Toward a Mechanistic Model of Stress Corrosion Cracking in PHWR Fuel Sheaths
Bibliographic record
Abstract
Abstract This work builds on the iodine-induced stress corrosion cracking (ISCC) model of Lewis and Kleczek by integrating the fuel performance model fuel and sheath modeling tool (FAST) to provide thermal and mechanical analysis of the fuel sheath, which was previously required as input parameters. The iodine transport methodology of the Lewis–Kleczek model has been modified to utilize the more mechanistic diffusion model in FAST, and an empirical surface multiplier term has been derived to predict iodine release rates under normal operating conditions based on measured release rates from in-reactor sweep gas tests. A fracture mechanics analysis is implemented using threshold stress intensity values and crack growth rates reported in literature. A correlation to predict crack initiation has been derived by analysis of a database of power histories with known ISCC defects. This correlation is based on the change in sheath hoop strain during a power ramp and is shown to be more accurate at discerning failure versus nonfailure than the correlations used in the previous Lewis–Kleczek model and FUELOGRAMS. Failure time prediction of the model is compared against power ramp test FFO-104 performed at the National Research Experimental (NRX) reactor. Fuel failure is predicted to occur 27% faster than experimentally measured; failure time is explored in a series of sensitivity studies to suggest areas for further development. The model improvements represent a step forward in the mechanistic modeling of stress corrosion cracking (SCC) in pressurized heavy water reactor (PHWR) nuclear fuel.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".