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Structural damage robust real time and data driven adaptive modeling for onli...

Structural damage robust real time and data driven adaptive modeling for online control Diagnosing structural damage and predicting its evolution has been a perpetual engineering issue. It is nowadays the topic of intensive research works addressing online damage detection and control, and benefiting from experimenta... ver más

31/05/2026

ENS Paris-Saclay

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

ECOLE NORMALE SUPERIEURE PARISSACLAY No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 3 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2021-04-08

ERC-2020-COG: ERC CONSOLIDATOR GRANTS

I+D

Cerrada hace 4 años

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

3 Participantes

ENS Paris-Saclay

1.94M€ | Lider

EDITORIAL ARANZADI

CNRS

126.00K€ | Participante

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Duración del proyecto: 61 meses Fecha Inicio: 2021-04-08
Fecha Fin: 2026-05-31

Líder del proyecto

ECOLE NORMALE SUPERIEURE PARISSACLAY No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Diagnosing structural damage and predicting its evolution has been a perpetual engineering issue. It is nowadays the topic of intensive research works addressing online damage detection and control, and benefiting from experimental and numerical advances made during the last decad. On the one hand, embedded sensor technologies nowadays provide high-resolution in situ information on the internal damage state of materials. On the other hand, the booming development of virtual twins and artificial intelligence leads to envision connected engineering structures with computer-based monitoring. Nevertheless, using large noisy data sets and high-fidelity damage models is hardly compatible with the real-time, robustness, and portability constraints of such a revolution. This is today a major limitation for its application to real systems. The objective of the project is thus to address key numerical challenges in order to truly permit a manageable and seamless dialog between damage data and simulations for large scale structures. The idea is to create a real-time feedback loop including the physical system in service and an adaptive numerical model of this system, such that: (i) the model is dynamically updated and enriched from observations (hybrid twin); (ii) damage diagnosis is continuously made from this model in order to drive the physical system appropriately. The project, focusing on composite structures, will consist in the design of innovative and effective numerical approaches for real-time data assimilation, predictive damage computation, and command law synthesis on evolutive systems, all supported by reduced order modeling. The advanced numerical tools will be implemented into a dedicated computing infrastucture, and practically assessed through a proof-of-concept using specific experimental facilities. Outcomes are expected to represent a major scientific and technological breakthrough for damage tolerance on real-life engineering structures.

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