In-Cylinder Cold-Flow Analysis - ‘A Comparison of Crank-Slider Engine and Crank-Rocker Engine’
Bibliographic record
Abstract
Researchers and engineers are continuously working to improve the overall efficiency of internal combustion engines by modifying the engine designs and configurations. As new engine designs are introduced, the in-cylinder flow behaviour becomes more and more complex. The maximum output and efficiency that can be achieved from a single cylinder engine depends upon the amount of air entrapped in the combustion chamber during intake stroke. A novel crank-rocker (C-R) engine was designed and fabricated in Universiti Teknologi PETRONAS (UTP), Malaysia and is currently under optimization phase. This paper narrates the in-cylinder cold-flow analysis of the C-R engine considering widely used industrial automotive software, Converge CFD. The turbulent behaviour of the C-R engine was compared with that of the conventional crank-slider engine. The initial and boundary conditions for the C-R engine simulations were set according to the benchmarked crank-slider engine. RNG k-ε turbulence model was used to generate the data plots for tumble & swirl ratios, cylinder pressure, TKE, turbulent dynamic viscosity and vorticity at cold-flow conditions. It was observed that the C-R engine has better air scavenging properties and can achieve better air-fuel mixing that can lead to emission-free combustion. This study will help in understanding the turbulent airflow behaviour within the curved cylinder under the influence of rocking piston motion, and its advantageous flow properties compared to those in crank-slider engines.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".