Technical Brief

Parallel Computing Enables Whole-Trip Train Dynamics Optimizations

[+] Author and Article Information
Qing Wu

Centre for Railway Engineering,
Central Queensland University,
Rockhampton, Queensland QLD4701, Australia
e-mail: q.wu@cqu.edu.au

Colin Cole

Centre for Railway Engineering,
Central Queensland University,
Rockhampton, Queensland QLD4701, Australia
e-mail: c.cole@cqu.edu.au

Maksym Spiryagin

Centre for Railway Engineering,
Central Queensland University,
Rockhampton, Queensland QLD4701, Australia
e-mail: m.spiryagin@cqu.edu.au

1Corresponding author.

Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF COMPUTATIONAL AND NONLINEAR DYNAMICS. Manuscript received May 26, 2015; final manuscript received November 22, 2015; published online December 16, 2015. Assoc. Editor: Dan Negrut.

J. Comput. Nonlinear Dynam 11(4), 044503 (Dec 16, 2015) (4 pages) Paper No: CND-15-1142; doi: 10.1115/1.4032075 History: Received May 26, 2015; Revised November 22, 2015

Due to the high computing demand of whole-trip train dynamics simulations and the iterative nature of optimizations, whole-trip train dynamics optimizations using sequential computing schemes are practically impossible. This paper reports advancements in whole-trip train dynamics optimizations enabled by using the parallel computing technique. A parallel computing scheme for whole-trip train dynamics optimizations is presented and discussed. Two case studies using parallel multiobjective particle swarm optimization (pMOPSO) and parallel multiobjective genetic algorithm (pMOGA), respectively, were performed to optimize a friction draft gear design. Linear speed-up was achieved by using parallel computing to cut down the computing time from 18 months to just 11 days. Optimized results using pMOPSO and pMOGA were in agreement with each other; Pareto fronts were identified to provide technical evidence for railway manufacturers and operators.

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Grahic Jump Location
Fig. 4

Pareto fronts of multiobjective optimization: (a) acceleration versus fatigue damage–pMOGA, (b) fatigue damage versus coupler force–pMOGA, (c) acceleration versus fatigue damage–pMOPSO, and (d) fatigue damage versus coupler force–pMOPSO

Grahic Jump Location
Fig. 2

Computing scheme for whole-trip train dynamics optimizations

Grahic Jump Location
Fig. 1

Parallelism of population-based optimization: (a) parallelism of computation and (b) parallelism of population




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