Spray and Thermal Analysis of Pressure and Air Atomized Nozzles for Electronic Cooling
Bibliographic record
Abstract
This study investigates the effectiveness of spray cooling for managing high heat generation in modern electronics, comparing the performance of two different nozzle types pressure atomized nozzles (PAN) and air atomized nozzles (AAN) .Our experimental setup consists of a pressure and air atomized nozzle for producing fine droplets of fluid.Through infrared thermography, we investigate temperature field distribution and heat flux evaluation on a heated SS-304 foil under various flow rates, heat fluxes, and fluid temperatures and nozzle to surface distance (N-SD).We are focusing mainly on the effect of different parameters in spray cooling at high heat flux such as nozzle to surface distance (N-SD), volumetric flow rate of fluid and fluid inlet temperature.Results show that AAN consistently achieves lower surface temperatures than PAN, demonstrating superior cooling efficacy.At 35°C and a flow rate of 0.1 L/min, AAN reduces average temperatures compared to PAN by 6.4°C, 7°C, and 6°C across heat fluxes ranging from 21.2 to 58.8 W/cm².The temperature decreases for each heat flux at a flow rate of 0.1 L/min and fluid inlet temperatures of 25°C and 35°C by 2.2°C to 11.2°C for the heat flux range of 21.2 to 58.8 W/cm² as the nozzle-to-surface distance increases from 20 mm to 30 mm.Infrared thermography offers localised insights of surface temperature distribution, which helps to evaluate accurate heat flux on substrate being heated.
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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 | 0.000 | 0.000 |
| 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".