Factors influencing the minimum arc sustaining electric field and associated current for a high-pressure-vortex-water-wall-arc lamp
Bibliographic record
Abstract
Pulsed high pressure arc lamps are often operated with a small sustaining current during the interpulse period in order to minimize the arc expansion time, to increase the lamps' operating lifetime and to enable operation at higher repetition frequencies. It is common to select a sustaining current level associated with an arc voltage which lies between the maximum DC source voltage and the minimum sustaining voltage. This paper shows that the radius of the arc tube, the amount of convection and radiation and the assumptions regarding the nature of the radiation model strongly influence the current value associated with the minimum sustaining voltage. Pressure and boundary temperature have only a slight effect on the current associated with the minimum sustaining voltage but affect the minimum sustaining voltage. Heat conduction plays the primary role in tubes with a small radius while optically thin radiation plays the primary role in tubes with a large radius. The simulation results were obtained using Patankar's control volume algorithm along with boundary conditions. The extended Ellenbass-Heller equation in cylindrical coordinates was solved. The numerical results for the electric field as a function of current are somewhat smaller than the experimentally derived results. Convection appears to be the reason for this difference.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".