Modeling of Air-Fuel Mixing in a Two-Stroke Direct Injection Engine
Bibliographic record
Abstract
This paper presents a numerical study on air-fuel mixing in a two-stroke direct injection spark ignition engine under homogeneous operation. The simulated engine is loop scavenged and uses an outwardly opening swirl injector. A generic mesh-snapping algorithm is developed to enable the moving piston to snap through transfer ports with complicated geometry. A spray model based on Linear Instability Sheet Atomization is used to describe the primary breakup of fuel sprays, and the initial rotational velocity of the conical sheet is determined from a CFD simulation of the nozzle internal flow. A wall film model accounting for the effect of contacting area is also developed to avoid the severe grid-dependence of the original film model in KIVA. Comparisons between simulations and experiments were made for sprays in quiescent ambient conditions, and a good agreement of the spray characteristics was obtained. The simulations were performed for four different injection timings. The model was shown to be capable to predict engine-out unburned hydrocarbon emission. It was also found that the mixture preparation around the spark plug is directly related to the engine-out emission, and leaner mixture tends to yield higher engine-out emission.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".