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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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Adhikari, S.
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (24/24 displayed)
- 2022Unfolding the mechanical properties of buckypaper composites: nano- to macro-scale coupled atomistic-continuum simulationscitations
- 2022Towards a novel application of wastewater-based epidemiology in population-wide assessment of exposure to volatile organic compounds.citations
- 2021Broadband dynamic elastic moduli of honeycomb lattice materials: a generalized analytical approachcitations
- 2021Voltage-dependent modulation of elastic moduli in lattice metamaterialscitations
- 2020Probing the Effective Young's Modulus of ‘Magic Angle’ Inspired Multi‐Functional Twisted Nano‐Heterostructurescitations
- 2019Probing the frequency-dependent elastic moduli of lattice materialscitations
- 2019Frequency domain homogenization for the viscoelastic properties of spatially correlated quasi-periodic latticescitations
- 2018Probing the shear modulus of two-dimensional multiplanar nanostructures and heterostructurescitations
- 2018Probing the shear modulus of two-dimensional multiplanar nanostructures and heterostructurescitations
- 2017Stochastic mechanics of metamaterialscitations
- 2017Stochastic natural frequency analysis of damaged thin-walled laminated composite beams with uncertainty in micromechanical propertiescitations
- 2017Metamodel based high-fidelity stochastic analysis of composite laminatescitations
- 2016Free-vibration analysis of sandwich panels with randomly irregular honeycomb corecitations
- 2016Fuzzy uncertainty propagation in composites using Gram-Schmidt polynomial chaos expansioncitations
- 2016Probabilistic analysis and design of HCP nanowirescitations
- 2016Pullout strength of graphene and carbon nanotube/epoxy compositescitations
- 2016Effective in-plane elastic properties of auxetic honeycombs with spatial irregularitycitations
- 2016Equivalent in-plane elastic properties of irregular honeycombs: an analytical approachcitations
- 2016Equivalent in-plane elastic properties of irregular honeycombscitations
- 2016Bottom up surrogate based approach for stochastic frequency response analysis of laminated composite platescitations
- 2015Stochastic natural frequency of composite conical shellscitations
- 2010Nanocomposites with auxetic nanotubescitations
- 2010Vibration and symmetry-breaking of boron nitride nanotubescitations
- 2009Effective elastic mechanical properties of single layer graphene sheetscitations
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article
Metamodel based high-fidelity stochastic analysis of composite laminates
Abstract
<p>This paper presents a concise state-of-the-art review along with an exhaustive comparative investigation on surrogate models for critical comparative assessment of uncertainty in natural frequencies of composite plates on the basis of computational efficiency and accuracy. Both individual and combined variations of input parameters have been considered to account for the effect of low and high dimensional input parameter spaces in the surrogate based uncertainty quantification algorithms including the rate of convergence. Probabilistic characterization of the first three stochastic natural frequencies is carried out by using a finite element model that includes the effects of transverse shear deformation based on Mindlin's theory in conjunction with a layer-wise random variable approach. The results obtained by different metamodels have been compared with the results of traditional Monte Carlo simulation (MCS) method for high fidelity uncertainty quantification. The crucial issue regarding influence of sampling techniques on the performance of metamodel based uncertainty quantification has been addressed as an integral part of this article.</p>