A numerical procedure for the wheel profile optimisation on railway vehicles

Abstract

The reduction of wear due to wheel–rail interaction is a fundamental aspect in the railway field, mainly correlated to safety, maintenance interventions and costs. In this work, the authors present an innovative wheel profile optimisation procedure, specifically designed with the aim of improving the wear and stability behaviour of the standard ORE S1002 wheel profile matched with the UIC60 rail profile canted at 1/20 rad, which represents the wheel–rail combination adopted in Italian railway line; this matching shows poor wear performance due to the non-conformal contact. A new wheel profile, conventionally named DR2, has been developed by the authors in collaboration with Trenitalia S.p.A. The DR2 wheel profile is designed to guarantee the same kinematic characteristics of the matching formed by ORE S1002 wheel profile and UIC60 rail profile with inclination angle [Formula: see text] equal to 1/40 rad, widely common in European railways and characterised by good performances in both wear and kinematic behaviour. The evolution of wheel profiles due to wear has been evaluated through a wear model developed and validated by the authors in previous works. In the present research the investigated trainset is the passenger vehicle ALSTOM ALn 501 “Minuetto”, which is usually equipped with wheelsets having the standard ORE S1002 wheel profile in Italian railways. The entire model has been simulated on a virtual track specifically developed to represent a statistical description of the whole Italian line; the innovative statistical approach has been employed to obtain accurate results in reasonable computational times. The data necessary to build the virtual track and the vehicle model were provided by Trenitalia S.p.A. and Rete Ferroviaria Italiana (RFI).

A fast nonlinear conjugate gradient based method for 3D concentrated frictional contact problems

“…The frictional contact problem has attracted interest from many researchers, due to various applications in the industry and engineering fields, e.g. rolling contact fatigue (RCF) [10], the fatigue life of machine elements [31], friction and wear [7,29,13]. This problem concerns two elastic bodies.…”

Rail wear and remaining life prediction using meta-models

“…This is due to the fact that the wear rate of wheels is higher. Hence, extensive descriptions on the wheel wear prediction procedure can be found in literature [1][2][3][4][5]. Enblom and Berg [6] proposed a procedure to predict rail wear similar to that of wheel wear.…”

Optimization of LMBP high-speed railway wheel size prediction algorithm based on improved adaptive differential evolution algorithm

“…Then, the numerical simulation analysis of the train wheel wear profile information is carried out. [4][5][6][7] The other is to analyze and predict the historical wear profile information obtained from the statistics and calculate the remaining service life of the wheel-set to propose a maintenance strategy, such as the time series model, 8 support vector machine, 9 gray prediction algorithm, 10 and Bayesian algorithm. 11 With the advent of the era of data explosion, big data technology and numerical analysis methods have gradually been applied to various device security predictions.…”