Work

Oil Transport Simulation and Oil Consumption Prediction with a Physics-Based and Data-Driven Digital Twin Model for Internal Combustion Engines

Year: 2025

Type: article

Abstract: Lubrication oil consumption (LOC) is one of the major sources of emissions from internal combustion (IC) engines; yet, analyzing and predicting it through modeling is challenging due to its multi-physics nature, which spans different time and length scales. In this work, a digital twin model is developed to simulate oil transport in the piston ring pack of IC engines and predict the resulting oil consumption with all major physical mechanisms considered. Three main contributors to LOC, namely, top ring up-scraping, oil vaporization on the liner, and reverse gas flows through the top ring gap, are included in the model. It was found that their behaviors are heavily dependent on the arrangement of the piston ring gaps. Therefore, with the ring rotation behavior still not resolved, the current model can predict the LOC range of a given engine profile. Results show that the predicted range can well encapsulate the experimentally measured LOC value.

Source: Lubricants

DOI: 10.3390/lubricants13100463

Authors: Xinlin Zhong, Tian Tian

Institution: Massachusetts Institute of Technology

Language: English

Topic: Tribology and Lubrication Engineering

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137