Induction charging of flat sprays for space charge generation
Bibliographic record
Abstract
Space charges play a key role in many industrial and agricultural applications such as electrostatic painting or application of insecticides in orchards and row crops. For generation of large sized space charge clouds, induction charging is the preferred method. Whereas corona charging often involves voltage in the order of 100 kV, induction charging may be achieved with voltages of less than a few thousand volts. The paper presents a novel construction of a spray unit with induction charging suitable for injecting large volumes of ionic space charge into a region. The experimental unit comprises: a flat spray liquid atomizing nozzle with high pressure air; two large size induction electrodes, and, an air curtain surrounding the electrodes to prevent the fine droplets from landing on the electrodes. The two large, flat induction electrodes sandwiching the flat spray have shown to generate excellent induction charging on the spray droplets achieving charge to mass ratios in the order of 4 mC/kg and ionic charge densities of 2 mC/m/sup 3/. The studied variables were: pressure and flow of water supply at the nozzle and the pressure and flow of the curtain The atomized charged spray cloud of droplets with a mean volume diameter of several micrometers, evaporates at certain distances from the nozzle forming a mixed charged cloud of both water ions and finer water droplets.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".