AI Summary & Key Takeaways
AI InsightThus, regression equations provide a robust framework for predicting specific friction work in both theoretical modelling and experimental validation for single-cylinder spark ignition engines.
📌 3 Key Takeaways
Abstract
Internal combustion engines and electric motors are competing against each other again after almost a century. Although some changes seem possible for passenger cars, the widespread use of internal combustion engines in commercial, rail and maritime transportation have continued. Mechanical friction remains a critical determinant of efficiency in internal combustion engines. This study investigates the variation in specific friction work within a single-cylinder spark ignition research engine. The identified independent variables were compression ratio, brake mean effective pressure or indicated mean effective pressure, engine speed and relative air/fuel ratio. Two different regression models were created using these parameters. Initially, in the base model that included the indicated mean effective pressure as the fourth independent variable, the R² value of the model was obtained as 88.5% by adding the interaction between compression ratio and engine speed. In the second model the brake mean effective pressure is incorporated into the model instead of the indicated mean effective pressure. With the addition of both interactions to this model, R2 was calculated as 92.5%. In the final part of the study, the created model was compared with different equations recommended in literature. However, it was determined that pumping losses were used differently in the equations in the studies examined. Consequently, approaches that include brake mean effective pressure in addition to engine speed were found to be more consistent with the experimental results. Thus, regression equations provide a robust framework for predicting specific friction work in both theoretical modelling and experimental validation for single-cylinder spark ignition engines.