# Presentation

(PP02) Inverse Coil Design by Machine Learning-based Optimization

SessionProject Posters Presentation

Event Type

Project Poster

AI/Machine Learning/Deep Learning

Clouds and Distributed Computing

Parallel Algorithms

Scientific Software Development

TimeTuesday, June 26th3:15pm - 3:45pm

LocationBooth N-230

DescriptionInductive power transfer is nowadays a popular and widely used technology, e.g. for charging mobile phones and heating cook ware. In such systems usually coplanar spiral coils are used in order to generate the necessary magnetic fields. However, with regard to energy efficiency and safety it is desirable to start from an optimal magnetic field and derive the necessary coil geometry from that.

In our previous work, we have shown that optimized coil geometries can be obtained using a parametric representation of the coil’s geometry, solving Biot-Savart’s law and optimizing the geometry by using Simulated Annealing. Depending on the number of optimization parameters the calculations take up to a few days on ’standard’ workstations. However, once additional components, like field focusing elements, are added to the magnetic circuit, the calculation of the magnetic field using solely Biot-Savart’s law is no longer valid. However, suitable calculation methods like finite element methods (FEM), which allow to calculate the magnetic field of complex magnetic circuits, tremendously increase the computation time of the magnetic field, thus drastically increasing the time for the whole optimization process.

In order to solve this issue, we propose a method that uses machine learning technologies providing a surrogate model for the complex magnetic circuit. Once trained, the surrogate model can replace a time-consuming FEM simulation, still providing an estimate of the magnetic field. As the calculation of the surrogate model will be several times faster than a full FEM simulation, this will speed up the entire optimization process.

In our previous work, we have shown that optimized coil geometries can be obtained using a parametric representation of the coil’s geometry, solving Biot-Savart’s law and optimizing the geometry by using Simulated Annealing. Depending on the number of optimization parameters the calculations take up to a few days on ’standard’ workstations. However, once additional components, like field focusing elements, are added to the magnetic circuit, the calculation of the magnetic field using solely Biot-Savart’s law is no longer valid. However, suitable calculation methods like finite element methods (FEM), which allow to calculate the magnetic field of complex magnetic circuits, tremendously increase the computation time of the magnetic field, thus drastically increasing the time for the whole optimization process.

In order to solve this issue, we propose a method that uses machine learning technologies providing a surrogate model for the complex magnetic circuit. Once trained, the surrogate model can replace a time-consuming FEM simulation, still providing an estimate of the magnetic field. As the calculation of the surrogate model will be several times faster than a full FEM simulation, this will speed up the entire optimization process.

Poster PDF