Thermodynamic treatment of uranium dioxide based nuclear fuel
Bibliographic record
Abstract
Abstract Many projects involving nuclear fuel rest on a quantitative understanding of the co-existing phases at various stages of burnup. Since the fission products have considerably different abilities to chemically associate with oxygen, and the metal-to-oxygen molar ratio is necessarily increasing, the chemical potential of oxygen is a function of burnup. Concurrently, well-recognized small fractions of new phases such as inert gas, noble metals, zirconates, etc. also develop. To further complicate matters, the dominant UO 2 fuel phase may be non-stoichiometric and most of the minor phases themselves have a variable composition dependent on temperature and possible contact with the coolant in the event of a sheathing breach. A thermodynamic database has been in development to predict the phases in partially burned CANDU (CANada Deuterium Uranium) nuclear fuel containing the major fission products. The building blocks are the standard Gibbs energies of formation of the many possible compounds expressed as a function of temperature. To these data are added mixing terms associated with the appearance of the component species in particular phases. In operational terms, the treatment rests on the ability to minimize the Gibbs energy in a multicomponent system using the algorithms developed by Eriksson. The treatment, considered applicable in the range 300 to 2000 °C, is capable of handling non-stoichiometry in the UO 2 fluorite phase, dilute solution behaviour of significant solute oxides, noble metal inclusions, a second metal solid solution U(Pd – Rh – Ru) 3 , zirconate, molybdate, and uranate solutions as well as other minor solid phases, and volatile gaseous species. The paper highlights the current capability of an ongoing project.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".