I lead the 'Digital Twin World' flagship postdoctoral research programme at the Department of Mechanical Engineering (DME). My current research focus is on condition monitoring of mechanical systems, such as wind turbines and ship propulsion systems. I develop and employ bond graph-based component-level digital twins to assist in predictive maintenance, fatigue life prognosis, and dynamic reliability assessment.
In August 2023, I completed my Ph.D. in Mechanical Engineering, specializing in simulation-based design and condition monitoring of mechanical systems. Currently, I am further enhancing my Ph.D. research findings with an interest in real-world industrial applications.
I graduated in 2009 with a degree in Materials Science and Engineering from University of Moratuwa (UoM), Sri Lanka. After gaining experience in the manufacturing industry, I transitioned to academia as a lecturer. My teaching areas include mechanics of materials, fluid mechanics, similitude modeling and experimental data analysis, and mechanical design.
In 2016, I completed my first master's degree in Sustainable Process Engineering from UoM. I subsequently earned my second master’s degree in Process Engineering from Memorial University of Newfoundland, Canada, where I applied machine learning for process fault detection, prediction, and diagnosis.
Using all my previous experience and qualifications, my goal is to synergize the strengths of the DME's four research groups (Materials to Products, Mechatronics, Marine and Arctic Technology, and Energy Conversion and Systems) under the theme of digital twins. By doing so, I aim to generate new knowledge that will contribute significantly to the advancement of circular economy, fostering sustainability and innovation.
