Stochastic critical stress intensity factor response of single edge notched laminated composite plate using displacement correlation method

Abstract

The second-order statistics of critical stress intensity factor (SIF) of single edge notched fiber reinforced composite plates with random system properties and subjected to uniaxial tensile loadings is investigated. This paper is an extension of reference (Lal and Kapania, 2013) by the present authors by considering more number of input random system parameters for higher accuracy. A C⁰ finite element method based on a higher-order shear deformation plate theory using displacement correlation method via isoparametric quarter point element is proposed for basic formulation. A stochastic finite element method using first-order perturbation technique and Monte Carlo simulation (MCS) is employed to examine the mean, coefficient of variance, and probability density faction of critical first mode SIF. The effect of different fiber orientations, crack length, plate thickness, a number of layers, and the lamination schemes with random system properties on the statistics of SIF of single edge crack laminated composite plate is evaluated. The tensile failure load is predicted using Hashin’s failure criteria. The present approach is validated with results available in literature and by employing independent MCS.     

Reliability analysis and optimization of in-plane functionally graded CNT-reinforced composite plates

Structural and Multidisciplinary Optimization - This paper focuses on the uncertainty quantification in the mechanical behavior of CNT-reinforced polymer composite and its effect on the...     

A framework combining meshfree analysis and adaptive kriging for optimization of stiffened panels

Structural and Multidisciplinary Optimization - A framework is developed for structural optimization using an Element Free Galerkin (EFG) method for analyzing the structure, a kriging for surrogate...     

Iterative One‐Pot α‐Glycosylation Strategy: Application to Oligosaccharide Synthesis

Based on the combined use of dimethylformamide (DMF) modulation and neighboring group participation, three iterative one‐pot α‐glycosylation methods, i.e., one‐pot (α,α)‐, one‐pot (β,α)‐, and one‐pot (α,β)‐glycosylations, were developed. These methods are applicable to a range of thioglycosyl donors, confer stereocontrol in α‐/β‐glycosidic bond formation, and thus provide for rapid access to oligosaccharides with various permutations of anomeric configurations. The utility of these one‐pot glycosylation methods is demonstrated in the synthesis of eight non‐natural and natural oligosaccharide targets, including the core 1 serine conjugate, core 8 serine conjugate, the D ‐Gal‐α(1→3)‐D ‐Glc‐α(1→3)‐L ‐Rha trisaccharide unit of the cell wall component in Streptococcus pneumoniae, and the D ‐Glc‐α(1→2)‐D ‐Glc‐α(1→3)‐D ‐Glc trisaccharide terminus of the N‐linked glycan precursor. Confirmation of the anomeric configurations of these oligosaccharides is evidenced by ¹H, ¹³C, ¹³C‐non‐proton decoupling, and heteronuclear correlation 2D NMR experiments. Global deprotection of selected oligosaccharide targets is illustrated.     

Offending estimates in covariance structure analysis: Comments on the causes of and solutions to Heywood cases.

In this article we discuss, illustrate, and compare the relative efficacy of three recommended approaches for handling negative error variance estimates (i.e., Heywood cases): (a) setting the offending estimate to zero, (b) adopting a model parameterization that ensures positive error variance estimates, and (c) using models with equality constraints that ensure nonnegative (but possibly zero) error variance estimates. The three approaches are evaluated in two distinct situations: Heywood cases caused by lack of fit and misspecification error, and Heywood cases induced from sampling fluctuations. The results indicate that in the case of sampling fluctuations the simple approach of setting the offending estimate to zero works reasonably well. In the case of lack of fit and misspecification error, the theoretical difficulties that give rise to negative error variance estimates have no ready-made methodological solutions. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Multi-dimensional optimization of functionally graded material composition using polynomial expansion of the volume fraction

The work of this paper proposes a method for multi-dimensional optimization of functionally graded materials (FGMs) composition. The method is based on using polynomial expansion of the volume fraction of the constituent materials. In this approach, the design variables are the coefficients of the polynomial expansion which to be determined through the optimization process. This method provides much more flexibility in the design compared to the methods based on the power-law, or the exponential-law which will in turn lead to more optimal designs. Also it requires much less number of design variables compared to the grid based approaches which is also utilized for two-dimensional optimization of FGMs structures. As an application of the proposed method, the optimization of a simply supported Aluminum plate reinforced with Silicon Carbide nano-particles is considered. Cost plays a very important role for this type of structures, since the cost of the reinforcements such as Silicon Carbide nano-particles, or carbon nano-tubes is too high. So the aim of the optimization process is to minimize the amount of the reinforcement required to satisfy certain performance criteria. Both static, and dynamic cases are considered in this work; a plate under a transverse pressure distribution is considered as the static case, and the panel flutter problem as the dynamic case.