Research

Science and technology development for a resilient renewable energy sector.

Research programme

Met2Adapt pioneers a disruptive and highly ambitious methodological framework that dynamically links advanced disciplines. By seamlessly cross-pollinating high-fidelity computational physics with state-of-the-art Artificial Intelligence (AI) and machine learning, we can rapidly model and optimize complex material behaviors. This theoretical powerhouse is firmly grounded in reality through rigorous, real-world empirical testing and continuous Structural Health Monitoring (SHM).

By streaming live data from integrated smart sensors into advanced Digital Twins—highly accurate, real-time virtual replicas of our physical infrastructure—we create a complete cyber-physical loop. This allows us to predict wear-and-tear, simulate extreme environmental stressors, and optimize performance long before an actual component degrades. Ultimately, this multidisciplinary nexus allows us to deploy highly tailored, bespoke solutions directly into the harsh, demanding environments that sustain Europe’s energy grid. By fortifying both onshore and offshore wind farms, and unlocking the immense potential of oceanic wave-energy converters, Met2Adapt is actively future-proofing the infrastructure that will define our renewable energy resilience.

Research work packages

Research

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WP1: Design

How can we engineer novel meta-material architectures with optimum vibration mitigation capabilities tailored to the specific mechanical and environmental stimuli pertinent to wind and wave energy production?

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WP2: Predict

Can we derive damage indices from fast yet reliable numerical simulation fusing physics and AI?

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WP3: Protect

Can we design meta-structures that are self-tuned, resilient, and adaptable to adverse environmental conditions?

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WP4: Sense

How can we enable a truly man-ageable and resilient green-energy production ecosystem via intelligent SHM and prognostics?

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WP1: Design

Lead: University of Trento

In WP1 partners join forces to deliver novel meta-material architectures that are truly reliable and durable for mitigating vibrations and extending the operational life of wind and wave energy critical components.

DC3 Multiscale Modelling and Machine Learning-Based Design of Innovative Vibration-Attenuating Metapanels

Supervisor: Prof. I. Kalogeris, Prof. I. Antoniadis, Prof. D. Chronopoulos · Host: National Technical University of Athens

DC4 Optimal devices, methodologies and digital twin models for the use of metastructural devices in Operational Wind Turbines

Supervisor: Prof. F. Magalhães, Mr. A. Graca, Prof. O. Bursi · Host: University of Porto

DC6 Self-tuning variable length systems for the vibration reduction of wind blades

Supervisor: Prof. F. Dal Corso, Prof. R. Springhetti, Prof. F. Magalhães, Mr. A. Graça · Host: University of Trento

DC12 Developing elasto-acoustic metamaterials for noise and vibration attenuation in wind turbine blades

Supervisor: Prof. D. Chronopoulos, Prof. E. Deckers, Prof. I. Kalogeris · Host: KU Leuven

DC14 Uncertainty Quantification and Technology Qualification for Advanced Wind Turbine Components

Supervisor: Prof. A. Kolios, Prof. Y. Guo, Prof. J. Chiachio · Host: DTU

WP2: Predict

Lead: National Technical University of Athens

We develop a new multi-level, multi-physics predictive modelling framework that provides rapid yet reliable estimates on the mechanical and acoustic performance of meta-materials and meta-structures for renewable energy production.

DC1 Resolving failure modes in complex composite components

Supervisor: Prof. S. Triantafyllou, Prof. I. Kalogeris, Prof. F. Magalhães · Host: National Technical University of Athens

DC2 Coupled chemo-mechanical interactions leading to fatigue damage in corrosive environments

Supervisor: Prof. S. Triantafyllou, Prof. E. Sapountzakis, Prof. A. Palermo · Host: National Technical University of Athens

DC9 Modelling of the dynamic response for offshore wind farms

Supervisor: Prof. D. Colquitt, Prof. A. Movchan, Prof. A. Palermo · Host: University of Liverpool

DC10 Development of deep learning-based surrogate models for predicting failure in meta-material composites

Supervisor: Prof. E. Kakouris, Prof. P. Brommer, Prof. S. Triantafyllou · Host: University of Warwick

WP3: Protect

Lead: University of Bologna

We design, build, and experimentally validate novel meta-structures for the protection of off-shore renewable energy production assemblies against adverse environmental conditions.

WP1 Discovery of new signalling elements

DC5 Nutrient sensor discovery

Supervisor: Prof. O. Bursi, Prof. M. F. Pantano, Dr. V. Dertimanis · Host: University of Trento

DC7 Optimal displacement of meta-structures for enhanced renewable energy harvesting

Supervisor: Prof. R. Archetti, Prof. A. Marzani, Prof. D. Colquitt · Host: University of Bologna

DC8 On the combined use of wave energy converters and meta-structures for fatigue-life extension of existing offshore structures

Supervisor: Prof. A. Palermo, Prof. R. Archetti, Prof. D. Colquitt · Host: University of Bologna

WP4: Sense

Lead: ETHZ

We harness the exotic properties of meta-materials into developing and establishing an innovative, and robust structural health monitoring and prognostics platform that is low-cost, fully autonomous, reliable and accurate.

WP1 Discovery of new signalling elements

DC11 GenAI-aided reliability assessment of mechanical metamaterials in offshore wind and wave energy applications

Supervisor: Prof. J. Chiachio, Prof. M.L. Jalón, Prof. A. Kolios · Host: University of Granada

DC13 Risk-based Maintenance Planning for Wind Turbine Components

Supervisor: Prof. A. Kolios, Prof. Y. Guo, Prof. J. Santos · Host: DTU

DC15 Meta-structures with autonomous sensing capacities

Supervisor: Prof. E. Chatzi, Dr. V. Dertimanis, Prof. A. Colombi · Host: ETHZ

DC16 Active meta-structures for enhanced damage detection and localization on wind turbine blades

Supervisor: Prof. E. Chatzi, Dr. V. Dertimanis, Prof. A. Colombi · Host: ETHZ