Modeling and CMOD Mapping of Surface-Cracked Wide Plates
Bibliographic record
Abstract
Curved wide plate (CWP) tests are frequently used to measure the tensile stress and strain capacity of pipeline girth welds. The parameters affecting the CWP measurement include specimen geometry and cooling setups. High-quality data is obtained when valid test conditions are confirmed. Crack mouth opening displacement (CMOD) is often measured in CWP tests. CMOD is a direct indicator of the amount of deformation at the cracked plane. It is an indirect indicator of the crack driving force (CDF) imparted on the crack. For a given test geometry and material, certain relationships can be derived between the measured CMOD and the more conventional representation of crack driving force, such as CTOD (crack tip opening displacement) and J-integral. Such relationships are a key element in fracture toughness testing standards. This kind of relationship is also particularly useful in strain-based design where CWP specimens are used for strain capacity and flaw growth prediction. In this paper finite element (FE) analysis is first used in modeling CWP testing conditions for X100 specimens with girth weld flaws to validate the test conditions. A novel approach called CMOD mapping is then developed to characterize the flaw behavior which, by making a direct use of CMOD test data from the CWP tests, is used to estimate the crack growth in the CWP. Finally analysis of strain limits using crack driving force (CDF) for the CWP specimens is also given by comparing experimental test data and FE estimation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".