A Mixed Numerical-Experimental Analysis for the Development of a Partially Stratified Compressed Natural Gas Engine
Bibliographic record
Abstract
This paper discusses a partially stratified technology for engines running lean on natural gas. A single cylinder research engine has been modified to enable direct injection of a small quantity of natural gas through the spark plug to the region of the electrodes, independent of the overall lean homogeneous charge. Thus, a Partially Stratified Charge (PSC) is formed within the chamber allowing significant extension of the lean limit of combustion. Although PSC has been shown to reduce NOX emissions and improve combustion efficiency, high hydrocarbon emissions have been observed and this was thought to be due to poor mixing of the injected fuel air charge. The mixed experimental-numerical activity described herein, carried out by the Universities of British Columbia of Vancouver and Roma Tor Vergata, is aimed at improving the micro-direct injection PSC process. The numerical study has enabled a detailed analysis of the fluid dynamics and combustion processes in the region of the electrodes, early in the combustion cycle. The model has been verified using experimental data from the single cylinder research engine. The results suggest that improving mixing of the PSC (by changing the way it is injected into the combustion chamber) can significantly reduce NOX. A multi-hole injection concept has been designed and numerically tested, indicating a potential improvement in both performance and emissions.
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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 | 0.000 | 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".