2026-09-19T00:27:54Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1290382026-06-17T14:44:54Zcom_1721.1_33226com_1721.1_7582com_1721.1_7581col_1721.1_145728col_1721.1_131022
Tian Tian.
Zhang, Qin,Ph.D.Massachusetts Institute of Technology.
Massachusetts Institute of Technology. Department of Mechanical Engineering.
Massachusetts Institute of Technology. Department of Mechanical Engineering
2021-01-05T23:14:40Z
2021-01-05T23:14:40Z
2020
2020
https://hdl.handle.net/1721.1/129038
1227042792
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2020
Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 177-183).
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.
by Qin Zhang.
Ph.D.
Ph.D. Massachusetts Institute of Technology, Department of Mechanical Engineering
183 pages
eng
Massachusetts Institute of Technology
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.
http://dspace.mit.edu/handle/1721.1/7582
Mechanical Engineering.
Fast modeling of multi-phase mixture transport in piston/ring/liner system via GAN-augmented progressive modeling
Thesis
application/pdf
2021-01-05T23:14:38Z
Publication
Doctoral
MechE
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="9a90ba02-33e2-4025-945d-6c13070e3657">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Fast modeling of multi-phase mixture transport in piston/ring/liner system via GAN-augmented progressive modeling</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2020</PublicationDate>
<Authors>
<Author>
<DisplayName>Zhang, Qin,Ph.D.Massachusetts Institute of Technology.</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Mechanical Engineering.</Keyword>
<Abstract>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.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>