Use of a genetic algorithm to optimize wheel profile geometry

Abstract

Wear is a very important subject for railway administrations. Therefore, it would be of interest to develop a methodology for designing wheel profile geometry in order to improve its behaviour in relation to this subject.Until now, existing approaches of this kind are based on the statistical study of wear wheel profiles, but this do not consider the characteristics of vehicles and railway tracks, and in addition, it is not an option when a vehicle is going to run, for example, over two different types of track over which mixed running has not previously happened.In the current paper, a new general methodology is presented for improving wheel profiles in relation to certain physical phenomena that arise during the running of the vehicle over the tracks. This methodology is based on the genetic algorithm (GA) technique. To show the power of this procedure, it is applied to an application case (of a tram-train in Spain), in which great improvements in the behaviour of wheel profile are achieved. In the light of this application, the importance of definition of indexes which control the evolution of the GA is shown.

Design of an optimised wheel profile for rail vehicles operating on two-track gauges

“…It should be pointed out that basing optimisation on this type of contact curve has a much lower computational cost than selecting optimisations based on dynamic simulations. Where dynamic simulations are included, calculation time can take weeks [8], whereas in the cases set out in this paper it is reduced to a matter of hours. The counterpart naturally lies in the fact that the use of contact curves contemplates global optimisation of dynamic behaviour, whereas with dynamic simulations it is possible to specify or elaborate on the reduction of specific parameters (risk of derailment, contact stresses) on a given curve radius or a representative routing.…”

“…Normally these variables are allocated to a number of points on the wheel profile plane that are interpolated subsequently to generate the complete profile. For example, it is possible to select n points on the y axis and allocate a variable z coordinate to each, and these coordinates constitute the vector of design variables [6][7][8][9][10]13]. The profile is unequivocally defined by performing interpolation of the points, with spline curves, for example.…”

“…Conventional optimisation methods alone may not produce satisfactory results, and frequently lead to relative minima that may be far from the optimum solution. Genetic Algorithms (GAs) can help to avoid local minima being obtained, which barely improve the initial behaviour of the wheel, and constitute a particularly useful method for this type of problem [6][7][8].…”

“…Persson subsequently used a similar methodology to study the potential optimisation of a rail profile on a suburban train [7]. In 2007 Novales et al [8] used similar methodology and software to optimise a rail profile for two different types of track: railway and light rail tracks. The objective function considered three ride quality parameters, evaluated through dynamic simulations: wear index, risk of derailment and contact stresses.…”

An Innovative and Efficient Method for Reverse Design of Wheel-Rail Profiles

“…Wu [8] designed subway wheel profiles based on the rail profile expansion method that are helpful in improving the compatibility of wheel-rail profiles, reducing the contact stress and finally reducing wheel-rail wear. Persson and Iwnicki [9] and Novales et al [10] sought wheel profiles matching with the vehicle suspension parameters directly through dynamic simulation and the genetic algorithm and comprehensively analyzed the constraint conditions of different wheel-rail characteristics such as comfort, lateral track force, derailment coefficient, wear and contact stress. However, this method is very time-consuming.…”