PM and NOx Reduction by Injection Parameter Alterations in a Direct Injected, Pilot Ignited, Heavy Duty Natural Gas Engine With EGR at Various Operating Conditions
Bibliographic record
Abstract
The use of pilot-ignited, direct-injected natural gas in a heavy-duty compression-ignition engine has been shown to reduce emissions while maintaining performance and efficiency. Adding recirculated exhaust gas (EGR) has been shown to further reduce emissions of nitric oxides (NOx), albeit at the cost of increased hydrocarbons (tHC), carbon monoxide (CO), and particulate matter (PM) emissions at high EGR fractions. Previous tests have suggested that reducing the delay between the diesel and natural gas injections, increasing the injection pressure, or adjusting the combustion timing have individually achieved substantial emissions benefits. To investigate the effectiveness of combining these techniques, and of using them over a wide range of operating conditions, a series of tests were carried out. The first set of tests investigated the interactions between these effects and the EGR fraction. For all the gaseous emissions, substantial interactions were found between the EGR level, combustion timing, and diesel-natural gas injection delay. Reductions in NOx were found to be cumulative, while PM emissions were mitigated by either advanced timing or shorter delays between the pilot and main fuel injections. The second test set studied the effects of the injection parameters over a range of engine speeds, equivalence ratios, and charge masses. It was found that the greatest NOx reductions were achieved by late timings and high EGR fractions, although at the cost of increased PM, tHC, and CO emissions as well as increased fuel consumption. The magnitudes of these increases were found to depend on engine operating condition. No single parameter combination was identified as being optimal for all operating conditions - to maximise emissions reductions and efficiency, it will be necessary to ‘tailor’ the injection process to the operating condition by varying the combustion timing and the relative ignition delay.
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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 | 0.000 |
| 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".