Cycle-to-Cycle Combustion Stability Evaluation of HCNG Blends in Multi-Cylinder Engines via Coefficient of Variation Analysis
Bibliographic record
Abstract
This study investigates the effect of hydrogen enrichment on combustion stability in a CNG-fueled multi-cylinder spark-ignition engine.Hydrogen was blended into CNG at 0%, 18%, 25%, and 30% by volume, and the blends were tested under constant-speed, fullload conditions.Combustion stability was assessed using the coefficient of variation (CoV) of peak cylinder pressure (Pmax), mass burn fraction at 50% (MBF50), and heat release rate (HRR).A correlation matrix analysis was employed to examine interrelationships between these stability parameters.Results indicated that the 18% HCNG blend provided the best overall improvement, achieving the lowest CoV values for MBF50 and HRR, and significantly reducing CoV_Pmax compared to pure CNG.Increasing hydrogen content to 25% maintained stability but introduced minor irregularities, while 30% hydrogen further improved Pmax consistency yet adversely impacted MBF50 and HRR stability.Correlation analysis highlighted a strong positive relationship between MBF50 and HRR stability, emphasizing the critical role of combustion phasing control.The study concludes that hydrogen enrichment in the range of 18-25% optimally enhances combustion stability without inducing combustion irregularities.These findings offer valuable insights for optimizing HCNG blends to achieve cleaner and more efficient future engine designs.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".