Stochastic dynamic analysis of twisted functionally graded plates

Karsh, P. K.; Mukhopadhyay, T.; Dey, Sudip

doi:10.1016/j.compositesb.2018.03.043

Cited by 75 publications

(11 citation statements)

References 70 publications

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“…In case of such complex input-output relationships, an efficient surrogate based Monte Carlo simulation approach can be adopted to carry out the search for minimum and maximum values of the output quantities of interest for a particular α-cut level. In this analysis, radial basis function [Beatson (1999), Fasshauer (1997, Hon and Mao (2001), Kansa (1990aKansa ( , 1990b, Kansa and Hon (2000)] is employed as a surrogate , Karsh et al (2018b), Maharshi et al (2018), Metya et al (2017), Mahata et al (2016), Dey et al (2015Dey et al ( , 2016dDey et al ( , 2016e, 2018b] of the actual finite element model of composites.…”

Section: Theoretical Formulation For Fuzzy Finite Element Analysis Of Compositesmentioning

confidence: 99%

Spatially varying fuzzy multi-scale uncertainty propagation in unidirectional fibre reinforced composites

Naskar

Mukhopadhyay

Sriramula

2019

Composite Structures

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This article presents a non-probabilistic fuzzy based multi-scale uncertainty propagation framework for studying the dynamic and stability characteristics of composite laminates with spatially varying system properties. Most of the studies concerning the uncertainty quantification of composites rely on probabilistic analyses, where the prerequisite is to have the statistical distribution of stochastic input parameters. In many engineering problems, these statistical distributions remain unavailable due to the restriction of performing large number of experiments. In such situations, a fuzzy-based approach could be appropriate to characterize the effect of uncertainty. A novel concept of fuzzy representative volume element (FRVE) is developed here for accounting the spatially varying non-probabilistic source-uncertainties at the input level. Such approach of uncertainty modelling is physically more relevant than the prevalent way of modelling non-probabilistic uncertainty without considering the ply-level spatial variability. An efficient radial basis function based stochastic algorithm coupled with the fuzzy finite element model of composites is developed for the multi-scale uncertainty propagation involving multi-synchronous triggering parameters. The concept of a fuzzy factor of safety (FFoS) is discussed in this paper for evaluation of safety factor in the non-probabilistic regime. The results reveal that the present physically relevant approach of modelling fuzzy uncertainty considering plylevel spatial variability obtains significantly lower fuzzy bounds of the global responses compared to the conventional approach of non-probabilistic modelling neglecting the spatially varying attributes.

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Section: Theoretical Formulation For Fuzzy Finite Element Analysis Of Compositesmentioning

confidence: 99%

Spatially varying fuzzy multi-scale uncertainty propagation in unidirectional fibre reinforced composites

Naskar

Mukhopadhyay

Sriramula

2019

Composite Structures

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“…In this section, a brief overview is given for the surrogate modelling approach on the basis of high dimensional model representation (HDMR) coupled with the diffeomorphic modulation under observable response preserving homotopy (DMORPH) algorithm. In general, the surrogate models (Dey et al (2016a(Dey et al ( , 2016f, 2018, Mukhopadhyay (2019), , Karsh et al (2018b), Maharshi et al (2018), Mahata et al (2016), Metya et al (2017)) are employed to reduce the number of function evaluations based on actual simulation/ experimental models in a Monte Carlo simulation (refer to figure 4) or a process involving iterative simulations (such as optimization), which need large number of realizations corresponding to random set of input parameters. The surrogate models can encompass any prospective combination of all the input variables within the analysis domain.…”

Section: Hdmr Based Surrogate Modelling Coupled With Dmorph Algorithmmentioning

confidence: 99%

Probabilistic micromechanical spatial variability quantification in laminated composites

Naskar

Mukhopadhyay

Sriramula

2018

Composites Part B: Engineering

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This article presents a probabilistic framework to characterize the dynamic and stability parameters of composite laminates with spatially varying micro and macro-mechanical system properties. A novel approach of stochastic representative volume element (SRVE) is developed in the context of two dimensional plate-like structures for accounting the correlated spatially varying properties. The physically relevant random field based uncertainty modelling approach with spatial correlation is adopted in this paper on the basis of Karhunen-Loève expansion. An efficient coupled HDMR and DMORPH based stochastic algorithm is developed for composite laminates to quantify the probabilistic characteristics in global responses. Convergence of the algorithm for probabilistic dynamics and stability analysis of the structure is verified and validated with respect to direct Monte Carlo simulation (MCS) based on finite element method. The significance of considering higher buckling modes in a stochastic analysis is highlighted. Sensitivity analysis is performed to ascertain the relative importance of different macromechanical and micromechanical properties. The importance of incorporating source-uncertainty in spatially varying micromechanical material properties is demonstrated numerically. The results reveal that stochasticity (/ system irregularity) in material and structural attributes influences the system performance significantly depending on the type of analysis and the adopted uncertainty modelling approach, affirming the necessity to consider different forms of source-uncertainties during the analysis to ensure adequate safety, sustainability and robustness of the structure.

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“…Gelecekte yapılacak çalışmaların yeni analiz yöntemleri kullanılarak uygulanabilirlikleri açısından faydalı olabileceğini belirttiler. Karsh ve arkadaşları [8], sonlu elemanlar yöntemini kullanarak FKM'lerin YSA 'da stokastik dinamik analizini yaptılar. Sonlu elemanlar yöntemiyle kurulan modelin Monte Carlo simülasyonuna göre doğrulamasını yaparak YSA tabanlı algoritmanın, sonuçları doğru hesapladığını belirttiler.…”

Section: Introductionunclassified

Modeling of 2D Functionally Graded Circular Plates with Artificial Neural Network

Demirbaş¹,

Çakir

2020

International Scientific and Vocational Studies Journal

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The thermo-mechanical properties of the functionally graded material (FGM) depend on the volumetric distribution that determines the material character, which is very important in order to overcome different operating conditions and stress levels. Three different training algorithms are used in an Artificial Neural Network (ANN) to determine the equivalent stress levels of a hollow disc that is functionally graded in two directions. The data set was created by choosing the most important four different equivalent stress values (σ_(eqv max max) ,σ_(eqv max min) ,σ_(eqv min max) ,σ_(eqv min min)) that determine the material structure in thermo-mechanical analysis. Performance estimation was performed in three different training algorithms (Gradient Descent Backpropagation, Gradient Descent with Momentum Backpropagation, BFGS Quasi-Newton Backpropagation Algorithm). In this study, termomechanical behaviour was numerically determined by using finite difference method at different compositional gradient upper values to train ANN.

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Stochastic dynamic analysis of twisted functionally graded plates

Cited by 75 publications

References 70 publications

Spatially varying fuzzy multi-scale uncertainty propagation in unidirectional fibre reinforced composites

Spatially varying fuzzy multi-scale uncertainty propagation in unidirectional fibre reinforced composites

Probabilistic micromechanical spatial variability quantification in laminated composites

Modeling of 2D Functionally Graded Circular Plates with Artificial Neural Network

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