Numerical study of the effect of surface wettability on performance of the spray cooling process
Bibliographic record
Abstract
The process of cooling caused by a water droplet contacting a surface has been extensively reported in the literature; however, the effect of surface wettability on the outcome of the cooling rate has yet to be analyzed.Due to optical limitations inside a liquid droplet, a three-dimensional (3D) computational fluid dynamics (CFD) model, including coupling between multiphase flow and the conjugated heat transfer module was developed to simulate the impact, spreading and transient heat transfer between a cold-water droplet and a heated surface.The total heat transfer results were calculated for both superhydrophobic and hydrophilic surfaces.The Navier-Stokes equation expressing the flow distribution of the liquid and the gas, coupled with the volume of fluid (VOF) method for tracking the liquid interface, was solved numerically using the finite volume methodology.The grid dependency test was examined for the 3D model, even though the convergence of the results was not exact.The 2 mm diameter water droplet with the Weber numbers 7, 25 and 62, which correspond to non-splashing regimes, were impinged onto two different surfaces.We showed that spray cooling on a superhydrophobic substrate was capable of improving the efficiency of the cooling process up to 40% compared to that of a hydrophilic surface.Additionally, the critical Weber regime was obtained for the optimal heat transfer between the droplet and the two substrates.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".