Experimental study of butanol droplet evaporation in a turbulent, high-pressure environment
Bibliographic record
Abstract
An experimental investigation is conducted to study the vaporization behavior of butanol droplets in a turbulent environment at elevated pressure. A single droplet of butanol, with a diameter of 500 µm, is suspended at the intersection point of two micro-fibers positioned in the center of a fan-stirred spherical chamber. The latter is equipped with eight axial fans to generate an isotropic and homogeneous turbulent flow field with nearly zero-mean velocity. In this experiment, turbulence intensity and ambient pressure were varied from 0 to 1.5 m/s and from 1 to 10 bar, respectively, while ambient temperature was kept at room conditions. The experimental findings revealed that the butanol droplet squared diameter exhibited a linear relationship with time for the majority of the droplet lifetime. This observation suggests that the d 2 -law is applicable across all the test conditions explored in this study. Results showed that the vaporization rate of a butanol droplet decreases with ambient pressure and increases with turbulence intensity. The vaporization rate of butanol falls between that of heptane and decane, with closer proximity to decane. Furthermore, the comparison of these fuels indicates that the influence of turbulence on the vaporization rate is more significant for fuels with lower vapor mass diffusivity. Finally, published correlations based on Damköhler and Reynolds numbers are verified and found to hold for the butanol droplet vaporization data.
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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 | 0.000 |
| 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) | 0.001 | 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".