Host
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Client & task
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Objectives
High-frequency (HF) power converters are central to modern energy-efficient systems, including renewable energy interfaces, electric mobility, data-center power supplies, and compact consumer electronics. Their performance and miniaturisation rely heavily on the availability of magnetic materials capable of operating at elevated frequencies with minimal losses. MnZn ferrites, long recognised for their low-loss behaviour in the hundreds of kilohertz range, are now being pushed toward the MHz and even sub-GHz domain. This shift requires a deeper understanding of how composition, processing routes, and geometric factors influence their electromagnetic performance.
This project addresses these needs by developing advanced MnZn ferrites, characterising their multidimensional behaviour, and integrating magneto-mechanical and AI-driven modelling to improve predictive design strategies. Sustainability considerations—including raw material availability, processing energy consumption, and end-of-life impact—are also embedded throughout the material development pipeline.
Objectives 1
Develop MnZn ferrite materials for HF power converters, including raw material selection, preferring doping, milling, and sintering.
Objectives 2
Evaluate how composition and processing affect electromagnetic performance, microstructure, and magnetic properties in different core shapes and sizes.
Objectives 3
Perform broadband magnetic measurements up to 1 GHz to study anisotropy variations related to core attributes and composition.
Objectives 4
Create magneto-mechanical models for predicting power losses in MnZn ferrites using thermodynamic and multiscale methods.
Objectives 5
Incorporate materials into AI training and validate in magnetic designs.
Objectives 6
Investigate sustainability aspects of raw materials availability, energy efficiency in production, recyclability, environmental impact and toxicity.
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Expected Results
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67%
Prediction of electromagnetic performance and validation of developed ferrites under dimensional scenarios and in real operating conditions
260%
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