Doctoral dissertation

Joonas Vesa: Computational methods facilitate the examination of soft magnetic composite materials

tohtorinhattu
The doctoral dissertation of MSc Joonas Vesa introduces methods to model soft magnetic composite materials computationally. The study demonstrates how the electromagnetic behaviour of soft magnetic composites arises from their particle scale structure. Computational modelling can be used to examine such properties of the materials that are difficult to measure directly.

Soft magnetic composite materials are manufactured from ferromagnetic powder that is coated with an insulation and then compacted. Inductor cores, transformer cores, and even parts of rotating electrical machines may be made of soft magnetic composites.

Computational methods allow to study how the electromagnetic behaviour of soft magnetic composites arises from their particle scale structure. The thesis introduces how imitations of soft magnetic composite geometries may be generated and used for finite element computations.

Localized material models are estimated computationally. The weaknesses of the approach are identified through case studies.

“The case studies show that measured effective B-H curves and measured frequency-dependent permeabilities and losses may be accurately replicated by computational methods. However, the microscale properties of the material models tend not to be unique. This uniqueness-related problem is quantified and partially dealt with," explains Joonas Vesa.

The doctoral dissertation of MSc Joonas Vesa in the field of electromechanics titled Geometry Imitations and Material Parameter Identifications for Soft Magnetic Composites will be publicly examined at the Faculty of Information Technology and Communication Sciences of Tampere University at 12 o’clock on Thursday 28 October. The event is organized online. Professor Hajime Igarashi from Hokkaido University will be the opponent while Associate Professor Paavo Rasilo will act as the custos.

The event can be followed via remote connection (Teams).

The dissertation is available online at http://urn.fi/URN:ISBN:978-952-03-2158-1

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