Capacitance Calculation Model in Corona Discharge Case
Bibliographic record
Abstract
The (I-V) characteristic pattern of the corona discharge case is very different from the pattern of ordinary electric circuits, so it is interesting to investigate. Several previous studies involved the concept of Maxwell's equations on several physical case models such as coaxial cylinder, electrohydrodynamic, and the electric wind. In this study, we use a capacitance calculation model for positive dc corona discharge in air, especially in calculating the (I-V) current-voltage characteristics of an electrode configuration model, often referred to as capacitively coupled plasma (CCP). The configuration model comprises active and passive electrodes, with the active electrode in the form of a pentagonal with the sharp end (in the middle) facing downwards in an upright position. The passive electrode under the active electrode has a large rectangular shape in a lying place. This configuration model is named The Chisel Eye and Midpoint-Plane (CEM-P). The analytical calculation of the (I-V) characteristics uses the geometric properties of the active electrode, which will produce a large corona current flow at the pointed electrode. These properties in analytical calculation manifest with the emergence of the corona flow multiplication factor at the sharp active electrode’s integration boundary condition called the shape sharpness factor k. The Python GUI Programming simulation program makes graphic simulations, Standard Deviation (SD), t-tests, and calculating the factor k (fitting curve value) between numerical calculations and research data. The values of the SD, the t-tests, and the Percentage of tangent points meet the requirements for a high level of accuracy for the four CEM-P configuration models of the (I-V) characteristics simulation graph with the graph has a relatively large percentage of tangent points values (82.35% – 94.44%).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".