Tools and methods for addressing the durability of rolling stock

Railway rollingstock is subjected to an arduous regime of load and vibration. This implies a likelihood of encountering fatigue cracking within rollingstock structures and components at some stage in the asset life cycle. Whilst there is an abundance of tools aimed at preventing the occurrence of fatigue cracking at the design stage–typically adopting the so called ‘safe life’ design philosophy–tools to assess the growth and tolerance of cracks once initiated are less routinely applied in the rail industry than in other industries such as aerospace and power generation. Fatigue life prediction of rollingstock components is exceptionally difficult and computationally intensive as calculations need to be made at each stage of the life of a component/structure. This is done to compute the stress intensity factors for each crack configuration so as to calculate the amount of crack growth, update the crack geometry, and then re-compute the stress intensity factors for this new geometry. To meet this challenge, this paper will discuss the issues associated with fatigue crack growth within rollingstock and provide an overview of the tools available to assess the defect tolerance of rollingstock structures and manage the key considerations to safely ensure the integrity of assets, whilst maintaining asset availability/productivity. tudy consists of the following areas of analysis. In the first stage, a 3D finite element model to evaluate the stress of rollingstock structure (with no cracking) is performed. The second stage of the (sequential) analysis is carried out for stress intensity factor of cracks in the rollingstock under service condition by using a semi-analytical solution technique that involves the use of an analytical solution combined with a numerical algorithm to assess fracture strength. In the third stage, the Hartman–Schijve approach is used to modelling crack growth. As the crack is not modelled explicitly a coarser mesh can be used thereby improving analysis time. This method is ideal for use on fatigue life prediction of rollingstock structures.