Researchers: Elahe Talebiahooie, Uday Kumar
Derailment is one of the potential risks in railway transportation, which is rare but its social, economic and environmental consequences is catastrophic. Practically, intervention levels and track quality indices are used to determine time for maintenance actions, which intended to control derailment risk. To efficiently control susceptibility of track sections to derailment, maintenance and inspection schedules should be optimized.
The aim of this project is to propose a practical framework to predict derailment likelihood based on mechanical simulation and track geometry historical data. The simulation will be performed in real time. Wheel flange climb and gauge widening and rail rollover are the two causes that will be assessed in this project.
Movable cellular automata (MCA) is the selected approach for the mechanical simulation of ballast, rail, sleepers and fasteners. The code of MCA will be developed in GPU to have a faster and more efficient simulation. The MCA is a method in computational solid mechanics based on the discrete concept. It provides advantages both of classical cellular automaton and discrete element methods.
1 km of railway will be simulated. With considering the ballast size as 38 mm and 1000×2×0.5 m as the dimension of the substructure, then 37×〖10〗^6 particles in the ballast and around 40×〖10〗^6 in total (also considering superstructure elements like rail, sleeper, fastening system, and weld) will be simulated in 1 km of railway. After performing the simulation, the transition rules in the MCA will be calibrated based on the historical data of track geometry