ICONE23-1994 FULLY COUPLED MODELING OF BURNUP DEPENDENT LIGHT WATER REACTOR FUEL PERFORMANCE USING COMSOL MULTIPHYSICS
Bibliographic record
Abstract
This paper presents the development of a light water reactor fuel performance code, which considers almost all the related physical models, including heat generation and conduction, species diffusion, thermomechanics (thermal expansion, elastic strain, densification, and fission product swelling strain), grain growth, fission gas production and release, gap heat transfer, mechanical contact, gap/plenum pressure with plenum volume, cladding thermal and irradiation creep and oxidation. All the equations are implemented into COMSOL Multiphysics finite-element platform with a 2D axisymmetric geometry of a fuel pellet and cladding. Comparisons are made for the simulation results between COMSOL and another simulation tool of BISON. The comparisons show the capability of our simulation tool to predict light water UO_2 fuel performances. In our modeling and simulation work, the performance of enhanced thermal conductivity UO_2-BeO fuel and newlyadopted corrosion resistant SiC cladding material was also studied. UO_2-BeO high thermal conductivity nuclear fuel would decrease fuel temperatures and facilitate a reduction in pellet cladding interaction through lessening thermal stresses that result in fuel cracking, relocation, and swelling. The safety of the reactor would be improved. However, for SiC cladding, although due to its high thermal expansion, the gap closure time is delayed, irradiation induced point defects and defect-clusters in the SiC crystal will dramatically decrease SiC thermal conductivity, and cause significant increase in the fuel temperature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".