Estimation of an Underground Cavity by the Particle Swarm Optimization of Equivalent Circuit Model for Multilayer Structures

2021 ◽  
Vol 141 (12) ◽  
pp. 1371-1379
Author(s):  
Koki Nakamura ◽  
Bojun Zheng ◽  
Tsugumichi Shibata
Micromachines ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 715
Author(s):  
Dongdong Chen ◽  
Jianxin Zhao ◽  
Chunlong Fei ◽  
Di Li ◽  
Yuanbo Zhu ◽  
...  

In order to improve the fabrication efficiency and performance of an ultrasonic transducer (UT), a particle swarm optimization (PSO) algorithm-based design method was established and combined with an electrically equivalent circuit model. The relationship between the design and performance parameters of the UT is described by an electrically equivalent circuit model. Optimality criteria were established according to the desired performance; then, the design parameters were iteratively optimized using a PSO algorithm. The Pb(ZrxTi1−x)O3 (PZT) ceramic UT was designed by the proposed method to verify its effectiveness. A center frequency of 6 MHz and a bandwidth of −6 dB (70%) were the desired performance characteristics. The optimized thicknesses of the piezoelectric and matching layers were 255 μm and 102 μm. The experimental results agree with those determined by the equivalent circuit model, and the center frequency and −6 dB bandwidth of the fabricated UT were 6.3 MHz and 68.25%, respectively, which verifies the effectiveness of the developed optimization design method.


Author(s):  
Arezoo Modiri ◽  
Kamran Kiasaleh

This chapter is intended to describe the vast intrinsic potential of the swarm-intelligence-based algorithms in solving complicated electromagnetic problems. This task is accomplished through addressing the design and analysis challenges of some key real-world problems, ranging from the design of wearable radiators to tumor detection tools. Some of these problems have already been tackled by solution techniques other than particle swarm optimization (PSO) algorithm, the results of which can be found in the literature. However, due to the relatively high level of complexity and randomness inherent to these problems, one has to resort to oversimplification in order to arrive at reasonable solutions utilizing analytical techniques. In this chapter, the authors discuss some recent studies that utilize PSO algorithm particularly in two emerging areas; namely, efficient design of reconfigurable radiators and permittivity estimation of multilayer structures. These problems, although unique, represent a broader range of problems in practice which employ microwave techniques for antenna design and microwave imaging.


2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Hamid Reza Mohammadi ◽  
Ali Akhavan

A cost effective off-line method for equivalent circuit parameter estimation of an induction motor using hybrid of genetic algorithm and particle swarm optimization (HGAPSO) is proposed. The HGAPSO inherits the advantages of both genetic algorithm (GA) and particle swarm optimization (PSO). The parameter estimation methodology describes a method for estimating the steady-state equivalent circuit parameters from the motor performance characteristics, which is normally available from the nameplate data or experimental tests. In this paper, the problem formulation uses the starting torque, the full load torque, the maximum torque, and the full load power factor which are normally available from the manufacturer data. The proposed method is used to estimate the stator and rotor resistances, the stator and rotor leakage reactances, and the magnetizing reactance in the steady-state equivalent circuit. The optimization problem is formulated to minimize an objective function containing the error between the estimated and the manufacturer data. The validity of the proposed method is demonstrated for a preset model of induction motor in MATLAB/Simulink. Also, the performance evaluation of the proposed method is carried out by comparison between the results of the HGAPSO, GA, and PSO.


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