This is a SpringerOpen journal published under the brand SpringerOpen. The research topics addressed by Advanced Modeling and Simulation in Engineering Sciences (AMSES) cover the vast domain of the advanced modeling and simulation of materials, processes and structures governed by the laws of mechanics. The emphasis is on advanced and innovative modeling approaches and numerical strategies. The main objective is to describe the actual physics of large mechanical systems with complicated geometries as accurately as possible using complex, highly nonlinear and coupled multiphysics and multiscale models, and then to carry out simulations with these complex models as rapidly as possible. In other words, this research revolves around efficient numerical modeling along with model verification and validation. Therefore, the corresponding papers deal with advanced modeling and simulation, efficient optimization, inverse analysis and simulation-based control. These challenging issues require multidisciplinary efforts – particularly in modeling, numerical analysis and computer science – which will be treated in this journal.

This paper introduces an interpolation method based on snapshot approach to reduce the order of a nonlinear model for magnetostatic problems and facilitate the evaluation of the corresponding system of equations. The solution of the reduced model for a set of given inputs is built by interpolating the right singular vectors, obtained from singular value decomposition of snapshots. To validate the efficiency of this method, this reduced model is compared with full-order finite-element model. Furthermore, the proposed method is analyzed with respect to a more conventional model order reduction that is combined with the discrete empirical interpolation method. The orthogonal interpolation method is the most effective method to reduce model of an interior permanent magnet synchronous machine especially in term of computational time.

Model Order Reduction (MOR) methods are applied in different areas of physics in order to reduce the computational time of large scale systems. It has been an active field of research for many years, in mechanics especially, but it is quite recent for magnetoquasistatic problems. Although the most famous method, the Proper Orthogonal Decomposition (POD) has been applied for modelling many electromagnetic devices, this method can lack accuracy for low order magnitude output quantities, like flux associated with a probe in regions where the field is low. However, the Balanced Proper Orthogonal Decomposition (BPOD) is a MOR method which takes into account these output quantities in its reduced model to render them accurately. Even if the BPOD may lead to unstable reduced systems, this can be overcome by a stabilization procedure. Therefore, the POD and stabilized BPOD will be compared on a 3D linear magnetoquasistatic field problem.

In this paper, a stabilized reduced-order model based on the gappy proper orthogonal decomposition (POD) technique is applied to an eddy-current problem with the non-linear material property. A classical magnetodynamic finite element formulation based on the magnetic vector potential is used as reference and starting point to build up the reduced models. The computational complexity of the non-linear problem might be reduced by the discrete empirical interpolation method-POD technique, but the eddy current effects may make this method unstable. In this paper, we propose an approach which is similar to the gappy method to successfully overcome this instability problem, while keeping the computational cost (time and memory) low.

The Proper Orthogonal Decomposition (POD) combined with the (Discrete) Empirical Interpolation Method (DEIM) can be used to reduce the computation time of the solution of a Finite Element (FE) model. However, it can lead to numerical instabilities. To increase the robustness, the POD_DEIM model must be constructed by preserving the structure of the full FE model. In this article, the structure preserving is applied for different potential formulations used to solve electromagnetic problems.

The proper generalized decomposition (PGD) is a model order reduction method which allows to reduce the computational time of a numerical problem by seeking for a separated representation of the solution. The PGD has been already applied to study an electrical machine but at standstill without accounting the motion of the rotor. In this paper, we propose a method to account for the rotation in the PGD approach in order to build an efficient metamodel of an electrical machine. Then, the machine metamodel will be coupled to its electrical and mechanical environment in order to obtain accurate results with an acceptable computational time on a full simulation.