Calculation of Stress Intensity Factor for Surface Flaws Using Universal Weight Functions With Piece-Wise Cubic Stress Interpolation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Linear elastic fracture mechanics based flaw evaluation procedures in ASME Section XI require calculation of the stress intensity factor. The method to calculate the stress intensity factor that is provided in the 2010 Edition of Appendix A of ASME Section XI is to fit the stress distribution ahead of the crack tip to a polynomial equation, and then use standardized influence coefficients. In PVP2011-57911, an alternate method for calculation of the stress intensity factor for a surface flaw that makes explicit use of the Universal Weight Function Method and does not require a polynomial fit to the actual stress distribution was proposed for implementation into Appendix A of Section XI. The alternate method provides closed-form solutions for the stress intensity factor. A numerical approximation is the assumed piece-wise linear variation of stress between discrete locations where stresses are known. For highly nonlinear stress distributions, piece-wise cubic interpolation of stress over intervals between discrete locations where stresses are known is an improvement over piece-wise linear interpolation. Investigation of a cubic interpolation of stress between discrete locations where stresses are known has been conducted. Closed-form equations for calculation of the stress intensity factor for a surface flaw were developed using the Universal Weight Function Method and generic piece-wise cubic interpolation of stress over intervals. Example calculations are provided to compare the results of stress intensity factors using piece-wise cubic interpolation with piece-wise linear interpolation.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it