Investigation of Transient High Current Ignition for Lean Mixture in Spark Assisted Compression Ignition
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Lean burn combustion is an effective strategy to reduce the in-cylinder temperature. Hence reduce NOx emissions and increase the thermal efficiency of the system. One essential aspect of successful combustion is the flame kernel initiation and development. However, as the fuel-air mixture becomes leaner, challenges arise in achieving a stable flame kernel initiation and a moderate speed of flame propagation. This empirical research aims to investigate the impact of the transient high current ignition strategy on flame kernel development, flame propagation and auto-ignition timing of lean Dimethyl Ether (DME). In this work, a rapid compression machine is employed at engine-relevant conditions, a pressure of ~15 bar and temperature of ~650K. Spark-assistance is applied at the end of compression to enable a spark-assisted compression ignition combustion mode. The spark event is initiated by a transient high current ignition system, which includes a traditional transistorized coil ignition and an in-parallel high-voltage capacitor for boosting the transient current, enabling high discharge energy (up to 11.3J). The combustion process is qualitatively assessed with high-precision pressure data acquisition along with high-speed images and quantitatively processed through image processing. Test results indicate that as spark energy increases, faster flame propagation is observed resulting in a shorter time requirement for reaching the auto-ignition in lean DME along with a shorter combustion duration.</div></div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".