A Thoria and Thorium Uranium Dioxide Nuclear Fuel Performance Model Prototype and Knowledge Gap Assessment
Bibliographic record
Abstract
Thorium-based fuel cycles can improve fuel sustainability within the nuclear power industry. The Canadian supercritical water-cooled reactor (SCWR) concept uses this path to achieve the sustainability requirement of the Gen-IV Forum. The study of thorium dioxide/thoria ThO2-based fuel irradiation behavior is significantly less advanced than that of uranium dioxide (UO2) fuel, although ThO2 possesses superior thermal conductivity, thermal expansion, higher melting temperature, and oxidation resistance that may improve both fuel performance and safety. The fuel and sheath modeling tool (FAST), a fuel performance model for UO2 fuel, was developed at the Royal Military College of Canada (RMCC). FAST capability has been extended to include thoria (ThO2), thorium uranium dioxide (Th,U)O2, and thorium plutonium dioxide (Th,Pu)O2 as fuel pellet materials, to aid in designing and performance assessment of Th-based fuels, including SCWR (Th,Pu)O2 fuel. The development and integration of ThO2 and (Th,U)O2 models into the existing FAST model led to the multipellet material FAST (MPM-FAST). Model development was performed in collaboration between RMCC and Canadian Nuclear Laboratories (CNL). This paper presents an outline of the ThO2 and (Th,U)O2 MPM-FAST model, a comparison between modeling results with postirradiation examination (PIE) data from a test conducted at CNL, and an account of the knowledge gap between our ability to model ThO2 and (Th,U)O2 fuel compared to UO2. Results are encouraging when compared to PIE data.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".