The Implementation of Poisson Field Analysis Within FLUENT to Model Electrostatic Liquid Spraying
Bibliographic record
Abstract
The process of electrostatic liquid spraying involves a combination of hydrodynamics, aerodynamics and electrostatics. Many parameters affect the process such as atomizing air pressure, liquid flow rate, nozzle-to-target distance, droplet size, charge-to-mass ratio, etc. The mechanical portion can be directly modeled with the computational fluid dynamics software, FLUENT. Although this software does not provide a direct solution for the electrostatic field, its user-defined functions can be used to solve the Poisson field by incorporating it into the general scalar transport equations within FLUENT. This enables the calculation of the electrostatic force on the charged droplets. The key to this technique is to find the space charge density for different charging models. An air-assisted electrostatic induction charging spray nozzle was modeled for both flat and spherical targets. Coupling between the airflow phase, the droplet discrete phase and the electrostatic field yields the trajectories of the charged droplets. Parameters such as droplet size, charge-to-mass ratio and nozzle-to-target distance were varied to demonstrate their effects on the motion of the charged droplets. The results show that the spray cloud expands with increased droplet charge-to-mass ratio and nozzle-to-target distance due to increased space charge. Thus they need to be independently controlled in order to increase the transfer efficiency and reduce drift.
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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 | 0.001 |
| 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".