A Hybrid Boundary Element Method-Reluctance Network Method for Open Boundary 3-D Nonlinear Problems

Araujo, Douglas Martins; Coulomb, Jean‐Louis; Chadebec, Olivier; Rondot, Loïc

doi:10.1109/tmag.2013.2281759

Cited by 15 publications

(4 citation statements)

References 10 publications

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“…However, BEM's main shortcoming is the difficulty of analyzing and solving saturated magnetic fields [30]. To tackle this issue, BEM is usually combined with another modeling method, such as FEA or magnetic equivalent circuit (MEC), to model SRMs [33], [34]. FEA or MEC are used to solve for the electromagnetic fields in the saturated nonlinear regions, while BEM analyzes fields in the linear region.…”

Section: A Numerical Modeling Methodsmentioning

confidence: 99%

Review of Machine Learning Applications to the Modeling and Design Optimization of Switched Reluctance Motors

et al. 2022

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This work presents a comprehensive review of the developments in using Machine Learning (ML)-based algorithms for the modeling and design optimization of switched reluctance motors (SRMs). We reviewed Machine Learning-based numerical and analytical approaches used in modeling SRMs. We showed the difference between the supervised, unsupervised and reinforcement learning algorithms. More focus is placed on supervised learning algorithms as they are the most used algorithms in this area. The supervised learning algorithms studied in this work include the feedforward neural networks, recurrent neural networks, support vector machines, extreme learning machines, and Bayesian networks. This work also discusses several essential aspects of the considered machine learning algorithms, such as core concept, structure, and computational time. It also surveys sample data acquisition methods and data size. Finally, comparisons between the different considered ML-based algorithms are conducted in terms of electric motor type, dataset inputs and outputs, and algorithm's structure and accuracy to provide a summary overview of the ML-based algorithms for SRMs modeling and design.INDEX TERMS Electric machine design, electric machine modeling, machine learning (ML), switched reluctance motor (SRM).

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Section: A Numerical Modeling Methodsmentioning

confidence: 99%

Review of Machine Learning Applications to the Modeling and Design Optimization of Switched Reluctance Motors

et al. 2022

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“…However, its major drawback is the difficulty of considering the magnetic saturation [21] due to non-symmetric and fully populated coefficient matrices. Therefore, BEM and FEM [88], [89] or BEM and MEC [90] are commonly combined together to model SRMs. The FEM or MEC estimates the magnetic fields in the nonlinear regions whereas the BEM solves the magnetic field in the linear regions.…”

Section: B Boundary Element Methods (Bem)mentioning

confidence: 99%

Electromagnetic Modeling Techniques for Switched Reluctance Machines: State-of-the-Art Review

Watthewaduge

Sayed

Emadi

et al. 2020

IEEE Open J. Ind. Electron. Soc.

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Switched Reluctance Machines (SRMs) are gaining more attention due to their simple and rugged construction, low manufacturing cost, and high-speed operation capability. An electromagnetic model of the machine is needed in the design and analysis processes. The required accuracy level of the model depends mainly on the application. A high-fidelity model is required to achieve a good design and predict the performance accurately. However, it requires high computational cost and longer simulation time. Other fast and less-comprehensive models with less computational burden could be utilized in the design and analysis of the motor drives. This paper extensively analyzes various electromagnetic modeling techniques of SRMs. Analytical, numerical, and hybrid models are considered. The paper investigates analytical models that are based on Maxwell's equations in addition to interpolation and curve fitting techniques. Numerical techniques such as Finite Element Method (FEM) and Boundary Element Method (BEM) are presented. Moreover, Magnetic Equivalent Circuit (MEC) method is discussed. Finally, potential research areas are proposed for the electromagnetic modeling of SRMs. INDEX TERMS Analytical model, boundary element method (BEM), electromagnetic modeling, finite element method (FEM), magnetic equivalent circuit (MEC) model, numerical model, switched reluctance machine (SRM).

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“…Finally, but importantly, the boundary element/integral method involves singular and hyper-singular boundary integrals, and improper evaluation of the integrals affects the accuracy and stability of the method. The disadvantages of the BEM are not negligible, but significant progress and special treatments have been made and are still going on [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Analytical Approach for Sharp Corner Reconstruction in the Kernel Free Boundary Integral Method during Magnetostatic Analysis for Inductor Design

Jin

Cao

et al. 2023

Energies

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It is very important to perform magnetostatic analysis accurately and efficiently when it comes to multi-objective optimization of designs of electromagnetic devices, particularly for inductors, transformers, and electric motors. A kernel free boundary integral method (KFBIM) was studied for analyzing 2D magnetostatic problems. Although KFBIM is accurate and computationally efficient, sharp corners can be a major problem for KFBIM. In this paper, an inverse discrete Fourier transform (DFT) based geometry reconstruction is explored to overcome this challenge for smoothening sharp corners. A toroidal inductor core with an airgap (C-core) is used to show the effectiveness of the proposed approach for addressing the sharp corner problem. A numerical example demonstrates that the method works for the variable coefficient PDE. In addition, magnetostatic analysis for homogeneous and nonhomogeneous material is presented for the reconstructed geometry, and results carried out using KFBIM are compared with the results of FEM analysis for the original geometry to show the differences and the potential of the proposed method.

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A Hybrid Boundary Element Method-Reluctance Network Method for Open Boundary 3-D Nonlinear Problems

Cited by 15 publications

References 10 publications

Review of Machine Learning Applications to the Modeling and Design Optimization of Switched Reluctance Motors

Review of Machine Learning Applications to the Modeling and Design Optimization of Switched Reluctance Motors

Electromagnetic Modeling Techniques for Switched Reluctance Machines: State-of-the-Art Review

Analytical Approach for Sharp Corner Reconstruction in the Kernel Free Boundary Integral Method during Magnetostatic Analysis for Inductor Design

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