from large amounts of data stored in databases, data warehouses,
or other information repositories. It can be used to help companies

to make better decisions to stay competitive in the marketplace.

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viscoelastic materials. Finally several invented cases are run to illustrate the importance
of the viscoelasticity phenomenon on the internal stress field throughout thick laminated
cylinders.
KEYWORDS: Polymer–matrix composites (PMCs); Creep; Elasticity; Analytical

modelling; Durability.

INTRODUCTION
The hollow cylinders or cylinders are very common structural elements, used in many
applications including trusses, hoses, piping systems and drive shafts. The effort to
improve oil production riser performance lead to the possible use of risers made of
polymer matrix composites to bring the oil to surface platforms in the offshore
exploration at waster depth of 2 km or more. Consequently the increasing use of

polymer matrix composites in civil engineering applications has renewed interest in

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problems of stress analysis of cylindrical laminated composite structures.
Many analytical works about stress analysis of composite cylindrical shells have been
done during the past years. This is related with the increase use of composite shells in
many applications, such as civil engineering structures and aeronautical industry. The
4

temperature of its internal surface is increased and the

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this regard, a remarkable number of studies have been accomplished
to disclose the vibrational behavior of such nano-structures
conveying fluid. Tuzun et al. [5], Amabili et al. [6], Yoon et al. [7],
Natsuki et al. [8], Wang et al. [9], Xia et al. [10] and Wang and Qiao
[11] made important contributions in this practical area. In this research,
we would undertake a reevaluation for computational
modeling of carbon nano-tubes conveying viscous fluid with some
fresh insights as well as we try to develop an innovative one
dimensional (1D) coupled fluid–structure interaction (FSI) equation
by considering slip condition on the nano-tube wall. Khosravian
and Rafii Tabar [12] studied the flow of viscous fluid through a
carbon nano-tube and established a new equation of motion of
pipe conveying fluid by considering the viscosity effect. They found
that a nano-tube conveying a viscous fluid was more stable against
vibration-induced buckling than a nano-tube conveying a non-viscous
fluid. Wang and Ni [13] reappraised the computational modeling
of carbon nano-tube conveying viscous fluid represented by
Khosravian and Rafii Tabar [12] and then corrected the FSI equation
and disclosed that the effect of viscosity of fluid flow on the
vibration and instability of CNTs could be ignored. Lee and Chang
[14] analyzed the influences of nonlocal effect, viscosity effect, aspect
ratio, and elastic medium constant on the fundamental frequency
of a single-walled carbon nano-tube (SWCNT) conveying

viscous fluid embedded in an elastic medium. They revealed that

the frequency increased as the values of the viscosity parameter increased.
Soltani et al. [15] developed a transverse vibrational model
for a viscous fluid-conveying SWCNT embedded in biological soft
tissue. Their investigation determined that the structural instability
and the associated critical flow velocity could be affected by
the viscosity of the fluid and the nonlocal parameter. Khoddami
et al. [16] studied electro-thermo nonlinear vibration and instability
of embedded double-walled Boron Nitride nano-tubes
(DWBNNTs) conveying viscous fluid based on nonlocal piezoelasticity
theory. They reported that increasing the small scale parameter
decreased the real and imaginary parts of frequency and
critical fluid velocity. Furthermore, they concluded that the effect
of fluid viscosity on the vibration and instability of DWBNNTs
might be ignored. In many recent studies various size-dependent
continuum theories have been developed for vibration and stability

analysis of CNTs conveying fluid. Lee and Chang [17], Zhen

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0927-0256/$ – see front matter  2013 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.commatsci.2013.04.047
⇑ Corresponding author. Tel.: +98 311 391 5248; fax: +98 311 391 2628.
E-mail addresses: m.mirramezani@me.iut.ac.ir (M. Mirramezani), hrmirdamadi@
cc.iut.ac.ir (H.R. Mirdamadi), ghayour@cc.iut.ac.ir (M. Ghayour).

Design of user interfaces and information graphics is poorly
understood, and somewhat hit-or-miss in terms of effectiveness.
A number of issues influence the success of a
design, and these run the gamut of the underlying human
behavior. A design must be good cognitively (can the user
easily understand the semantic structure of the design?),
perceptually (can they effortlessly interpret the visual information
present in the design?), and socially (does the
design fit into the user’s workflow? will they want to use
it?). Here we focus on perceptual aspects of design.
Perhaps the most important aspect of human vision for design
is perceptual organization. Perceptual organization
refers to phenomena in which the visual system quickly and
seemingly effortlessly transforms individual feature estimates
into perception of coherent regions, structures, and
objects. These phenomena were first studied in detail by
the Gestalt psychologists, who produced a set of qualitative
Gestalt principles that govern pattern perception [1, 2], including
but not limited to: the tendency of things to group if
they are nearby (the Gestalt law of proximity); if they share
similar features (the law of similarity), or are smooth and
continuous (the law of good continuation). The duals of perceptual
grouping are important phenomena in their own right:

we quickly and effortlessly perceive boundaries between

certain visual textures, perceive edges between coherent regions
in an image, and quickly detect unusual items that
seem to “pop out” from the background. Examples of perceptual
grouping phenomena are given in Figure 1.
Following the visual system’s “rules” of visual organization
makes interpretation of visual aspects of designs effortless:
a user easily sees which labels refer to which parts of a diaa
b
c
d
e
f
Figure 1: Perceptual grouping examples, including
grouping by proximity & similarity (a, b), and grouping

by good continuation (c, d). (e) A user interface; what

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is the percept? (f) A graph, from [5]. Will a user
perceive the trend of the data?