Experimental studies on upward-facing multi-nozzle spray cooling for external cooling of reactor pressure vessels with a four-nozzle system
Bibliographic record
Abstract
To enhance the thermal safety limits, the present study experimentally investigates the performance of spray cooling on a downward-facing surface. The experiments were conducted on the KTH-SPAYCOR facility. It employs four pressure-swirl full-cone nozzles arranged in a 2 × 2 array to produce a multi-spray pattern on a thin SA302B foil with a relatively large surface area of 120 mm × 80 mm. The tests were carried out under steady-state conditions, achieved through stepwise increments in surface heat flux, until the onset of dry spots or ultimate burnout was detected. Seven tests were performed to examine the effects of nozzle-to-surface distance and the flowrate, while maintaining a fixed surface inclination angle of 30°. The cooling mechanism combines direct droplet impingement and flushing by water film. The experimental results demonstrated the potential applicability of the multi-nozzle array system for cooling large surface areas, such as the reactor pressure vessel lower head. Specifically, the cooling limit was observed to enhance with increasing NTSD and flowrate, reaching a peak value of 2.33 MW/m 2 at a flow rate of 13 lpm. However, a slight decline in the cooling limit was noted when the flowrate exceeded this optimal flowrate. Additionally, increasing NTSD and flowrate enhanced the uniformity of the heater temperature distribution. To demonstrate the system's performance under high heat flux conditions, the ultimate critical heat flux was measured at flow rates of 10 lpm and 13 lpm, yielding values at 1.97 MW/m 2 and 2.50 MW/m 2 , respectively.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".