Dr Evangelos Papatheou
Telephone: 01392 724655
Extension: (Streatham) 4655
Evangelos Papatheou is a Lecturer in the College of Engineering Mathematics and Physical Sciences of the University of Exeter. He has graduated from the Department of Mechanical Engineering & Aeronautics of the University of Patras (Greece). He then completed a PhD in the Department of Mechanical Engineering of the University of Sheffield on the subject of vibration-based Structural Health Monitoring (SHM) with a pattern recognition approach.
He continued working as a post-doctoral researcher in the Dynamics Research Group (University of Sheffield) and the Centre for Engineering Dynamics (University of Liverpool), where he worked in various projects which included among others, the design, development and testing of a smart energy harvesting suspended-load backpack, active control of structures with applications in aeroelasticity, and nonlinear dynamics. Before he moved in Exeter he was working part-time in two EPSRC projects 'Disease surveillance for structures and systems' involving population-based SHM, and 'Engineering Nonlinearity' involving nonlinearity in structural dynamics.
Previous applications of SHM involved aerospace structures (full-scale aircaft wings) and also wind turbines.
Evangelos is currently very interested in finding solutions to what he considers as the main challenges of the pattern recognition approach to SHM: the acquisition/creation of reliable databases from damaged structures (under different damaged states), and the study/mitigation of the effect/influence of environmental conditions on the SHM methodologies.
Currently there is a funded PhD position available, please get in touch for more information.
Structural Health Monitoring (SHM), specifically vibration-based SHM with a pattern recognition approach.
Active control, Hardware in-the-loop (HIL) simulators, real-time hybrid testing of structures.
Vibration testing and Modal analysis.
Combination of model-based approaches (Finite Element Analysis) with pattern recognition and machine learning.
Smart systems (with applications to structures and also humans).