It is well known that one of the most significant causes of train derailments within the U.S. is due to rail fracture. Despite this fact, a reliable model for predicting fatigue fracture in rails has not yet been deployed within the U.S. We have recently been developing an advanced computational algorithm for predicting crack evolution in ductile solids subjected to long-term cyclic loading. In this part of the UTCRS, we will continue to adapt this model to the prediction of crack growth in rails. Concomitantly, with funding provided by MxV Rail, we have recently completed a decade-long series of experiments designed to provide data usable for the purpose of developing just such a model. We therefore possess the ability to both predict crack growth due to cyclic fatigue in rails, as well as to utilize our previously obtained experimental results to validate our predictive methodology. We have therefore begun the following rather challenging task of: 1) modifying the computational model for predicting crack growth for application to cyclic fatigue in rails; 2) developing an experimental protocol for obtaining the material properties required to deploy our computational fracture model (described in our companion proposal entitled Experimental Determination of Crack Growth in Rails Subjected to Long-Term Cyclic Fatigue Loading); 3) demonstrate the effectiveness of our model for predicting the effects of long-term cyclic loading on rail fracture; and 4) develop a procedure based on our model for railway engineers to utilize to determine when rails should be inspected and potentially removed from service for cause, thereby increasing rail safety. This project will be carried out with direct interaction and supervision by MxV Rail.
Our model will be compared to our previously obtained experimental results, and if our model is successful, it will provide a more accurate means of determining not only when track cracking requires the replacement of track sections, but also if/when damaged sections of track can be safely continued in service, thereby providing a technologically based accurate tool for assessing track worthiness in the field. The two-way coupled multiscale computational algorithm under development herein is based on the prior works of Allen and co-workers [Souza et al., 2008, Souza et al., 2009]. Based on our initial results, we are optimistic that this relatively new and unique approach to modeling rail head fracture due to long-term cyclic fatigue will produce a tool capable of dramatically improving our ability to assess track worthiness of rails with internal imperfections.
