Discharge Characteristics of Current Boosted Spark Events Under Flow Conditions
Bibliographic record
Abstract
With the advancement of spark ignition engines, lean or diluted in-cylinder charge is often used to improve the engine performance. Enhanced in-cylinder charge motion is widely applied under such conditions to promote the flame propagation, which raise challenges for the spark ignition system. In this work, the spark discharging process is investigated under different flow conditions via both optical diagnosis and electrical measurement. Results show that the spark plasma channel is stretched under flow conditions. A higher discharge current can maintain the stretched spark plasma for a longer duration. Re-strikes are observed when the spark plasma is stretched to a certain extent. The frequency of re-strikes increases with increased flow velocity and decreased discharge current level. The discharge duration reduces with the increased flow velocity. The effects of gas flow on the ignition and flame kernel development are studied in a constant volume optical combustion chamber with premixed lean and stoichiometric methane air mixture. Two spark strategies with low and high discharge current are used for the ignition. The flame propagation speed of both lean and stoichiometric mixtures increases with the increased gas flow velocity. A higher discharge current level retains a more stable spark channel and improves the flame kernel development for both lean and stoichiometric conditions, especially under the higher gas flow velocity of 20 m/s.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".