Model Order Reduction for Rotating Electrical Machines

The simulation of electric rotating machines is both computationally expensive and memory intensive. To overcome these costs, model order reduction techniques can be applied. The focus of this contribution is especially on machines that contain non-symmetric components. These are usually introduced during the mass production process and are modeled by small perturbations in the geometry (e.g., eccentricity) or the material parameters. While model order reduction for symmetric machines is clear and does not need special treatment, the non-symmetric setting adds additional challenges. An adaptive strategy based on proper orthogonal decomposition is developed to overcome these difficulties. Equipped with an a posteriori error estimator the obtained solution is certified. Numerical examples are presented to demonstrate the effectiveness of the proposed method

Model Order Reduction applied to a linear Finite Element model of a squirrel cage induction machine based on POD approach

The Proper Orthogonal Decomposition (POD) approach is applied to a linear Finite Element (FE) model of a squirrel cage induction machine. In order to obtain a reduced model valid on the whole operating range, snapshots are extracted from the simulation of typical tests such as at locked rotor and at the synchronous speed. Then, the reduced model of the induction machine is used to simulate different operating points with variable rotation speed and the results are compared to the full FE model to show the effectiveness of the proposed approach

Multidisciplinary Optimization Formulation for the Optimization of Multirate Systems

International audienceMultidisciplinary optimization strategies are widely used in static case and can be extended to a problem with a time-domain model in order to reduce optimization time. The waveform relaxation method is a fixed-point approach applied to waveforms which allows the coupling of dynamic models. By using the individual discipline feasibility strategy, the coupling is transferred to the optimization problem and leads to a high decrease of the number of model evaluations compared to the multidisciplinary feasibility strategy. The drawback of this approach might be the increased number of optimization variables but it is coped through an efficient way to compute the derivatives of time-dependent variables