Engine Efficiency of a Leidenfrost Droplet Transporting System
Bibliographic record
Abstract
Leidenfrost droplet transporting engine energy efficiencies were calculated to determine its feasibility and practicality for various industrial purposes. The engine relied on the Leidenfrost effect to transport water droplets across a superheated aluminum surface with ratchet-like topology (Cole et al., 2015; Linke et al., 2006; Wells et al., 2015). An established protocol was used, permitting an unbiased analysis of only relevant data. Acceleration-time data was collected using Logger Pro 3® motion-tracking software and work was calculated using a Riemann summation technique. A power meter measured the hot plate’s total power input over 3-hours. Average trial times were used to determine each trial’s energy input, and engine efficiencies were subsequently calculated. Droplet size and ratchet angle were varied as parameters in attempt to optimize engine efficiency. The results indicate that this linear Leidenfrost system has an extremely low average percent efficiency (2.86E-07%), analogous to that of a rotational Leidenfrost system (Wells et al., 2005). Varying the droplet size or ratchet angle, as a variation and extension of previous studies, did not influence the efficiency to any statistically meaningful extent.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".