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Stochastic free vibration analyses of composite shallow doubly curved shells – A Kriging model approach
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In this paper, the Kriging model approach for stochastic free vibration analysis of composite shallow doubly curved shells is presented. And the convergence studies and error analysis are carried out to ensure the accuracy of present approach.Abstract:
This paper presents the Kriging model approach for stochastic free vibration analysis of composite shallow doubly curved shells. The finite element formulation is carried out considering rotary inertia and transverse shear deformation based on Mindlin’s theory. The stochastic natural frequencies are expressed in terms of Kriging surrogate models. The influence of random variation of different input parameters on the output natural frequencies is addressed. The sampling size and computational cost is reduced by employing the present method compared to direct Monte Carlo simulation. The convergence studies and error analysis are carried out to ensure the accuracy of present approach. The stochastic mode shapes and frequency response function are also depicted for a typical laminate configuration. Statistical analysis is presented to illustrate the results using Kriging model and its performance.read more
Citations
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The Design and Analysis of Experiments
TL;DR: This book by a teacher of statistics (as well as a consultant for "experimenters") is a comprehensive study of the philosophical background for the statistical design of experiment.
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Metamodel based high-fidelity stochastic analysis of composite laminates: A concise review with critical comparative assessment
TL;DR: In this article, the authors present 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.
Journal ArticleDOI
A critical assessment of Kriging model variants for high-fidelity uncertainty quantification in dynamics of composite shells
TL;DR: In this paper, a critical comparative assessment of Kriging model variants for surrogate based uncertainty propagation considering stochastic natural frequencies of composite doubly curved shells is presented, where the effect of noise in uncertainty propagation is addressed by using the Stochastic kriging.
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Uncertain natural frequency analysis of composite plates including effect of noise – A polynomial neural network approach
Sudip Dey,S. Naskar,Tanmoy Mukhopadhyay,Uwe Gohs,Axel Spickenheuer,Lars Bittrich,Srinivas Sriramula,Sondipon Adhikari,Gert Heinrich,Gert Heinrich +9 more
TL;DR: The effect of noise on a PNN based uncertainty quantification algorithm is explored and the convergence of the proposed algorithm for stochastic natural frequency analysis of composite plates is verified and validated with original finite element method (FEM).
References
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Journal Article
The Design and Analysis of Experiments
TL;DR: This book by a teacher of statistics (as well as a consultant for "experimenters") is a comprehensive study of the philosophical background for the statistical design of experiment.
Book
Statistics for spatial data
Noel A Cressie,Noel A Cressie +1 more
TL;DR: In this paper, the authors present a survey of statistics for spatial data in the field of geostatistics, including spatial point patterns and point patterns modeling objects, using Lattice Data and spatial models on lattices.
Journal ArticleDOI
A comparison of three methods for selecting values of input variables in the analysis of output from a computer code
TL;DR: In this paper, two sampling plans are examined as alternatives to simple random sampling in Monte Carlo studies and they are shown to be improvements over simple sampling with respect to variance for a class of estimators which includes the sample mean and the empirical distribution function.
Journal ArticleDOI
5. Statistics for Spatial Data
Mike Rees,N. Cressie +1 more
TL;DR: Cressie et al. as discussed by the authors presented the Statistics for Spatial Data (SDS) for the first time in 1991, and used it for the purpose of statistical analysis of spatial data.