Ultra-fast Surrogate Model for Magnetic Field Computation of a Superconducting Magnet Using Multi-layer Artificial Neural Networks

Abstract

Due to the inherent nonlinear and sophisticated nature of superconducting wires/tapes, magnetic field computation of superconducting magnets by means of finite element methods (FEMs) is a time-consuming and complicated procedure. Although Legendre series method (LSM) was proposed as an alternative of FEMs, LSMs are not still fast enough. In current research, a surrogate model based on multi-layer artificial neural networks (ANNs) was introduced for the first time to dramatically reduce the computation time of a magnetic resonance imaging (MRI) magnet. To do this, firstly, the data related to the magnetic field were extracted based on LSM simulations for around 5000 different coil geometries. After that, the geometries of coils were used as inputs to a semi-deep learning ANN-based model in MATLAB software package. The minimum magnetic field in diameter spherical volume, maximum and minimum of total magnetic field were considered as outputs of the model, known as field indices. Then, ANN model was trained to calculate these field indices for any coil geometry. By doing so, magnetic field indices were estimated with a high accuracy based on the target values and also with extremely higher speed, comparing to FEM and LSM. Results showed that it takes 15 to 17 s for the proposed model to calculate the field indices for 750 different geometries whereas it takes for LSM-based model about 4 h.