Thermal Design Options of New Pressure Channel for SCWRs
Bibliographic record
Abstract
This paper focuses on thermal-design options of a new pressure channel for SuperCritical Water-cooled Reactors (SCWRs). The objectives of this paper are to estimate heat losses from the coolant to the moderator for a preliminary fuel-channel design and to investigate effects of the insulator thickness and moderator pressure on the overall heat losses. In order to fulfill the objectives, the heat losses for an existing reactor were calculated and compared with available values from open literature. These calculations became the basis for calculation of the heat loss for the chosen new fuel-channel design. MATLAB, and NIST REFPROP software were utilized for programming and calculation of thermo-physical properties as needed, respectively. Heat losses for different thicknesses of the ceramic insulator were calculated. These calculations showed that the heat losses for the optimum thickness of insulator, which was calculated to be 7 mm, were about 31 MW. In current CANDU reactors the operating pressure of the moderator is close to the atmospheric pressure; higher operating pressures will allow operation of the moderator at higher temperature while preventing occurrence of boiling in the calandria vessel. Higher moderator temperatures will results in a lower temperature difference between the coolant and the moderator, hence reducing the heat sink from the coolant to the moderator. The effect of the moderator pressure on the heat loss was investigated, which showed that the heat loss can be reduced by increasing the operating pressure of the moderator by approximately 1 MW per 0.1 MPa increase in pressure.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 0.001 |
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".