An analytical study of tribological parameters between piston ring and cylinder liner in internal combustion engines
Bibliographic record
Abstract
This paper presents a model to study the effect of piston ring dynamics on basic tribological parameters that affect the performance of internal combustion engines by using dynamics analysis software (AVL Excite Designer). The paramount tribological parameters include friction force, frictional power losses, and oil film thickness of piston ring assembly. The piston and rings assembly is one of the highest mechanically loaded components in engines. Relevant literature reports that the piston ring assembly accounts for 40% to 50% of the frictional losses, making it imperative for the piston ring dynamics to be understood thoroughly. This analytical study of the piston ring dynamics describes the significant correlation between the tribological parameters of piston and rings assembly and the performance of engines. The model was able to predict the effects of engine speed and oil viscosity on asperity and hydrodynamic friction forces, power losses, oil film thickness and lube oil consumption. This model of mixed film lubrication of piston rings is based on the hydrodynamic action described by Reynolds equation and dry contact action as described by the Greenwood–Tripp rough surface asperity contact model. The results in the current analysis demonstrated that engine speed and oil viscosity had a remarkable effect on oil film thickness and hydrodynamic friction between the rings and cylinder liner. Hence, the mixed lubrication model, which unifies the lubricant flow under different ring–liner gaps, is needed via the balance between the hydrodynamic and boundary lubrication modes to obtain minimum friction between rings and liner and to ultimately help in improving the performance of engines.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".