Improvement on Energy Efficiency of the Spark Ignition System
Bibliographic record
Abstract
Future clean combustion engines tend to increase the cylinder charge to achieve better fuel economy and lower exhaust emissions. The increase of the cylinder charge is often associated with either excessive air admission or exhaust gas recirculation, which leads to unfavorable ignition conditions at the ignition point. Advanced ignition methods and systems have progressed rapidly in recent years in order to suffice the current and future engine development, and a simple increase of energy of the inductive ignition system does not often provide the desired results from a cost-benefit point of view. Proper design of the ignition system circuit is required to achieve certain spark performances. In this paper, inductive coils with different primary and secondary inductances as well as turning ratios were tested to investigate the effect of inductive ignition system parameters on spark discharge characteristics under different control parameter such as charging voltage and charging duration. Based on the results from the electrical measurement, a simplified circuit model was then employed to perform a systematic study in order to improve the performance of the inductive ignition system. Furthermore, different ignition strategies based on inductive coil system including multi-coil discharge strategy, repetitive discharge strategy and dual coil discharge strategy were studied, and energy transfer efficiency was compared with traditional single spark strategy. Special attention was paid to energy losses inside the electrical path of the ignition system, and energy transfer efficiency from the primary coil to the spark gap was analyzed.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".