Simulation of particle trajectories in tribo-powder coating
Bibliographic record
Abstract
The modeling of the tribo-powder coating process requires the solution of the electrostatic field problem and the equation for particle motion. Electric field distribution is a function of the space charge density, which is associated with the particle concentration. The particle movement results from the balance of electrical, inertial, air drag and gravitational forces. Therefore, both problems are mutually coupled. In this paper, an iterative algorithm is presented to simulate this problem; the electric field is determined by means of the finite element method, whereas the time-dependent explicit fourth order Runge-Kutta algorithm is used to predict the particle trajectories. Both problems are solved iteratively until a self-consistent solution is found. The results of simulation show the effects of different parameters which characterize the process such as the powder particle size, charge/mass ratio, distance to a coating object, powder outflow and assisted air velocity on the shape of the powder jet.
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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 | 0.000 | 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) | 0.000 | 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".