Topology Optimization Using Basis Functions for Improvement of Rotating Machine Performances

Sasaki, Hidenori; Igarashi, Hajime

doi:10.1109/tmag.2017.2759784

Cited by 23 publications

(12 citation statements)

References 9 publications

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“…where G i is the Gaussian function centered at the ith element barycenter, b i is the ith normalized function and w i is the ith weight. For the two-material case (e.g., iron and air), the elemental material is expressed as [133]…”

Section: ) Normalized Gaussian Network Methodsmentioning

confidence: 99%

Topology Optimization for Electromagnetics: A Survey

et al. 2022

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The development of technologies for the additive manufacturing, in particular of metallic materials, is offering the possibility of producing parts with complex geometries. This opens up to the possibility of using topological optimization methods for the design of electromagnetic devices. Hence, a wide variety of approaches, originally developed for solid mechanics, have recently become attractive also in the field of electromagnetics. The general distinction between gradient-based and gradient-free methods drives the structure of the paper, with the latter becoming particularly attractive in the last years due to the concepts of artificial neural networks. The aim of this paper is twofold. On one hand, the paper aims at summarizing and describing the state-of-art on topology optimization techniques while on the other it aims at showing how the latter methodologies developed in non-electromagnetic framework (e.g., solid mechanics field) can be applied for the optimization of electromagnetic devices. Discussions and comparisons are both supported by theoretical aspects and numerical results.

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Section: ) Normalized Gaussian Network Methodsmentioning

confidence: 99%

Topology Optimization for Electromagnetics: A Survey

et al. 2022

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“…In topology optimization, the material distribution is determined, allowing the generation and elimination of holes such that the cost function is minimized. Here, we adopt the NGnet-based on/off method, which has been shown to be fairly suitable for the optimization of electric apparatus, such as electric motors [18] and wireless power transfer devices [19]. In this method, the material distribution (for example, magnetic core/air) is determined from the sign of the shape function , defined by The NGnet on/off method is schematically shown in Fig.…”

Section: A Shape Optimization Based On Gamentioning

confidence: 99%

Fast Multi-Objective Optimization of Electromagnetic Devices Using Adaptive Neural Network Surrogate Model

Sato

Igarashi

2022

IEEE Trans. Magn.

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This paper presents a fast population-based multi-objective optimization of electromagnetic devices using an adaptive neural network surrogate model. The proposed method does not require any training data or construction of a surrogate model before the optimization phase. Instead, the neural network surrogate model is built from the initial population in the optimization process, and then it is sequentially updated with high-ranking individuals. All individuals were evaluated using the surrogate model. Based on this evaluation, high-ranking individuals are re-evaluated using high-fidelity electromagnetic field computation. The suppression of the execution of expensive field computations effectively reduces the computing costs. It is shown that the proposed method works two to four times faster-maintaining optimization performance than the original method that does not use surrogate models.

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“…They have been studied since around 2011. The EC or SI is also applied to the electric machine design [19]- [21].…”

Section: Circuit Machine and System Designmentioning

confidence: 99%

“…iii) [60] iv) [46] Auto-encoder -Unsupervised learning is possible -Application is limited iv) [47]- [49] Intelligent Optimization Algorithms GA -Ability to deal with non-linear, non-differentiable, multimodal, combinatorial optimization -Concerns about initial convergence -Strong problem dependence i) [17], [20], [21], [64] ii) [10] iii) [11], [62], [63] PSO -Ability to deal with non-linear, non-differentiable -Concerns about initial convergence -Strong problem dependence ii) [67] iii) [43], [65], [66] Ant Colony Optimization -Ability to deal with non-linear, non-differentiable -Concerns about initial convergence -Strong problem dependence i) [19] Multi-Objective Optimization -Ability to deal with non-linear, non-differentiable, multimodal, combinatorial optimization -Concerns about initial convergence -Strong problem dependence -High calculation cost i) [18] ii) [69] iii) [57], [68] INTERNATIONAL JOURNAL of RENEWABLE ENERGY RESEARCH F. Kurokawa…”

Section: Detection or Prediction Of Degradation Failure And Abnormalitymentioning

confidence: 99%

Recent Research Trends of Artificial Intelligence Applications in Power Electronics

Kurokawa

Tanaka

Furukawa

et al. 2021

IJRER

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The artificial intelligence (AI) is one of the most significant topics to obtain an optimal solution in various applications. Focusing on the AI applications to the power electronics, this paper presents their tendency and future trend systematically and exhaustively. In the process, IEEE Xplore is chosen for the paper investigation and the result is arranged chronologically. Furthermore, several considerable AI methods for the power electronics, such as the neural network, deep learning, genetic algorithm, particle swarm optimization, and multi-objective optimization, are explained. Several published papers with respect to AI applications to the power electronics in the renewable energy system are also introduced.

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Topology Optimization Using Basis Functions for Improvement of Rotating Machine Performances

Cited by 23 publications

References 9 publications

Topology Optimization for Electromagnetics: A Survey

Topology Optimization for Electromagnetics: A Survey

Fast Multi-Objective Optimization of Electromagnetic Devices Using Adaptive Neural Network Surrogate Model

Recent Research Trends of Artificial Intelligence Applications in Power Electronics

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