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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".