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MA: Fachverband Magnetismus
MA 42: Posters Magnetism I
MA 42.53: Poster
Mittwoch, 18. März 2020, 15:00–18:00, P3
Predictive Design of Induction Coil Geometries using Neural Networks — •Simon Bekemeier1 and Christian Schröder1,2 — 1Bielefeld Institute for Applied Materials Research (BIfAM), Computational Materials Science and Engineering (CMSE), Bielefeld University of Applied Sciences, Department of Engineering Sciences and Mathematics, Interaktion 1, 33619 Bielefeld, Germany — 2Faculty of Physics, Bielefeld University, Universitätsstraße 25, 33615 Bielefeld, Germany
Nowadays, inductive power transfer is an established technology with its most common application in induction hobs. Such appliances usually use planar coils with homogeneous winding distances. With regard to energy efficiency, comfort and electromagnetic compatibility it is desirable to start from an optimal magnetic field distribution and derive the necessary coil geometry from it.
Unknown, highly non-linear functional relations can be modelled using neural networks with relative ease. In this contribution, we use a deep convolutional auto-encoder to predict the relationship between coil geometries and the respective magnetic fields. To achieve this, the current-path and the coil's magnetic field are presented to the neural network in spatially discretized form. By using the current-path as input and the magnetic field as output, the neural net is trained to find coil geometries, which produce a desired magnetic field. Furthermore, a neural net can be used as a surrogate model to speed up an iterative optimization approach in comparison to using a conventional simulation.