A Preliminary Study of the Discharge Current and Spark Energy for the Multi-Coil Offset Strategy
Bibliographic record
Abstract
To overcome the unfavorable operation conditions caused by lean/diluted charges in modern Spark Ignited (SI) engines, various advanced ignition systems have been proposed in the past. Among them, the dual-coil and multi-coil Transistor Coil Ignition (TCI) systems with offset discharge strategy caused significant attention in literature because they can generate a continuous spark with high spark energy being delivered into the cylinder. Comparing with the dual-coil system, a multi-coil system is capable to apply more flexible control strategies and generate a higher discharge current. However, the spark energy and transfer efficiency of the multi-coil system are still worthy to investigate as they are important performance indicators for a TCI system. In this paper, the discharge characteristics of the dual-coil and triple-coil strategies under both quiescent and flow conditions were studied firstly by experimental methods. Based on the test results, a numerical model for the multi-coil system was developed. Simulations of gradually adding more coils up to ten to the multi-coil system were conducted. Spark energy and transfer efficiency for each simulation conditions were calculated and compared to investigate the influence of the coil number on the discharge current and spark energy of the multi-coil offset strategy.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".