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A transformer model is used as a benchmark for testing various methods to solve 3D nonlinear periodic eddy current problems. This paper aims to set up a nonlinear magnetic circuit problem to assess the solving procedure of the nonlinear equation system for determining the influence of various special techniques on the convergence of nonlinear iterations and hence the computational time.

Using the T,ϕ-ϕ formulation and the harmonic balance fixed-point approach, two techniques are investigated: the so-called “separate method” and the “combined method” for solving the equation system. When using the finite element method (FEM), the elapsed time for solving a problem is dominated by the conjugate gradient (CG) iteration process. The motivation for treating the equations of the voltage excitations separately from the rest of the equation system is to achieve a better-conditioned matrix system to determine the field quantities and hence a faster convergence of the CG process.

In fact, both methods are suitable for nonlinear computation, and for comparing the final results, the methods are equally good. Applying the combined method, the number of iterations to be executed to achieve a meaningful result is considerably less than using the separated method.

To facilitate a quick analysis, a simplified magnetic circuit model of the 3D problem was generated to assess how the different ways of solutions will affect the full 3D solving process. This investigation of a simple magnetic circuit problem to evaluate the benefits of computational methods provides the basis for considering this formulation in a 3D-FEM code for further investigation.

The purpose of this paper is to introduce a new method to model a stranded wire efficiently in 3D finite element simulations.

In this method, the stranded wires are numerically approximated with the Cauer ladder network (CLN) model order reduction method in 2D. This approximates the eddy current effect such as the skin and proximity effect for the whole wire. This is then projected to a mesh which does not include each strand. The 3D fields are efficiently calculated with the CLN method and are projected in the 3D geometry to be used in simulations of electrical components with a current vector potential and a homogenized conductivity at each time step.

In applications where the stranded wire geometry is known and does not change, this homogenization approach is an efficient and accurate method, which can be used with any stranded wire configuration, homogenized stranded wire mesh and any input signal dependent on time steps or frequencies.

In comparison to other methods, this method has no direct frequency dependency, which makes the method usable in the time domain for an arbitrary input signal. The CLN can also be used to interconnected stranded cables arbitrarily in electrical components.

The purpose of this paper is the modelling and estimation of inrush currents while energising power devices under no load conditions. An analytical representation of the nonlinear B-H curve serves for considering the hysteresis behaviour in the numerical model.

The model is implemented into a standard finite element formulation to compute transient problems.

Inrush currents behave like faults in power distribution facilities. Its prior estimation helps to distinguish between operating conditions and faults.

The magnetic cores may become extremely magnetised. At such high material saturations, the material characteristics are not measurable accurately. Hence, the results depend on the extrapolation of the B-H curves.

The use of first-order reversal curves within the major hysteresis loops helps in a convenient way to estimate peak and shape of the inrush currents.

The Cauer ladder network (CLN) model order reduction (MOR) method is applied to an industrial inductor. This paper aims to to anaylse the influence of different meshes on the CLN method and their parameters.

The industrial inductor is simulated with the CLN method for different meshes. Meshes considering skin effect are compared with equidistant meshes. The inductor is also simulated with the eddy current finite element method (ECFEM) for frequencies 1 kHz to 1 MHz. The solution of the CLN method is compared with the ECFEM solutions for the current density in the conductor and the total impedance.

The increase of resistance resulting from the skin effect can be modelled with the CLN method, using a uniform mesh with elements much larger than the skin depth. Meshes taking account of the skin depth are only needed if the electromagnetic fields have to be reconstructed. Additionally, the convergence of the impedance is used to define a stopping criterion without the need for a benchmark solution.

The work shows that the CLN method can generate a network, which is capable of mimicking the increase of resistance usually accompanied by the skin effect without using a mesh that takes the skin depth into account. In addition, the proposed stopping criterion makes it possible to use the CLN method as an a priori MOR technique.

This paper aims to introduce a non-invasive and convenient method to detect a life-threatening disease called aortic dissection. A Bayesian inference based on enhanced multi-sensors impedance cardiography (ICG) method has been applied to classify signals from healthy and sick patients.

A 3D numerical model consisting of simplified organ geometries is used to simulate the electrical impedance changes in the ICG-relevant domain of the human torso. The Bayesian probability theory is used for detecting an aortic dissection, which provides information about the probabilities for both cases, a dissected and a healthy aorta. Thus, the reliability and the uncertainty of the disease identification are found by this method and may indicate further diagnostic clarification.

The Bayesian classification shows that the enhanced multi-sensors ICG is more reliable in detecting aortic dissection than conventional ICG. Bayesian probability theory allows a rigorous quantification of all uncertainties to draw reliable conclusions for the medical treatment of aortic dissection.

This paper presents a non-invasive and reliable method based on a numerical simulation that could be beneficial for the medical management of aortic dissection patients. With this method, clinicians would be able to monitor the patient’s status and make better decisions in the treatment procedure of each patient.

For an accurate simulation of the temperature distribution inside an electrical machine a method for deriving the convective heat transfer coefficient numerically would be desirable. The purpose of this paper is to present a reliable simulation setup, which is able to reproduce the measured convective heat transfer coefficient at certain spots on the end windings of an electric machine.

The heat flux density on certain spots on the end windings of an induction motor have been measured with heat flux sensors, in order to find out the convective heat transfer coefficient. To identify the air mass flow inside a cooling duct of an encapsulated cooling circuit during the operation of the motor, the pressure loss inside the duct has been measured. The measured data for temperature and air mass flow have been used as boundary conditions for the identification of the convective heat transfer coefficient with a commercial software for computational fluid dynamics (CFD).

The measured data for the local convective heat transfer coefficients have been compared to the results of the numerical simulation for various rotational velocities. The quality of the simulated convective heat transfer coefficient depending on the rotational velocity meets the measured values. Owing to the used simplified model, the quantity of the measured values differ strongly around the simulated coefficient for the convective heat transfer.

The derivation of the convective heat transfer is a challenging subject in CFD but has become more reliable with the invention of the SST and the SAS‐SST turbulence model. In the present work, measurements on the end windings have been compared to simulation results derived with the SAS‐SST turbulence model.