Air Fuel Dilution in a Pilot Ignited Direct Injection Natural Gas Engine: Pollutants, Performance, and System Level Considerations
Bibliographic record
Abstract
Abstract Dilution of natural gas fuel with air for use in a pilot ignited direct injection natural gas engine was investigated to evaluate the impact of this strategy on emissions and engine performance. A representative heavy-duty mode (mid to high-load at medium speed) was considered and the equivalence ratio (Φ) and exhaust gas recirculation (EGR) rates were varied from this representative mode. Air dilution resulted in a significant reduction in several pollutants: 90 to 97% reductions in black carbon particulate matter, 45 to 95% reductions in carbon monoxide, 68 to 85% reductions in total unburnt hydrocarbons. NOx emissions were found to increase by between 1.5 and 2.5x, depending on Φ and EGR, for a fixed combustion phasing. Beyond the emissions improvements, the gross indicated thermal efficiency increased by 2.5 percentage points at both high and low EGR rates. At higher EGR rates, this improvement was due to improved combustion efficiency, while the mechanism for efficiency improvement at lower EGR rates was unclear. The application of air-fuel dilution requires compressed air (> 300 bar) to mix with natural gas at high pressures. A system level analysis considered the compression power required by an industrial 3-stage reciprocating compressor and indicated that the gross indicated thermal efficiency improvements could compensate for the compression requirements for engine operation at high Φ.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".