Predicting and Optimizing Two-Stroke Engine Performance Using Multidimensional CFD
Bibliographic record
Abstract
One-dimensional unsteady gas dynamics dominate the prediction and optimization of two-stroke engine performance. Its application in engines with complicated geometry is, however, limited because the flow through the engine is three dimensional in nature. Multidimensional CFD has the capacity to capture the effect of complicated flow fields. However, most existing CFD studies include either only one cylinder with a partial exhaust system or just a separate exhaust manifold, and boundary conditions need to be fed from experimental data. It is found in this study that such simplifications may yield misleading results. In a previous study, the authors extended a multidimensional CFD code, KIVA to simulate a multi-cylinder engine together with a full exhaust manifold. The need for exhaust pressure boundary conditions was thus eliminated. In continuation of this study, a crankcase model was first developed to dynamically predict the crankcase pressure. A reed valve model proposed by Blair, et al. was used to predict the flow into the crankcase through the reed valve. Thus the code is capable of predicting engine performance without any input of dynamic boundary conditions. The developed code was first rigorously tested against a single cylinder engine at a wide range of engine speeds. The computed exhaust pressure trace agrees well with the measurement. It shows that the predicted trapped oxygen mass is a good indicator of engine power. The test was then extended to a twin-cylinder loop scavenged engine with a folded exhaust manifold. The predicted exhaust pressure matches experiments, and the predicted trapped oxygen mass agrees well with the estimate based on the measured fuel consumption and emission data. The code was finally used to optimize a production two-stroke engine, and the improvement predicted by the simulation was realized in dynamometer tests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".