Building Fast Stochastic Surrogate Models for Extracting RL Parameters of Wound Inductors Modeled Using FEM

Autor: Geoffrey Lossa, Christophe Geuzaine, Zacharie De Greve, Olivier Deblecker
Rok vydání: 2020
Předmět:
Zdroj: 2020 IEEE 19th Biennial Conference on Electromagnetic Field Computation (CEFC).
DOI: 10.1109/cefc46938.2020.9451442
Popis: In this work, fast stochastic surrogate models are derived for extracting RL parameters of wound inductors using the Finite Element method. To this end, the Representative Volume Element (RVE) technique is employed to convert the geometrical uncertainties (e.g. due to conductor positions in the winding window) into material uncertainties (complex permeability and conductivity). The dimensionality of the stochastic input space is in that way reduced, thereby allowing the use of the Polynomial Chaos Expansion (PCE) technique for building the stochastic surrogate.
Databáze: OpenAIRE