ENERCOMP Seminar Series
From Sensing to Safety: A Digital Twin Framework for Smart Air Vehicles
Thursday, May 22nd, 2025
ABSTRACT
Structural Health Monitoring (SHM) is emerging as a cornerstone of next-generation smart structures, enabling the shift from schedule-based maintenance to condition-based strategies. By continuously assessing structural integrity, SHM enhances operational safety, reduces downtime, and extends asset life. In air vehicles specifically, SHM has the potential to deliver safer, lighter, and greener aircraft through on-demand insights into structural health, unlocking the full potential of advanced composites and supporting the transition to sustainable aviation.
This talk presents a holistic health assessment framework for composite aircraft panels, designed to manage the full lifecycle of structural integrity data; from sensor measurements to reliability-informed maintenance planning. Specifically, guided wave-based sensing is used to detect, localize, and quantify damage (diagnosis) and if damage is identified, its capability to reliably fulfil upcoming missions is quantified (prognosis). A comprehensive building-block (both for the numerical and experimental campaigns) underpins the framework, enabling systematic uncertainty quantification and propagation across scales. To ensure practical applicability, computationally intensive tasks are shifted to an offline phase, where probabilistic surrogate models are trained to replicate high-fidelity simulations. These surrogates enable rapid predictions during aircraft operation, transforming raw sensor data into actionable reliability assessments.
By closing the loop from sensing to decision-making, the proposed framework valorizes data-rich environments and positions SHM as a critical enabler of intelligent, evidence-based maintenance in the aviation sector.
About the speaker
Dr. Ilias Giannakeas is a structural engineer and researcher with expertise in numerical modelling, reliability analysis, and the integration of machine learning with physics-based approaches for predictive analytics and digital twins. He holds a Diploma in Civil Engineering from the National Technical University of Athens, where he specialised in structural engineering. He went on to complete an MSc in Structural Integrity at the Department of Mechanical and Aerospace Engineering, Brunel University London, followed by a PhD in Numerical Modelling and Fracture Mechanics at Brunel’s Department of Civil Engineering.
Dr Giannakeas is currently a Research Associate at Imperial College London’s Department of Aeronautics, contributing to EU-funded projects such as AVATAR, where he develops advanced methods for structural health monitoring, damage modelling, and real-time reliability assessment in aerospace applications. His work has also spanned across multiple sectors including marine, additive manufacturing and waste-water treatment plants where he applied machine learning models to drive process optimization.
He has authored 12 peer-reviewed journal articles, contributed to a book chapter, and presented at several international conferences. His research has received over 280 citations (h-index: 10), and his work has been recognised through awards and fellowships, including a Marie Skłodowska-Curie Fellowship for intelligent decision-making in the shipping industry.