| People | Locations | Statistics |
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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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Lindgaard, Esben
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (21/21 displayed)
- 2024An accurate forming model for capturing the nonlinear material behaviour of multilayered binder-stabilised fabrics and predicting fibre wrinklingcitations
- 2024That’s how the preform crumples: Wrinkle creation during forming of thick binder-stabilised stacks of non-crimp fabricscitations
- 2024MatrixCraCS: Automated tracking of matrix crack development in GFRP laminates undergoing large tensile strains
- 2024Parametric study on the effect of material properties, tool geometry, and tolerances on preform quality in wind turbine blade manufacturingcitations
- 2023Benchmark test for mode I fatigue-driven delamination in GFRP composite laminatescitations
- 2023Benchmark test for mode I fatigue-driven delamination in GFRP composite laminates: Experimental results and simulation with the inter-laminar damage model implemented in SAMCEFcitations
- 2022Simulation of Wrinkling during Forming of Binder Stabilized UD-NCF Preforms in Wind Turbine Blade Manufacturingcitations
- 2022Delamination toughening of composite laminates using weakening or toughening interlaminar patches to initiate multiple delaminationscitations
- 20213D progressive fatigue delamination model:Deliverable 5.1
- 20213D progressive fatigue delamination model
- 2021A simple MATLAB draping code for fiber-reinforced composites with application to optimization of manufacturing process parameterscitations
- 2021Transition-behaviours in fatigue-driven delamination of GFRP laminates following step changes in block amplitude loadingcitations
- 2021UPWARDS Deliverable D5.4:Report and data on the effect of fatigue loading history on damage development
- 2021A continuum damage model for composite laminatescitations
- 2019Formulation of a mixed-mode multilinear cohesive zone law in an interface finite element for modelling delamination with R-curve effectscitations
- 2019Parametric study of the effect of wrinkle features on the strength of a tapered wind turbine blade sub-structurecitations
- 2019An evaluation of mode-decomposed energy release rates for arbitrarily shaped delamination fronts using cohesive elementscitations
- 2019Experimental characterization of delamination in off-axis GFRP laminates during mode I loadingcitations
- 2017A benchmark study of simulation methods for high-cycle fatigue-driven delamination based on cohesive zone modelscitations
- 2016Post-buckling optimization of composite structures using Koiter's methodcitations
- 2015Simulation Methods for High-Cycle Fatigue-Driven Delamination using Cohesive Zone Models - Fundamental Behavior and Benchmark Studies
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document
Simulation Methods for High-Cycle Fatigue-Driven Delamination using Cohesive Zone Models - Fundamental Behavior and Benchmark Studies
Abstract
A novel computational method for simulating fatigue-driven delamination cracks in composite laminated structures under cyclic loading based on a cohesive zone model [2] and new benchmark studies with four other comparable methods [3-6] are presented. The benchmark studies describe and compare the traction-separation response in the cohesive zone and the transition phase from quasistatic to fatigue loading for each method. Furthermore, the accuracy of the predicted crack growth rate is studied and compared for each method. It is shown that the method described in [2] is significantly more accurate than the other methods [3-6]. Finally, studies are presented of the dependency and sensitivity to the change in different quasi-static material parameters and model specific fitting parameters. It is shown that all the methods except [2] rely on different parameters which are not possible to determine experimentally and/or depend on the problem and are therefore not possible to determine in advance which is needed in order to do predictive simulation studies.