Numerical analysis of the scavenging process in a large Two-Stroke engine using varied turbulence models

Improving fuel efficiency across all machines is crucial for reducing emissions and lowering energy usage. Automobiles and machinery depend on lubricants to ensure smooth operation and effective energy transfer. In mechanical systems, lubrication is key to minimizing wear between contacting surfaces. While the design of these materials aims for optimal machine performance, wear and tear can decrease efficiency, resulting in higher fuel and energy consumption. This study aims to evaluate how various turbulence methods influence scavenging air dynamics and subsequently affect the interaction between oil spray and scavenging air in a two-stroke engine. In the other words, it is grounded in the essential role of scavenging air in the efficiency and performance of two-stroke engines. This research is significant for its potential to optimize two-stroke engine performance. By better understanding how turbulence influences the interaction between oil spray and scavenging air, it can lead to improved engine designs. Utilizing swirl injection principle (SIP), lubricant injectors spray oil into swirling scavenging air within the cylinder. The study formulates a precise 3D model of uniflow scavenging air, incorporating Reynolds-Averaged Navier-Stokes (RANS) approaches − specifically k-ε and k-ω − to simulate turbulence. The research examines flow characteristics during scavenging, comparing predictive performance of k-ε and k-ω models in replicating in-cylinder pressure, velocity fields, and spray distribution. Both models reasonably predict in-cylinder pressure and exhibit alignment with experimental data on velocity fields. However,k-ε excels in tangential velocity prediction. The study analyzes scavenging performance based on operational parameters and examines oil distribution and spreading efficiency using Lagrangian particle distribution. Comparison of contour plots generated by k-ε and k-ω simulations with experimental data reveals similarities and differences, particularly in oil mass distribution on the cylinder wall. The k-ε model demonstrates a broader spray pattern, closer to experimental observations and anticipated behavior, suggesting its superiority in depicting oil spray formation in the two-stroke engine. Therefore, the study recommends the use of the k-ε turbulence model for more precise simulations.