Thermodynamic treatment of uranium dioxide based nuclear fuel
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 it