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 m and 100 m). n-Heptane was chosen to compare the effects of surface tension on the evaporation process. Initial surface temperatures were varied from 60C to 300C. 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 (60C to 120C), 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 120C), 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 m 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 m and 100 m substrates at surface temperatures of 225 o C and 285 o C respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".