Changes In The Leidenfrost Transition Point During The Evaporation Of Water And N-Heptane Droplets On Hot, Porous Stainless Steel Surfaces
Bibliographic record
Abstract
The impact and evaporation of droplets impinging on a heated porous substrate is relevant to applications such as fire suppression by sprinkler systems, spray cooling of heated surfaces, and the deposition of fuel droplets on combustor walls.Design involving these sub systems requires an understanding of the heat and mass transfer between the droplet and porous surface.An experimental study was done in which pure water and n-heptane droplets were deposited onto porous, stainless steel surfaces made from sintered powders with varying pore size (5 μm and 100 μm).n-Heptane was chosen to compare the effects of surface tension on the evaporation process.Initial surface temperatures were varied from 60°C to 300°C.Results were compared with those for droplet evaporation on a solid, impermeable stainless steel surface.On porous surfaces, it is difficult to determine when a droplet has completely evaporated from video images, since liquid penetrates into the surface pores.At low wall temperatures (60°C to 120°C), droplet evaporation was measured by placing the heated surface on a digital scale and recording the weight decrease as a function of time.At high wall temperatures (above 120°C), video techniques were employed to capture evaporation times.At high wall temperatures, the porous surfaces were the most effective at vaporizing both the pure water, and n-heptane droplets, resulting in the lowest evaporation times.At low wall temperatures the porous surfaces became less effective at transferring heat to both the water and n-heptane.For n-heptane the evaporation times increased on the porous surfaces as compared to the impermeable surface.Similar results could be seen with the water on the 100 μm surface.The Leidenfrost transition point was shown to increase with porosity.Droplet levitation was not achieved with the water on the porous surfaces, however due to the lower heat of vaporization of n-heptane it was achieved on the 5 μm and 100 μm substrates at surface temperatures of 225 o C and 285 o C 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 | 0.000 | 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 | 0.000 |
| 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".