Vaporization and Combustion of a Soybean Biodiesel Droplet in Turbulent Environment at Elevated Ambient Pressure
Bibliographic record
Abstract
This article reports experimental data on the vaporization and combustion of soybean (derived) biodiesel droplet in turbulent environment at elevated ambient pressure and temperature conditions. Test conditions consisted of varying turbulence intensity and ambient pressure while keeping ambient temperature constant at 473 K. Soybean biodiesel droplet was formed using an in-house developed injector, and suspended onto the tip of a quartz fiber in the center point of a spherical vessel. The initial diameter of the formed droplet ranged between 1.00 mm and 1.50 mm. The characterization of the turbulent field, generated by four pairs of axial fans, revealed that turbulence is essentially isotropic and homogeneous with nearly zero-mean flow within the 40-mm-diameter volume in the center of the vessel. The experimental results showed that the biodiesel droplet vaporization and burning followed the d2-law. More importantly, the biodiesel droplet vaporization rate was shown to depend on both turbulence and ambient pressure. The droplet vaporization results showed that the effect of turbulence becomes more effective with increasing ambient pressure. In addition, turbulence was found to enhance the biodiesel droplet burning rate only at elevated ambient pressure. Heat loss from the flame predominates at high levels of turbulence, which consequently causes droplet flame extinction.
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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".