Axial Power and Coolant-Temperature Profiles for a Non-Re-Entrant Pressure-Tube Supercritical Water-Cooled Reactor Fuel Channel
Bibliographic record
Abstract
The axial power and coolant-temperature distributions in a fuel channel of the Generation IV pressure-tube super-critical water-cooled reactor (PT-SCWR) are found using coupled neutronics-thermal-hydraulics calculations. The simulations are performed for a channel loaded with a fresh, 78-element Th-Pu fuel assembly. Neutronics calculations are performed using the DONJON diffusion code using two-group homogenized cross sections produced using the lattice code DRAGON. The axial coolant temperature profile corresponding to a certain axial linear heat generation rate is found using a code developed in-house at University of Ontario Institute of Technology (UOIT). The effect of coolant density, coolant temperature, and fuel temperature variation along the channel is accounted for by generating macroscopic cross sections at several axial positions. Fixed-point iterations are performed between neutronics and thermal-hydraulics calculations. Neutronics calculations include the generation of two-group macroscopic cross sections at several axial positions, taking into account local parameters such as coolant temperature and density and average fuel temperature. The coolant flow rate is adjusted so that the outlet temperature of the coolant corresponds to the SCWR technical specifications. The converged axial power distribution is found to be asymmetric, resembling a cosine shape skewed toward the inlet (reactor top).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".