Mechanical Property Database Development and Creep Prediction of Candidate Generation IV SCWR Alloys
Bibliographic record
Abstract
To contribute to the design and materials selection of the Generation IV SuperCritical Water Reactors (Gen IV SCWR), a high-temperature mechanical property database of candidate alloys has been developed and creep models have been applied to predict long-term creep strength. The alloys in the database include representative ferritic/martensitic (F/M) steels, stainless steels, iron-nickel–base alloys, Ni-base alloys, and oxide dispersion strengthened (ODS) alloys with an emphasis on candidate austenitic and super-austenitic stainless steels. The mechanical properties were evaluated and examined with reference to ASME Section III Subsection NH code and the preliminary fuel cladding requirements in Canadian and Japanese conceptual SCWR designs. Traditional creep prediction models (i.e., Monkman-Grant model and Larson-Miller parameter) and the recent Wilshire-Scharning model were assessed and the constants of creep models were obtained. The predicted average maximum allowable stresses at temperatures and lifetimes of interest to Gen IV SCWR fuel cladding applications are presented.
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 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) | 0.003 | 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".