First principles Candu fuel model and validation experimentation
Bibliographic record
Abstract
Many modeling projects on nuclear fuel rest on a quantitative understanding of the co- existing phases at various stages of burnup. Since the various fission products have considerably different abilities to chemically associate with oxygen, and the O/M ratio is slowly changing as well, the chemical potential (generally expressed as an equivalent oxygen partial pressure) 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 UO2 fuel phase may be non-stoichiometric and most of minor phases have a variable composition dependent on temperature and possible contact with the coolant in the event of a sheathing defect. A Thermodynamic Fuel Model to predict the phases in partially burned CANDU nuclear fuel containing many major fission products has been under development. This model is capable of handling non-stoichiometry in the UO2 fluorite phase, dilute solution behaviour of significant solute oxides, noble metal inclusions, a second metal solid solution U(Pd-Rh-Ru)3, zirconate and uranate solutions as well as other minor solid phases, and volatile gaseous species. The treatment is a melding of several thermodynamic modeling projects dealing with isolated aspects of this important multi-component system. To simplify the computations, the number of elements has been limited to twenty major representative fission products known to appear in spent fuel. The proportion of elements must first be generated using SCALES-5. Oxygen is inferred from the concentration of the other elements. Provision to study the disposition of very minor fission products is included within the general treatment but these are introduced only on an as needed basis for a particular purpose. The building blocks of the model 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. To validate the model, coulometric titration experiments relating the chemical potential of oxygen to the moles of oxygen introduced to SIMFUEL are underway. A description of the apparatus to oxidize and reduce samples in a controlled way in a H2/H2O mixture is presented. Existing measurements for irradiated fuel, both defected and non-defected, are also being incorporated into the validation process.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".