SCW Pressure-Channel Nuclear Reactors: Some Design Features and Concepts
Bibliographic record
Abstract
Concepts of nuclear reactors cooled with water at supercritical pressures were studied as early as the 1950s and 1960s in the USA and Russia. After a 30-year break, the idea of developing nuclear reactors cooled with supercritical water (SCW) became attractive again as the ultimate development path for water-cooling. The main objectives of using SCW in nuclear reactors are 1) to increase the thermal efficiency of modern nuclear power plants (NPPs) from 33–35% to about 40–45%, and 2) to decrease capital and operational costs and hence decrease electrical energy costs (∼$1000 US/kW). SCW NPPs will have much higher operating parameters compared to modern NPPs (pressure about 25 MPa and outlet temperature up to 625 °C), and a simplified flow circuit, in which steam generators, steam dryers, steam separators, etc., can be eliminated. Also, higher SCW temperatures allow direct thermo-chemical production of hydrogen at low cost, due to increased reaction rates. Pressure-channel SCW nuclear reactor concepts are being developed in Canada and Russia. Design features related to both channels and fuel bundles are discussed in this paper. Also, Russian experience with operating supercritical steam heaters at NPP is presented. The main conclusion is that development of SCW pressure-channel nuclear reactors is feasible and significant benefits can be expected over other thermal energy systems.
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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 | 0.000 |
| 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 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".