Uncertainty quantification in buckling strength of variable stiffness laminated composite plate under thermal loading

Sharma, Narayan; Nishad, Mohamed; Maiti, Dipak Kumar; Sunny, Mohammed Rabius; Singh, B. N.

doi:10.1016/j.compstruct.2021.114486

Cited by 38 publications

(14 citation statements)

References 52 publications

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“…26 Another key issue for composite laminates subjected to thermal loading is buckling of the plates. The thermal buckling of plate was firstly reported by Gossard et al 27 A consistent body of literature have recently analyzed thermal buckling behavior of angle-ply composite laminated thin plates subjected to various types of temperature loading [28][29][30][31] . Although the laminated composite plates have already been investigated for their thermal performance, the influence of the Bouligand-type arrangement of layers on their thermal buckling behavior have not been reported so far.…”

Section: Introductionmentioning

confidence: 97%

Thermal buckling behavior of Bouligand inspired laminated composite plates

Yang

Xie

2022

Journal of Composite Materials

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Natural Bouligand structure is a special class of hierarchical architectures, which have proven to be effective in enhancing its mechanical properties. In this work, we demonstrate that the thermal buckling capacity of Carbon Fibre Reinforced Plastics can also be improved by mimicking bio-inspired Bouligand architectures. A finite element approach using commercial software ABAQUS is developed to predict the critical buckling temperature of the Bouligand inspired laminated plates under thermal loading. Numerical predictions show a good agreement with analytical results obtained from the literature. The effectiveness of Bouligand-type composite laminates in enhancing thermal buckling behavior is first confirmed by comparing with unidirectional, cross-ply and quasi-isotropic laminates. We then present the effects of pitch angle and ply thickness on the thermal buckling temperature of bio-inspired Bouligand-type laminates. Finally, we conclude that the improved mechanical and thermal properties can be simultaneously achieved by the Bouligand structure of laminates as the pitch angle is increased. The present study highlights the high thermal buckling behavior of biomimetic Bouligand structure, offering a possibility in developing biomimetic composites with good trade-off between mechanical properties and thermal performance.

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Section: Introductionmentioning

confidence: 97%

Thermal buckling behavior of Bouligand inspired laminated composite plates

Yang

Xie

2022

Journal of Composite Materials

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“…Kumar et al (2019) implemented an artificial neural network (ANN) model to investigate the effect of material and geometric uncertainty on the buckling response of sandwich plates. In recent work, PNN based surrogate model was used to predict the influence of the uncertain material properties on the free vibration response (Sharma et al, 2020), aeroelastic response (Swain et al, 2020), and the buckling response (Sharma et al, 2021a). Sharma et al (2021b) adopted response surface-based first-order perturbation technique to analyze the stochastic aeroelastic response of laminated plate with variable fiber spacing.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty analysis of galloping based piezoelectric energy harvester system using polynomial neural network

Dash

Sharma

Maiti

et al. 2022

Journal of Intelligent Material Systems and Structures

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This paper deals with the impact of uncertain input parameters on the electrical power generation of galloping-based piezoelectric energy harvester (GPEH). A distributed parameter model for the system is derived and solved by using Newmark beta numerical integration technique. Nonlinear systems tend to behave in a completely different manner in response to a slight change in input parameters. Due to the complex manufacturing process and various technical defects, randomness in system properties is inevitable. Owing to the presence of randomness within the system parameters, the actual power output differs from the expected one. Therefore, stochastic analysis is performed considering uncertainty in aerodynamic, mechanical, and electrical parameters. A polynomial neural network (PNN) based surrogate model is used to analyze the stochastic power output. A sensitivity analysis is conducted and highly influenced parameters to the electric power output are identified. The accuracy and adaptability of the PNN model are established by comparing the results with Monte Carlo simulation (MCS). Further, the stochastic analyses of power output are performed for various degrees of randomness and wind velocities. The obtained results showed that the influence of the electromechanical coefficient on power output is more compared to other parameters.

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“…Multiscale methodologies have been developed to simulate the probabilistic mechanical properties of composite structures because of the uncertainty in the mechanical properties’ transfers from the micro-level to the structural level [ 6 , 7 , 8 ], as presented in Figure 1 . Therefore, in probabilistic analyses of complex composite structures, the determination of the probabilistic mechanical properties of composite laminae is of great importance [ 9 , 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Micromechanics-Model-Based Probabilistic Analysis Method for the Elastic Properties of Unidirectional CFRP Composites

Shan

Zhao

2022

Materials

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Considerable uncertainties in the mechanical properties of composites not only prevent them from having efficient applications but also threaten the safety and reliability of structures. In order to determine the uncertainty in the elastic properties of unidirectional CFRP composites, this paper develops a probabilistic analysis method based on a micromechanics theoretical model and the Monte Carlo simulation. Firstly, four commonly used theoretical models are investigated by calculating the deterministic elastic parameters of three unidirectional CFRP composites, which are compared with experimental outcomes. According to error analyses, the bridging model is the most brilliant one, with errors lower than 6%, which suggests that it can be used in probabilistic analyses. Furthermore, constituent parameters are regarded as normally distributed random variables, and the Monte Carlo simulation was used to obtain samplings based on the statistics of constituent parameters. The predicted probabilistic elastic parameters of the T800/X850 composite coincide with those from experiments, which verified the effectiveness of the developed probabilistic analysis method. According to the probabilistic analysis results, the statistics of the elastic parameters, the correlations between the elastic parameters, and their sensitivity to the constituent’s properties are determined. The moduli E11, E22, and G12 of the T800/X850 composite follow the lognormal distribution, namely, ln(E11)~N[5.15, 0.0282], ln(E22)~N[2.15, 0.0242], and ln(G12)~N[1.48, 0.0382], whereas its Poisson’s ratio, v12, obeys the normal distribution, namely, v12~N(0.33, 0.0122). Additionally, the correlation coefficients between v12 and E11/E22/G12 are small and thus can be ignored, whereas the correlation coefficients between any two of E11, E22, and G12 are larger than 0.5 and should be considered in the reliability analyses of composite structures. The developed probabilistic analysis method based on the bridging model and the Monte Carlo simulation is fast and reliable and can be used to efficiently evaluate the probabilistic properties of the elastic parameters of any unidirectional composite in the reliability design of structures in engineering practice.

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Uncertainty quantification in buckling strength of variable stiffness laminated composite plate under thermal loading

Cited by 38 publications

References 52 publications

Thermal buckling behavior of Bouligand inspired laminated composite plates

Thermal buckling behavior of Bouligand inspired laminated composite plates

Uncertainty analysis of galloping based piezoelectric energy harvester system using polynomial neural network

A Novel Micromechanics-Model-Based Probabilistic Analysis Method for the Elastic Properties of Unidirectional CFRP Composites

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