A novel Model Order Reduction (MOR) technique is developed to compute high-dimensional parametric solutions for electromagnetic fields in synchronous machines. Specifically, the intrusive version of the Proper Generalized Decomposition (PGD) is employed to simulate a Permanent-Magnet Synchronous Motor (PMSM). The result is a virtual chart allowing real-time evaluation of the magnetic vector potential as a function of the operation point of the motor, or even as a function of constructive parameters, such as the remanent flux in permanent magnets. Currently, these solutions are highly demanded by the industry, especially with the recent developments in the Electric Vehicle (EV). In this framework, standard discretization techniques require h...
The number of electrical machines used in modern road-vehicles is continuously increasing to meet re...
In this paper, the proper orthogonal decomposition and the Arnoldi-based Krylov subspace methods are...
In the control of electric drives, inaccurate estimation of the motor parameters affects the robustn...
A novel Model Order Reduction (MOR) technique is developed to compute high-dimensional parametric so...
A novel Model Order Reduction (MOR) technique is developed to compute high-dimensional parametric so...
The Proper Generalized Decomposition (PGD) is a model order reduction method which allows to reduce ...
International audienceA novel model order reduction (MOR) technique is presented to achieve fast and...
Among the model order reduction techniques, the Proper Generalized Decomposition (PGD) has shown its...
In this paper, proper orthogonal decomposition method is employed to build a reduced-order model fro...
Due to fine discretization in space and time, the simulation of transient electromagnetic phenomena ...
Model Order Reduction (MOR) methods are more and more applied on many di erent elds of physics in o...
In the domain of numerical computation, Proper Generalized Decomposition (PGD), which consists of ap...
Model order reduction approaches aim at reducing the computational complexity of numericalmodels. Th...
Manufacturing new Magnetic Resonance Imaging (MRI) scanners represents a computational challenge to ...
The number of electrical machines used in modern road-vehicles is continuously increasing to meet re...
In this paper, the proper orthogonal decomposition and the Arnoldi-based Krylov subspace methods are...
In the control of electric drives, inaccurate estimation of the motor parameters affects the robustn...
A novel Model Order Reduction (MOR) technique is developed to compute high-dimensional parametric so...
A novel Model Order Reduction (MOR) technique is developed to compute high-dimensional parametric so...
The Proper Generalized Decomposition (PGD) is a model order reduction method which allows to reduce ...
International audienceA novel model order reduction (MOR) technique is presented to achieve fast and...
Among the model order reduction techniques, the Proper Generalized Decomposition (PGD) has shown its...
In this paper, proper orthogonal decomposition method is employed to build a reduced-order model fro...
Due to fine discretization in space and time, the simulation of transient electromagnetic phenomena ...
Model Order Reduction (MOR) methods are more and more applied on many di erent elds of physics in o...
In the domain of numerical computation, Proper Generalized Decomposition (PGD), which consists of ap...
Model order reduction approaches aim at reducing the computational complexity of numericalmodels. Th...
Manufacturing new Magnetic Resonance Imaging (MRI) scanners represents a computational challenge to ...
The number of electrical machines used in modern road-vehicles is continuously increasing to meet re...
In this paper, the proper orthogonal decomposition and the Arnoldi-based Krylov subspace methods are...
In the control of electric drives, inaccurate estimation of the motor parameters affects the robustn...