Effect of operating condition on particulate matter and nitrogen oxides emissions from a heavy-duty direct injection natural gas engine using cooled exhaust gas recirculation
Bibliographic record
Abstract
Two methods for reducing nitrogen oxides (NO X ) emissions from direct injection, compression ignition, heavy-duty engines are exhaust gas recirculation (EGR) and the high-pressure direct injection of natural gas. Tests combining these two techniques were carried out on a single-cylinder research engine (SCRE) based on a modified heavy-duty automotive engine. No attempt was made to optimize the engine's combustion chamber or the injector geometry for EGR operation. The SCRE's independent charge-air system allowed for more controlled testing over a wider range of test variables than can be carried out by a standard engine. These tests investigated the effects of cooled EGR on particulate matter (PM) and NO X emissions while varying the injection timing, engine speed, equivalence ratio and intake manifold pressure. The results suggested that, with EGR, higher equivalence ratios reduced power-specific NO X but increased PM emissions. Increasing the charge mass at a constant EGR fraction resulted in significant reductions in PM, at the cost of slightly increased NO X By advancing the injection timing at high EGR fractions, PM emissions and fuel efficiency were improved, with only a slight increase in NO X emissions compared to the more retarded injection timings. The engine speed influenced the amount of EGR that could be recirculated, with lower speeds resulting in higher achievable EGR fractions. These results suggest that EGR fractions in excess of 20 per cent can achieve NO X reductions beyond 75 per cent, without causing unacceptable increases in PM emissions or significant reductions in fuel efficiency.
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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 | 0.001 |
| 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 | 0.000 |
| 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".