Stochastic aeroelastic analysis of laminated composite plate with variable fiber spacing

Sharma, Narayan; Swain, Prasant Kumar; Maiti, Dipak Kumar; Singh, B. N.

doi:10.1177/00219983211040860

Cited by 19 publications

(12 citation statements)

References 53 publications

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“…The developed model for the present study is validated by various works of literature available for VFSC laminates. Table 1 depicts the comparison drawn from Houmat 41 and Sharma et al 11 to the present study for fundamental natural frequency for different composite materials combined with various boundary conditions. This table validates the closeness of the present results with the references.…”

Section: Resultsmentioning

confidence: 99%

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Stochastic buckling response of variable fiber spacing composite plate under thermal environment

Chandrakar,

Sharma,

Maiti

2023

Journal of Composite Materials

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The effectiveness of variable fiber spacing composite (VFSC) laminates subjected to thermal loading is compared with uniformly distributed fiber composite (UDFC) laminates. The higher-order shear deformation theory (HSDT) is adopted to develop a FE model for estimating critical thermal buckling load. Three types of fiber distribution patterns are considered to study the effect of the non-uniform spacing distribution of fiber on thermal buckling, and the results are compared with UDFC. Further, a surrogate model based on the radial basis function network (RBFN) is developed to examine the impact of composite properties uncertainty on the critical buckling response of UDFC and VFSC laminates under thermal loading. The RBFN model is found to be highly efficient and possesses a close match with Monte Carlo simulation (MCS). Though the VFSC laminates possess a large deviation (standard deviation) in the buckling temperature, the stochastic effect (standard deviation/mean) shows relatively minimal variation in VFSC and UDFC laminates. The effect of each composite property is investigated for different lamination sequences and boundary conditions. From the sensitivity analysis, the thermal expansion coefficient of matrix [Formula: see text] is found to be the most sensitive property for all the investigated cases. Lastly, the RBFN model is utilized to quantify reliability parameters for multiple stochasticity levels.

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Section: Resultsmentioning

confidence: 99%

“…Changes in flutter coalescent modes are connected to fiber distribution patterns, according to the study. In the recent work, Sharma et al 11 performed a dynamic aeroelastic analysis of VFSC. The enhancement of flutter characteristics by using VFSC in conjunction with the cantilever boundary condition has been concluded.…”

Section: Introductionmentioning

confidence: 99%

Stochastic buckling response of variable fiber spacing composite plate under thermal environment

Chandrakar,

Sharma,

Maiti

2023

Journal of Composite Materials

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“…The mathematical formulation and prediction accuracy of the RS method for aeroelastic analysis of composite plates are discussed in the ref. 60 The computation of the FORM starts with the converting of a non-normal random input variable (X) into a standard normal variable (U). The shortest distance between the origin and the performance function curve is represented by the reliability index (boldU*).…”

Section: Reliability Analysismentioning

confidence: 99%

Uncertainty quantification in free vibration and aeroelastic response of variable angle tow laminated composite plate

Sharma

Swain

Maiti

2023

Journal of Composite Materials

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The influence of uncertainty in material properties on free vibration and aeroelastic response of variable angle tow (VAT) laminated plates is presented in this work. Free vibration analysis is conducted by a finite element model based on third-order shear deformation theory. The FE model is further coupled with MSC. Nastran to carry out aeroelastic analysis of VAT laminates in the subsonic regime. MSC. Nastran helps to extract the aerodynamic influence coefficient matrix at discrete values of reduced frequencies using Doublet Lattice Method. The accuracy and adaptability of the highly efficient radial basis neural network (RBFN) based surrogate model developed for uncertainty analysis are checked by comparing the result with that of the Monte Carlo simulation. Stochastic analysis of natural frequencies and flutter characteristics of VAT laminates are conducted through various parametric studies. Variance-based sensitivity analysis is used to calculate the contribution of individual material properties to the free vibration and aeroelastic response of VAT laminates. Further, the influence of the highly sensitive properties on the structural failure probability is estimated. This research can be leveraged to predict the probability of failure of the structure at different levels of material uncertainty present in it.

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“…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. A radial basis neural network-based surrogate model was used by Karsh et al (2020) to study the effect of random material properties on natural frequencies of functionally graded cantilever plates.…”

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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Stochastic aeroelastic analysis of laminated composite plate with variable fiber spacing

Cited by 19 publications

References 53 publications

Stochastic buckling response of variable fiber spacing composite plate under thermal environment

Stochastic buckling response of variable fiber spacing composite plate under thermal environment

Uncertainty quantification in free vibration and aeroelastic response of variable angle tow laminated composite plate

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

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