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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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Sinchuk, Yuriy
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (8/8 displayed)
- 2024A numerical multi-scale method for analyzing the rate-dependent and inelastic response of short fiber reinforced polymers : modeling framework and experimental validationcitations
- 2024Study of self-heating and local strain rate in polyamide-6 and short fibre glass/polyamide-6 under tension through synchronised full-field strain and temperature measurementscitations
- 2022A computationally efficient multi-scale strategy for predicting the elasto-plastic behaviour of short fiber composites
- 2022Sinchuk et al. Variational and Deep Learning Segmentation of Very-Low-Contrast X-ray Computed Tomography Images of Carbon/Epoxy Woven Composites
- 2022X-ray CT based multi-layer unit cell modeling of carbon fiber-reinforced textile composites: Segmentation, meshing and elastic property homogenizationcitations
- 2022A hierarchical multi-scale analytical approach for predicting the elastic behavior of short fiber reinforced polymers under triaxial and flexural loading conditionscitations
- 2021Geometrical and deep learning approaches for instance segmentation of CFRP fiber bundles in textile compositescitations
- 2020Variational and Deep Learning Segmentation of Very-Low-Contrast X-ray Computed Tomography Images of Carbon/Epoxy Woven Compositescitations
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article
A hierarchical multi-scale analytical approach for predicting the elastic behavior of short fiber reinforced polymers under triaxial and flexural loading conditions
Abstract
This paper presents a computationally efficient multi-scale analytical framework for predicting the effectiveelastic response of short fiber reinforced polymers (SFRPs) under triaxial and flexural loading conditions wherethe details of microstructure such as core/shell thickness, volume fraction distribution, fiber misalignment andfiber length variation are objectively taken into account. To this end, the mean-field homogenization and finiteelement approaches are compared to calculate the elastic response of SFRPs at the microscopic level while theorientation averaging approach is used to address the effects of fiber misalignment. The obtained mechanicalbehavior is then linked to an enhanced laminate theory to predict the effective triaxial and bending macrostructuralbehavior considering the core/shell effects and variation of volume fraction through the thickness.Using the second-order homogenization technique, the numerical validation of the proposed analytical approachis investigated based on the micro- and meso-scale analyses. Furthermore, the potential of the proposed strategyis demonstrated for hybrid composites. Finally, the accuracy of the suggested model is thoroughly studied usingthe available experimental tests in literature where the statistical information about the details of SFRP microstructuresis presented.