An Evaluation of the Protection Against Local Failure in ASME Section VIII, Division 2: Finite Element Model Considerations
Bibliographic record
Abstract
The local failure criteria was a relatively new inclusion in the 2007 Edition of ASME Section VIII, Division 2. The elastic-plastic evaluation criteria for this failure mechanism was brand new to the Code. This failure mechanism introduced to the Code the concept of local strain limits based on the triaxial state of stress. In implementing the evaluation of this failure mechanism, an interesting phenomenon was discovered. The triaxiality was to be calculated “everywhere” in the model, and used to create a limiting strain to be compared to the plastic strain at the point where said triaxiality was calculated. As is well known, in the finite element method, stresses and strains are calculated at the Gauss Points, and extrapolated to the nodes. This uses the element shape function. One question that arises is: if the triaxiality is calculated at the Gauss Points, can it be extrapolated to the nodes using the same element shape functions? Another question that arises is: can the extrapolated values of stress and strain be adequately used to calculate the triaxiality at the nodes? This paper discusses the validity of both approaches, and introduces a third option: a material property called Ductile Damage Initiation Criteria, and provides and example that demonstrate all three methods. Recommendations are provided about the best approach to be implemented.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".