top of page

DOCTORAL CANDIDATE 10

PROJECT TITLE

Digital Twins for the SHM of Smart CFRP Aircraft Sandwich Structures

Host Institution:

KU Leuven (KUL)

PhD enrolment:

KU Leuven (KUL)

Orange White Modern Gradient  IOS Icon (5).png

IOANNIS NIKOLAOS KOMIS

EDUCATION

MEng in Mechanical Engineering - Aristotle University of Thessaloniki, Greece
PhD in Mechanical Engineering - KU Leuven, Belgium

ABOUT

Ioannis Nikolaos (Giannikos) Komis is a PhD candidate at KU Leuven. He graduated with a diploma in Mechanical Engineering from the Aristotle University of Thessaloniki (AUTH), where he specialised in structural and system dynamics. During his master’s thesis, he worked on the design and optimisation of floater substructures for semi-submersible offshore wind turbines. In parallel with his studies, he participated in two CubeSat projects, contributing as a mechanical and structural analysis engineer.

His doctoral research at KU Leuven focuses on the development of digital twins for ultrasound-based structural health monitoring (SHM) of aeronautical composite sandwich structures. These digital twins aim to accurately replicate the behaviour of their physical counterparts, enabling cost-effective numerical experiments under realistic operational and environmental conditions. By integrating digital twin simulations, real time measurements, and machine learning algorithms, this approach supports improved damage detection, more reliable condition assessment, and optimized maintenance strategies. Ultimately, the proposed methodology has the potential to reduce maintenance costs, limit over-dimensioning of critical components, and contribute to lighter and more sustainable aircraft designs, leading to lower fuel consumption and reduced environmental impact.

Outside of research, he enjoys exercise, cooking and playing boardgames which help him maintain a healthy balance between work and personal life.

Objectives

- Build a numerical twin to simulate structural responses​.
- Combine simulation and experimental data into a data library​.
- Apply model updating and Bayesian networks for improved predictions​.

02

Expected results:

- A validated DT model for damage detection and tracking.​
- Reliable Remaining Useful Life (RUL) predictions​.
- Support safer, more sustainable aircraft structures​.

03

bottom of page