Optimization Techniques in the Design of Electromagnetic Devices

Ţopa, Vasile; Simion, Emil; Munteanu, Călin

doi:10.1007/978-1-4615-1961-4_42

Cited by 3 publications

(11 citation statements)

References 6 publications

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“…As in [3], the chosen device operates at a frequency of 1 (kHz), having the excitation current applied to the primary coil. All three coils depicted in brown in Figure 1 (one primary and two secondary coils) have 100 turns with the cross-section of the conductor of 1 × 10 −6 (m 2 ).…”

Section: Methodsmentioning

confidence: 99%

“…As the authors of the work [3] demonstrate, the objective function shows a series of local minima, and so the use of stochastic tools such as NSGA-II and -III is preferred to tackle the problem as opposed to the deterministic algorithms.…”

Section: Initial Model Valuementioning

confidence: 99%

“…The primary feature of an LVDT device lies in its ability to exhibit a linear response to displacement. The constraints encountered during the optimization of such a device involve maximizing linearity while considering a predefined tolerance value, as demonstrated in [3]. Moreover, the benefits of introducing finite-element modeling in the development of LVDTs have been proven in [4].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, our objective is to address the issue of LVDT linearization using two optimization methods: the NSGA-II and the NSGA-III algorithms, which are integrated into the system described in [17]. Our contribution lies in applying these methods to enhance the performance of the LVDT by increasing its working distance in comparison to the initial analyzed model in [3], through a simple yet efficient approach. By modifying the internal geometrical configuration (while maintaining the external dimensions unchanged), we achieved notable results regarding the above-mentioned goals.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimization of an Inductive Displacement Transducer

Mociran,

Gliga

2023

Sensors

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This paper presents the optimization of an inductive displacement transducer or linear variable differential transformer (LVDT). The method integrates design software (SolidWorks 2023), simulation tools (COMSOL Multiphysics), and MATLAB. The optimization phase utilizes the non-dominated sorting genetic algorithm (NSGA)-II and -III to fine-tune the geometry configuration by adjusting six inner parameters corresponding to the dimension of the interior components of the LVDT, thus aiming to improve the overall performance of the device. The outcomes of this study reveal a significant achievement in LVDT enhancement. By employing the proposed methodology, the operational range of the LVDT was effectively doubled, extending it from its initial 8 (mm) to 16 (mm). This expansion in the operational range was achieved without compromising measurement accuracy, as all error values for the working range of 0–16 (mm) (NSGA-II with a maximum final relative error of 2.22% and NSGA-III with 2.44%) remained below the imposed 3% limit. This research introduces a new concept in LVDT optimization, capitalizing on the combined power of NSGA-II and NSGA-III algorithms. The integration of these advanced algorithms, along with the interconnection between design, simulation, and programming tools, distinguishes this work from conventional approaches. This study fulfilled its initial objectives and generated quantifiable results. It introduced novel internal configurations that substantially improved the LVDT’s performance. These achievements underscore the validity and potential of the proposed methodology in advancing LVDT technology, with promising implications for a wide range of engineering applications.

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Section: Methodsmentioning

confidence: 99%

Section: Initial Model Valuementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimization of an Inductive Displacement Transducer

Mociran,

Gliga

2023

Sensors

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“…In the particular example of the cloak, optimization has been shown to not only give simpler material parameters using homogenous shells but also better performance [46]. Simple numerical optimization techniques have been widely applied to a variety of science and engineering problems and are most effective with a well-defined objective function that seeks maximum/minimum over a limited parameter space using a set of constraints [47,48]. Ideally, the algorithm finds a global optimum and the problem is considered solved.…”

Section: Theoretical Methods To Design Em Media/devicesmentioning

confidence: 99%

Rapid Prototyping across the Spectrum: RF to Optical 3D Electromagnetic Structures

Allen¹,

Allen²,

Wenner³

2015

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Multi‐terminal resistances optimal design using spline boundary elements and genetic algorithms

Munteanu

Ţopa

Simion

et al. 2001

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

Self Cite

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This paper proposes a method for multi‐terminal resistor optimal design based on a stochastic optimization algorithm coupled with a high accuracy boundary element method numerical analysis module using spline boundary elements. The aim of the optimization process is to find out the optimal shape of the resistor so that the values of the partial resistances between its terminals can be globally reduced. In the first part of the paper, a short introduction to the boundary element method formulation using spline interpolation of the boundary unknowns is performed. Then, the genetic algorithm optimization method is briefly described, emphasising the numerical implementation particularities related to the actual application. In the second part of the paper, three numerical examples are proposed and the final conclusions are outlined.

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Optimization Techniques in the Design of Electromagnetic Devices

Cited by 3 publications

References 6 publications

Optimization of an Inductive Displacement Transducer

Optimization of an Inductive Displacement Transducer

Rapid Prototyping across the Spectrum: RF to Optical 3D Electromagnetic Structures

Multi‐terminal resistances optimal design using spline boundary elements and genetic algorithms

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