Power Distribution and Fuel Centerline Temperature in a Pressure-Tube Supercritical Water-Cooled Reactor (PT SCWR)
Bibliographic record
Abstract
SuperCritical Water-cooled nuclear Reactor (SCWR) designs are one of six nuclear-reactor concepts being developed under the Generation IV International Forum (GIF) initiative. A generic pressure-tube SCWR consists of distributed fuel channels with coolant inlet and outlet temperatures of 350 and 625°C at 25 MPa, respectively. Such reactor coolant outlet conditions allow for high thermal efficiencies of SCW Nuclear Power Plant (NPP) of about 45–50%. In addition to high thermal efficiencies, SCWR designs provide the means for co-generation of hydrogen through thermochemical processes such as the Cu–Cl cycle. The main objective of this paper is to determine the power distribution inside the core of an SCWR by using a lattice code - DRAGON and a diffusion code - DONJON. As a result of these calculations, heat-flux profiles in all fuel channels were determined. Consequently, the heat-flux profile in a channel with the maximum thermal power was used as an input into a thermal-hydraulic code, which was developed in MATLAB in order to calculate a fuel centerline temperature for UO2 and UC nuclear fuels. Results of an analysis showed that the fuel centerline temperature of UC was significantly lower than that of UO2. This paper also studies effects of energy groups on multi-group diffusion calculations and proposes nine energy groups for further neutronic studies related to SCWRs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".