Sensitivity Analysis Using the Virtual Work Principle for 2-D Magnetostatic Problems

Brun, Olivier; Chadebec, Olivier; Ferrouillat, Pauline; Niyonzima, Innocent; Siau, Jonathan; Meunier, G.; Gerbaud, Laurent; Vi, Frédéric; Floch, Yann Le

doi:10.1109/tmag.2023.3237030

Cited by 4 publications

(10 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where v k are the shape functions hereafter chosen from P1 conforming piece-wise linear space and ν a domain-wise reluctivity function defined in (2).…”

Section: Case Study and Performance Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

Topology Optimization of the Harmonic Content for Torque Ripple Minimization

Gauthey,

Hassan,

Messine

et al. 2024

IEEE Trans. Magn.

View full text Add to dashboard Cite

This paper proposes a formulation to minimize the torque ripple of switched reluctance machines by means of topology optimization. The novelty lies in targeting critical harmonics which allows for more control, both in the design of the rotor and the harmonic content of the torque, during the ripple minimization. An efficient gradient-based algorithm is used to solve the optimization problem and the gradient is obtained using the adjoint variable method.

show abstract

“…where v k are the shape functions hereafter chosen from P1 conforming piece-wise linear space and ν a domain-wise reluctivity function defined in (2).…”

Section: Case Study and Performance Evaluationmentioning

confidence: 99%

“…In the past decades, they have been considered as potential candidates for electrical and hybrid mobility. For that purpose, several studies [1], [2] have addressed its two main drawbacks: low mean torque and high ripple. These performances are highly linked with the rotor and stator geometries and the current control methods.…”

Section: Introductionmentioning

confidence: 99%

Topology Optimization of the Harmonic Content for Torque Ripple Minimization

Gauthey,

Hassan,

Messine

et al. 2024

IEEE Trans. Magn.

View full text Add to dashboard Cite

show abstract

“…Deriving the weak formulation of ( 4) and inserting the ansatz and test function expansions from (8), we obtain the system of equations…”

Section: Discretizationmentioning

confidence: 99%

“…However, in the optimization of electric machines, the focus has been on either parameter optimization or free-form optimization. There is less work on combining these two approaches, e.g., [8,9]. However, combining the two promises better results because it offers more design freedom.…”

Section: Optimizationmentioning

confidence: 99%

“…However, there is a noticeable gap in research that concurrently addresses both free-shape and parameter optimization and their combined effects on electric machine performance. Only a few studies have appeared at this intersection, using FEM for their investigations [8]. In contrast to these methods, our study [9] introduces a gradient-based optimization framework within the IGA context, combining both parameter and shape optimization.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust Design Optimization of Electric Machines with Isogeometric Analysis

Komann,

Wiesheu,

Ulbrich

et al. 2024

Mathematics

View full text Add to dashboard Cite

In electric machine design, efficient methods for the optimization of the geometry and associated parameters are essential. Nowadays, it is necessary to address the uncertainty caused by manufacturing or material tolerances. This work presents a robust optimization strategy to address uncertainty in the design of a three-phase, six-pole permanent magnet synchronous motor (PMSM). The geometry is constructed in a two-dimensional framework within MATLAB®, employing isogeometric analysis (IGA) to enable flexible shape optimization. The main contributions of this research are twofold. First, we integrate shape optimization with parameter optimization to enhance the performance of PMSM designs. Second, we use robust optimization, which creates a min–max problem, to ensure that the motor maintains its performance when facing uncertainties. To solve this bilevel problem, we work with the maximal value functions of the lower-level maximization problems and apply a version of Danskin’s theorem for the computation of generalized derivatives. Additionally, the adjoint method is employed to efficiently solve the lower-level problems with gradient-based optimization. The paper concludes by presenting numerical results showcasing the efficacy of the proposed robust optimization framework. The results indicate that the optimized PMSM designs not only perform competitively compared to their non-robust counterparts but also show resilience to operational and manufacturing uncertainties, making them attractive for industrial applications.

show abstract