Physics-Informed Deep Learning-Based Modeling of a Novel Elastohydrodynamic Seal for Supercritical CO2 Turbomachinery

Abstract

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 Elasto-Hydrodynamic (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 µm levels. 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.