Constrained Algorithm for the Selection of Uneven Snapshots in Model Order Reduction of a Bearingless Motor

Mukherjee, Victor; Far, Mehrnaz Farzam; Martin, Floran; Belahcen, Anouar

doi:10.1109/tmag.2017.2664505

Cited by 13 publications

(9 citation statements)

References 103 publications

(101 reference statements)

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“…A tradeoff between computational effort and accuracy is achieved by distributing the snapshots only in the first current period. For static problems, greedy algorithm-based snapshot methods can be used to further improve the decomposition but are unfeasible in terms of computational effort in context of eddy current problems (Mukherjee et al, 2017). The two methods are exemplary depicted in Figure 1.…”

Section: Snapshot Methodmentioning

confidence: 99%

Model order reduction techniques applied to magnetodynamic T-Ω-formulation

Müller

Crampen

Henneron

et al. 2020

COMPEL

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Purpose The purpose of this paper is to use different model order reduction techniques to cope with the computational effort of solving large systems of equations. By appropriate decomposition of the electromagnetic field problem, the number of degrees of freedom (DOF) can be efficiently reduced. In this contribution, the Proper Generalized Decomposition (PGD) and the Proper Orthogonal Decomposition (POD) are used in the frame of the T-Ω-formulation, and the feasibility is elaborated. Design/methodology/approach The POD and the PGD are two methods to reduce the model order. Particularly in the context of eddy current problems, conventional time-stepping algorithms can lead to many numerical simulations of the studied problem. To simulate the transient field, the T-Ω-formulation is used which couples the magnetic scalar potential and the electric vector potential. In this paper, both methods are studied on an academic example of an induction furnace in terms of accuracy and computational effort. Findings Using the proposed reduction techniques significantly reduces the DOF and subsequently the computational effort. Further, the feasibility of the combination of both methods with the T-Ω-formulation is given, and a fundamental step toward fast simulation of eddy current problems is shown. Originality/value In this paper, the PGD is combined for the first time with the T-Ω-formulation. The application of the PGD and POD and the following comparison illustrate the great potential of these techniques in combination with the T-Ω-formulation in context of eddy current problems.

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Section: Snapshot Methodmentioning

confidence: 99%

Model order reduction techniques applied to magnetodynamic T-Ω-formulation

Müller

Crampen

Henneron

et al. 2020

COMPEL

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“…Many methods to diminish these problems have been presented in the literature such as; the hybrid analytical-FEM model [47,48], the model order reduction [49][50][51], and sparse subspace learning (SSL) [52], etc. These methods have their own limitations as they rely on statistical and interpolation techniques, which are different for different kinds of machines.…”

Section: Introductionmentioning

confidence: 99%

The Cluster Computation-Based Hybrid FEM–Analytical Model of Induction Motor for Fault Diagnostics

et al. 2020

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This paper presents a hybrid finite element method (FEM)–analytical model of a three-phase squirrel cage induction motor solved using parallel processing for reducing the simulation time. The growing development in artificial intelligence (AI) techniques can lead towards more reliable diagnostic algorithms. The biggest challenge for AI techniques is that they need a big amount of data under various conditions to train them. These data are difficult to obtain from the industries because they contain low numbers of possible faulty cases, as well as from laboratories because a limited number of motors can be broken for testing purposes. The only feasible solution is mathematical models, which in the long run can become part of advanced diagnostic techniques. The benefits of analytical and FEM models for their speed and accuracy respectively can be exploited by making a hybrid model. Moreover, the concept of cloud computing can be utilized to reduce the simulation time of the FEM model. In this paper, a hybrid model being solved on multiple processors in a parallel fashion is presented. The results depict that by dividing the rotor steps among several processors working in parallel, the simulation time reduces considerably. The simulation results under healthy and broken rotor bar cases are compared with those taken from a laboratory setup for validation.

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“…Model reduction has been a popular field for researchers in the last few decades due to its applications in various complex problems [1][2][3][4]. Model reduction is basically a problem of reducing the order of a higher-order model by retaining its dominant and important characteristics such as steady-state error, transient response, stability, and so on.…”

Section: Introductionmentioning

confidence: 99%

Factorization‐based frequency‐weighted optimal Hankel‐norm model reduction

Kumar

Sreeram

2019

Asian Journal of Control

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In this paper, we present frequency‐weighted optimal Hankel‐norm model reduction algorithms for linear time‐invariant continuous‐time systems by representing an original higher‐order system into new fictitious systems. The new system representations are derived through factorization of the resulting sub‐matrices that are obtained after transformations. As the proposed approaches are factorization dependent, additional results with both approaches are included using another factorization of the fictitious input–output and weight matrices. The proposed algorithms generate stable reduced models with double‐sided weights and provide a substantial improvement in the weighted error. A numerical example is given to compare the efficacy of the proposed algorithms with the well‐known frequency‐weighted techniques.

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Constrained Algorithm for the Selection of Uneven Snapshots in Model Order Reduction of a Bearingless Motor

Cited by 13 publications

References 103 publications

Model order reduction techniques applied to magnetodynamic T-Ω-formulation

Model order reduction techniques applied to magnetodynamic T-Ω-formulation

The Cluster Computation-Based Hybrid FEM–Analytical Model of Induction Motor for Fault Diagnostics

Factorization‐based frequency‐weighted optimal Hankel‐norm model reduction

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