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Parallel computing enables whole-trip train dynamics optimizations

journal contribution
posted on 06.12.2017, 00:00 by Qing WuQing Wu, Colin ColeColin Cole, Maksym SpiryaginMaksym Spiryagin
Due to the high computing demand of whole-trip train dynamicssimulations and the iterative nature of optimizations, whole-triptrain dynamics optimizations using sequential computing schemesare practically impossible. This paper reports advancements inwhole-trip train dynamics optimizations enabled by using the parallelcomputing technique. A parallel computing scheme forwhole-trip train dynamics optimizations is presented and discussed.Two case studies using parallel multiobjective particleswarm optimization (pMOPSO) and parallel multiobjectivegenetic algorithm (pMOGA), respectively, were performed to optimizea friction draft gear design. Linear speed-up was achievedby using parallel computing to cut down the computing time from18 months to just 11 days. Optimized results using pMOPSO andpMOGA were in agreement with each other; Pareto fronts wereidentified to provide technical evidence for railway manufacturersand operators.

History

Volume

11

Issue

4

Start Page

044503-1

End Page

044503-4

Number of Pages

4

eISSN

1555-1423

ISSN

1555-1415

Location

United States

Publisher

The American Society of Mechanical Engineers

Language

en-aus

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

School of Engineering and Technology (2013- ); TBA Research Institute;

Author Research Institute

Centre for Railway Engineering

Era Eligible

Yes

Journal

Journal of computational and nonlinear dynamics.

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