Post-processing Topology Optimization of an Electromagnetic Actuator Using a Hybrid Method of Genetic Algorithm and Local Importance Measure (LIM)

被引:0
|
作者
Ruzbehi, Shabnam [1 ]
Hahn, Ingo [1 ]
机构
[1] Univ Erlangen Nurnberg, Inst Elect Drives & Machines, Erlangen, Germany
关键词
topology optimization; genetic algorithms; metaheuristic algorithm; hybrid algorithm; local optimization; global optimization;
D O I
10.1109/icpes47639.2019.9105417
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
In the modern design and to improve the operation characteristics of electrical machines and electromagnetic devices an optimal structure of the machine's active and flux guiding parts is required. In the conventional optimization geometric or parameter optimization is common. In these methods, just the boundaries of the shape can be changed. In this study topology optimization as a powerful tool is used. It gives more freedom to the designer to gain higher performance and to approach more close to the consumer goals. In this work, a combinatorial optimization method based on a meta-heuristic algorithm combined with a local method based on a Local Importance Measure is offered to improve the results. The aim of this research is to seek for material distribution inside the electromagnetic actuator to have a lighter weight and the maximum possible force or torque. It is possible to extend this method to the different active parts of electrical actuators and machines satisfying different goals.
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