Parametric Damage Mechanics Empowering Structural Health Monitoring of 3D Woven Composites
Article dans une revue avec comité de lecture
Auteur
Résumé
This paper presents a data-driven structural health monitoring (SHM) method by the use of so-called reduced-order models relying on an offline training/online use for unidirectional fiber and matrix failure detection in a 3D woven composite plate. During the offline phase (or learning) a dataset of possible damage localization, fiber and matrix failure ratios is generated through high-fidelity simulations (ABAQUS software). Then, a reduced model in a lower-dimensional approximation subspace based on the so-called sparse proper generalized decomposition (sPGD) is constructed. The parametrized approach of the sPGD method reduces the computational burden associated with a high-fidelity solver and allows a faster evaluation of all possible failure configurations. However, during the testing phase, it turns out that classical sPGD fails to capture the influence of the damage localization on the solution. To alleviate the just-referred difficulties, the present work proposes an adaptive sPGD. First, a change of variable is carried out to place all the damage areas on the same reference region, where an adapted interpolation can be done. During the online use, an optimization algorithm is employed with numerical experiments to evaluate the damage localization and damage ratio which allow us to define the health state of the structure.
Fichier(s) constituant cette publication
Cette publication figure dans le(s) laboratoire(s) suivant(s)
Documents liés
Visualiser des documents liés par titre, auteur, créateur et sujet.
-
Article dans une revue avec comité de lecture
JACOT, Maurine; CHAMPANEY, Victor;
TORREGROSA JORDAN, Sergio;
CORTIAL, Julien;
CHINESTA SORIA, Francisco (EDP Sciences, 2024-03)
Resolving Partial Differential Equations (PDEs) through numerical discretization methods like the Finite Element Method presents persistent challenges associated with computational complexity, despite achieving a satisfactory ... -
Article dans une revue avec comité de lectureDEROUICHE, Khouloud; GAROIS, Sevan; CHAMPANEY, Victor; DAOUD, Monzer; TRAIDI, Khalil;
CHINESTA SORIA, Francisco (MDPI AG, 2021)
Data-driven modeling provides an efficient approach to compute approximate solutions for complex multiphysics parametrized problems such as induction hardening (IH) process. Basically, some physical quantities of interest ... -
Article dans une revue avec comité de lectureLOREAU, Tanguy; CHAMPANEY, Victor; HASCOËT, Nicolas; MOURGUE, Philippe; DUVAL, Jean-Louis;
CHINESTA SORIA, Francisco (MDPI AG, 2021)
For better designing manufacturing processes, surrogate models were widely considered in the past, where the effect of different material and process parameters was considered from the use of a parametric solution. The ... -
Article dans une revue avec comité de lectureCHAMPANEY, Victor;
CHINESTA SORIA, Francisco;
CUETO, Elias (Springer Science and Business Media LLC, 2022-04-05)
Smart manufacturing implies creating virtual replicas of the processing operations, taking into account the material dimension and its multi-physics transformation when forming processes operate. Performing efficient, that ... -
Article dans une revue avec comité de lectureTORREGROSA, Sergio; CHAMPANEY, Victor;
AMMAR, Amine; HERBERT, Vincent;
CHINESTA SORIA, Francisco (Elsevier BV, 2022-10)
Nowadays data is acquiring an indisputable importance in every field including engineering. In the past, experimental data was used to calibrate state-of-the art models. Once the model was optimally calibrated, numerical ...