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- 저자 : Junguo Cui (검색결과 2 건)
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Cui Junguo,Mei Lianpeng,Xiao Wensheng,Liu Zhanpeng 대한전기학회 2024 Journal of Electrical Engineering & Technology Vol.19 No.3
Three methods i.e. Response surface, Taguchi, and improved Taguchi are used for multi-objective design optimization of the drilling permanent magnet synchronous motor (DPMSM) in this study. The optimization objectives are determined according to the performance requirements of the DPMSM aiming to maximize efciency and minimize the torque ripple coefcient. 4 Of 8 electromagnetic parameters were screened as signifcant factors by factorial experiment. The response surface approach uses data from fnite element analysis to ft a polynomial model between objectives and factors. The particle swarm optimization with constrict factor (PSOCF) algorithm is used for determining the Pareto frontier. In the Taguchi method, the efects of the noise factors on the optimization objective are considered. The combinations of signifcant factor levels for diferent objectives obtained from orthogonal experiments were fused into an optimal combination based on the infuence proportion of factors. In the improved Taguchi method, the optimization objectives are fused into multiple performance characteristic indexes (MPCI) based on fuzzy inference. The MPCI is optimized by the Taguchi method. Then the sequential experiments are used to increase the range and efectiveness of the optimization. The characteristics and the optimization results of the three methods were compared and the improved Taguchi method was verifed based on a test of the prototype motor.
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Research on facility layout optimization algorithm of deep-water semi-submersible drilling platform
Hongyan Wang,Wensheng Xiao,Lei Wu,Keke Wei,Congcong Xu,Chao Hou,Junguo Cui,Jie Li 대한기계학회 2019 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.33 No.2
We aimed to create a facility layout design of semi-submersible drilling platform (DSDP) with performance constraints. The Boltzmann survival mechanism of simulated annealing algorithm was introduced into the replacement strategy of genetic algorithm to form an improved genetic algorithm called genetic and simulated annealing algorithm (GASA). The new algorithm alleviates the “combination explosion” and premature convergence of the traditional genetic algorithm. The layout problem of DSDP is efficiently solved by using this new algorithm. When the number of layout objects increases, GASA’s performance is better than that of the genetic algorithm.
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