Using Reformer Gas to Enhance HCCI Combustion of CNG in a CFR Engine
Bibliographic record
Abstract
This paper describes use of reformer gas (RG) to alter and control combustion in a CNG-fueled HCCI engine. Experimental work used a mixture of simulated RG (75% H2 and 25% CO) to supplement base CNG fueling in a CFR engine upgraded to achieve high compression ratios. RG was used to improve the engine's operating performance and to control combustion onset in experiments conducted at three different compression ratios. A combination of high compression ratio (18.5) and high intake temperature (140°C) was observed to be appropriate to run the CNG-fueled CFR engine in HCCI mode. RG replacement of CNG altered combustion characteristics and expanded the operating range on the lean side. Use of RG decreased knock severity and reduced NOx emission. At constant relative air/fuel ratio (λ) it advanced combustion timing, moving the maximum cylinder pressure earlier in the cycle and increasing maximum pressure. Overall, the combustion efficiency increased slightly though there was also an increase in indicated specific CO, possibly because of the CO content in any unburnt fuel.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".