RESEARCH ARTICLE


Fast Method to Evaluate Payload Effect on In-Train Forces of Freight Trains



Gabriele Arcidiacono1, Rossella Berni2, Luciano Cantone3, *, Nedka D. Nikiforova2, Pierpaolo Placidoli1
1 Department of Innovation and Information Engineering, Marconi University, Via Plinio, 44, 00193 Rome, Italy
2 Department of Statistics, Computer Science and Applications, University of Florence, “G. Parenti”, Viale Morgagni, 59, 50134, Florence, Italy
3 Department of Engineering for Enterprise “Mario Lucertini”,University of Rome “Tor Vergata”, Via del Politecnico, 1, 00133 Rome, Italy


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Creative Commons License
© 2018 Arcidiacono et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Department of Engineering for Enterprise “Mario Lucertini”, University of Rome “Tor Vergata”, Via del Politecnico, 1, 00133 Rome, Italy; Tel: +39 06 7259 7133; E-mail:luciano.cantone@uniroma2.it


Abstract

Introduction:

This paper introduces a fast method to evaluate the effect of payload distribution on in-train forces.

Methods:

The method is based on Strong Orthogonal Arrays (SOA) and the excellent space-filling properties of Latin Hypercube Design (LHD): SOA-based-LHD is proved to be very efficient in spanning the range of in-train forces for different types of trains (also considering distributed power/braking) and trains operations.

Results:

The distribution of the percentage of braked mass is used to consider the effect of payload distribution on in-train forces. Because of its computational efficiency, the method proposed here can be satisfactorily employed to perform an optimization analysis of train composition.

Keywords: Freight trains, Payload distribution, Longitudinal train dynamics (LTD), Statistical approach, Strong orthogonal array (SOA), TrainDy.