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Advancing Oil Consumption Prediction with 3-D Multiphase CFD: Insights into Piston Design Impacts Simerics, Incorporated
- Format:
- Book
- Conference/Event
- Author/Creator:
- Mohapatra, Chinmoy K., author.
- Schlautman, Jeff, author.
- Manne, Venkata Harish Babu, author.
- Schroeder, Debera, author.
- Srinivasan, Chiranth, author.
- Conference Name:
- WCX SAE World Congress Experience (2026-04-14 : Detroit, Michigan, United States)
- Language:
- English
- Subjects (All):
- Single cylinder engines.
- Computational fluid dynamics (CFD).
- Particulate matter (PM).
- Local Subjects:
- Single cylinder engines.
- Computational fluid dynamics (CFD).
- Particulate matter (PM).
- Physical Description:
- 1 online resource
- Place of Publication:
- Warrendale, PA SAE International 2026
- Summary:
- Engine oil consumption contributes to hydrocarbon and particulate emissions, catalyst degradation, and reduced thermal efficiency. Reducing it is essential for meeting emission standards and improving engine reliability. This study introduces a 3-D Computational Fluid Dynamics (CFD) framework that captures micron-scale gaps in the piston-ring-cylinder system while accounting for ring dynamics. The model leverages Simerics-MP+ featuresincluding a novel mesh motion strategy and Mismatched Grid Interface (MGI) couplingto resolve fine crevice regions alongside coarser bulk domains. It incorporates piston translation, ring motion, and crankshaft rotation, and uses the Volume of Fluid (VOF) method to capture multiphase interactions in thin oil films. Compared to experiments, this approach offers detailed flow visualization in optically inaccessible regions at lower cost and complexity. Unlike traditional 1-D models, it captures nonlinear behaviors without relying heavily on parameter tuning. Applied to a single-cylinder engine, the model evaluates oil transport in two piston designs under fixed RPM and undeformed bore conditions. Results highlight piston geometry's role in oil consumption, and qualitative validation against experiments confirms the model's predictive capabilities. This CFD framework provides valuable insights to guide low-emission, high-efficiency engine design
- Notes:
- Vendor supplied data
- Access Restriction:
- Restricted for use by site license
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