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Performance evaluation and design optimization using differential evolutionary algorithm of the pantograph for the high-speed train
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  • Performance evaluation and design optimization using differential evolutionary algorithm of the pantograph for the high-speed train
  • Performance evaluation and design optimization using differential evolutionary algorithm of the pantograph for the high-speed train
저자명
Lee. Jin-Hee,Kim. Young-Guk,Paik. Jin-Sung,Park. Tae-Won
간행물명
Journal of mechanical science and technology
권/호정보
2012년|26권 10호|pp.3253-3260 (8 pages)
발행정보
대한기계학회
파일정보
정기간행물|ENG|
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이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
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기타언어초록

The global trend in the railway industry is the effort to increase the maximum speed and stability of a train. For an electric railway vehicle to meet this driving performance, stable electric power should be supplied by a catenary system. Various factors affect the current collection performance, most important of which is the dynamic characteristics of a pantograph. In this paper, the sensitivity analysis and design optimization of a pantograph for a high-speed train were conducted using a finite element method. The dynamic catenary-pantograph interaction was analyzed by using the commercial finite element analysis software, SAMCEF. The pantograph was modeled as a three degrees of freedom mass-spring-damper system, and the pre-sag of the contact and messenger wire due to gravity was implemented. The span data of a high-speed line was applied in the analysis model. And the dynamic characteristics of the pantograph model were obtained by a performance test. The reliability of the simulation model was verified by comparing the analysis contact force results with the test data. By simulation, the mean contact force and its standard deviation etc. were evaluated, and then sensitivity of the pantograph was analyzed. Based on the sensitivity analysis results, the specification of the pantograph was optimized. In the optimization process, response surface analysis and differential evolutionary algorithm were applied to define the regressive function and to determine the optimum values for stable current collection performance. Finally, the improvement of the current collection performance was verified by comparing the optimum specification results with the original specification.