TY - GEN
T1 - A DESIGN STUDY OF AN ELASTO-HYDRODYNAMIC SEAL FOR sCO2 POWER CYCLE BY USING PHYSICS INFORMED NEURAL NETWORK
AU - Islam, Mohammad Towhidul
AU - Hassan, Mohammad Fuad
AU - Lyathakula, Karthik Reddy
AU - Cesmeci, Sevki
AU - Xu, Hanping
AU - Tang, Jing
N1 - Publisher Copyright:
Copyright © 2023 by ASME.
PY - 2023
Y1 - 2023
N2 - Supercritical carbon dioxide (sCO2) is a promising alternative working fluid in power cycles. It offers several benefits, including improved efficiencies, lower power costs, lower water usage, and reduced equipment footprint. At the subcomponent level, there is a lack of suitable shaft end seals to minimize the sCO2 leakage in the turbomachinery, which would otherwise penalize the efficiencies by up to 0.65 percent, for example, for a 500 MWe utility-scale power plant when existing sealing technologies are used such as labyrinth seals. For a potential solution, we propose a simple sleeve-like Elasto-Hydrodynamic (EHD) seal concept that provides low leakage, minimum wear, and no stress concentration. In this work, a numerical study has been performed to model the structural deformation of the EHD seal and fluid flow by using Lame's equation and simplified Reynold's equation, respectively. However, the analytical solutions for this type of modeling require a set of coupled differential and highly non-linear equations. Conventional approaches for optimizing the solution often end in convergence issues and require selecting specific flow rates. Therefore, the design equations used in this work have been solved by adopting the Physics Informed Neural Network (PINN) to mitigate these issues. Through using PINN analysis, the throttling behavior of the seal was successfully demonstrated. The deformation of the seal was observed to occur at an axial length of approximately 18mm, while a fixed root condition was imposed at 26.5mm. The initial clearance within the seal was 0.2566mm, but it was observed to decrease as the pressure increased within the range of 60MPa to 90MPa. The fluid flow inside the region also decreased from 0.68kg/s to 0.42kg/s at this high-pressure range. The proposed analysis can be used to design EHD seals for specific cases when more comprehensive simulation models are not readily available or are deemed to be costly.
AB - Supercritical carbon dioxide (sCO2) is a promising alternative working fluid in power cycles. It offers several benefits, including improved efficiencies, lower power costs, lower water usage, and reduced equipment footprint. At the subcomponent level, there is a lack of suitable shaft end seals to minimize the sCO2 leakage in the turbomachinery, which would otherwise penalize the efficiencies by up to 0.65 percent, for example, for a 500 MWe utility-scale power plant when existing sealing technologies are used such as labyrinth seals. For a potential solution, we propose a simple sleeve-like Elasto-Hydrodynamic (EHD) seal concept that provides low leakage, minimum wear, and no stress concentration. In this work, a numerical study has been performed to model the structural deformation of the EHD seal and fluid flow by using Lame's equation and simplified Reynold's equation, respectively. However, the analytical solutions for this type of modeling require a set of coupled differential and highly non-linear equations. Conventional approaches for optimizing the solution often end in convergence issues and require selecting specific flow rates. Therefore, the design equations used in this work have been solved by adopting the Physics Informed Neural Network (PINN) to mitigate these issues. Through using PINN analysis, the throttling behavior of the seal was successfully demonstrated. The deformation of the seal was observed to occur at an axial length of approximately 18mm, while a fixed root condition was imposed at 26.5mm. The initial clearance within the seal was 0.2566mm, but it was observed to decrease as the pressure increased within the range of 60MPa to 90MPa. The fluid flow inside the region also decreased from 0.68kg/s to 0.42kg/s at this high-pressure range. The proposed analysis can be used to design EHD seals for specific cases when more comprehensive simulation models are not readily available or are deemed to be costly.
KW - Elasto-Hydrodynamic (EHD)
KW - Machine Learning
KW - Neural Networks (NNs)
KW - Supercritical CO2
UR - http://www.scopus.com/inward/record.url?scp=85174580169&partnerID=8YFLogxK
U2 - 10.1115/POWER2023-108802
DO - 10.1115/POWER2023-108802
M3 - Conference article
AN - SCOPUS:85174580169
T3 - American Society of Mechanical Engineers, Power Division (Publication) POWER
BT - Proceedings of ASME Power Applied R and D 2023, POWER 2023
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME Power Applied R and D 2023, POWER 2023
Y2 - 6 August 2023 through 8 August 2023
ER -