Permanent magnets are a critical component of electric motors and generators in many applications, the most important of which are wind turbines and hybrid/electric vehicles. The rapid growth of these sectors has resulted in an increased demand for high performance Nd-Fe-B-based permanent magnets but the long-term sustainability of using global resources of rare earth elements like Nd and Dy at this high rate is questionable. There is a clear need to develop a rare-earth-free permanent magnet. A digital twin is a set of information which fully describes the structure and properties of a physical object. This is highly challenging as the magnetic state of a material depends not only on its physical structure and magnetic properties but also on its magnetic and thermal history. The digital twin of a permanent magnet has the potential to play a vital role in the development of novel permanent magnets, and in real-time monitoring of the performance of magnets in applications. Obtaining the digital twin of a permanent magnet would therefore deliver important contributions to the digitalisation of materials science, environmental sustainability, clean energy and electromobility. In this project the rare-earth-free magnet, MnAl-C, will be taken as a model system and an enhanced micromagnetic model will be developed. Advanced characterisation combined with magnetic measurements and domain images will form the basis for the simulations. A machine learning model will then be developed and data assimilation will be employed in order to reduce the offset between predicted and measured magnetic properties. The trained model represents the microscale component of the digital twin of a MnAl-C permanent magnet.


Duration 01/02/2024 - 31/01/2027
Funding Bundesländer (inkl. deren Stiftungen und Einrichtungen)

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
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