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Liste des publications du LMPS

Affichage des publications 631 à 640 sur 1305 au total

Mechanical engineering

Damage Detection in a Polymer Matrix Composite from 4D Displacement Field Measurements

Standard Digital Volume Correlation (DVC) approaches enable quantitative analyses of specimen deformation to be performed by measuring displacement fields between discrete states. Such frameworks are thus limited by the number of scans (due to…

Ana Mandić, Viktor Kosin, Clément Jailin, Zvonimir Tomičević, Benjamin Smaniotto, François Hild

Materials -

Electric power

Multimaterial Filtering applied to Topology Optimization of a Permanent Magnet Synchronous Machine

This paper proposes a generalized multi-material filtering formalism in density-based topology optimization. This work’s novelty is the consideration of the periodic and anti-periodic boundary conditions commonly used to simulate electrical machines…

Théodore Cherrière, S Hlioui, François Louf, Luc Laurent

17th International Workshop on Optimization and Inverse Problems in Electromagnetism 2023 -

Engineering Sciences

Shear loading dominated damage mechanisms and strain localization studied by in situ 3D laminography imaging and Digital Volume Correlation for AA2198-T8

Damage and strain changes during shear dominated loading were assessed in situ via synchrotron laminography. In combination with digital volume correlation (DVC), local strain fields inside the material were determined thanks to the contrast…

Ante Buljac, Xiang Kong, Lukas Helfen, François Hild, Thilo F. Morgeneyer

Mechanics of Materials -

Engineering Sciences

Estimation of laser-induced damage depth from surface image features

In laser damage experiments, damage initiation and growth are typically monitored by imaging the surface of the tested fused silica sample, ignoring their bulk morphology. The depth of a damage site in fused silica optics is considered to be…

Guillaume Hallo, Yanis Abdelmoumni-Prunes, Sylvain Grosjean, Jérôme Néauport, Chloé Lacombe, Laurent Lamaignère, François Hild

Applied optics -

Engineering Sciences

Proper Generalized Decomposition stereocorrelation to measure kinematic fields for high speed impact on laminated glass

Stereocorrelation (SC) is a powerful tool for measuring 3D surface deformations in mechanical tests. However, it may suffer from convergence issues for large displacements or speckle changes. A global space-time formulation is proposed herein using…

Xuyang Chang, Corentin Le Gourriérec, François Hild, Léonard Turpin, Myriam Berny, Stéphane Roux

Computer Methods in Applied Mechanics and Engineering -

Neural and Evolutionary Computing

A neural network-based data-driven local modeling of spotwelded plates under impact

Solving large structural problems with multiple complex localized behaviors is extremely chal- lenging. To address this difficulty, both intrusive and non-intrusive Domain Decomposition Methods (DDM) have been developed in the past, where the…

Afsal Pulikkathodi, Elisabeth Longatte-Lacazedieu, Ludovic Chamoin, Juan-Pedro Berro Ramirez, Laurent Rota, Malek Zarroug

Mechanics & Industry -

Biomechanics

Mechanistic insights on age-related changes in heart-aorta-brain hemodynamic coupling using a pulse wave model of the entire circulatory system

Age-related changes in aortic biomechanics can impact the brain by reducing blood flow and increasing pulsatile energy transmission. Clinical studies have shown that impaired cardiac function in heart failure patients is associated with cognitive…

Arian Aghilinejad, Faisal Amlani, Sohrab Mazandarani, Kevin King, Niema Pahlevan

AJP - Heart and Circulatory Physiology -

Engineering Sciences

A modified Constitutive Relation Error (mCRE) framework to learn nonlinear constitutive models from strain measurements with thermodynamics-consistent Neural Networks

The damage of mechanical structures is a permanent concern in engineering, related to issues of durability and safety. The theme is currently the subject of various research activities; a typical example is the ERC project DREAM-ON (2021-2026), in…

Antoine Benady, Emmanuel Baranger, Ludovic Chamoin

XI Conference on Adaptive Modeling and Simulation -

Engineering Sciences

A modified Constitutive Relation Error framework to learn nonlinear constitutive laws using physics-augmented Neural Networks

A novel data-driven approach to learn constitutive law with observable data is proposed in the context of structural health monitoring (SHM). Constitutive laws are represented by a physics-augmented neural network taking strain in input and free…

Antoine Benady, Ludovic Chamoin, Emmanuel Baranger

IACM Mechanistic Machine Learning and Digital Engineering for Computational Science Engineering and Technology. -