Impact of Discharge Current Profiling on Ignition Characteristics of Hydrogen/Methane Blends
Bibliographic record
Abstract
Abstract For future SI engines, the ignition processes of an air-fuel mixture are often subjected to a fuel-lean mixture of considerably higher density, high intake boost, and high compression ratio to further improve engine efficiency. The ignition systems for future gasoline engines should effectively ignite the mixture and secure the flame kernel until it develops into self-sustainable propagation. In this paper, the impact of discharge current profile on flame kernel formation and development processes of methane-hydrogen/air mixtures under engine-like conditions are experimentally investigated in a rapid compression machine. The discharge current during the glow phase is modulated to change the energy discharge profiles. A Field-programmable gate array based multi-task control system is established to effectively control and stabilize the discharge current amplitude and duration for different ignition strategies. The ignition and combustion process are characterized via simultaneous high-speed direct imaging and in-cylinder pressure measurement. The ignition delay is analyzed with respect to the in-cylinder pressure under various boundary conditions such as fuel blending ratio and spark discharge parameters, with a focus on the efficacy of ignition strategies under various hydrogen/methane blending ratios.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".