Modeling Ranges of Cyclic Variability for HCCI Ignition Timing Control
Bibliographic record
Abstract
The probability distribution shape of the ignition timing ensemble allows detection of unstable operation near misfire in Homogeneous Charge Compression Ignition (HCCI) engines. The acceptable range of cyclic variation in HCCI combustion timing is determined by linking the experimental measurements with the shape factors of Generalized Extreme Value (GEV) probability distribution. A combined physical-statistical model is incorporated to analyze the range of cyclic variability in CA50 (crank angle of 50% mass fraction fuel burnt) for two single-cylinder engines. The model is validated with the experimental data at 227 operating points with five different Primary Reference Fuels (PRF). Good agreement between simulation and the experiment with an average error of 0.36 crank angle degree for predicting standard deviation of CA50 is obtained. Low, medium and high cyclic variability zones are identified as a function of intake manifold pressure, equivalence ratio, and intake manifold temperature. This information can be integrated into the design of an engine controller strategy to maintain acceptable levels of cyclic variation during a commanded engine load change.
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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) | 0.000 | 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".