Modeling of Scavenging and Plugging in a Twin-Cylinder Two Stroke Engine Using CFD
Bibliographic record
Abstract
<div class="htmlview paragraph">This paper presents a study on multi-dimensional CFD modeling of scavenging and plugging in a twin-cylinder two-stroke engine. A general-purpose CFD code, KIVA, was extended to track an arbitrary number of moving pistons. The code was also modified to allow piston snapping through complicated transfer ports. Thus, a multi-cylinder simulation together with a full exhaust manifold to fully account for the interaction between scavenging and plugging becomes possible. The developed code is intended to be a numerical tool for exhaust-manifold design and optimization. The studied engine is a five-port loop scavenged twin-cylinder engine with a cylinder displacement of 432 cc. The computed exhaust pressure was compared with measured data, and reasonably good agreement was obtained. The results were also compared with those from a one-dimensional gas dynamics model, which over-predicts the plugging intensity while under-predicting the pressure loss in the exhaust manifold. It is also suggested that a simulation that only includes a partial exhaust manifold may not be adequate to predict details of the in-cylinder air/fuel mixing when a uniform boundary condition is applied at the exhaust port because the predicted in-cylinder swirl level is significantly different from that of a full exhaust manifold simulation. The effect of plugging pulse on the flow pattern and the air-fuel mixing was also examined. It is shown that the plugging pulse not only increases the trapped mass, but also enhances the in-cylinder tumble motion and promotes air/fuel mixing.</div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".