Laskennallinen moduuli termokemiallisten tasapainojen laskentaan ydinpolttoaineessa
Bibliographic record
Abstract
A computational module was developed in this work for the calculation of thermochemical equilibria bearing in mind the application of the module to chemical phenomena in nuclear fuel. Gibbs energy minimization principles set forth by Gunnar Eriksson in the SOLGASMIX program were applied for the calculation of thermochemical equilibria along with some improvements published in the literature. The underlying theory behind Gibbs energy minimization is presented, and the program applying this theory developed in this work is described in detail. To increase understanding of the chemical phenomena to be modelled with the program, chemistry of nuclear fuel in operation was reviewed. In the review, the chemistry of the actinides uranium and plutonium along with the fission products formed in irradiated nuclear fuel were explored. Published thermochemical treatments of irradiated nuclear fuel and the applications of thermochemical modelling to nuclear fuel were reviewed to gain knowledge of state of research in this matter. Thermodynamic data for this system in the form of the Royal Military College of Canada Fuel Thermochemical Treatment was found to be publicly available, and this data was applied in this work. The program developed in this work was tested and in many cases was found to calculate results comparable to other, established programs. Even difficult cases possible in the application of irradiated nuclear fuel were evaluated with the new program. Finally, a simulation of oxygen potential in a nuclear fuel rod under irradiation was performed with the new program with fission product amounts generated by the point depletion code ORIGEN and temperatures and pressures in the fuel rod calculated with the fuel performance code FRAPCON.
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 | 0.001 |
| 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 | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.011 |
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".