Discovery Early Career Researcher Award - Grant ID: DE210100273
Funder
Australian Research Council
Funding Amount
$407,679.00
Summary
Supercomputing to understand track buckling and related train derailments. This project aims to understand the contributions of railway train forces to a dangerous and high-cost track dynamic behaviour called buckling; by developing a supercomputing method that unlocks the capability for large-scale 3D train-track interaction research for railway trains of up to 250 vehicles. This project expects to generate new knowledge regarding track buckling, train derailments and train-track dynamics. Expe ....Supercomputing to understand track buckling and related train derailments. This project aims to understand the contributions of railway train forces to a dangerous and high-cost track dynamic behaviour called buckling; by developing a supercomputing method that unlocks the capability for large-scale 3D train-track interaction research for railway trains of up to 250 vehicles. This project expects to generate new knowledge regarding track buckling, train derailments and train-track dynamics. Expected outcomes include a new supercomputing method for train-track dynamics and derailment research and a science-based technique to assess track buckling safety. This project should provide significant benefits to the rail industry including enhanced rail safety, lower maintenance costs and improved transport efficiency.Read moreRead less
Condition-Based Maintenance Optimisation for Queensland’s Railways. Rail maintainers currently use time-based (scheduled) approaches to balance the costs and benefits of inspections and maintenance. Changing to condition-based maintenance has the potential to reduce costs and improve track condition. This project aims to enable this approach for rail by developing: 1) new track degradation prediction techniques combining Big Data and engineering knowledge; 2) new on-board sensing capabilities fo ....Condition-Based Maintenance Optimisation for Queensland’s Railways. Rail maintainers currently use time-based (scheduled) approaches to balance the costs and benefits of inspections and maintenance. Changing to condition-based maintenance has the potential to reduce costs and improve track condition. This project aims to enable this approach for rail by developing: 1) new track degradation prediction techniques combining Big Data and engineering knowledge; 2) new on-board sensing capabilities for frequent, low-cost track monitoring; 3) novel inspection and maintenance optimisation methods to efficiently allocate resources. The knowledge generated by this project is expected to decrease maintenance costs, safety risk, and track closures and therefore enhance the affordability and reliability of rail travel.Read moreRead less