Skip navigation
Please use this identifier to cite or link to this item:
Title: A Critical Assessment of Kriging Model Variants for High-Fidelity Uncertainty Quantification in Dynamics of composite Shells
Authors: Mukhopadhyay T.
Chakraborty S.
Dey S.
Adhikari S.
Chowdhury, Rajib
Published in: Archives of Computational Methods in Engineering
Abstract: This paper presents a critical comparative assessment of Kriging model variants for surrogate based uncertainty propagation considering stochastic natural frequencies of composite doubly curved shells. The five Kriging model variants studied here are: Ordinary Kriging, Universal Kriging based on pseudo-likelihood estimator, Blind Kriging, Co-Kriging and Universal Kriging based on marginal likelihood estimator. First three stochastic natural frequencies of the composite shell are analysed by using a finite element model that includes the effects of transverse shear deformation based on Mindlin’s theory in conjunction with a layer-wise random variable approach. The comparative assessment is carried out to address the accuracy and computational efficiency of five Kriging model variants. Comparative performance of different covariance functions is also studied. Subsequently the effect of noise in uncertainty propagation is addressed by using the Stochastic Kriging. Representative results are presented for both individual and combined stochasticity in layer-wise input parameters to address performance of various Kriging variants for low dimensional and relatively higher dimensional input parameter spaces. The error estimation and convergence studies are conducted with respect to original Monte Carlo Simulation to justify merit of the present investigation. The study reveals that Universal Kriging coupled with marginal likelihood estimate yields the most accurate results, followed by Co-Kriging and Blind Kriging. As far as computational efficiency of the Kriging models is concerned, it is observed that for high-dimensional problems, CPU time required for building the Co-Kriging model is significantly less as compared to other Kriging variants. © 2016, CIMNE, Barcelona, Spain.
Citation: Archives of Computational Methods in Engineering(2017), 24(3): 495-518
Issue Date: 2017
Publisher: Springer Netherlands
ISSN: 11343060
Author Scopus IDs: 56419590700
Author Affiliations: Mukhopadhyay, T., College of Engineering, Swansea University, Swansea, United Kingdom
Chakraborty, S., Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, India
Dey, S., Leibniz-Institut für Polymerforschung Dresden e.V., Dresden, Germany
Adhikari, S., College of Engineering, Swansea University, Swansea, United Kingdom
Chowdhury, R., Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, India
Corresponding Author: Mukhopadhyay, T.; College of Engineering, Swansea UniversityUnited Kingdom; email:
Appears in Collections:Journal Publications [CE]

Files in This Item:
There are no files associated with this item.
Show full item record

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.