Fast modeling of multi-phase mixture transport in piston/ring/liner system via GAN-augmented progressive modeling

As a continued effort to advance the understanding of the power cylinder system and design capacities, we develop a modeling framework for multi-phase macro mixture transport that integrates all length scales, time scales and flow regimes using a hybrid approach combining deterministic modeling and machine learning. This framework considers various mechanical and physical processes including ring dynamics, gas flow, oil redistribution and multi-phase transport to paint a detailed picture of the global lubrication environment in the piston/ring/liner system. The main contributions of this thesis can be summarized as the following: 1) designed a modular architecture that decouples various processes to manage complex dependencies, 2) achieved fast inference of flow separation and vortices near ring gaps by a physics-informed Generative Adversarial Network, and 3) established a lower bound estimation of oil consumption based on the "healthy system" oil distribution pattern. This thesis provides a powerful modeling methodology that can achieve fast modeling and monitoring of oil consumption and PM emissions from IC engines, which is of immediate economic, environmental and health concern.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Zhang, Qin,Ph.D.Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• Tian Tian.

• Mechanical Engineering.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/129038

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/129038