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Condition Assessment and Damage Localisation in Fluid-Filled Buried Sandwich (FFBS) Pipe Using Guided Wave and Impedance Fusion Technique (GIFT) Under Various Exposure Conditions

Obiettivo

The safe and reliable transport of clean fuels such as hydrogen and biofuels depends on pipelines that can withstand demanding service conditions. Sandwich-structured pipelines are focused due to their unprecedented lightweight properties and superior mechanical resilience. However, buried sandwich-pressurised pipelines face persistent vulnerabilities, including embrittlement, corrosion, and delamination, that can threaten both environmental safety and energy reliability. To mitigate these risks, the project proposes an innovative, multidisciplinary Structural Health Monitoring (SHM) system for the pipelines by integrating cutting-edge non-destructive techniques, Guided Wave (GW) propagation and Electromechanical Impedance (EMI) with advanced machine learning (ML) and data fusion for robust, real-time monitoring and damage detection. Execution consists of four coordinated levels: i. a comprehensive numerical model to simulate the pipeline responses under varied operational and environmental conditions, ii. Experimental setup optimised for real-world deployment to collect high-fidelity sensor data under controlled and field conditions. iii. development of a hybrid ML-driven decision framework enhanced by data fusion for robust, accurate, and real-time damage monitoring. iv. Hybrid probability-weighted RAPID imaging tool for precise defect visualisation and localisation, elevating the sensitivity and accuracy of the damage detection system. The project’s multidisciplinary approach combines solid and fluid mechanics, electrostatics, signal processing, and advanced data analytics, delivering improved diagnostic reliability, predictive capabilities, and adaptable tools for other fluid storage applications, fully supporting EU sustainable energy objectives. Dedicated knowledge transfer to the host institution and structured training in advanced NDE, modelling, and AI-driven analytics, strengthening the fellow's skills and supporting career development in research.

Parole chiave

Programma(i)

Argomento(i)

Meccanismo di finanziamento

HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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(si apre in una nuova finestra) HORIZON-MSCA-2025-PF

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Coordinatore

KAUNO TECHNOLOGIJOS UNIVERSITETAS
Contributo netto dell'UE
€ 181 136,16
Indirizzo
K DONELAICIO 73
LT-44029 Kaunas
Lituania
Regione
Lietuva Vidurio ir vakarų Lietuvos regionas Kauno apskritis
Tipo di attività
Higher or Secondary Education Establishments
Collegamenti
Costo totale
Nessun dato

Partner (2)