Interpretation of adjoint sensitivity analysis for shape optimal design of electromagnetic systems

Lee, Hyang-Beom; Ida, Nathan

doi:10.1049/iet-smt.2014.0197

Cited by 3 publications

(3 citation statements)

References 7 publications

(6 reference statements)

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“…Thus, no numerical noise due remeshing is observed as opposed to finite-difference. We implemented a 2D FEM code and an AVM code based on the discrete approach [12] for the gradient computation. The implementation of the AVM code is cumbersome since it requires intrusive manipulation of the FEM code itself.…”

Section: Adjoint Variable Methodsmentioning

confidence: 99%

Branch and Bound Algorithm Based on Prediction Error of Metamodel for Computational Electromagnetics

et al. 2020

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Metamodels proved to be a very efficient strategy for optimizing expensive black-box models, e.g., Finite Element simulation for electromagnetic devices. It enables the reduction of the computational burden for optimization purposes. However, the conventional approach of using metamodels presents limitations such as the cost of metamodel fitting and infill criteria problem-solving. This paper proposes a new algorithm that combines metamodels with a branch and bound (B&B) strategy. However, the efficiency of the B&B algorithm relies on the estimation of the bounds; therefore, we investigated the prediction error given by metamodels to predict the bounds. This combination leads to high fidelity global solutions. We propose a comparison protocol to assess the approach’s performances with respect to those of other algorithms of different categories. Then, two electromagnetic optimization benchmarks are treated. This paper gives practical insights into algorithms that can be used when optimizing electromagnetic devices.

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Section: Adjoint Variable Methodsmentioning

confidence: 99%

Branch and Bound Algorithm Based on Prediction Error of Metamodel for Computational Electromagnetics

et al. 2020

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“…The adjoint variable is inserted in (7), which is responsible for the connect the design variables of the problem (x, y) and the objective function [20], [30]:…”

Section: Sensitivity Analysismentioning

confidence: 99%

Methodology for Minimizing Electric Field at Ground Level from Transmission Lines Using Sensitivity Analysis

Paganotti

Saldanha

Lisboa

et al. 2021

J. Microw. Optoelectron. Electromagn. Appl.

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“…To tackle all these issues, some researchers have proposed to use the adjoint variable method for computing the gradient from the mathematical model of the electromagnetic phenomena [6,7,19]. This approach requires an intrusive manipulation of the FE code to calculate the gradient efficiently and accurately.…”

mentioning

confidence: 99%

The Adjoint Variable Method for Computational Electromagnetics

2022

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Optimization using finite element analysis and the adjoint variable method to solve engineering problems appears in various application areas. However, to the best of the authors’ knowledge, there is a lack of detailed explanation on the implementation of the adjoint variable method in the context of electromagnetic modeling. This paper aimed to provide a detailed explanation of the method in the simplest possible general framework. Then, an extended explanation is offered in the context of electromagnetism. A discrete design methodology based on adjoint variables for magnetostatics was formulated, implemented, and verified. This comprehensive methodology supports both linear and nonlinear problems. The framework provides a general approach for performing a very efficient and discretely consistent sensitivity analysis for problems involving geometric and physical variables or any combination of the two. The accuracy of the implementation is demonstrated by independent verification based on an analytical test case and using the finite-difference method. The methodology was used to optimize the parameters of a superconducting energy storage device and a magnet press and the optimization of the topology of an electromagnet. The objective function of each problem was successfully decreased, and all constraints stipulated were met.

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Interpretation of adjoint sensitivity analysis for shape optimal design of electromagnetic systems

Cited by 3 publications

References 7 publications

Branch and Bound Algorithm Based on Prediction Error of Metamodel for Computational Electromagnetics

Branch and Bound Algorithm Based on Prediction Error of Metamodel for Computational Electromagnetics

Methodology for Minimizing Electric Field at Ground Level from Transmission Lines Using Sensitivity Analysis

The Adjoint Variable Method for Computational Electromagnetics

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