Thermal Aspects of Using Thoria Fuel in SuperCritical Water-Cooled Nuclear Reactors
Bibliographic record
Abstract
SuperCritical Water-cooled Reactors (SCWRs) are one of six next-generation nuclear-reactor design options under consideration worldwide. These nuclear-reactor design options are included in the major international treaties such as: Generation IV International Forum (GIF) and INternational PROject on innovative nuclear reactors and fuels (INPRO). SCWR coolant is light water, which operates at supercritical pressures and temperatures. Typical SCWR coolant operating parameters are 25 MPa and 350–625°C. These SCWR operating conditions significantly increase the thermal efficiency of a SCW Nuclear Power Plant (NPP) (about 45 – 50%) compared to that of existing NPPs (30 – 35%). Also, SCWRs use significantly higher water parameters than existing water-cooled reactors, because of this they can support hydrogen co-generation. Previous thermal-design fuel-channel option studies for SCWRs have shown that the use of uranium dioxide (UO2) fuel at supercritical water conditions might be unacceptable as the fuel centerline temperature is close to or even exceeds the industry accepted limit of 1850°C. Alternative fuels with a higher thermal conductivity have to be considered. Thoria (ThO2) fuel is a suitable alternative to UO2 due to its higher thermal conductivity. Thoria fuel is beneficial because it complies with the Non-Proliferation Treaty and there are plenty of reserves worldwide. Therefore, ThO2 fuel and its suitability with SCWR use are considered in this paper.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".