Assessment of artificial neural network for thermohydrodynamic lubrication analysis
Industrial Lubrication and Tribology
ISSN: 0036-8792
Article publication date: 5 June 2020
Issue publication date: 13 November 2020
Abstract
Purpose
In this study, artificial neural networks (ANNs) are constructed and validated by using the bearing data generated numerically from a thermohydrodynamic (THD) lubrication model. In many tribological simulations, a surrogate model (meta-model) for obtaining a fast solution with sufficient accuracy is highly desired.
Design/methodology/approach
The THD model is represented by two coupled partial differential equations, a simplified generalized Reynolds equation, considering the viscosity variation across the film thickness direction and a transient energy equation for the 3-D film temperature distribution. The ANNs tested are having a single- or dual-hidden-layer with two inputs and one output. The root-mean-square error and maximum/minimum absolute errors of validation points, when comparing with the THD solutions, were used to evaluate the prediction accuracy of the ANNs.
Findings
It is demonstrated that a properly constructed ANN surrogate model can predict the THD lubrication performance almost instantly with accuracy adequately retained.
Originality/value
This study extends the use of ANNs to the applications other than the analyses dealing with experimental data. A similar procedure can be used to build a surrogate model for computationally intensive tribological models to have fast results. One of such applications is conducting extensive optimum design of tribological components or systems.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2020-0109/
Keywords
Acknowledgements
This study was supported by the Ministry of Science and Technology of ROC, contract number MOST 108–2221-E-182–047, and Chang Gung University, project number BMRP 375.
Citation
Wang, N. and Tsai, C.-M. (2020), "Assessment of artificial neural network for thermohydrodynamic lubrication analysis", Industrial Lubrication and Tribology, Vol. 72 No. 10, pp. 1233-1238. https://doi.org/10.1108/ILT-03-2020-0109
Publisher
:Emerald Publishing Limited
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