OPTIMUMS

Optimization of Sensor Placement Methodology for Structural Health Monitoring

 Coordinatore NATIONAL TECHNICAL UNIVERSITY OF ATHENS - NTUA 

 Organization address address: HEROON POLYTECHNIOU 9 ZOGRAPHOU CAMPUS
city: ATHINA
postcode: 15780

contact info
Titolo: Prof.
Nome: Georgios
Cognome: Tsamasphyros
Email: send email
Telefono: -7721477
Fax: -7721478

 Nazionalità Coordinatore Greece [EL]
 Totale costo 30˙000 €
 EC contributo 22˙499 €
 Programma FP7-JTI
Specific Programme "Cooperation": Joint Technology Initiatives
 Code Call SP1-JTI-CS-2009-01
 Funding Scheme JTI-CS
 Anno di inizio 2010
 Periodo (anno-mese-giorno) 2010-01-01   -   2010-07-31

 Partecipanti

# participant  country  role  EC contrib. [€] 
1    NATIONAL TECHNICAL UNIVERSITY OF ATHENS - NTUA

 Organization address address: HEROON POLYTECHNIOU 9 ZOGRAPHOU CAMPUS
city: ATHINA
postcode: 15780

contact info
Titolo: Prof.
Nome: Georgios
Cognome: Tsamasphyros
Email: send email
Telefono: -7721477
Fax: -7721478

EL (ATHINA) coordinator 20˙249.00
2    GMI AERO SAS

 Organization address address: Rue Buffault 9
city: PARIS
postcode: 75009

contact info
Titolo: Mr.
Nome: Roland
Cognome: Chemama
Email: send email
Telefono: -42821112
Fax: -42829806

FR (PARIS) participant 2˙250.00

Mappa


 Word cloud

Esplora la "nuvola delle parole (Word Cloud) per avere un'idea di massima del progetto.

fe    structure    outputs    algorithm    network    problem    training    mathematical    sensors    data    strain    desired    structures    finite    genetic    aeronautical    neural    damage    resolution    basic    networks   

 Obiettivo del progetto (Objective)

'The basic problem of damage detection is to deduce the existence of a defect in a structure from measurements taken at sensors distributed on the structure. Especially in aeronautical structures, cracks, delaminations and debondings are typical types of damages often encountered. The problem is essentially one of pattern recognition. Artificial neural networks show considerable promise for damage diagnosis. In the most basic, supervised learning, approach to deriving a neural network, the network is presented with pairs of data vectors, the input being the vector of measurements from the system and the output being the desired damage classification. At each presentation of the data, the internal structure of the network is modified, in order to bring the actual network outputs into correspondence with the desired outputs. This iterative procedure is terminated when the network outputs have the required properties over the whole training set. In a structural application, the training data may be provided by finite element (FE) analysis. This has the advantage of allowing a large range of boundary conditions and static/dynamic load cases to be analysed. FE analysis may be a little unrealistic as there is no limit on the spatial resolution of the data which is obtained, e.g. strains. In reality, the number of sensors available will be limited and this will, of course, place restrictions on the resolution of data. As a result, it is necessary in practice to optimise the number and location of sensors for a given problem. The main objective of the current proposal is to develop a mathematical algorithm for optimal strain sensor (strain gauges, fiber Bragg grating or other) placement in aeronautical composite structures for maximum damage detectability. The mathematical method to be used will be a genetic algorithm based on neural networks. The genetic algorithm will be trained from finite element analyses simulating impact scenarios (damage initiation) and operational'

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