Bibliographic record
Abstract
Combustion diagnostics of highly diluted air fuel mixtures are of great importance for reducing the carbon footprint of various types of combustion systems. Flame detection techniques, such as ion sensing and optical diagnostics, have been reported for diagnosing combustion status. In this paper, a flame front detection technique based on active plasma probing is introduced and analyzed. Unlike the conventional ion sensing used in internal combustion engines, a separate electrode gap is used to detect the flame front arrival. Further, the voltage potential across the electrodes of the spark plug probe is modulated actively to be slightly below the breakdown threshold prior to flame arrival. At the arrival of the flame front, the ions in the flame tend to decrease the breakdown voltage threshold and trigger a breakdown event. An optically accessible constant volume combustion vessel is employed to investigate the efficacy of such active plasma probing for the detection of the flame front under quiescent and flow conditions. Three types of fuels are used in the tests, including methane, propane, and DME, with an air fuel ratio sweep (from stoichiometric to extremely lean) for each fuel. Efforts are made to characterize the probing criteria of minimum, yet adequate voltage to succeed in the detection of the arrival and departure of the flame front most sensitively and reliably, for all three types of fuels under various mixture strengths. For comparison, conventional ion current measurements are conducted under identical background conditions to benchmark the efficacy of flame detection. Results show that the active plasma probing can promptly detect the arrival and departure of the flame front, robustly regardless of the fuel type and excess air ratios. The precise control of the detecting voltage is key for reliable flame detection with high sensitivity.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".