A Simplified Circuit Model for the Emulation of Glow Phase during Spark Discharge
Bibliographic record
Abstract
The ever-growing demand to meet the stringent exhaust emission regulations have driven the development of modern gasoline engines towards lean combustion strategies and downsizing to achieve the reduction of exhaust emission and fuel consumption. Currently, the inductive ignition system is still the dominant ignition system applied in Spark Ignited (SI) engines. It is popular due to its simple design, low cost and robust performance. The new development in spark ignition engines demands higher spark energy to be delivered by the inductive ignition system to overcome the unfavorable ignition conditions caused by the increased and diluted in-cylinder charge. To meet this challenge, better understanding of the inductive ignition system is required. The development of a first principle model for simulation can help in understanding the working mechanism of the system in a better way. In this paper, an empirical model was proposed to emulate the relation between the equivalent ion channel resistance of the spark plug gap and the discharge current during the glow phase of a spark discharge. This empirical model was then embedded into a simplified physics based spark ignition circuit model which had been developed previously. Model validation was conducted against measurement results. Compared to the previous model, the proposed model can capture the electrical characteristics better during the glow phase of the spark discharge. Parametric analysis was presented using the model to study the effects caused by changing the ignition coil settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".