An Interim Approach to Determine Dynamic Ductile Fracture Resistance of Modern High Toughness Pipeline Steels
Bibliographic record
Abstract
The ductile fracture toughness of steel is used to assess the ability of a pipeline to resist long running ductile fractures in a burst event. In modern low carbon clean steels with high toughness, conventional measures of ductile fracture toughness (standard Charpy and DWTT energy) are under review, and alternatives are being studied. The major factor causing concern is the inability of these tests to isolate the energy associated with crack propagation from the total energy absorbed during the specimen fracture. This is significant in modern high toughness steels because their initiation toughness is extremely high. To resolve crack propagation energy, a novel modification was evaluated for both Charpy and DWTT specimens by employing a back-slot including a snug fitting shim to replace the removed material. In most cases, this modification was effective in curtailing the load-displacement trace when the propagating crack interacted with the slot on the backside of the specimen, without affecting the initial portion of the trace; this allowed crack propagation energies to be resolved. The propagation energy determined by this method is compared with the total energy and conventional test parameters. The crack propagation energy values inferred based on this should be validated, in future burst test.
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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".