Fast Optimization of a Linear Actuator by Space Mapping Using Unique Finite-Element Model

Vivier, Stéphane; Lemoine, Didier; Friedrich, Guy

doi:10.1109/tia.2011.2162050

Cited by 3 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each motion system needs to be optimized individually depending on its application. Much work has been published on optimizing electrical machines using single-objective optimization algorithms, such as genetic algorithms [1] and space-mapping algorithms [2], [3]. However, a Pareto front that shows the optimal designs for a few key features can often be of great use for those who make complex designs using those motors.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Finite-Element Method Optimization That Reduces Computation Resources Through Subdomain Model Assistance, for Surface-Mounted Permanent-Magnet Machines Used in Motion Systems

2023

View full text Add to dashboard Cite

Surface-mounted permanent-magnet (PM) motors are widely used in motion control systems because of their high peak torque-to-inertia ratio. These motors exhibit high magnetic saturation to produce high peak torque. A precise finite-element model (FEM) is needed to optimize these motors. It requires high computation power, especially for multi-objective optimizations. On the contrary, subdomain models require low computing power but are inaccurate when magnetic saturation occurs. To solve this problem, we use a subdomain-assisted FEM optimization method (SAFEMOM) that combines subdomain models for preoptimization and FEM for refined optimization. We provide the quantitative measurement methods to compare SAFEMOM with FEM-only optimization. If the constraints in the magnetic fluxes in the subdomain part of SAFEMOM are based on the actual saturation value of the materials, then the advantage of SAFEMOM is not significant. The machines in this study use non-linear material with a knee point at 1.3 T and hence show heavy magnetic saturation above 1.5 T. In that case, contrary to what could be intuitively thought, we need to increase the magnetic flux density limit to 2.6 T -3.0 T in SAFEMOM to have a significant advantage. SAFEMOM reduces about 80% of computing time to obtain a slightly better convergence than the one using FEM only. Also, if limited computing resource is allowed, SAFEMOM gives an error reduced by a factor of nearly eight compared to the optimization error using FEM only. Those results are validated on a family of surface-mounted permanent-magnet machines with a wide range of design parameters.

show abstract

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Finite-Element Method Optimization That Reduces Computation Resources Through Subdomain Model Assistance, for Surface-Mounted Permanent-Magnet Machines Used in Motion Systems

2023

View full text Add to dashboard Cite

show abstract

“…OSM has been used in the electromechanics domain without a prior introduction of input SM. For example, it was adopted in the optimization of an internal permanent magnet synchronous machine (Vivier et al, 2011) using two models built on a finite element analysis with different accuracies, and was also combined in a three level output mapping using models with three different levels of accuracies (Ben Ayed et al, 2012;Koziel and Bandler, 2010).…”

Section: Introductionmentioning

confidence: 99%

Radial output space mapping for electromechanical systems design

Hage-Hassan

Remy

Krebs

et al. 2014

COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering

View full text Add to dashboard Cite

International audiencePurpose – The purpose of this paper is to set a relation through adaptive multi-level optimization between two physical models with different accuracies; a fast coarse model and a fine time consuming model. The use case is the optimization of a permanent magnet axial flux electrical machine. Design/methodology/approach – The paper opted to set the relation between the two models through radial basis function (RBF). The optimization is held on the coarse model. The deduced solutions are used to evaluate the fine model. Thus, through an iterative process a residue RBF between models responses is built to endorse an adaptive correction. Findings – The paper shows how the use of a residue function permits, to diminish optimization time, to reduce the misalignment between the two models in a structured strategy and to find optimum solution of the fine model based on the optimization of the coarse one. The paper also provides comparison between the proposed methodology and the traditional approach (output space mapping (OSM)) and shows that in case of large misalignment between models the OSM fails. Originality/value – This paper proposes an original methodology in electromechanical design based on building a surrogate model by means of RBF on the bulk of existing physical model

show abstract

Comparison of three space mapping techniques on electromagnetic design optimization

Gong

Gillon

Bracikowski

2018

COMPEL

View full text Add to dashboard Cite

PurposeThis paper aims to investigate three low-evaluation-budget optimization techniques: output space mapping (OSM), manifold mapping (MM) and Kriging-OSM. Kriging-OSM is an original approach having high-order mapping. Design/methodology/approachThe electromagnetic device to be optimally sized is a five-phase linear induction motor, represented through two levels of modeling: coarse (Kriging model) and fine.The optimization comparison of the three techniques on the five-phase linear induction motor is discussed. FindingsThe optimization results show that the OSM takes more time and iteration to converge the optimal solution compared to MM and Kriging-OSM. This is mainly because of the poor quality of the initial Kriging model. In the case of a high-quality coarse model, the OSM technique would show its domination over the other two techniques. In the case of poor quality of coarse model, MM and Kriging-OSM techniques are more efficient to converge to the accurate optimum. Originality/valueKriging-OSM is an original approach having high-order mapping. An advantage of this new technique consists in its capability of providing a sufficiently accurate model for each objective and constraint function and makes the coarse model converge toward the fine model more effectively.

show abstract

Fast Optimization of a Linear Actuator by Space Mapping Using Unique Finite-Element Model

Cited by 3 publications

References 6 publications

A Multi-Objective Finite-Element Method Optimization That Reduces Computation Resources Through Subdomain Model Assistance, for Surface-Mounted Permanent-Magnet Machines Used in Motion Systems

A Multi-Objective Finite-Element Method Optimization That Reduces Computation Resources Through Subdomain Model Assistance, for Surface-Mounted Permanent-Magnet Machines Used in Motion Systems

Radial output space mapping for electromechanical systems design

Comparison of three space mapping techniques on electromagnetic design optimization

Contact Info

Product

Resources

About