Universal Weight Function Consistent Method to Fit Polynomial Stress Distribution for Calculation of Stress Intensity Factor
Bibliographic record
Abstract
Procedures for the analytical evaluation of flaws in nuclear pressure boundary components are provided in Section XI of the ASME B&PV Code. The flaw evaluation procedure requires calculation of the stress intensity factor. Engineering procedures to calculate the stress intensity factor are typically based on a polynomial equation to represent the stress distribution through the wall thickness, where the polynomial equation is fitted using the least squares method to discrete data point of stress through the wall thickness. However, the resultant polynomial equation is not always an optimum fit to stress distributions with large gradients or discontinuities. Application of the weight function method enables a more accurate representation of the stress distribution for the calculation of the stress intensity factor. Since engineering procedures and engineering software for flaw evaluation are typically based on the polynomial equation to represent the stress distribution, it would be desirable to incorporate the advantages of the weight function method while still retaining the framework of the polynomial equation to represent the stress distribution when calculating the stress intensity factor. A method to calculate the stress intensity factor using a polynomial equation to represent the stress distribution through the wall thickness, but which provides the same value of the stress intensity factor as is obtained using the Universal Weight Function Method, is provided in this paper.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".