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Naji, M. |
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Motta, Antonella |
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Aletan, Dirar |
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Mohamed, Tarek |
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Ertürk, Emre |
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Taccardi, Nicola |
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Kononenko, Denys |
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Petrov, R. H. | Madrid |
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Alshaaer, Mazen | Brussels |
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Bih, L. |
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Casati, R. |
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Muller, Hermance |
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Kočí, Jan | Prague |
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Šuljagić, Marija |
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Kalteremidou, Kalliopi-Artemi | Brussels |
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Azam, Siraj |
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Ospanova, Alyiya |
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Blanpain, Bart |
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Ali, M. A. |
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Popa, V. |
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Rančić, M. |
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Ollier, Nadège |
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Azevedo, Nuno Monteiro |
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Landes, Michael |
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Rignanese, Gian-Marco |
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Karakoç, Alp
Aalto University
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (18/18 displayed)
- 2024Design, Fabrication, and Characterization of 3D-Printed Multiphase Scaffolds Based on Triply Periodic Minimal Surfacescitations
- 2023Effects of leaflet curvature and thickness on the crimping stresses in transcatheter heart valvecitations
- 2023Low-cost thin film patch antennas and antenna arrays with various background wall materials for indoor wireless communicationscitations
- 2022Predicting the upper-bound of interlaminar impact damage in structural composites through a combined nanoindentation and computational mechanics techniquecitations
- 2022Simplified indentation mechanics to connect nanoindentation and low-energy impact of structural composites and polymers
- 2021Effect of single-fiber properties and fiber volume fraction on the mechanical properties of Ioncell fiber compositescitations
- 2021Exploring the possibilities of FDM filaments comprising natural fiber-reinforced biocomposites for additive manufacturingcitations
- 2021Mild alkaline separation of fiber bundles from eucalyptus bark and their composites with cellulose acetate butyratecitations
- 2020Data-Driven Computational Homogenization Method Based on Euclidean Bipartite Matchingcitations
- 2020Mechanical and thermal behavior of natural fiber-polymer composites without compatibilizerscitations
- 2020A predictive failure framework for brittle porous materials via machine learning and geometric matching methodscitations
- 2020Comparative screening of the structural and thermomechanical properties of FDM filaments comprising thermoplastics loaded with cellulose, carbon and glass fiberscitations
- 2020Comparative screening of the structural and thermomechanical properties of FDM filaments comprising thermoplastics loaded with cellulose, carbon and glass fiberscitations
- 2019Machine Learning assisted design of tailor-made nanocellulose filmscitations
- 2018Stochastic fracture of additively manufactured porous compositescitations
- 2016Shape and cell wall slenderness effects on the stiffness of wood cell aggregates in the transverse planecitations
- 2016Modeling of wood-like cellular materials with a geometrical data extraction algorithmcitations
- 2013Effective stiffness and strength properties of cellular materials in the transverse planecitations
Places of action
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article
Data-Driven Computational Homogenization Method Based on Euclidean Bipartite Matching
Abstract
Image processing methods combined with scanning techniques - for example, microscopy or microtomography - are now frequently being used for constructing realistic microstructure models that can be used as representative volume elements (RVEs) to better characterize heterogeneous material behavior. As a complement to those efforts, the present study introduces a computational homogenization method that bridges the RVE and material-scale properties in situ. To define the boundary conditions properly, an assignment problem is solved using Euclidean bipartite matching through which the boundary nodes of the RVE are matched with the control nodes of the rectangular prism bounding the RVE. The objective is to minimize the distances between the control and boundary nodes, which, when achieved, enables the bridging of scale-based features of both virtually generated and image-reconstructed domains. Following the minimization process, periodic boundary conditions can be enforced at the control nodes, and the resultingboundary value problem can be solved to determine the local constitutive material behavior. To verify the proposed method, virtually generated domains of closed-cell porous, spherical particle-reinforced, and fiber-reinforced composite materials are analyzed, and the results are compared with analytical Hashin-Shtrikman and Halpin-Tsai methods. The percent errors are within the ranges from 0.04% to 3.3%, from 2.7% to 14.9%, and from 0.5% to 13.2% for porous, particle-reinforced, and fiber-reinforced composite materials, respectively, indicating that the method has promising potential in the fields of image-based material characterization and computational homogenization. ; Peer reviewed