ICONE23-1375 Pressure-Drop Analysis of a Re-Entrant Fuel Channel In a Pressure-Channel Supercritical Water-Cooled Reactor
Bibliographic record
Abstract
Modern Generation-III Nuclear Power Plants (NPPs) equipped with water-cooled reactors have gross thermal efficiencies of approximately 30-36%, which are significantly less than those of advanced fossil-fuel and natural-gas thermal power plants (55-62%). Therefore, global effort to progress Generation-IV reactor concepts and NPPs are required to meet the demand for clean, non-fossil-based electrical production. The main objective of this paper is to determine a pressure drop across a fuel channel of a SuperCritical Water-cooled Reactor (SCWR). A generic 1200-MWel SCWR has inlet and outlet temperatures of 350°C and 650°C, respectively, and an inlet pressure of 25 MPa. With such high operating temperatures and pressures, an SCWR NPP can achieve gross thermal efficiencies of approximately 45 - 48%, which is a substantial improvement over the currently operating Generation-III NPPs. For this purpose, a Re-Entrant-Channel design and its associated 78-element fuel bundle are selected as a basis for this analysis. An investigation of a pressure drop resulting from friction, gravity, acceleration and local losses at supercritical conditions has been performed on a basis of one-dimensional steady-state analysis. With this objective, a thermal-hydraulic code has been created with MATLAB, which calculates the pressure drop across a 5-m heated length of a vertical fuel channel. The total pressure drop across the channel was estimated to be about 60 kPa.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".