Physics-Informed Deep Learning-Based Proof-of-Concept Study of a Novel Elastohydrodynamic Seal for Supercritical CO2 Turbomachinery

Lyathakula, Karthik Reddy; Cesmeci, Sevki; DeMond, Matthew; Hassan, Mohammad Fuad; Xu, Hanping; Tang, Jing

doi:10.1115/1.4063326

Title: Physics-Informed Deep Learning-Based Proof-of-Concept Study of a Novel Elastohydrodynamic Seal for Supercritical CO₂ Turbomachinery

Journal Article · 28 September 2023 · Journal of Energy Resources Technology

DOI: https://doi.org/10.1115/1.4063326 · OSTI ID:2338024

Lyathakula, Karthik Reddy ^[1]; Cesmeci, Sevki ^[2]; DeMond, Matthew ^[2]; Hassan, Mohammad Fuad ^[2]; Xu, Hanping ^[3]; Tang, Jing ^[3]

North Carolina State University, Raleigh, NC (United States); Georgia Southern University
Georgia Southern Univ., Statesboro, GA (United States)
Ultool, LLC , Duluth, GA (United States)

Supercritical carbon dioxide (sCO₂) power cycles show promising potential of higher plant efficiencies and power densities for a wide range of power generation applications such as fossil fuel power plants, nuclear power production, solar power, and geothermal power generation. sCO₂ leakage through the turbomachinery has been one of the main concerns in such applications. To offer a potential solution, we propose an elastohydrodynamic (EHD) seal that can work at elevated pressures and temperatures with low leakage and minimal wear. The EHD seal has a very simple, sleeve-like structure, wrapping on the rotor with minimal initial clearance at micron levels. Here, in this work, a proof-of-concept study for the proposed EHD seal was presented by using the simplified Reynolds equation and Lame’s formula for the fluid flow in the clearance and for seal deformation, respectively. The set of nonlinear equations was solved by using both the conventional Prediction–Correction (PC) method and modern Physics-Informed Neural Network (PINN). It was shown that the physics-informed deep learning method provided good computational efficiency in resolving the steep pressure gradient in the clearance with good accuracy. The results showed that the leakage rates increased quadratically with working pressures and reached a steady-state at high-pressure values of 15~20 MPa, where Q = 300 g/s at 20 MPa for an initial seal clearance of 255 μm. This indicates that the EHD seal could be tailored to become a potential solution to minimize the sCO₂ discharge in power plants.

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Research Organization:: Ultool, LLC, Duluth, GA (United States); Georgia Southern Univ., Statesboro, GA (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: SC0020851

OSTI ID:: 2338024

Journal Information:: Journal of Energy Resources Technology, Journal Name: Journal of Energy Resources Technology Journal Issue: 12 Vol. 145; ISSN 0195-0738

Publisher:: ASMECopyright Statement

Country of Publication:: United States

Language:: English

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30 DIRECT ENERGY CONVERSION
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sealing
gas leakage

Title: Physics-Informed Deep Learning-Based Proof-of-Concept Study of a Novel Elastohydrodynamic Seal for Supercritical CO2 Turbomachinery

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Title: Physics-Informed Deep Learning-Based Proof-of-Concept Study of a Novel Elastohydrodynamic Seal for Supercritical CO₂ Turbomachinery