Prediction of the reaction forces of spiral-groove gas journal bearings by artificial neural network regression models

Iseli, Elia; Schiffmann, Jürg

doi:10.1016/j.jocs.2020.101256

Cited by 9 publications

(1 citation statement)

References 24 publications

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“…The outcomes were used to train the proposed ANNs. Recently, Iseli and Schiffmann [25] presented neural network regression models in order to predict the nonlinear static and linearized dynamic forces of spiral grooved gas bearings. Yang and Palazzolo [26], examined the applicability of the Reynolds equations in a thermo elastohydrodynamic titling pad journal bearing model, with ANNs.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Misaligned Journal Bearings Using Neural Networks

Kokkinidis¹,

Nikolakopoulos²

2022

Tribol. Ind.

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Utilization of smart systems, i.e. software tools that incorporate artificial intelligence (AI), in engineering applications increases. This fact is due to their ability to study the performance of complicated systems, producing results quicker and easier than typical analytical models. This article is focused on the advantages of using Artificial Neural Networks (ANNs) to solve the problem of a misaligned hydrodynamic journal bearing. Firstly, the Reynolds equation is solved using finite difference method (FDM) for different operating and misalignment conditions. The results are used to train four (4) artificial neural networks, one for each design parameter. Afterwards, the networks are tested for several operational characteristics and compared with the results of the finite difference method. The outcome is that the force and the torque can be predicted with maximum error of approximately 5% with less computational cost than the finite difference method.

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Section: Introductionmentioning

confidence: 99%