Polynomial neural network based probabilistic hydrodynamic analysis of two-lobe bearings with stochasticity in surface roughness
Polynomial neural network based probabilistic hydrodynamic analysis of two-lobe bearings with stochasticity in surface roughness
This paper investigates the probabilistic response of two-lobe bearings considering the uncertainty in eccentricity ratio, preload value, bearing clearance, supply pressure, oil viscosity and surface roughness. To simulate stochasticity in input variables, Monte Carlo simulation (MCS) is carried out in conjunction with the Reynolds equation using finite difference method. Polynomial neural network based machine learning model is used as a surrogate model to increase the efficiency of MCS. To assess the relative importance of the stochastic input parameters, a sensitivity analysis is carried out. The physically insightful new probabilistic results, presented here covering a wide spectrum of uncertainty sources including surface roughness, make it evident that different forms of source-uncertainties have a significant effect on the critical performance of bearings.
Machine learning based analysis of bearings, Stochasticity in surface roughness, Polynomial neural network, Two-lobe journal bearing
Roy, B.
3fe01df7-8105-4dc4-85c8-31376530fcda
Mukhopadhyay, T.
2ae18ab0-7477-40ac-ae22-76face7be475
Dey, S.
fe4c7b4d-5927-4986-a4e3-e4d2ac4fb791
1 October 2022
Roy, B.
3fe01df7-8105-4dc4-85c8-31376530fcda
Mukhopadhyay, T.
2ae18ab0-7477-40ac-ae22-76face7be475
Dey, S.
fe4c7b4d-5927-4986-a4e3-e4d2ac4fb791
Roy, B., Mukhopadhyay, T. and Dey, S.
(2022)
Polynomial neural network based probabilistic hydrodynamic analysis of two-lobe bearings with stochasticity in surface roughness.
Tribology International, 174, [107733].
(doi:10.1016/j.triboint.2022.107733).
Abstract
This paper investigates the probabilistic response of two-lobe bearings considering the uncertainty in eccentricity ratio, preload value, bearing clearance, supply pressure, oil viscosity and surface roughness. To simulate stochasticity in input variables, Monte Carlo simulation (MCS) is carried out in conjunction with the Reynolds equation using finite difference method. Polynomial neural network based machine learning model is used as a surrogate model to increase the efficiency of MCS. To assess the relative importance of the stochastic input parameters, a sensitivity analysis is carried out. The physically insightful new probabilistic results, presented here covering a wide spectrum of uncertainty sources including surface roughness, make it evident that different forms of source-uncertainties have a significant effect on the critical performance of bearings.
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More information
Accepted/In Press date: 21 June 2022
Published date: 1 October 2022
Additional Information:
Funding Information:
BR acknowledges the financial support received from Ministry of Education (MoE), Govt. of India, during the period of this research work. TM would like to acknowledge the financial support through initiation grant from IIT Kanpur .
Publisher Copyright:
© 2022 Elsevier Ltd
Keywords:
Machine learning based analysis of bearings, Stochasticity in surface roughness, Polynomial neural network, Two-lobe journal bearing
Identifiers
Local EPrints ID: 483937
URI: http://eprints.soton.ac.uk/id/eprint/483937
ISSN: 0301-679X
PURE UUID: 52ce89d2-80ab-4e25-ab13-4dac090f0a69
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Date deposited: 07 Nov 2023 18:31
Last modified: 18 Mar 2024 04:10
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Contributors
Author:
B. Roy
Author:
T. Mukhopadhyay
Author:
S. Dey
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