A New Approach of Reliability Design For Creep Rupture Property
Bibliographic record
Abstract
Generally, creep rupture data of a heat-resistant steel can be compressed into a narrow band by using a temperature-time parametric method such as the Larson–Miller or Manson–Haferd method. In order to describe the scattering of the data, the current paper proposes a “Z parameter” method to represent the magnitude of the deviation of the rupture data to master curve. Statistical analysis shows that the scattering of the Z parameter for several types of steels is supported by normal distribution. Using this method, it is possible to achieve unified analysis of the creep rupture data in various temperature and stress conditions. Stress-time temperature parameter-reliability curves (σ-TTP-R curves), stress-rupture time-reliability curves (σ-tr-R curves), and allowable stress-temperature-reliability curves ([σ]-T-R curves) are proposed, which could embrace the reliability concept into creep rupture property design.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".