Design and Analysis of Homopolar Electrodynamic Bearing with Radial Magnets

Rura, David; Bárta, Jan; Klima, Petr

doi:10.1109/icem51905.2022.9910773

Cited by 1 publication

(1 citation statement)

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“…Incontrast in Ref. [22] radial magnets were employed there was 450% increase in load bearing capacity. It is necessary to perform sensitivity analysis because there is no proven reference for obtaining initial dimensions of EDB.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Radial Electrodynamic Bearing Using Artificial Neural Network

Supreeth,

Bekinal,

Shivamurthy

2024

IEEE Access

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This article focuses on the prediction of essential bearing characteristics and optimization of electrodynamic bearing (EDB). Initially, a sensitivity analysis was conducted, manipulating key design parameters to assess their impact on electric pole frequency (𝜔) , stiffness (k), and damping (c). Subsequently, the data derived from the sensitivity analysis was employed as input for training an artificial neural network (ANN) model. The ANN model was developed and trained with six inputs using various algorithms and different hidden neuron configurations to forecast essential bearing characteristics. Three distinct artificial neural network models (for ω, c and k) were created. Notably, Bayesian Regularization with 10 hidden neurons exhibited superior performance, demonstrating the least average error. In the final stage, the ANN model was utilized to optimize the EDB through the Bonobo Optimization (BO) algorithm in MATLAB. The optimization results were validated using COMSOL Multiphysics, where essential bearing characteristics were determined by fitting an analytical model to simulation outcomes. These outcomes were then compared with the ANN model predictions, affirming the applicability of ANN models in both predicting and optimizing EDB performance.

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Section: Introductionmentioning

confidence: 99%