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See detailModel Order Reduction: Application to Electromagnetic Problems
Paquay, Yannick ULiege

Doctoral thesis (2017)

With the increase in computational resources, numerical modeling has grown expo- nentially these last two decades. From structural analysis to combustion modeling and electromagnetics, discretization ... [more ▼]

With the increase in computational resources, numerical modeling has grown expo- nentially these last two decades. From structural analysis to combustion modeling and electromagnetics, discretization methods–in particular the finite element method–have had a tremendous impact. Their main advantage consists in a correct representation of dynamical and nonlinear behaviors by solving equations at local scale, however the spatial discretization inherent to such approaches is also its main drawback. In- deed, it usually leads to (very) large systems of equations—requiring abundance of computational resources, usually far too much for quasi-real time simulations. In this dissertation, model order reduction of numerical models from finite element discretization is analyzed to efficiently and accurately downsize the number of degrees of freedom in static and dynamic, linear and nonlinear electromagnetic applications. In particular, an in-depth review of state of the art model order reduction methods is performed in view of the aforementioned problems. To this end, the proper orthogonal decomposition is considered to limit the number of unknowns in the resolution process. Nonlinear sampling methods such as: the missing point estimation approach and discrete empirical interpolation method, are compared to reduce the assembly phase. The parametric dependencies are taken into account by resorting to global reduced basis and nonlinear interpolation on manifolds techniques. Finally, a novel decoupled approach for the reduction of a coupled nonlinear magnetodynamic three-phase energy converter with external electric circuits is proposed and analyzed by combining all the aforementioned methods—impressively reducing the computational cost by 95%. This dissertation is genuinely geared towards the application of a priori known meth- ods on a variety of different numerical models of electromagnetic devices. Additional automatic algorithms which eliminate the arbitrary choices of numerical reduction parameters are proposed and compared to reference methods proposed in the litera- ture. The following applications have been considered: a 2D inductor-core system to first illustrate and provide understanding of the proposed methods, a 2D single phase transformer, a 2D three-phase transformer and a 3D microwave antenna. [less ▲]

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See detailModel Order Reduction of Nonlinear Eddy Current Problems Using Missing Point Estimation
Paquay, Yannick ULiege; Bruls, Olivier ULiege; Geuzaine, Christophe ULiege

in Model Reduction of Parametrized Systems (2017)

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See detailProper orthogonal decomposition versus Krylov subspace methods in reduced-order energy-converter models
Hasan M.D., Rokibul; V. Sabariego, Ruth; Geuzaine, Christophe ULiege et al

Conference (2016, April)

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See detailNonlinear Interpolation on Manifold of Reduced Order Models in Magnetodynamic Problems
Paquay, Yannick ULiege; Bruls, Olivier ULiege; Geuzaine, Christophe ULiege

in IEEE Transactions on Magnetics (2016), 52(3), 1-4

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See detailReduced Order Model for Accounting for High Frequency Effects in Power Electronic Components
Paquay, Yannick ULiege; Geuzaine, Christophe ULiege; Hasan, Md. Rokibul et al

in IEEE Transactions on Magnetics (2016)

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See detailA General Review of Model Order Reduction Techniques for Magnetodynamic Problems
Paquay, Yannick ULiege

Scientific conference (2015, September 15)

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See detailNonlinear Interpolation on Manifold of Reduced Order Models in Magnetodynamic Problems
Paquay, Yannick ULiege; Bruls, Olivier ULiege; Geuzaine, Christophe ULiege

Poster (2015, June)

Proper Orthogonal Decomposition (POD) is an efficient model order reduction technique for linear problems in computational sciences, recently gaining popularity in electromagnetics. However, its ... [more ▼]

Proper Orthogonal Decomposition (POD) is an efficient model order reduction technique for linear problems in computational sciences, recently gaining popularity in electromagnetics. However, its efficiency has been shown to considerably degrade for nonlinear problems. In this paper, we propose a reduced order model for nonlinear magnetodynamic problems by combining POD with an interpolation on manifolds, which interpolates the reduced bases to efficiently construct the desired solution. [less ▲]

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See detailReduced Order Model for Accounting for High Frequency Effects in Power Electronic Components
Paquay, Yannick ULiege; Geuzaine, Christophe ULiege; Hasan, Md. Rokibul et al

Poster (2015, June)

This paper proposes a reduced-order model of power electronic components based on the proper orthogonal decomposition. Starting from a full-wave finite-element model and several snapshots/frequencies, the ... [more ▼]

This paper proposes a reduced-order model of power electronic components based on the proper orthogonal decomposition. Starting from a full-wave finite-element model and several snapshots/frequencies, the reduced-order (RO) model is constructed. Local field values (e.g. magnetic flux density, electric current density, magnetic or electric field) and global quantities (e.g. characteristic complex impedance, joule losses) can be determined for the intermediate frequencies with a very low computational cost and high accuracy. Particular attention is paid to the choice of the most suitable snapshots by means of three different greedy algorithms, the performance of which is compared. We adopt an automatic greedy algorithm that depends only on the RO model. [less ▲]

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See detailMultiscale Modeling of Electrical Energy Systems
Plumier, Frédéric ULiege; Paquay, Yannick ULiege

Scientific conference (2015)

Detailed reference viewed: 48 (11 ULiège)