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Comparison of
multiple surrogate models probing uncertainty in natural
frequency of hybrid functionally graded sandwich cylindrical
shells
Vaishali, Pradeep K. Karsh and Sudip Dey |
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Vol. 17 (2022), No. 2, 97–121
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Abstract |
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This paper presents the surrogate-based predictive dynamic behavior of hybrid functionally graded sandwich cylindrical shells. The material properties are graded based on the power law for functionally graded structures to comply with high strength and stiffness at an elevated temperature. At the same time, the sandwich composite is employed to ensure weight sensitivity. A comparative evaluation of the relative efficacy and accuracy of each surrogate model is studied to map the uncertainties in the first three natural frequencies of these structures. The predictive analysis is computationally investigated by using finite elements. The outcomes of various surrogate models are compared with the traditional Monte Carlo simulation (MCS) method. The effects of sampling are observed to be pivotal on the performance of surrogate models such as artificial neural network (ANN), Gaussian process regression (GPR), linear regression model (LRM), multivariate adaptive regression splines (MARS), polynomial neural network (PNN), radial basis function (RBF) and support vector regression (SVR). Based on compound effects of uncertainties, parametric studies are conducted by employing the parameters such as power-law exponent, temperature, shell thickness, and twist angle. The in-depth insight of sensitivity analysis is portrayed for comparative assessment on variabilities in the output quantity of interest (QoI). The numerical outcomes show the significant effect of source-uncertainties for hybrid FG-sandwich cylindrical shells. The mode shapes for the first three natural modes of such structure are also obtained. |
Keywords
hybrid functionally graded composite, cylindrical shells,
natural frequency, finite element, surrogate model, Monte
Carlo simulation, moment-independent sensitivity
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Milestones
Received: 25 February 2021
Revised: 31 October 2021
Accepted: 3 December 2021
Published: 10 December 2022
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Authors |
| Vaishali | |
| Department of Mechanical
Engineering National Institute of Technology Silchar Assam India |
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| Pradeep K. Karsh | |
| Department of Mechanical
Engineering Parul Institute of Engineering & Technology Parul University Vadodara India |
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| Sudip Dey | |
| Department of Mechanical
Engineering National Institute of Technology Silchar Assam India |
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