3 FAD ductile tearing instability analysis that requires J-R curves, i.e., crack resistance curves, for the assessment. However, J-R curves are usually unavailable for most pipelines. To overcome this technical barrier, efforts have been made to estimate the J-R curve indirectly from commonly available toughness data, such as the Charpy V-notched Impact Energy CVN values, by correlating the upper-shelf CVN value (energy) to the ductile fracture resistance J-R curve. In this paper, the theoretical background and studies made by various researchers on this topic are reviewed. Attempts made by the present study to establish correlations between CVN and J-R curves for linepipe materials are then presented. Application of this CVN-JR correlation to API 579 Level 3 FAD tearing instability assessment for failure pressure predictions is demonstrated with examples. The accuracy of the correlation is analyzed and reported.
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.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".