Power Cycles of Generation III and III+ Nuclear Power Plants
Bibliographic record
Abstract
It is well known that electrical power generation is the key factor for advances in industry, agriculture, and standard of living. In general, electrical energy can be generated by (1) nonrenewable energy sources such as coal, natural gas, oil, and nuclear; and (2) renewable energy sources such as hydro, wind, solar, biomass, geothermal, and marine. However, the main sources for electrical energy generation are (1) thermal—primarily coal and secondary natural gas, (2) “large” hydro, and (3) nuclear. Other energy sources might have a level of impact in some countries. Modern advanced thermal power plants have reached very high thermal efficiencies (55–62%). In spite of that, they are still the largest emitters of carbon dioxide into the atmosphere. Therefore, reliable non–fossil fuel energy generation, such as nuclear power, is becoming more and more attractive. However, current nuclear power plants (NPPs) are way behind in thermal efficiency (30–42%) compared to the efficiency of advanced thermal power plants. Therefore, it is important to consider various ways to enhance the thermal efficiency of NPPs. This paper presents a comparison of thermodynamic cycles and layouts of modern NPPs and discusses ways to improve their thermal efficiencies.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".