Computational Study of Reactivity Controlled Compression Ignition (RCCI) Combustion in a Heavy-Duty Diesel Engine Using Natural Gas
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Reactivity controlled compression ignition (RCCI) combustion employs two fuels with a large difference in auto-ignition properties that are injected at different times to generate a spatial gradient of fuel-air mixtures and reactivity. Researchers have shown that RCCI offers improved fuel efficiency and lower NOx and Soot exhaust emissions when compared to conventional diesel diffusion combustion. The majority of previous research work has been focused on premixed gasoline or ethanol for the low reactivity fuel and diesel for the high reactivity fuel.</div><div class="htmlview paragraph">The increased availability of natural gas (NG) in the U.S. has renewed interest in the application of compressed natural gas (CNG) to heavy-duty (HD) diesel engines in order to realize fuel cost savings and reduce pollutant emissions, while increasing fuel economy. Thus, RCCI using CNG and diesel fuel warrants consideration. A computational study was performed on a 15L HD diesel engine to examine trade-offs of pollutant emissions, fuel consumption, peak cylinder pressure and maximum cylinder pressure rise rate. The results from the model indicated that an RCCI combustion strategy had the potential of 17.5% NOx reduction, 78% soot reduction and a 24% decrease in fuel consumption when compared to a conventional diesel combustion strategy using the same air-fuel ratio (AFR) and exhaust gas recirculation (EGR) rate, at the rated power operating condition. This was attained while meeting peak cylinder pressure and maximum cylinder pressure rise rate constraints.</div></div>
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".