Conference Agenda

Deep learning has achieved remarkable success in diverse applications; however, its use in solving partial differential equations (PDEs) has emerged only recently. Here, we present a feasibility study of applying physics informed deep learning methods for solving PDEs related to the physical laws of electromagnetics. The methodology uses automatic differentiation, and the loss function is formulated based on the underlying PDE and boundary conditions. The feasibility of the method is shown using an electrostatic analysis of a boundary value problem. The results show appreciable agreement with the solution obtained through traditional numerical analysis.

In this work isogeometric mortaring is used for the simulation of a six pole permanent magnet synchronous machine. Isogeometric mortaring is especially well suited for the efficient computation of rotating electric machines as it allows for an exact geometry representation for arbitrary rotation angles without the need of remeshing. The appropriate B-spline spaces needed for the solution of Maxwell’s equations and the corresponding mortar spaces are introduced. Unlike in classical finite element methods their construction is straightforward in the isogeometric case.

The Proper Orthogonal Decomposition (POD) approach is an efficient model order reduction method in the domain of numerical computation of electrical machines. This approach leads to a reduction of the degrees of freedom and the simulation effort of non-linear field problems. In this paper the POD approach is used to solve steady-state operating points of a non-linear squirrel-cage induction machine. Therefore, the POD approach is combined with a hybrid simulation approach of the induction machine that signifiantly decreases the number of simulation time steps for the steady state simulation by shortening the transient build-up of the rotor flux in the machine.