4.3 Article

Performance evaluation and design optimization using differential evolutionary algorithm of the pantograph for the high-speed train

Journal

JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY
Volume 26, Issue 10, Pages 3253-3260

Publisher

KOREAN SOC MECHANICAL ENGINEERS
DOI: 10.1007/s12206-012-0833-5

Keywords

Catenary; Design of experiments; Differential evolutionary algorithm; High-speed train; Pantograph

Funding

  1. National Research Foundation of Korea [핵C6A1105] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

Ask authors/readers for more resources

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.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.3
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available