Permanent magnets are a critical component of electric motors and generators in a broad spectrum of applications. The rapid growth of the wind power and electromobility sectors has resulted in a greatly increased demand for highest performance Nd-Fe-B-based permanent magnets. Limited global resources of rare earth elements recquire the development of reduced or rare-earth-free permanent magnets.

The aim of this project is to produce a digital twin of rare earth-free MnAl-C permanent magnets. This requires a complete, quantitative description of the microstructure in three dimensions and detailed knowledge of the role of the different components of the microstructure in determining the magnetic properties. This information forms the basis for micromagnetic simulations of the magnetic properties based on realistic, simulated microstructures. The digital twin is evaluated by simulating the magnetic state of the material after exposure to a specific magnetic field history and comparing the results with the experiment. Machine learning is used to establish the relationship between experiment and simulation and to generate a realistic model of MnAl-C magnets.

actual

On 25.4.2022 the first virtual kick-off meeting took place. Participants from IFW Dresden were Thomas G. Woodcock and Panpan Zhao. From UWK Krems Thomas Schrefl, Harald Özelt and Markus Gusenbauer participated. The main topic was the discussion about the next steps, prioritization of the work plan, all with focus on the upcoming project period. In WP1 and WP2 at IFW Dresden, we are now working on ways to generate 3D information on grain structures and to analyze material segregation of individual types of interfaces, such as triple junctions. In WP4, the micromagnetic part at UWK, the individual interfaces are analyzed with distributions of coercivity. In parallel, we will work on a toolchain to convert 3D microstructure measurements into simulation models. Grain structure statistics will help to generate artificial microstructures similar to the experimental measurements.

On 20.5. we were able to inspire young and old at the Long Night of Research 2022. The influence of permanent magnets on electromobility was demonstrated at a hands-on station. Those interested were able to find out how magnets influence the performance of electric motors with simple experiments. With our project we were able to show how important it is to develop permanent magnets without critical rare earths materials.

Lange Nacht der Forschung 2022 Markus Gusenbauer, Harald Özelt

https://www.donau-uni.ac.at/de/aktuelles/veranstaltungen/2022/lange-nacht-der-forschung-2022.html

Lange Nacht der Forschung 2022

On July 13, 2022, we organized a workshop at the Junge Uni on the Krems campus. Children between the ages of 10 and 13 learned about how an electric motor works. They were able to make a small electric motor and experience how the strength of a magnet affects the power of a motor. Thomas Schrefl, Harald Özelt and Markus Gusenbauer were the supervising researchers at the workshop.

Junge Uni 1

JUnge Uni 2

Markus Gusenbauer gave a talk at JEMS2022 on July 26, 2022 entitled: "Coercivity analysis of twin boundaries". The talk was virtually shown at the hybrid event in Warsaw, Poland (https://jems2022.pl).

On August 31, 2022 Harald Özelt gave a talk at CMAM 2022 (Computational Methods in Applied Mathematics) in Vienna. The title of the talk was: "Machine learning as building block for macromagnetic simulations".

cmam2022

 

@ Details:

Auftraggeber:

FWF, Projekt: I 5266-N

Details

Duration 01/03/2022 - 28/02/2025
Funding FWF
Program
Department

Department for Integrated Sensor Systems

Center for Modelling and Simulation

Principle investigator for the project (University for Continuing Education Krems) Dipl.-Ing.(FH) Dr. Markus Gusenbauer

Publications

Zhao, P.; Gusenbauer, M.; Oezelt, H.; Wolf, D.; Gemming, T.; Schrefl, T.; Nielsch, K.; Woodcock, T. G. (2022). Nanoscale chemical segregation to twin interfaces in t-MnAl-C and resulting effects on the magnetic properties. Journal of Materials Science & Technology, Vol. 134: 22-32

Lectures

Machine learning as building block for macromagnetic simulations

CMAM 2022, 31/08/2022

Coercivity analysis of twin boundaries with demagnetization negligible models in arbitrary field direction

JEMS 2022, 26/07/2022

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