Optimization of the Canadian SCWR Core Using Coupled Three-Dimensional Reactor Physics and Thermal-Hydraulics Calculations
Bibliographic record
Abstract
The Canadian supercritical water-cooled reactor (SCWR) design is part of Canada's Generation IV reactor development program. The reactor uses batch fueling, light water above the thermodynamic critical point as a coolant and a heavy water moderator. The design has evolved considerably and is currently at the conceptual design level. As a result of batch fueling, a certain amount of excess reactivity is loaded at the beginning of each fueling cycle. This excess reactivity must be controlled using a combination of burnable neutron poisons in the fuel, moderator poisons, and control blades interspersed in the heavy water moderator. Recent studies have shown that the combination of power density, high coolant temperatures, and reactivity management can lead to high maximum cladding surface temperatures (MCST) and maximum fuel centerline temperatures (MFCLT) in this design. This study focuses on improving both the MCST and the MFCLT through modifications of the conceptual design including changes from a 3 to 4 batch fueling cycle, a slightly shortened fuel cycle (although exit burnup remains the same), axial graded fuel enrichment, fuel-integrated burnable neutron absorbers, lower reactivity control blades, and lower reactor thermal powers as compared to the original conceptual design. The optimal blade positions throughout the fuel cycle were determined so as to minimize the MCST and MFCLT using a genetic algorithm and the reactor physics code PARCS. The final design was analyzed using a fully coupled PARCS-RELAP5/SCDAPSIM/MOD4.0 model to accurately predict the MCST as a function of time during a fueling cycle.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".