Initial Multidisciplinary Study of Oxidized Chromium-Coated Zirconium Alloy for Fuel Cladding of SCW-SMR Concept: Weight-Gain and Thermal Conductivity Measurements and Coating Cost Evaluation
Bibliographic record
Abstract
One of the challenges of small modular reactors (SMRs) in comparison with large reactors is the greater difficulty in achieving high burnups in smaller cores. With greater neutron leakage through the periphery, a key factor is the neutron economy of the fuel cladding. However, all large supercritical water-cooled reactor (SCWR) concepts have employed neutron-absorbing stainless steels and nickel-based alloys in order to meet all the requirements in terms of corrosion and thermalhydraulics. In order to achieve higher burnups and extend the time between refueling in a SCW-SMR, the use of chromium-coated zirconium alloy as a potential fuel cladding candidate has been explored. Chromium coatings up to a few micrometers thick have shown improved oxidation resistance of zirconium-based claddings under operating conditions relevant to SCWR concepts. In this study, Zr-2.5Nb alloy (UNS R60904) from pressure tube samples was coated using a physical vapor-deposition (PVD) method. Oxidation tests were performed on coated samples at 500 °C and approximately 25 MPa in a refreshed autoclave. The effects of the oxide on heat transfer and hydraulic resistance are also discussed in this study. Last, but not least, this study evaluates the coating cost of the fuel cladding with chromium in a vacuum plasma spray process.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".