Work

Reduced Basis Method for Simulating Thermal Transients in Electric Machines

Year: 2026

Type: preprint

Abstract: Reduced Basis (RB) methods provide low-dimensional approximations of parametrized partial differential equations with controllable accuracy. We discuss the construction of RB approximations for transient thermal simulation of an induction motor and compare their performance to Finite Element (FE) models and Lumped Parameter Thermal Networks (LPTNs) after calibration to measurements. Numerical results demonstrate that RB models can achieve FE accuracy, allow for a fully automatic construction, and offer computational efficiency comparable to traditional LPTNs.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2608.05904

Authors: Egger, Herbert, Haslhofer, Eva-Maria

Institutions: Johann Radon Institute for Computational and Applied Mathematics, Johannes Kepler University of Linz

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funder: Austrian Science Fund

Awards: G2328542868, G8590477180

OpenAlex ID: W7201841191

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