Implementation of A Gibbs Energy Minimizer In A Fission-Product Release Computer Program
Bibliographic record
Abstract
SOURCE 2.0 is the Canadian computer program for calculating fractional release of fission products from the UO2 fuel matrix. In nuclear accidents, fission-product release from fuel is one of the physical steps required before radiation dose from fission products can affect the public. Fission-product release calculations are a step in the analysis path to calculating dose consequences to the public from postulated nuclear accidents. SOURCE 2.0 contains a 1997 model of fission-product vaporization by B.J. Corse et al. based on lookup tables generated with the FACT computer program. That model was tractable on computers of that day. However, the understanding of fuel thermochemistry has advanced since that time. Additionally, computational resources have significantly improved since the time of the development of the Corse model and now allow incorporation of the more-rigorous thermodynamic treatment. Combining the newer Royal Military College of Canada (RMC) thermodynamic model of irradiated uranium dioxide fuel, a new model for fission-product vaporization from the fuel surface, a commercial user-callable thermodynamics subroutine library (ChemApp), an updated nuclide list, and updated nuclear physics data, a prototype computer program based on SOURCE IST 2.0P11 has been created that performs thermodynamic calculations internally. The resulting prototype code (with updated and revised data) provides estimates of 140La releases that are in better agreement with experiments than the original code version and data. The improvement can be quantified by a reduction in the mean difference between experimental and calculated release fractions from 0.70 to 0.07. 140La is taken to be representative of “low-volatile” fission products. To ensure that the existing acceptable performance for noble gases and volatile fission products is not adversely affected by the changes, comparisons were also made for a representative noble gas, 85Kr, and a representative volatile fission-product, 134Cs. These nuclides have the largest dataset in the SOURCE 2.0 validation test suite. This improvement provides increased confidence in the safety margin for equipment qualification in Loss-of-Coolant Accidents with Loss of Emergency Core Cooling.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".