June 2022
The treatment of thermal mixing in inter pad grooves of a fluid film bearing is essential due to its influence on the heat transfer with the rotating shaft and stationary bearing. Lower fidelity models that either neglect or over approximate thermal groove mixing may lead to premature bearing or machinery failure, most commonly from babbitt thermally induced fatigue. Conventional models rely on bulk flow and thermal analyses yielding a single temperature at the groove outlet into the pad inlet. The high uncertainty of this approach carries over into downstream predictions for bearing life, stiffness and damping, and machinery vibration predictions. Contrary to a uniform temperature, CFD-Conjugate heat transfer studies reveal large gradient temperature distributions varying in both the radial and axial directions at the groove outlet, especially with jet lubrication implemented with multiple nozzles. These distributions vary continuously with time as the spinning shaft and bearing pads vibrate. A direct CFD simulation thus becomes computationally prohibitive.
The present work introduces a novel approach which yields highly detailed lubricant temperature distributions at the pad inlets in a computationally economical manner. This is implemented with a surrogate groove model via a deep convolutional autoencoder neural network based on CFD (Computational Fluid Dynamics) data. The trained Convolutional Neural Network (CNN) shows excellent prediction capability for 2D temperature distribution at a circumferential groove outlet. The trained CNN is combined with a rotor-bearing model, and the combined model is verified by full CFD results and experimental data. In addition, this approach is expanded to include various oil injection types, illustrating their detailed heat transfer to the rotating shaft and bearing.