Nicolas Zerbib
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Nicolas Zerbib has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Abderrahmane Bendali
INSA-Toulouse, IMT UMR CNRS 5219
1 shared publication - Abel Sancarlos
ESI Group, Rungis Cedex, France
1 shared publication - Chady Ghnatios
Department of Mechanical Engineering, University of North Florida, Jacksonville, USA
1 shared publication - Elías Cueto
Keysight-UZ Chair of the National Strategy on Artificial Intelligence Aragon Institute of Engineering Research (I3A) Universidad de Zaragoza Zaragoza Spain
1 shared publication - Francisco Chinesta
PIMM Research Laboratory, UMR 8006 CNRS-ENSAM-CNAM, Arts et Metiers Institute of Technology, Paris, France
1 shared publication - Jean Louis Duval
ESI Group, 3bis rue Saarinen, 94528 Rungis, France
1 shared publication
Latest Publications
- Open Access
Fast Computation of Multi-Parametric Electromagnetic Fields in Synchronous Machines by Using PGD-Based Fully Separated Representations
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 highly time-consuming simulations when analyzing, for instance, the noise and vibration in electric motors. The proposed approach is able to construct a virtual chart within a few minutes of off-line simulation, thanks to the use of a fully separated representation in which the solution is written from a series of functions of the space and parameters coordinates, with full space separation made possible by the use of an adapted geometrical mapping. Finally, excellent performances are reported when comparing the reduced-order model with the more standard and computationally costly Finite Element solutions.
Open AccessFast Computation of Multi-Parametric Electromagnetic Fields in Synchronous Machines by Using PGD-Based Fully Separated Representations
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 highly time-consuming simulations when analyzing, for instance, the noise and vibration in electric motors. The proposed approach is able to construct a virtual chart within a few minutes of off-line simulation, thanks to the use of a fully separated representation in which the solution is written from a series of functions of the space and parameters coordinates, with full space separation made possible by the use of an adapted geometrical mapping. Finally, excellent performances are reported when comparing the reduced-order model with the more standard and computationally costly Finite Element solutions.
- 3 October 2013
- Published by Oxford University Press (OUP) in IMA Journal of Numerical Analysis
- Vol. 34 (3) , 1240-1265
Localized adaptive radiation condition for coupling boundary and finite element methods applied to wave propagation problems
The wave propagation problems addressed in this paper involve a relatively large and impenetrable surface on which a comparatively small penetrable heterogeneous material is positioned. Typically the numerical solution of such problems is by coupling boundary and finite element methods. However, a straightforward application of this technique gives rise to some difficulties that are mainly related to the solution of a large linear system whose matrix consists of sparse and dense blocks. To face such difficulties, the adaptive radiation condition technique is modified by localizing the truncation interface only around the heterogeneous material. Stability and error estimates are established for the underlying approximation scheme. Some alternative methods are recalled or designed making it possible to compare the numerical efficiency of the proposed approach.
- 3 October 2013
- Published by Oxford University Press (OUP) in IMA Journal of Numerical Analysis
- Vol. 34 (3) , 1240-1265
Localized adaptive radiation condition for coupling boundary and finite element methods applied to wave propagation problems
The wave propagation problems addressed in this paper involve a relatively large and impenetrable surface on which a comparatively small penetrable heterogeneous material is positioned. Typically the numerical solution of such problems is by coupling boundary and finite element methods. However, a straightforward application of this technique gives rise to some difficulties that are mainly related to the solution of a large linear system whose matrix consists of sparse and dense blocks. To face such difficulties, the adaptive radiation condition technique is modified by localizing the truncation interface only around the heterogeneous material. Stability and error estimates are established for the underlying approximation scheme. Some alternative methods are recalled or designed making it possible to compare the numerical efficiency of the proposed approach.