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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| Vydáno v: | International journal of heat and mass transfer Ročník 188; s. 122639 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Oxford
Elsevier Ltd
01.06.2022
Elsevier BV |
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| ISSN: | 0017-9310, 1879-2189 |
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| Abstract | •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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| AbstractList | 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. •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. |
| ArticleNumber | 122639 |
| Author | Palazzolo, Alan Yang, Jongin |
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| Cites_doi | 10.1115/1.4041021 10.1016/j.ijheatmasstransfer.2021.120997 10.1016/j.ijheatmasstransfer.2020.120743 10.1115/1.4041130 10.1115/1.3254629 10.1115/1.4041720 10.1080/10402004.2018.1469805 10.1016/j.ijheatmasstransfer.2020.120783 10.1016/j.ijheatmasstransfer.2021.121075 10.1007/s10494-015-9622-4 10.1016/j.ijheatmasstransfer.2021.121199 10.1016/j.triboint.2017.08.025 10.1016/j.triboint.2009.12.002 |
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| SubjectTerms | 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 |
| Title | Deep convolutional autoencoder augmented CFD thermal analysis of bearings with inter pad groove mixing |
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