Stochastic buckling behaviour of laminated composite structures with uncertain material properties

Nguyen, Hoang Hai; Hien, Ta Duy; Lee, Jae Hong; Nguyen‐Xuan, H.

doi:10.1016/j.ast.2017.01.028

Cited by 33 publications

(9 citation statements)

References 51 publications

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“…In the basic spectral representation method, as shown in Figure 4, 2D-1V can be expressed as a stochastic field, depending on the mean, standard deviation, and the correlation distance between variables [37]. Since the uniform random phase angle Φ in Equation ( 4) is determined depending on and , it needs to generate two folds of × number of values in the range of [0, 2π].…”

Section: Consideration Methods Of Spatial Randomness Of Material's Elastic Modulusmentioning

confidence: 99%

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Inverse Estimation Method of Material Randomness Using Observation

et al. 2020

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This study proposes a method for inversely estimating the spatial distribution characteristic of a material’s elastic modulus using the measured value of the observation data and the distance between the measurement points. The structural factors in the structural system possess temporal and spatial randomness. One of the representative structural factors, the material’s elastic modulus, possesses temporal and spatial randomness in the stiffness of the plate structure. The structural factors with randomness are typically modeled as having a certain probability distribution (probability density function) and a probability characteristic (mean and standard deviation). However, this method does not consider spatial randomness. Even if considered, the existing method presents limitations because it does not know the randomness of the actual material. To overcome the limitations, we propose a method to numerically define the spatial randomness of the material’s elastic modulus and confirm factors such as response variability and response variance.

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Section: Consideration Methods Of Spatial Randomness Of Material's Elastic Modulusmentioning

confidence: 99%

Section: Consideration Methods Of Spatial Randomness Of Material's Elastic Modulusmentioning

confidence: 99%

Inverse Estimation Method of Material Randomness Using Observation

et al. 2020

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“…And a big load capacity can be achieved by employing composite material. Unfortunately as a typical multi-phase material, mechanical property of composite material is variable and uncertain [11][12][13]. The variation of mechanical property leads to a relatively big variation of structural performance [14].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty quantification and global sensitivity analysis for composite cylinder shell via data-driven polynomial chaos expansion

Chen

Xin-hu

Shen

et al. 2022

J. Phys.: Conf. Ser.

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The mechanical properties of composite material exhibit inherent variation with uncertainty. Uncertainties in material properties propagate and result in uncertainties of mechanical performance of structure made of composite material. Polynomial chaos expansion (PCE) is implemented to carry out uncertainty quantification (UQ) and global sensitivity analysis (GSA) of cylinder shell made of composite material for this paper. A case study concerning eigenvalue buckling load of composite cylinder shell is investigated. Design of experiment (DOE) is conducted by utilizing Latin hypercubic sampling. Then data-driven PCE is established and later validated. Statistical moments (mean and standard deviation) and Sobol sensitivity indices of eigenvalue buckling load are obtained respectively. It is found that the PCE can serve as an efficient approach to handle UQ and GSA in engineering applications.

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“…Li et al (2018a) proposed the spectral SIGA for linear elasticity problems. Nguyen et al (2017) generated random fields by the spectral representation method to investigate the response variability of the buckling load of composite structures. Shahrokhabadi and Vahedifard (2018) combined isogeometric and generation random fields of water in the soil to model seepage in unsaturated soils.…”

Section: Introductionmentioning

confidence: 99%

Perturbation based stochastic isogeometric analysis for bending of functionally graded plates with the randomness of elastic modulus

Nguyen

2020

Lat. Am. j. solids struct.

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We propose, in this paper, stochastic isogeometric analysis (SIGA) is a type of non-statistic approach in which combines the perturbation technique with the standard isogeometric analysis, in particular for static behavior of functionally graded plates with the uncertain elastic modulus. We assume that the spatial random variation of elastic modulus can be modeled as a two dimensional Gaussian random field in the plane of the plate. The random field is discretized to set of random variables using the integration point method. The system equations of SIGA are created using the NURBS functions for approximation displacement fields in conjunction with the first-order and second-order perturbation expansions of random fields, stiffness matrix, displacement fields. Besides the non-statistic approach, Monte Carlo simulation is presented for validation. The accuracy and appropriateness of the non-statistic approach are demonstrated via comparisons of the present results with those given by the stochastic finite element method in the literature and by the Monte Carlo analysis as well. The numerical examples are employed to investigate the effect of the randomness of elastic modulus and system parameters on the first and second statistical moments of displacement.

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Stochastic buckling behaviour of laminated composite structures with uncertain material properties

Cited by 33 publications

References 51 publications

Inverse Estimation Method of Material Randomness Using Observation

Inverse Estimation Method of Material Randomness Using Observation

Uncertainty quantification and global sensitivity analysis for composite cylinder shell via data-driven polynomial chaos expansion

Perturbation based stochastic isogeometric analysis for bending of functionally graded plates with the randomness of elastic modulus

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