Modeling internal combustion engine three-piece oil control ring coupling reduced order oil transport based on neural network

Abstract

Reducing emission and improving efficiency of internal combustion engines are the major focuses in modern automotive industry. Lubrication oil leakage contributes to particle formation and piston ring friction occupies 1/3 to 1/2 of total mechanical losses in engines. In almost all of modern gasoline engines, three-piece oil control ring (TPOCR) is used considering its low-cost and satisfying oil control performance in low load work conditions. While TPOCR will see high oil consumption at high load, high speed working conditions. This raises our interest in modeling work to predict the TPOCR dynamics and oil transport and explain the oil control mechanism. This master thesis work is focusing on building a three-piece oil control ring model coupling the oil transport. First, a 2D dynamics model for three pieces is established as the main frame. Second, oil transport in different zones will be modelled in different ways considering the length scales. Specially for the oil movement behind the ring, a novel approach is introduced by using neural networks to learn and run the reduced order modeling of computational fluid dynamics (CFD), to speed up the calculation. The model is then applied on a 2D laser induced fluorescence (2DLIF) engine and produces consistent simulation results with experimental observation. Further parametric study on oil transport will be discussed to build a complete picture of oil transport around TPOCR.