open access

https://doi.org/10.14943/doctoral.k16516

https://hdl.handle.net/2115/95194

Study on optimal design of electromagnetic devices and acceleration by machine learning

日本語

hysteresis loss

2025-03-25

Hokkaido University

xii, 86p.

The rapid growth of communication traffic in the information society has led to demands for higher performance and smaller network equipment. This has created a need for new design methods for microwave circuits used in wireless communications. In the design of electromagnetic devices, the optimal design approach is often used, where the difference between the target characteristics and the characteristics computed by electromagnetic simulation is defined as the objective function, and the shape that minimizes this difference is found. Topology optimization (TO), one of the optimal design methods, allows a high degree of design freedom and can find innovative structures. However, TO has not been sufficiently applied to circuits composed of microstrip lines (MSLs), which are the basic elements of microwave circuits, and its effectiveness is not yet known. In addition, although TO using evolutionary computation can perform global searches, its computational cost is a problem. Devices such as inductors and motors are being used at higher frequencies to miniaturize electrical and electronic equipment, and it has become important to accurately evaluate the hysteresis loss (hysloss), which increases with frequency. Accurate hysloss analysis requires more computational cost than simple analysis methods using approximation formulas, and the increase in computational cost is a problem in the optimal design of inductors and motors, where a large number of hysloss analyses are performed. To address the above problem of computational cost in optimal design, surrogate model methods are known to replace time-consuming computation with machine learning. The typical surrogate model method that only replaces computation assumes that the surrogate model can accurately predict the characteristics. On the other hand, it is difficult to create a surrogate model with high accuracy for filter circuits composed of MSLs, and if an inaccurate surrogate model is used for optimal design, the optimal design will fail due to prediction errors. The purpose of this study is to improve (develop and accelerate the TO method) the optimal design of electromagnetic devices, especially microwave circuits and electrical devices. The following three problems are considered. 1.The effectiveness of topology optimization for microwave circuits is unknown because there are few examples of its application to microwave circuits. 2.The typical surrogate model method, which only replaces the electromagnetic computation, cannot be applied to the TO of MSL circuits because it is difficult to accurately predict the characteristics of microwave filter circuits. 3.Existing accurate hysloss analysis methods are time-consuming to perform, which is a problem when many hysloss analyses are required, such as for optimal design. In Chapter 3, to solve Problem 1, we design a filter circuit and a power divider circuit composed of MSLs using TO and verify the effectiveness of the TO method. Two TO methods, the Gaussian basis function method (NGnet method) and the geometry projection method, are considered in this study, and conventional design methods are also compared. The optimal design results show that the NGnet method can shorten the circuit length for filter circuit design better than the conventional method. The comparison of the two TO methods shows that the geometry projection method is more suitable for designing MSL circuits than the NGnet method for two reasons: it can utilize stub structures, and it is less likely to generate flat meshes. In Chapter 4, to solve Problem 2, we propose a method for predicting the accuracy of surrogate model and an optimal design method based on this method. The proposed method uses a convolutional neural network to predict the accuracy of the surrogate model, and switches between electromagnetic field analysis and surrogate model based on the prediction results to perform optimal design. The proposed method can avoid optimization failures (misleading) caused by surrogate model prediction errors and accelerate the optimization time by a factor of 9. The proposed method enables TO using surrogate model even for MSL filter circuits, for which it is difficult to create a highly accurate surrogate model. In Chapter 5, to solve Problem 3, we propose a method to perform hysloss analysis of electromagnetic devices with high accuracy and speed using machine learning. The proposed method applies a surrogate model to each section of the input waveform divided by its extreme values, taking advantage of the fact that hysteresis characteristics are characterized by the extreme values of the input waveform. As a result of applying the proposed method to the hysloss analysis of an inductor, the computational time was reduced by 2,000 times while obtaining the same analysis results as those obtained by conventional high-precision hysloss analysis methods. Furthermore, the optimal design of inductors that minimize hysloss using the proposed method resulted in a 2.5-fold reduction in the time required, including the time required to create surrogate models.

2025-03-25

甲第16516号

博士(情報科学)

(主査) 教授 五十嵐 一, 教授 野口 聡, 教授 北 裕幸

情報科学院（情報科学専攻）

博士論文

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