Iron losses are a key limitation in the design of electrical machines and drives. The push towards compact, high-power-density drives has raised operating frequencies, sharply increasing these losses. Industry still lacks tools that accurately predict their level and distribution. This three-year research program is jointly funded by two research funding bodies.
It brings together two complementary research groups: one specializing in computational electromagnetics and finite element methods, and another specializing in electrical machines and magnetic materials.
What you will do.
- Extend the energy-based (EB) hysteresis model to account for anisotropy and rotational losses.
- Further develop neural surrogate and homogenization methods to enable fast and accurate iron loss predictions within design optimization loops.
- Build experimentally validated material libraries for electrical steels, in the form of neural networks directly usable in finite element simulations.
Profile
- A PhD in computational science, applied mathematics, electrical engineering, physics or a related field.
- A strong background in numerical methods for PDEs (finite elements in particular) and/or scientific machine learning.
- Solid programming skills (Python, C++, PyTorch or JAX).
- Knowledge of electromagnetics is an asset but not a requirement.
- Fluency in English.
How to apply
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