Data-Driven Model-Order Reduction for Magnetostatic Problem Coupled With Circuit Equations

Pierquin, Antoine; Henneron, Thomas; Clénet, S.

doi:10.1109/tmag.2017.2771358

Cited by 13 publications

(6 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, nonlinear problems are still extremely challenging [5], [6]. The by far more demanding nonlinear ECPs are currently under intensive investigations, as can be seen in [4], [7], and [8].…”

Section: Introductionmentioning

confidence: 99%

MSFEM and MOR to Minimize the Computational Costs of Nonlinear Eddy-Current Problems in Laminated Iron Cores

Hollaus¹,

Schöberl²,

Schobinger³

2020

IEEE Trans. Magn.

View full text Add to dashboard Cite

The multiscale finite-element method (MSFEM) reduces the computational costs for the simulation of eddy currents (ECs) in laminated iron cores compared with the standard finite-element method (SFEM) essentially. Nevertheless, the complexity of the resulting problem is still too large to solve it conveniently. The idea is to additionally exploit model order reduction (MOR). Snapshots (SNSs) for a reduced basis are cheaply calculated by the MSFEM. Numerical simulations of a small transformer show exceptional performance. This is well demonstrated by the overall EC losses and by the distribution of the magnetic-flux density, both with respect to those obtained by the MSFEM.

show abstract

Section: Introductionmentioning

confidence: 99%

MSFEM and MOR to Minimize the Computational Costs of Nonlinear Eddy-Current Problems in Laminated Iron Cores

Hollaus¹,

Schöberl²,

Schobinger³

2020

IEEE Trans. Magn.

View full text Add to dashboard Cite

show abstract

“…Consequently, the order of the system model will become extremely large in order to consider these stray parameters to accurately simulate the transient performance of a modern engineering electric and electronic system [1][2][3][4][5]. In this respect, the model order reduction (MOR) technique is come into being [6][7][8][9]. Moreover, the model reduction method based on moment matching has been widely used in the analysis of the integrated circuit interconnection [10].…”

Section: Introductionmentioning

confidence: 99%

An Improved Structure-Preserving Reduced-Order Interconnect Macromodeling for Large-Scale Equation Sets of Transient Interconnect Circuit Problems

et al. 2021

View full text Add to dashboard Cite

The widely applied Structure-Preserving Reduced-order Interconnect Macromodeling (SPRIM) algorithm cannot retain the reciprocity or the block structure of the circuit matrices of the original system, being inherent to RCL circuits; and consequently it is harder to synthesize a Passive Reduced-order Interconnect Macromodeling Algorithms (PRIMA) model into a practical circuit. In order to overcome this deficiency, this paper proposes an improved SPRIM algorithm to maintain the structure features of the input-output correlation matrix. Firstly, the algorithm in this paper reorders the modified nodal analysis (MNA) equations. An Input-Output structure Preserving Order Reduction (IOPOR) technique applicable to SPRIM is then proposed. Also, one combines the IOPOR technique with the conventional SPRIM algorithm to obtain a new projection matrix that can maintain the input and output correlation matrix of the original system. The feasibility and effectiveness of the proposed algorithm are verified through numerical analysis and calculation of typical problems.The capacitances-submatrix of the reduced-order system. ex LThe inductances-submatrix of the reduced-order system. n GThe resistances-submatrix of the reduced-order system. BThe input-output correlation matrix of the reduced-order system.

show abstract

“…Indeed this method consists in interpolating the nonlinear terms of the full model by calculating only some of their entries. Consequently inference based or data-driven MOR strategies [12]- [15] are interesting because they are non intrusive and allow to obtain a reduced model only from full model solutions. Nevertheless, if datadriven strategy is well defined when the non-linear expression is clearly identified [12], it could be more complex to deal with if the nonlinear behaviour is not known.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Data-Driven Model Order Reduction Applied to Circuit-Field Magnetic Problem

Pierquin¹,

Henneron

2021

IEEE Trans. Magn.

Self Cite

View full text Add to dashboard Cite

As in most of the domains in physics, finite element formulation is a very common method for electromagnetic fields computation. Since many years both proper orthogonal decomposition and empirical interpolation method are also often used in a model order reduction context. If these methods are efficients, their application is intrusive because it requires an access to the matrices and the assembly step. To avoid such an aspect, a data-driven model order reduction based on proper orthogonal decomposition with an approximation of the nonlinear terms by radial basis functions interpolation is applied to a magnetostatic problem coupled with circuit equations. The nonlinear reduced order model only needs solutions of a finite element analysis to be generated.

show abstract

Data-Driven Model-Order Reduction for Magnetostatic Problem Coupled With Circuit Equations

Cited by 13 publications

References 7 publications

MSFEM and MOR to Minimize the Computational Costs of Nonlinear Eddy-Current Problems in Laminated Iron Cores

MSFEM and MOR to Minimize the Computational Costs of Nonlinear Eddy-Current Problems in Laminated Iron Cores

An Improved Structure-Preserving Reduced-Order Interconnect Macromodeling for Large-Scale Equation Sets of Transient Interconnect Circuit Problems

Nonlinear Data-Driven Model Order Reduction Applied to Circuit-Field Magnetic Problem

Contact Info

Product

Resources

About