Generalized approach to estimation of strains and stresses at blunt V‐notches under non‐localized creep
Bibliographic record
Abstract
Abstract Geometrical discontinuities such as notches need to be carefully analysed by engineers because of the stress concentration generated by them. Notches become even more important when the component is subjected, in service, to very severe conditions, such as high‐temperature fatigue and imposed viscoplastic behaviour such as creep. The knowledge of strains and stresses in such stress concentration zones is essential for an efficient and safe design process. The aim of the paper is to present an improvement and extension of the existing notch‐tip creep stress–strain analysis method developed by Nuñez and Glinka, validated for U‐notches only, to a wide variety of blunt V‐notches. The key in obtaining the extension to blunt V‐notches is the substitution of the Creager–Paris equations with the more generalized Lazzarin–Tovo solution, allowing a unified approach to the evaluation of linear elastic stress fields in the neighbourhood of both cracks and notches. Numerous examples have been analysed to date, and the stress fields obtained according to the proposed method were compared with appropriate finite element data, resulting in a very good agreement. In view of the promising results discussed in the paper, authors are considering possible further extension to sharp V‐notches and cracks introducing the concept of the strain energy density.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".