Study on Thermal Efficiency of SuperCritical Water NPPs
Bibliographic record
Abstract
As the demand for emission-free energy increases, the continued improvement of Nuclear Power Plants (NPPs) and their thermal efficiencies is crucial to fulfilling that demand. Current NPPs, especially, with water-cooled reactors, have significantly lower thermal efficiencies (32–36%) compared to those of modern advanced thermal power plants (55–62%). Even Generation-III+ water-cooled NPPs will have thermal efficiencies not higher than 37–38%. Therefore, to be competitive on the energy market, new nuclear reactors and NPPs, so-called, Generation-IV concepts, should be designed and commissioned. The paper discusses the vital role that thermal efficiency plays with respect to how far nuclear reactors can be more cost efficient and competitive. An evaluation of thermal efficiencies has been carried out for SuperCritical Water (SCW) NPPs with Rankine “steam”-turbine power cycle using the IAEA DEsalination Thermodynamic Optimization Program (DE-TOP). Various options for improving thermal efficiencies of SCW NPPs have been studied. This study was performed in support of possible designs of the first experimental SCW reactors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".