Hybrid digital twin for conditional lubricant oil transport simulation and oil consumption prediction in internal combustion engines

Abstract

Reducing lubricant oil consumption (LOC) has long been an interest to engine manufacturers in an effort to reduce overall emissions from internal combustion (IC) engines. However, directly correlating engine operating conditions with LOC rates is a challenging task given its complex and multi-physics nature. Building on previous experimental observations and modeling efforts, a baseline hybrid model, which combines traditional physics-based numerical simulation with modern machine learning techniques, was developed. As the first step, the model is to compute the oil exiting the top ring gap with given oil supply rate and locations to the second land. Studies performed using the model reveal a strong dependency of LOC on engine speed, load as well as the relative locations of piston ring gaps. In particular, the close proximity of the oil source on second land to the top ring gap and the reverse gas flow at low load conditions can both induce high LOC. Piston ring gap rotations and the different oil supply mechanisms to the piston ring pack are also found to heavily influence the oil consumption behavior and oil film thickness distribution pattern. The present work is the first step to a more comprehensive model for predicting LOC for healthy systems of piston ring packs. The efficiency of the model made it possible to study the oil redistribution that takes many engine cycles to reach equilibrium and rotation of the ring gaps that could happen in reality at a time scale of minutes and even longer.