Physics-Informed Deep Learning-based Modeling of a Novel Elastohydrodynamic Seal for Supercritical CO2 Turbomachinery
Faculty Mentor
Dr. Sevki Cesmeci
Location
Poster 216
Session Format
Poster Presentation
Academic Unit
Department of Mechanical Engineering
Background
-
Supercritical CO2 (sCO2) power cycles offer many benefits, including thermal efficiencies greater than 50%, less water and fuel usage, overall lower capital costs, and lower electricity costs [1].
-
Many of the conventional seal designs, such as labyrinth seal, brush seal, compliant foil seal, and finger seals, have limitations that are hindering the development of sCO2 power cycles [6].
-
To offer a potential solution, we propose an Elasto-Hydrodynamic (EHD) seal that can work at elevated pressures and temperatures with low leakage and minimal wear.
-
In this work, a proof-of-concept study for the proposed EHD seal was presented by using the Reynolds equation and Lame’s formula for the fluid flow in the clearance and for seal deformation, respectively [7].
Keywords
Allen E. Paulson College of Engineering and Computing Student Research Symposium, Supercritical CO2, sCO2, Pressure-Correction, PC
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Presentation Type and Release Option
Presentation (File Not Available for Download)
Start Date
2022 12:00 AM
January 2022
Physics-Informed Deep Learning-based Modeling of a Novel Elastohydrodynamic Seal for Supercritical CO2 Turbomachinery
Poster 216
