Development of a Procedure for the Calculation of J-R Curves From Pressed-Notch Drop Weight Tear Test Specimens
Bibliographic record
Abstract
Over the last few years, there have been ongoing efforts funded by TransCanada PipeLines Limited to develop a more fundamentally based procedure to extract a true measure of the dynamic steady-state fracture toughness of linepipe steels. As part of this effort, considerable research has focused on the drop-weight tear test (DWTT) specimen as the baseline specimen to be used in this development. Using instrumented DWTT test equipment, dynamic load-displacement histories were extracted from the experiments. In addition, a visual measure of the crack growth, and the crack-tip-opening angle (CTOA) were obtained from high-speed video equipment. A procedure has been developed using detailed three-dimensional finite element analyses that calculate the J-integral as a function of crack growth for these dynamic experiments. The results from these analyses have been verified using published procedures on similar scale specimens. This paper presents the development of a procedure for calculating the dynamic J-R curve from the load-displacement trace of a pressed-notch DWTT specimen. The slope of the JM-R curves generated provides technical insight into the range of steady-state propagation that occurs during these experiments. The slope of the JM-R curve (dJM/da) is compared to the visually measured CTOA values and conclusions about the relationship between these parameters and the steady-state propagation energy are made. These results are key in the development of a procedure to predict steady-state fracture propagation from laboratory specimen data.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".