A Study on the Effect of Stratified Mixture Formation on Combustion Characteristics in a Constant Volume Combustion Chamber
Bibliographic record
Abstract
It is well known that a lean burn engine caused by stratified mixture formation has many kinds of advantages to combustion characteristics, such as higher thermal efficiency and lower CO, NOx levels than conventional homogeneous mixture combustion. Although this combustion can achieve low fuel consumption, it produces much unburned hydrocarbon and soot because of inhomogeneity of the charge mixture in the combustion chamber. Therefore, it is necessary to investigate the effect of mixture formation on combustion characteristics in order to obtain the stable lean combustion. In this paper, fundamental studies for stratified combustion were carried out using a constant volume combustion chamber. The effect of mixture formation on the combustion characteristics in the chamber was examined experimentally. In addition, the effect of turbulence on stratified charge combustion process was observed by schlieren photography. From this study, as the swirl intensity increases, (Sv)max is rapidly enhanced and the period of combustion is shortened. We also find that the stratification degree can be quantified by using burning velocity and it was controlled by induced air pressure and turbulent intensity.
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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".