Investigation on Electrostatical Breakup of Bio-Oil Droplets
Bibliographic record
Abstract
In electrostatic atomization, the input electrical energy causes breaking up of the droplet surface by utilizing a mutual repulsion of net charges accumulating on that surface. In this work a number of key parameters controlling the bio-oil droplet breakup process are identified and these correlations among the droplet size distribution, specific charges of droplets and externally applied electrical voltages are quantified. Theoretical considerations of the bag or strip breakup mechanism of biodiesel droplets experiencing electrostatic potential are compared to experimental outcomes. The theoretical analysis suggests the droplet breakup process is governed by the Rayleigh instability condition, which reveals the effects of droplets size, specific charge, surface tension force, and droplet velocities. Experiments confirm that the average droplet diameters decrease with increasing specific charges and this decreasing tendency is non-monotonic due to the motion of satellite drops in the non-uniform electrical field. The measured specific charges are found to be smaller than the theoretical values. And the energy transformation from the electrical energy to surface energy, in addition to the energy loss, Taylor instability breakup, non-excess polarization and some system errors, accounts for this discrepancy. The electrostatic force is the dominant factor controlling the mechanism of biodiesel breakup in electrostatic atomization.
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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".