Experimental-Numerical Analysis of Nitric Oxide Formation in Partially Stratified Charge (PSC) Natural Gas Engines
Bibliographic record
Abstract
Lean burn natural gas engines have high potential in terms of efficiency and NOx emissions in comparison with stoichiometric natural gas engines, and much lower particulate emissions than diesel engines. They are a promising solution to meet the increasingly stringent exhaust emission targets for both light and heavy-duty engines. Partially Stratified-Charge (PSC) is a novel concept which was conceived by prof. Evans (University of British Columbia, Vancouver). This technique allows to further limit pollutant emissions and improve efficiency of an otherwise standard spark-ignition engine fuelled by natural gas, operating with lean air-fuel ratio. The potential of the PSC technique lies in the control of load without throttling by further extending the lean flammability limit. It provides a relatively rich air-fuel mixture (fuel amount is less than 5% of the main charge) in the vicinity of the spark plug, while maintaining an ultra-lean homogeneous charge in the main combustion chamber area. A stable ignition kernel is then allowed to form in this region, to support the subsequent combustion process. The PSC technique does not require any modifications to the basic engine unless the adoption of a special spark-plug: this is one of its main advantages. Since PSC is a relatively new technique, a thorough understanding of the physical processes involved is mandatory to guide the design process. To that aim, a joint experimental-numerical analysis has been performed in this paper, applied to a Ricardo Single Cylinder Research Engine equipped with the PSC spark-plug. Traditional homogeneous/stoichiometric and lean PSC operating conditions have been compared in terms of performance and NOx emissions, to finally put into evidence the coupling between PSC injection patterns and NOx formation mechanism.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".