Nitrogen Oxide Production in a Diesel Engine Fueled by Natural Gas
Bibliographic record
Abstract
The effect of large exhaust gas re-circulation (EGR) quantities on NOx production in a natural-gas-fueled direct-injection heavy-duty diesel engine has been tested over a range of speed, load, and timing in controlled experiments with a single-cylinder engine. At the highest EGR ratio, as much as 50% of the cylinder- out NOx was NO2. NOx results correlated well with oxygen mole fraction in the unburned gas because of the direct dependence of flame temperature on this quantity. Within the range of measurements, speed and load had little or no effect on the relationship between oxygen mole fraction and NOx production. A multi-zone model for estimating combustion rate, flame temperature, wall heat transfer, and NOx production from engine operating conditions and the record of cylinder pressure development with crank angle, was used to interpret experimental measurements. The model showed the incompatibility of test data with the normal form of the extended Zeldovich model on NO production. However a modified form of it served to correlate experimental data with oxygen mole fraction - which was a nearly linear function of flame temperature. The model also served to represent the effects of engine timing (defined here as the crank angle corresponding to 50% cumulative indicated heat release) on NOx production. At highest EGR (lowest oxygen mole fraction) the NOx emissions were of the order of 1 g/kg of fuel. At this condition CO and unburned hydrocarbon emissions were high, indicating the need for enhanced burning rate.
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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) | 0.000 | 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".