Tilt Pad Bearing Distributed Pad Inlet Temperature With Machine Learning—Part I: Static and Dynamic Characteristics

Abstract

Uncertainty in mixing coefficients MC for estimating pad leading edge film temperature in tilt pad journal bearings, reduces the reliability of predicted characteristics. A 3D Hybrid Between Pad (HBP) model, utilizing CFD and machine learning ML, is developed to provide the radial and axial temperature distributions at the leading-edge. This provides a ML derived, 2D film temperature distribution in place of a single uniform temperature. This has a significant influence on predicted journal temperature, dynamic coefficients, and Morton Effect response. An innovative Finite-Volume-Method (FVM) solver significantly increases computational speed, while maintaining comparable accuracy with CFD. Part I provides methodology and simulation results for static and dynamic characteristics, while Part II applies this to Morton Effect response.