Comparison of Strain Range Measures and Environmental Fatigue Calculation Methodologies for the Stepped Pipe Tests
Bibliographic record
Abstract
Various strain measure formulas exist at this time for the calculation of the strain amplitude required for fatigue calculations, and various methodologies have been suggested in the years 2005 through 2008 to take into account — in general — the environmental effects on fatigue (EAF = Environmentally-Assisted Fatigue). The purpose of this technical paper is to compare these strain measure formulas and these EAF methodologies for the case of the thermal fatigue tests of a stainless steel stepped pipe for which results have been published in the Proceedings of the 2004 PVP Conference [1]. Thermal transient finite element analyses and cyclic elasto-plastic finite element analyses were performed to obtain the thermal gradients through the pipe thickness and the resulting strain ranges. These strain ranges are based on the various strain measure definitions presented at the 2001/2005 PVP Conferences (see Ref. [2] and [3]). These various strain measure definitions were compared. Using one of the stepped pipe inside surface locations and using one of the strain range values (out of the various strain measure definitions), the allowable number of design cycles has been calculated, based on the currently mandated methodologies for the environmental effects on fatigue (EAF). These methodologies are the EAF methodologies to be applied in the United States for future fatigue calculations, either for license renewal of the currently operating nuclear power plants or for the design of new plants. The fatigue results are compared and discussed for their implication in component design.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".