This thesis deals with computational challenges related to simulation of electrical machines. Electromagnetic fields and heat conduction in the machines are modeled by partial differential equations (PDEs), which are treated numerically by using the finite element (FE) method combined with appropriate time integration schemes. The uniting theme of this work is the prediction of iron losses in electrical machines. Simulating energy losses and mechanical torque in an electrical machine involves computation of the energy in the system, and a time integration method may introduce numerical errors in such computations. Additional complications are caused by a moving subdomain in a machine, and the fact that the resulting discretized problem does not lead into a system of ordinary differential equations (ODEs), but to a differential-algebraic equation (DAE). All this has to be taken into account to construct proper time integration schemes, which is the first topic of thesis. A core of an electrical machine often consists of a hysteretic ferromagnetic material. Conventionally, hysteresis is neglected in electromagnetic simulations, as its inclusion is complicated and computationally expensive. In this thesis, we propose and test numerically a method to incorporate the Jiles-Atherton magnetic hysteresis model into a FE simulation. The last article approaches the iron loss prediction from an inverse problem perspective. The iron loss acts as an unknown heat source term in the heat equation, and the source is reconstructed from a limited number of temperature measurements conducted on and inside the machine. A computational framework and temperature sensor placement optimization is proposed and numerically tested in the thesis.
|Translated title of the contribution||Laskennallisia ongelmia sähkökoneiden numeerisessa mallintamisessa|
|Publication status||Published - 2020|
|MoE publication type||G5 Doctoral dissertation (article)|
- electric machines
- inverse problem
- numerical methods