Model Order Reduction for Rotating Electrical Machines

Zeger Bontinck (), Oliver Lass (), Oliver Rain () and Sebastian Schöps ()
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Zeger Bontinck: Technische Universität Darmstadt, Graduate School of Computational Engineering
Oliver Lass: Technische Universität Darmstadt, Department of Mathematics, Chair of Nonlinear Optimization
Oliver Rain: Robert Bosch GmbH
Sebastian Schöps: Technische Universität Darmstadt, Graduate School of Computational Engineering

A chapter in Reduced-Order Modeling (ROM) for Simulation and Optimization, 2018, pp 121-140 from Springer

Abstract: Abstract The simulation of electric rotating machines is both computationally expensive and memory intensive. To overcome these costs, model order reduction techniques can be applied. The focus of this contribution is especially on machines that contain non-symmetric components. These are usually introduced during the mass production process and are modeled by small perturbations in the geometry (e.g., eccentricity) or the material parameters. While model order reduction for symmetric machines is clear and does not need special treatment, the non-symmetric setting adds additional challenges. An adaptive strategy based on proper orthogonal decomposition is developed to overcome these difficulties. Equipped with an a posteriori error estimator, the obtained solution is certified. Numerical examples are presented to demonstrate the effectiveness of the proposed method.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-75319-5_6

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DOI: 10.1007/978-3-319-75319-5_6

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