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Parallel computing enables whole-trip train dynamics optimizations
journal contribution
posted on 2017-12-06, 00:00 authored by Qing WuQing Wu, Colin ColeColin Cole, Maksym SpiryaginMaksym SpiryaginDue 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
11Issue
4Start Page
044503-1End Page
044503-4Number of Pages
4eISSN
1555-1423ISSN
1555-1415Location
United StatesPublisher
The American Society of Mechanical EngineersPublisher DOI
Full Text URL
Language
en-ausPeer 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