Deep convolutional autoencoder augmented CFD thermal analysis of bearings with inter pad groove mixing

•Bearing groove model via a deep convolutional autoencoder training based on CFD data.•Consideration of 2D temperature distribution at pad leading edge of fluid-film.•Combination of rotor-bearing model and convolutional neural network.•Proposed model validation through comparison to full CFD and ava...

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Bibliographic Details
Published in:International journal of heat and mass transfer Vol. 188; p. 122639
Main Authors: Yang, Jongin, Palazzolo, Alan
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 01.06.2022
Elsevier BV
Subjects:
Artificial neural networks
Computational fluid dynamics
Deep learning
Economic models
Groove mixing
Grooves
Heat transfer
Heat treatment
Inlets
Lubricants
Lubricants & lubrication
Mathematical models
Neural networks
Rotating shafts
Rotor-bearing heat transfer
Stiffness
Temperature distribution
Thermal analysis
Vibration damping
Deep learning
Groove mixing
Thermal analysis
Rotor-bearing heat transfer
ISSN:0017-9310, 1879-2189
Online Access:Get full text
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Summary:•Bearing groove model via a deep convolutional autoencoder training based on CFD data.•Consideration of 2D temperature distribution at pad leading edge of fluid-film.•Combination of rotor-bearing model and convolutional neural network.•Proposed model validation through comparison to full CFD and available test data.•Accurate heat transfer and temperature prediction for a rotor-bearing system. 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.
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ISSN:0017-9310
1879-2189
DOI:10.1016/j.ijheatmasstransfer.2022.122639