Effects of Spark Discharge Energy Scheduling on Flame Kernel Formation under Quiescent and Flow Conditions
Bibliographic record
Abstract
The breakdown phase is considered to have the highest electric-thermal energy transfer efficiency among all the discharge modes in a conventional spark ignition process. In this study, an external capacitor is connected in parallel with the spark plug in order to enhance the discharge energy and power during the breakdown phase. A constant volume combustion chamber is used to investigate the high power spark discharge under different background pressures and with varied flow velocities. Results show that the added parallel capacitance is effective in redistributing the spark energy. With the increase in parallel capacitance, the breakdown power and energy increase, though at the cost of reduced glow phase energy. The breakdown energy also increases with the increased background pressure. Then combustion tests are carried out to study the effects of the breakdown power enhanced spark on flame propagation under both quiescent and flow conditions via optical diagnosis. Firstly, the spark discharge characteristics are investigated from the shadowgraph images recorded during the spark discharge in air where no combustion is involved. The high temperature field generated by spark plasma can be viewed from the shadowgraph images. A larger high-temperature region with enhanced turbulence is obtained with the enhanced breakdown power. Combustion test results prove that this enlarged turbulent high-temperature region is effective in promoting the flame propagation. Subsequently, the spark discharge and combustion under flow conditions are studied. Results reveal that the high power discharge does not bring benefit under the tested flow conditions with a flow velocity of about 15 m/s and background pressure of 4 bar absolute.
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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) | 0.000 | 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 | 0.001 |
| 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".