ArticlePDF Available

Abstract and Figures

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.
Content may be subject to copyright.
1
Probabilistic micromechanical spatial variability quantification in
laminated composites
S. Naskara, T. Mukhopadhyayb, S. Sriramulaa
aSchool of Engineering, University of Aberdeen, Aberdeen, UK
bDepartment of Engineering Science, University of Oxford, Oxford, UK
Email address: tanmoy.mukhopadhyay@eng.ox.ac.uk (Tanmoy Mukhopadhyay)
Abstract
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.
Keywords: composite laminate; micromechanical random field; spatially correlated material properties;
stochastic natural frequency; stochastic buckling load; stochastic mode shape
2
Contents
1. Introduction ........................................................................................................................................................ 2
2. Stochastic dynamics and stability analysis of composite plates ...................................................................... 6
3. HDMR based surrogate modelling coupled with DMORPH algorithm ........................................................ 8
4. Stochastic representative volume element based framework for uncertainty quantification ................... 12
4.1. Concept of SRVE ....................................................................................................................................... 12
4.2. Characterization of correlated material properties based on Karhunen-Loève expansion ................... 14
4.3. Description of the uncertainty quantification framework ........................................................................ 17
4.3.1. Monte Carlo simulation ...................................................................................................................... 17
4.3.2. Modelling of source-uncertainty at the input level ............................................................................. 18
4.3.3. Propagation of uncertainty based on HDMR coupled with DMORPH algorithm ............................. 20
5. Results and discussion ...................................................................................................................................... 22
5.1. Stochastic dynamic analysis ...................................................................................................................... 24
5.1.1. Validation and convergence study ...................................................................................................... 24
5.1.2. Results for stochastic dynamic analysis.............................................................................................. 26
5.2. Stochastic stability analysis ....................................................................................................................... 38
5.2.1. Validation and convergence study ...................................................................................................... 38
5.2.2. Results of stochastic stability analysis ................................................................................................ 43
6. Summary and perspective ................................................................................................................................ 50
7. Conclusion ......................................................................................................................................................... 55
Acknowledgements ................................................................................................................................................... 56
References ................................................................................................................................................................. 60
1. Introduction
Composite structures are widely used in modern aerospace, construction, marine and automobile
applications because of high strength and stiffness with lightweight and tailorable properties. Even though
laminated composite structures have the advantage of modulating large number of design parameters to
achieve various application-specific requirements, this concurrently brings the challenge of manufacturing
the structure according to exact design specifications. Large-scale production of such structures according
to the requirements of industry is often subjected to large amount of variability arising from unavoidable
manufacturing imperfections (such as intra-laminate voids and excess matrix voids, excess resin between
plies, incomplete curing of resin, porosity, variations in lamina thickness and fibre properties), lack of
experiences and complexity of the structural configuration. The issue aggravates further due to uncertain
3
operational and environmental factors and the possibility of incurring different forms of damages and
defects during the service life. In general, uncertainties can be broadly classified into three categories,
aleatoric (due to variability in structural parameters), epistemic (due to lack of adequate information about
the system) and prejudicial (due to absence of stochasticity characterization of the structural system)
(Agarwal et al. (2004), Oberkampf et al. (2001), Sriramula and Chryssanthopoulos (2009), Dey et al.
(2018a)). Composite structures being susceptible to multiple forms of uncertainties, the structural
performances are often subjected to a significant element of risk. Thus it is of prime importance in case of
composite structures to quantify the effect of source-uncertainties so that an inclusive design paradigm
could be adopted to avoid any compromise in the aspects of safety and serviceability.
Composite structures have received immense attention from the engineers and scientists
concerning their static, dynamic and stability behaviour (Chakrabarti et al. (2011, 2013), Biswal et al.
(2016), Dey et al. (2016d), Mandal et al. (2017), Kumari and Behera (2017). Recent studies on the
vibration and buckling analysis of advanced lightweight structures (like composites and FGM) in the
deterministic regime include non-homogeneity, non-linear behaviour, shear deformation, rotary inertia
and effect of elastic foundation in the analysis (Sofiyev et al. (2012, 2017), Sofiyev and Kuruoglu
(2014), Haciyev et al. (2018)). Following several decades of deterministic studies, the aspect of
considering the effect of uncertainty in material and structural attributes have recently started receiving
due attention from the scientific community. Both probabilistic (Sakata et al. (2008), Goyal and Kapania,
(2008), Manan and Cooper (2009), Dey et al. (2016a, 2016e, 2018b, 2019), Naskar et al. (2017b), Naskar
and Sriramula (2017a, 2017b, 2017c), Naskar (2017)) as well as non-probabilistic (Dey et al. (2016b),
Pawar et al. (2012)) approaches have been investigated to analyse the influence of variability in the
material and structural attributes of composite structures. Plenty of researches have been reported based
on intrusive methods to quantify the uncertainty of composite structures (Lal and Singh (2010), Scarth
and Adhikari (2017)), wherein the major drawback can be identified as the requirement of intensive
analytical derivation and lack of the ability to obtain complete probabilistic description of the response
quantities for systems with spatially varying attributes. A non-intrusive method based on Monte Carlo
simulation, as adopted by many researchers (Dey et al. (2016a, 2016g, 2015d), Mukhopadhyay and
4
Fig. 1 (a) Typical distribution of a material property E1 along a cross-sectional view (X-Z plane) of two
laminae for a random realization in case of the layer-wise random variable approach (b) Typical
distribution of a material property E1 along a cross-sectional view (X-Z plane) of two laminae for a
random realization in case of the random field approach
Adhikari (2016c)), can obtain comprehensive probabilistic descriptions for the response quantities of
composite structures. Besides consideration of random variability in material and structural attributes,
recent studies related to uncertainty quantification of laminated composite structures include the effect of
environmental (Dey et al. (2015a)), operational (Dey et al. (2015b)) and service life conditions (Naskar et
al. (2017), Karsh et al. (2018a)) following the non-intrusive approach. A careful consideration of
available scientific literature unveils that most of the studies conducted so far to quantify the effect of
uncertainty in composite structures are based on a ply-level random variable based approach, where the
spatial variation of stochastic parameters in the laminae is neglected. In the previous studies, the material
and structural properties of a lamina are assumed constant spatially (i.e. along the x-y plane) for a
particular realization (refer to figure 1(a)). Modelling of uncertainty in composite structures based on such
random variable based approach is of limited practical resemblance. Therefore, it is essential to consider
5
the effect of spatial variability in the material properties (Kazimierz and Kirkner (2001)) of the laminae to
quantify the effect of uncertainty accurately.
We aim to quantify the effect of spatially varying lamina properties in this article to characterize
the probabilistic descriptions for the dynamics and stability characteristics of composite plates. The aspect
of spatial variation of lamina properties is illustrated in figure 1(b) for a random realization (i.e. a typical
sample of the Monte Carlo simulation), wherein it can be noticed that the stochastic attributes vary in the
x-y plane as well as along the z-axis (i.e. for different laminae). Most of the previous investigations in this
field have not considered the spatial variation of stochastic attributes as shown in figure 1 (Dey et al.
(2016a)). In practical situations, the stochastic attributes often being spatially correlated, it is essential to
account for the effect of such correlation to create a physically realistic model of uncertainty. We aim to
consider the spatially correlated material attributes in composite laminates based on Karhunen-Loeve
expansion (Karhunen (1947), Loève (1977)). However, even after ensuring a physically relevant
uncertainty model of composite laminates, as discussed above, the issue of propagation of uncertainty
following a computationally viable framework still remains to be addressed. The aspect of
computationally efficient uncertainty propagation in context to composite laminates is discussed in the
next paragraph.
Uncertainty quantification based on Monte Carlo simulation is a popular approach because of the
ability to obtain a comprehensive probabilistic description of the response quantities. However, the major
lacuna of this approach is that a Monte Carlo simulation requires thousands of expensive finite element
(FE) simulations to be carried out corresponding to the random realizations. Thus, direct Monte Carlo
simulation has limited practical use due to the computational intensiveness. To mitigate this lacuna we
have developed a surrogate modelling approach based on the high dimensional model representation
(HDMR) technique coupled with the diffeomorphic modulation under observable response preserving
homotopy (DMORPH) algorithm (Li and Rabitz (2012)) for accounting correlated spatially varying
attributes, wherein the uncertainty propagation can be realized following an efficient mathematical
medium.
6
In the present analysis, uncertainty of the system properties is considered in the elementary
micromechanical level to comprehensively analyse the dynamic and stability characteristics of composite
laminates. Thus a probabilistic approach is followed, wherein the effect of uncertainty is included in the
elementary micromechanical-level first and then the effects are propagated towards the global responses
via an efficient surrogate of the actual finite element model. For this purpose, the idea of stochastic
representative volume element (SRVE) is proposed in the context of two-dimensional plate-like
structures. This article hereafter is organized as, section 2: governing equations for analysing the
stochastic dynamics and stability of composite laminates; section 3: brief description of the surrogate
model based on HDMR coupled with the DMORPH algorithm; section 4: description of the SRVE
approach of uncertainty quantification considering spatially correlated material properties hinged upon the
Karhunen-Loève expansion; section 5: results and discussion demonstrating the influence of spatially
varying properties on the global responses of composite laminates; section 6: summary and perspective of
the present study in context to the available scientific literature; section 7: conclusion.
2. Stochastic dynamics and stability analysis of composite plates
In present article, a laminated composite plate with thickness h, length L and width b is analysed
as shown in figure 2 and 3. The governing equation for stochastic free vibration analysis of a composite plate
without damping can be expressed as (refer to the APPENDIX for detail formulation)
 
 
 
( ) ( ) 0MK
   
 

 
(1)
where
 
)()()(
ee KKK
. In the finite element formulation of this study, an eight noded element is
considered, wherein each node has five degrees of freedom (two rotations and three translations). The natural
frequencies
 
k
and mode shapes
()
f
k
S
of the composite plate are obtained by solving an eigenvalue
problem based on QR iteration algorithm (Bathe (1990), Rayleigh (1945))
(2)
where
nk ,....,3,2,1
. The superscript f is used to denote the frequency analysis. Here the orthogonality
relationship is satisfied as
7
(a) (b)
Fig. 2 (a) Force acting on the plate (b) Moment acting on the plate
Fig. 3 Laminated composite plate with layer number
[ ( )] [ ( )] ( )
f T f
i k ik
S M S
 
and
2
[ ( )] [ ( )] ( ) [ ( )]
f T f
i k k ik
S K S
 
(3)
where
nki ,....3,2,1,
and the Kronecker delta functions
ik
=0 for
ki
;
ik
=1 for
ki
. The problem of
stability analysis is solved through another eigenvalue problem as:
 
( ) ( )[ ( )] ( )
b b b
e k e k
K S K S
 
(4)
where
()
b

is the stochastic buckling load factor and
b
k
S
gives the buckling modeshapes. The
superscript b is used to denote the buckling analysis.
8
3. HDMR based surrogate modelling coupled with DMORPH algorithm
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, 2016f, 2018, 2015e), Mukhopadhyay (2019), Mukhopadhyay et al. (2016b, 2016c), 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.
Thousands of sets of the design input parameters can be generated and pseudo analyses for each set can be
efficiently executed by adopting the corresponding surrogate based prediction models. The development
of surrogate models is performed in three typical steps: selection of optimal sample points (which are able
to collect information of the whole design space) to construct surrogate model, evaluation of responses
(i.e. output) corresponding to each of the sample points and formulation of the mathematical/ statistical
prediction model to obtain an efficient input-output relationship based on the sample set (containing a set
of input parameters and corresponding output parameters).
The present HDMR (Dey et al. (2015c, 2016c, 2017) Mukhopadhyay et al. (2015, 2016a)) based
surrogate modelling algorithm is particularly suitable for high dimensional systems (i.e. large number of
input parameters) and correlated system properties. The HDMR can form an efficient model to predict the
random output responses (e.g. natural frequency and buckling load) in the stochastic analysis domain.
This approach is able to treat both independent as well as correlated input variables. The function of D-
MORPH here is to verify the component function orthogonality following a hierarchical approach. The
present formulation decomposes function
)(S
with the component functions by input parameters,
),...,,( 21 kk
SSSS
. As the input parameters are considered to be independent in nature, the component
functions can be projected by vanishing condition. In the present analysis, the component functions is
portrayed, wherein a unified framework for general HDMR dealing with both correlated as well as
9
Fig. 4 Surrogate based analysis of stochastic system (Here
()x
and
( ( ))yx
are the symbolic
representation of stochastic input parameters and output responses respectively.
denotes the
stochasticity of parameters.)
independent variables is developed. For various stochastic input parameters, the output quantity is
calculated as follows (Li and Rabitz (2012), Li et al. (2002a, 2002b))
).,....,,(.......),()()( 21.......12
1 1
0kkkk
kk
ikkji jiijii SSSSSSS
 
 
(5)
kkuuu SS )()(
(6)
where
0
is the zero-th order component function, which represents the mean value.
)( ii S
and
),( jiij SS
represent first and second order component functions, respectively. The expression
).,....,,( 21.......12 kkkk SSS
is the residual contribution by input parameters. Here
},....,2,1{ kku
indicates the subset wherein
kku
. Note that
u
includes
(
u
), which is an empty set. According
to Hooker’s definition, the correlated variables can be expressed as
dSSwSSgArgkkuS ku uu
kkuRLg
uu u
u)()()(min)}|({
2
}),({ 2
(7)
10
0)()(,, uiuu dSdSSwSuikku
(8)
0)(,)()()()(:, vvuuvvuuv SgSdSSwSgSguv
(9)
The parameter
)(S
is obtained from the design points. Assuming H to be a Hilbert space on the basis of
{h1, h2, . . . , hkk}, the bigger subspace
'
H
(
'
H
) can be expanded by the extended basis {h1, h2, . . . ,
hkk, hkk+1, . . . , hm}. Then the subspace
'
H
is decomposed as
' ' '
H H H

(10)
where
'
H
represents the orthogonal complement subspace of
'
H
within the subspace
'
H
. Component
functions of a second order HDMR expansion can be obtained from basis functions
}{
(Li et al.
(2006)),
kk
ri
i
r
i
rii SS 1
)0( )()(
(11)
)()()]()([),( 1 1
)0(
1
)()( j
j
q
l
p
l
qi
i
p
ji
pq
kk
rj
j
r
jji
ri
i
r
iji
rjiij SSSSSS
 
(12)
The HDMR based expansions for
samp
N
sample points of
S
is represented by a linear system of
algebraic equations
RJ ˆ
(13)
where
represents a matrix (
samp
N
×
t
~
), where the elements are basis functions at
samp
N
values of
S
; J
denotes a vector having
t
~
dimension of all the unknown combination coefficients;
R
ˆ
is a vector having
samp
N
dimension, where the
l
-th element is
0
)( )(
l
S
.
)(l
S
is the
l
-th sample of
S
, and
0
represents
the average value of all
)( )(l
S
. Regression equation for the least squares can be written as
R
N
J
NT
samp
T
samp
ˆ
11
(14)
Because of using extended bases, some of the rows of the above expression become identical and these
can be removed for obtaining an underdetermined algebraic system of equation
VJA ˆ
(15)
11
This has many solutions for
J
composing a manifold
t
Y~
. Thus a solution
J
from
Y
is found to force
the HDMR component functions that satisfy hierarchical orthogonal condition. DMORPH regression can
provide a solution for ensuring the additional condition
)()()(
)( lvAAIlv
dlldJ t
(16)
where
is an orthogonal projector having the following properties
2
and
T
(17)
T
2
(18)
The free function vector can be adopted for ensuring the wide domain for
)(lJ
and to reduce the cost
))(( lJ
simultaneously
JlJ
lv
))((
)(
(19)
Then,
0
))(())((
)(
))(()())(())((
JlJ
P
JlJ
P
lvP
JlJ
llJ
JlJ
llJ
T
TT
(20)
The cost function is obtained in a quadratic form
JBJT
2
1
(21)
where, B denotes a positive definite symmetric matrix and
J
is expressed as
VAUVUVJ Trtrt
Trt
tˆ
)( ~
1
~~
(22)
Here the last columns
)
~
(rt
in
U
and
V
are obtained as
rt
U
~
and
rt
V
~
which is obtained by
decomposition of
B
T
rV
S
UB
00
0
(23)
The solution
J
in Y , which is unique, indicates the minimized cost function. Here DMORPH regression
is adopted to obtain J ensuring the HDMR component functions’ orthogonality in a hierarchical manner.
12
4. Stochastic representative volume element based framework for uncertainty quantification
4.1. Concept of SRVE
In this paper a concept of stochastic representative volume element (SRVE) is proposed for two-
dimensional plate-like structures to account for the effect of spatial randomness of material properties.
According to this approach, each of the representative units (structural element) is considered to be
stochastic in nature, instead of considering the homogenized mechanical properties of a conventional
representative volume element (RVE) throughout the entire solid domain. As per the traditional approach,
one RVE is analysed typically and the assumption is that a single RVE can represent the entire analysis
domain (Sriramula and Chryssanthopoulos (2009), Mukhopadhyay et al. (2017a, 2017b, 2018a)).
However, this approach of analysis can lead toeroneous outcomes, specially in case of stochastic systems
with spatial randomness in material and other attributes. To analyse such systems, it is essential to account
for the effect of the distribution of stocahstic mechanical properties along the spatial location of different
zones of a plate-like structure.
According to the present approach, the entire plate is assumed to be consisted of a finite number of
SRVEs. Thus mechanical properties of a SRVE are dependent on its stochastic material and structural
properties. Following this framework, it becomes feasible to consider the spatial randomness in a
structural system more realistically. The global responses (such as natural frequencies and buckling loads)
of the plate are computed by propagating the mechanical information acquired in the elementary local
level (SRVEs) towards the global level by combing (/assembling) the SRVEs applying the principles of
solid mechanics (finite element approach in the present study). Recently, a similar concept has been
proposed for analyzing hexagonal honeycomb-like lattices having spatial irregularity (Mukhopadhyay and
Adhikari (2016a, 2016b, 2017a), Mukhopadhyay et al. (2018a), Mukhopadhyay (2017)), wherein multiple
representative unit cell elements (RUCE) are analysed instead of the conventional approach of considering
a single unit cell. The entire lattice structure is assumed to be consisted of several RUCEs and the global
13
Fig. 5 SRVE based approach for analyzing spatially random two dimensional systems
mechanical properties of the entire irregular lattice can be computed by assembling the RUCEs based on
equilibrium and compatibility conditions. The concept of SRVE for analyzing one dimensional beam-like
structures with random material properties and crack density is first adopted by Naskar et al. (2017). In
this paper, we have generalized the concept for stochastic analysis of two-dimensional plate-like
structures with randomly inhomogeneous form of uncertainty (Mukhopadhyay and Adhikari (2017b)). It
can be noted that spatially correlated material properties can be conveniently accounted in this approach
based on the Karhunen-Loève expansion. The adoption of SRVEs in a plate-like structure is shown in the
figure 5, wherein the two-dimensional space is divided into a finite number of stochastic elements
(SRVEs) having dimensions of l1 and l2 in two mutually perpendicular directions of the two-dimensional
structure. Each of the SRVEs possesses different material and structural properties. Here, a parameter
characteristic length (
r
) can be defined as:
12
12
1
d
ll
rL L N
 
, where
d
N
denotes the number of divisions
along the two dimensions of the plate. As per the proposed concept of SRVE, the size (/number) of SRVE
is independent of the discretization in a finite element based numerical solution that could be adopted for
dynamic/ stability analysis of the composite plate. The size (/number) of SRVE would normally be
govorned by the spatial distribution of structural and material attributes along with the correlation length.
Once the size of a SRVE is decided, they could be discretized following conventional finite element
analysis using a mesh convergence study. In the present analysis, we have only considered spatial
14
variation of material properties; but future studies could include random spatial variation of
microstructural properties (such as micro-scale damage) using the proposed SRVE based framework. In
such problems, appropriate finite element meshing schemes would need to be adopted for each of the
SRVEs.
4.2. Characterization of correlated material properties based on Karhunen-Loève expansion
Random fields are useful for modelling variables which have correlated spatial randomness. When
spatial variation of structural and material properties are considered in a randomly inhomogeneous
structural system, the properties are often found to be spatially correlated. The conventional approach to
deal with such random fields is to discretize it into different finite number of random variables. Available
schemes for discretizing the random fields can be classified into three groups, point discretization (e.g.,
midpoint (Klintworth and Stronge (1988)), shape function (Liu et al. (1986a, b)), integration point
method (Matthies et al. (1997)), optimal linear estimate (Li and Der (1993))); series expansion (e.g.,
orthogonal series expansion (Zhang and Ellingwood (1994))), and average discretization (e.g., spatial
average (Vanmarcke (1983), Vanmarcke and Grigoriu (1983)), weighted integral (Deodatis (1991),
Deodatis and Shinozuka (1991))).
The beneficial alternative for discretizing the random field is representing it in a generalized
Fourier type of series such as Karhunen-Loève (KL) expansion (Karhunen (1947); Loève, (1977)). Let us
consider a random field
 
,
x
with covariance function
12
( , )xx
defined in the probability space
( , , )FP
. The KL expansion for
 
,
x
can be expressed in the following form
 
1
,i i i
i
 
 
x x x
(24)
where
 
 
i

represents a set of random variables with no correlation.
 
i
and
 
 
i
x
denote the
eigenvalues and eigenfunctions of the covariance kernel
12
( , )xx
, satisfying the integral equation
 
1 2 1 1 2
( , )
N
i i i
d
 
x x x x x
(25)
In practice, the infinite series in Equation (24) is truncated as
15
   
1
,N
i i i
if
 

x x x
(26)
The above expression approaches to
 
,
x
in a mean square sense for
N
. The finite element
method (Huang et al. (2001)) can be applied to compute the eigensolutions for any covariance function
and domain in the random field. In case of linear and exponential covariance functions along with a
simple domain, the eigen solutions are feasible to be obtained analytically (Huang et al (2001)). Once
()s
and its eigen solutions are computed, the parameterization of
 
,
x
is carried out by the KL
approximation of Gaussian image,
     
1
,N
i i i
i
Gf
 




x x x
(27)
As per Equation (27), the KL approximation gives a parametric representation of
 
,
x
with M
random variables. It can be noted that this is not the only available method for discretizing the random
field
 
,
x
. However, KL expansion has uniqueness and error minimization properties that make it a
superior choice over the other methods (Huang et al. (2001)).
In the present article, the stochastic material properties (micro/ macro-mechanical) are modelled as
random fields and these are discretized using the KL expansion. To be specific, lognormal random fields
are considered for modelling the correlated material properties. The covariance function is expressed as
 
 
1 2 1 22eyz
y y b z z b
zz

 

(28)
where
y
b
and
z
b
represent the two planar directions. These parameters control the rate of covariance decay.
The eigensolutions of the covariance function can be obtained by solving the integral analytically (refer to
Equation 25)
 
1
2
1
2
2 2 1 1 2 2 1 1 1 1
, ( , ; , ) ,
aa
i i i
a
a
y z y z y z y z dy dz
 
(29)
where,
11
a y a  
and
22
a z a  
. Assuming that the eigen-solution can be separated in y and z
directions and substituting the covariance function
16
 
 
 
 
 
2 2 2 2
,yz
i i i
y z y z  
(30)
 
 
 
 
 
2 2 2 2
,yz
i i i
y z y z
 
(31)
, the solution of equation (29) can be reduced to the product of the solutions of two equations having the
form
   
 
 
 
1
12
1
()
1 2 2
y
ay y b
y y y
i i i
a
y e y dy

 
(32)
Solution of the above equation, which is the eigensolution of an exponential covariance kernel with a one-
dimensional random field, can be obtained as
   
 
 
 
 
2
22
*2
**2 2
*
*
2
cos for odd
sin 2
2
sin 2for even
sin 2
2
z
z
i
ii
i
i
i
i
ii
i
i
i
bi
b
a
bi
b
a






 
 
(33)
Where
1
y
bb
or
1
z
b
and
1
aa
or
2
a
. The parameter
is either y or z. Here
i
and
*
i
are computed
from the solutions of the following equations
tan( ) 0
ii
ba


and
tan( ) 0
ii
ba


, respectively. It
can be noted that the KL expansion was formulated for discretization following gaussian random fields. In
case of lognormal random fields, as considered in the present study, the KL expansion is formulated on its
gaussian image. In the current study of laminated composite plates, the spatially correlated properties are
parameterized by the respective mean values (considered to be same as the deterministic value of a
particular parameter), the coefficient of variation (COV) and two correlation parameters. The degree of
stochasticity is defined as the coefficient of variation (
) of a particular stochastic input parameter.
17
4.3. Description of the uncertainty quantification framework
4.3.1. Monte Carlo simulation
Uncertainty quantification is part of modern structural analysis problems. Practical structural
systems need to face uncertainty, variability and ambiguity on a constant basis. Even after having
unprecedented access to the information due to recent improvement in various technologies, it is
impossible to accurately predict future structural behaviour during its service life. Monte Carlo
simulation, a computerized mathematical technique, lets us realize all the possible outcomes of a
structural system leading to better and robust designs for the intended performances. This technique was
first used by the engineers and scientists developing the atom bomb and it was named after a Monaco
resort town Monte Carlo. Since the introduction during World War II, this technique has been applied to
model various physical and conceptual systems across different fields covering engineering,
manufacturing, energy, finance, insurance, project management, transportation and environment.
Monte Carlo simulation (MCS) furnishes a range of prospective outcomes along with their
respective probability of occurrence. This technique performs uncertainty quantification by forming
probabilistic simulations of all prospective results accounting a wide range of values from the probability
distributions of any factor having an inherent uncertainty. This method simulates the outputs multiple
times, using a different set of random values each time, drawn from the probability distribution of
stochastic input parameters. Depending upon the nature of stochasticity, a Monte Carlo simulation may
involve thousands or tens of thousands of realizations (/function evaluations) before it can provide a
converged result depicting the distributions of possible outcome values of the response quantities of
interest. Thus Monte Carlo simulation provides not only a comprehensive idea of what could happen, but
also how likely it is to happen i.e. the probability of occurrence.
The mean (/expected) value of a function
()fx
having an n dimensional vector of random variables
and a joint probability density function
()x
, is expressed as
   
()
fE f x f x x dx




(34)
18
The variance of the function
()fx
is given as,
   
 
2
2()
ff
Var f x f x x dx
 

 

(35)
The multidimensional integrals, as shown in equation (34) and (35) are difficult to compute analytically
various types of joint density functions. Moreover, the integrand function
()fx
may not always be
available in an analytical form for the problem under consideration. Thus the only alternative way is to
calculate it by numerical means. It can be computed using the MCS approach, wherein N number of
sample points is generated following a suitable sampling scheme in the random variable space of n-
dimensions. The N number of samples drawn from a dataset must follow the same distribution as
()x
.
The function
()fx
is computed at each of the sampling points of the sample set
 
1,............, N
xx
.
Thus, the integral for the expected value can be expressed in the form of averaging operator as
 
1
1N
i
fi
E f x f x
N



(36)
In a similar manner, using sampled values of MCS as above, the equation (35) leads to
 
 
2
2
1
11
N
i
ff
i
Var f x f x
N


 

(37)
Thus the statistical moments can be obtained using a brute force MCS based approach, which is often
computationally very intensive due to the evaluation of function
()
i
fx
corresponding to the N-sampling
points , where N ~ 104. The noteworthy fact in this context is the adoption of a surrogate based Monte
Carlo simulation approach in the present study that reduces the computational burden of traditional (i.e.
brute force) Monte Carlo simulation to a significant extent.
4.3.2. Modelling of source-uncertainty at the input level
The stochasticity in material properties (micro/ macro- mechanical properties) and geometric
properties (like ply-orientation angle and thickness of plate) are considered as stochastic input parameters
for analyzing the probabilistic dynamic and buckling characteristics of laminated composite plates. In the
i
x
i
x
19
present article, two separate forms of analysis have been performed considering the stochasticity in micro-
mechanical and macro-mechanical properties to understand and ascertain the cascading effect in
uncertainty propagation on a comparative basis. For analysing the effect of various source-uncertainties,
the following four cases of stochasticity are considered
(i) Compound effect for the simultaneous variation of macro-mechanical material properties (such as
Young’s moduli, shear moduli, mass density and Poisson’s ratio) and geometric properties (such as ply-
orientation angle and thickness of laminae)
 
1 2 12 13 23
1 2 3
1(1,1) 1( , ) 2(1,1) 2( , ) 12(1,1) 12( , )
45
13(1,1) 13( , ) 23(1,1) 23(
( ), ( ), ( ), ( ), ( ), ( ), ( ), ( ), ( )
( .... ), ( .... ), ( .... ),
( .... ), ( ....
macro e l macro e l macro e l
macro e
C
macro
l macro
E E G G G t
E E E E G G
G G G
g
G
 

 
  6
, ) (1,1) ( , )
7 8 9
(1,1) ( , ) (1,1) ( , ) (1,1) ( , )
), ( .... ),
( .... ), ( .... ), ( .... )
e l macro e l
macro e l macro e l macro e l
tt




 


   


(38)
(ii) Compound effect for the simultaneous variation of micro-mechanical material properties such as
Young’s moduli of fibre and matrix, shear moduli of fibre and matrix, Poisson ratios of fibre and matrix,
mass densities of fibre and matrix and volume fraction along with geometric properties (ply orientation
angle and thickness of laminae)
 
12
1 2 3 4
1 (1,1) 1 ( , ) 2 (1,1) 2 ( , ) (1,1) ( , ) (1,1) ( , )
( ), ( ), ( ), ( ), ( ), ( ), ( ), ( ), ( ), ( ), ( ), ( )
( .... ), ( .... ), ( .... ), ( .... ),
f f m f m f m f m f
micro f f e l micro f f e l micro m m e l micr
C
m
o f f e l
mi
icr
c
o
r
E E E G G V t
E E E E E E G G
g
 
 
 

5 6 7 8
(1,1) ( , ) (1,1) ( , ) (1,1) ( , ) (1,1) ( , )
9 10 11
(1,1) ( , ) (1,1) ( , ) (1,1) ( , )
( .... ), ( ...., ), ( ...., ), ( .... ),
( .... ), ( .... ), ( .... ),
o m m e l micro f f e l micro m m e l micro f f e l
micro m m e l micro f f e l micro f f e l micr
GG
VV

     
  12 (1,1) ( , )
( .... )
o f f e l
tt







(39)
(iii) Individual effect for the variation of a single macro-mechanical property
 
 
1(1,1) ( , )
( ) ( .... )
I
macro M macro M M e l
g
 

(40)
(iv) Individual effect for the variation of a single micro-mechanical property
 
 
1(1,1) ( , )
( ) ( .... )
I
micro m micro m m e l
g
 

(41)
Here
is a symbolic operator that generates a set of input parameters for carrying out the Monte Carlo
simulation. The parameters
         
1 , 2 , 12( , ) 13 , 23 , , , ,
( ) ( ) ( ) ,
, , , , ,,,,
i j i j i j i j i j i j i j i j i j
E E G G G t

denote the longitudinal
Young’s modulus, transverse Young’s modulus, shear moduli, Possoin’s ratio, mass density, ply
20
orientation angle and thickness of lamina respectively (with conventional notations) for the ith SRVE
situated in the jth layer, where i = 1, 2, 3,…, e and j = 1, 2, 3, …, l. For the stochasticity in micro-
mechanical properties,
1 ( , ) 2 ( , ) ( , ) ( , ) ( , ) ( , ) ( , ) ( , ) ( , ) ( , ) ( , ) ( , )
, , , , , , , , , , ,
f i j f i j m i j f i j m i j f i j m i j f i j m i j f i j i j i j
E E E G G V t
 
denote (following conventional notations) Young’s moduli of fibre in longitudinal and transverse
directions, Young’s modulus of matrix, shear modulus of fibre and matrix, Poisson’s ratio of fibre and
matrix, mass density of fibre and matrix and volume fraction, ply orientation angle and thickness
respectively corresponding to
th
i
layer, respectively for the ith SRVE situated in the jth layer, where i = 1, 2,
3,…, e and j = 1, 2, 3, …, l. The quantities
M
and
m
denote any one of the macromechanical and
micromechanical properties. The material properties are considered to vary spatially (correlated variation
following the KL expansion) for both the macromechanical and micromechanical analyses. However,
considering practical aspects of modelling uncertainty in the geometric parameters, spatial variation is not
considered for ply orientation angle and thickness of lamina; rather a layer-wise uncorrelated random
variation is considered for the two geometric parameters (i.e.
(1, ) (2, ) (3, ) ( , )
...
j j j e j
 
 
and
(1, ) (2, ) (3, ) ( , )
...
j j j e j
t t t t  
, for the jth lamina).
4.3.3. Propagation of uncertainty based on HDMR coupled with DMORPH algorithm
In a typical problem of uncertainty analysis, there are normally three aspects that need to be dealt
with. The first aspect is source-uncertainty modelling at the input level, which is discussed in the
preceding subsection. After the uncertainty in material and structural attributes is modelled in a
practically relevant way, the next concern is propagating the effect of uncertainty from the local input-
level to the global level of quantifying output responses following a bottom-up framework. As discussed
in section 1, direct Monte Carlo simulation being a computationally intensive approach, we have adopted
a surrogate based uncertainty propagation scheme for the present analysis. To achieve computational
efficiency, a HDMR based surrogate modelling framework coupled with the DMORPH algorithm is
developed in conjunction with the probabilistic finite element model of composite plates as presented in
figure 6.
21
Fig. 6 Flowchart for stochastic macro and micro mechanical analysis of laminated composites based on
surrogate models (Representative figures of finite element analysis, sobol’s quasi-random sampling,
surrogate modelling and uncertainty quantification are shown corresponding to the respective steps)
The surrogate models are constructed by choosing the sample points optimally from a domain Rn.
All input variables are rescaled in the range of 0 xi 1, where xi denotes the ith input parameter.
Generation of the random sample points is an important aspect to form the HDMR model because the
quality of the random sample points governs the convergence rate and prediction accuracy. Quasi-random
22
sequences (Niederreiter (1992)) (such as Halton (Halton (1960)), Sobol (Sobol (1967)), Faure (Faure
(1992))) having low discrepancy are often used for generating random sample points that ensures a
uniform distribution of input sample points in the design domain. It essentially results in a faster
convergence rate compared to pseudo-random sample points. In this work, Sobol sequence is used to
generate the input sample set as it shows a better convergence rate than Faure and Halton sequences
(Galanti and Jung (1997)). It can be noted in this context that in a surrogate based approach, first the
surrogate model is constructed using few optimally chosen design points. The same number (number of
design points) of finite element simulations (\function evaluations) is required to be performed for the
surrogate model formation. Here the HDMR model replaces the original finite element model (expensive)
effectively by an efficient mathematical/statistical model. After the HDMR based surrogate model is
constructed, thousands of virtual simulations can be conducted for various random combinations of the
input parameters using the efficient HDMR model.
5. Results and discussion
In this article, numerical results for stochastic dynamic and stability analyses are presented for a
three layered graphite-epoxy angle-ply ([45o/-45o/45o]) composite square plate, unless otherwise
mentioned. A practically relavent randomly inhomogeneous (Mukhopadhyay and Adhikari (2017b))
model of stochasticity with spatially correlated system parameters are considered for characterizing the
first three modes of vibration and buckling of composite plates. Results are presented for two distinct
cases: stochasticity in micromechanical and macromechanical material properties (refer to equation 38
41). The deterministic micromechanical properties (E-glass
21 43xK
Gevetex/
3501 6
epoxy) of
composite material are shown in Table 1 (Soden et al. (1998)). Applying Halpin-Tsai principle (Jones
(1999)) the deterministic macromechanical material properties are obtained with a volume fraction (
f
V
)
of 0.61 (refer to Table 2). Thus, for the case of stochasticity in micromechanical properties, the material
atributes presented in Table 1 are assumed as the source of stochasticity along with the uncertain
geometric parameters (
C
micro
g
) and thereby the macromechanical material properties are obtained based on
Halpin- Tsai principle to perform further analysis for quantifying uncertainty. For the case of stochasticity
23
Table 1 Deterministic micromechanical material properties of composites
Property
Value
Longitudinal Young’s modulus of fibre (
1f
E
)
80 GPa
Transverse Young’s modulus of fibre (
2f
E
)
80 GPa
Poisson's ratio of fibre (
f
)
0.2
Shear modulus of matrix (
f
G
)
33.33 GPa
Mass density of fibre (
f
)
2.55 gm/cc
Mass density of matrix (
m
)
1.265 gm/cc
Young’s modulus of matrix (
m
E
)
4.2 GPa
Shear modulus of matrix (
m
G
)
1.567 GPa
Poisson's ratio of matrix (
m
)
0.34
Fibre volume fraction (
f
V
)
0.61
Table 2 Deterministic macro-mechanical material properties of composites (
f
V
= 0.61)
Property
Value
Longitudinal Young’s modulus (
1
E
)
50.438 GPa
Transverse Young’s modulus (
2
E
)
9.952 GPa
Poisson's ratio (
12
)
0.2546
In-plane shear modulus (
12
G
)
3.742 GPa
Mass density (
)
2.049 gm/cc
Shear modulus (
13
G
)
3.742 GPa
Transverse shear modulus (
23
G
)
2.094 GPa
24
in macromechanical material properties, the analysis commences one step ahead in the hierarchy i.e. the
source-uncertainty is assumed in the macromechanical properties (as shown in Table 2) along with
uncertain geometric parameters (
C
macro
g
). Subsequently, the results obtained from these two different types
of analyses are compared to ascertain the cascading effect in stochasticity. Non-dimensional results are
presented following the scheme mentioned in the caption of Table 3 and 4.
5.1. Stochastic dynamic analysis
5.1.1. Validation and convergence study
In the surrogate assisted stochastic analysis of laminated composites, two different forms of
validation and convergence study are needed to be carried out. The first validation is for the finite element
model of composite plate along with mesh convergence study. A second type of validation is also needed
here concerning the performance (efficiency and accuracy) of the surrogate model in predicting the
responses along with a convergence study for minimizing the number of design points required for
forming surrogate models.
The results for validation and convergence study of the finite element code of a composite plate are
shown in Table 3, wherein non-dimensional natural frequencies are validated with the results available in
scientific articles (Liew and Huang (2003)). Based on the results presented in Table 3, a mesh size of 9
9
is adopted for the finite element analysis hereafter. The optimum number of samples (drawn from Sobol
sequence) to construct the surrogate models are decided based on the comparative performance (four
different statistical parameters: minimum value, maximum value, mean value and standard deviation)
with respect to direct Monte Carlo simulation. The convergence results for macro and micro mechanical
analyses showing the values of absolute error with different sample size are presented in figure 7 and 8.
From the figures it is evident that a sample size of 1024 provides reasonably accurate results for the
natural frequencies. To further examine the prediction capabilities of the surrogate models, scatter plots
are presented for the macro and micro mechanical analyses in figures 9(a-c) and 10(a-c), respectively.
Negligible deviation of the sample points from the diagonal line corresponding to the sample size of 1024
indicates the accuracy of prediction. The comparative probability density function plots on the basis of
25
Table 3 The convergence study of frequency parameters [
2
20
( ) ( )
h
bD
, where
3
2
012 21
12(1 )
Eh
D

] for cross-ply
(0 / 90 /0 )
o o o
simply supported (SSSS) rectangular laminates
t/b
Mesh
Mode 1
Mode 2
Mode 3
Present
FEM
Liew (1996)
Present
FEM
Liew (1996)
Present
FEM
Liew (1996)
0.001
5×5
6.8817
6.6253
9.7192
9.4653
24.4234
22.4607
6×6
6.7864
9.6212
24.0928
7×7
6.7056
9.5312
23.4168
8×8
6.6258
9.4684
22.4802
9×9
6.6251
9.4644
22.4520
10×10
6.6210
9.4401
22.2811
0.20
5×5
3.6867
3.5940
6.1626
5.7784
8.1347
7.3972
6×6
3.6314
6.0189
7.9167
7×7
3.6145
5.9180
7.6512
8×8
3.5978
5.7337
7.3368
9×9
3.5913
5.7331
7.3345
10×10
3.5839
5.7036
7.2366
surrogate based MCS corresponding to the sample size of 1024 and direct MCS are presented for the
macro and micro mechanical analyses in figures 9(d-f) and 10(d-f), respectively. A good agreement
between the probabilistic descriptions of natural frequencies corroborates the accurate prediction
capability of the surrogate models for further analyses. It can be noted in this context that computational
time required is exorbitently high for evaluating the probabilistic responses through full scale MCS
because of the involvement of large number of finite element simulations (~104). However, in case of the
present surroagate based method, although a same sample size as the direct MCS is considered, the
requirement of carrying out actual finite element simulations is much lesser compared to the direct MCS
approach. Here it is equal to the number of samples required to form the HDMR based surrogate model
(i.e. 1024). Hence, the computational intensiveness (time and effort) in terms of finite element analyses
26
(a) Maximum
(b) Minimum
(c) Mean
(d) Standard Deviation
Fig. 7 Absolute percentage error in minimum value, maximum value, mean value and standard deviation
with respect to direct Monte Carlo simulation for macromechanical material properties for a coefficient of
variation of 0.6 in the stochastic input parameters (
C
macro
g
).
are decreased significantly in comparison to full-scale direct MCS.
5.1.2. Results for stochastic dynamic analysis
Having the FE model and the surrogate model validated, as shown in the preceding subsection,
stochastic results are presented in this subsection for the first three modes of vibration for a composite
plate with correlated spatially varying material properies (
C
macro
g
and
C
micro
g
). Figure 11 shows the
probabilistic descriptions for first three natural frequencies considering different degrees of stochasticity
for macro-mechanical and micro-mechanical material properties. For both the micro and macro
mechanical analyses, response bounds are noticed to substantially increase with the increasing degree of
stochasticity along with a marginal change in mean values. The probabilistic descriptions of the natural
frequencies differ from each other on the basis of the adopted type of analysis. A micromechanical
analysis, which is more accurate for considering the source uncertainty at a more elementary level, shows
higher degree of variability in the global responses due to the cascading effect in stochasticity.
27
(a) Maximum
(b) Minimum
(c) Mean
(d) Standard Deviation
Fig. 8 Absolute percentage error in maximum, minimum, mean and standard deviation with respect to
original MCS considering a coefficient of variation of 0.6 in the stochastic input parameters (
C
micro
g
).
The probabilistic variations for first three natural frequencies are investigated for various laminate
configurations considering the
 
//
 
family of composites for stochasticity in micro-mechanical
properties. The probabilistic descriptions presented in figure 12 show that the mean values, depending on
the effective stiffness of the structure, decrease up to a critical value of
45o
, and after this point they
increase again for higher values of
. Figure 13 shows the influence of different boundary conditions on
the stochastic natural frequencies of composite plates. Results are presented for simply supported (SSSS)
and fixed (CCCC) boundaries at all the four edges considering stochasticity in micro-mechanical
properties. The range of natural frequencies is found to vary depending on the stiffness of the system,
following a similar trend as deterministic analysis. Figure 14 shows the effect of aspect ratio of the
composite laminated plates on the probabilistic variation of natural frequencies considering stochasticity
in micro-mechanical material properties. The stochastic natural frequencies are noticed to reduce with the
increase in aspect ratio.
28
Fundamental natural frequency
(a)
(d)
Second natural frequency
(b)
(e)
Third natural frequency
(c)
(f)
Fig. 9 (a-c) Scatter plots for HDMR based analysis (normalized on the basis of respective deterministic
values) for various sample sizes of Sobol sequence considering the stochasticity in macro-mechanical
attributes (
C
macro
g
) with respect to direct MCS (with coefficient of variation of 0.6 in the stochastic input
parameters) ; (d-f) Probability density function (pdf) of natural frequencies (normalized with respect to
the corresponding deterministic values) for macro-mechanical attributes (
C
macro
g
) obtained by direct MCS
and HDMR model based on a sample size of 1024 (with coefficient of variation of 0.6 in the stochastic
input parameters)
29
Fundamental natural frequency
(a)
(d)
Second natural frequency
(b)
(e)
Third natural frequency
(c)
(f)
Fig. 10 (a-c) Scatter plots for HDMR based analysis (normalized on the basis of respective deterministic
values) for various sample sizes of Sobol sequence considering the stochasticity in micro-mechanical
attributes (
C
micro
g
) with respect to direct MCS (with coefficient of variation of 0.6 in the stochastic input
parameters) ; (d-f) Probability density function (pdf) of natural frequencies (normalized with respect to
the corresponding deterministic values) for micro-mechanical attributes (
C
micro
g
) obtained by direct MCS
and HDMR model based on a sample size of 1024 (with coefficient of variation of 0.6 in the stochastic
input parameters)
30
(a)
(b)
(c)
Fig. 11 Probability density function (pdf) plots for macro (
C
macro
g
) and micro (
C
micro
g
) mechanical analyses
of natural frequencies for different degree of stochasticity (
). Normalized results are used with respect to
the corresponding deterministic values for plotting the pdfs. The bar plots in the inset indicate the
percentage increase of stochastic bounds for a micromechanical analysis with respect to the
macromechanical analysis.
Figure 15 presents the relative coefficient of variation (RCOV) for individual stochastic effect of
different uncertain input parameters considering both the macro (
I
macro
g
) and micro (
I
macro
g
) mechanical
analyses. To obtain these figures, MCS are carried out for the variation of each of the micro and macro
mechanical material parameters individually. The figures can provide a clear understanding regarding the
relative sensitivity of various stochastic system parameters (input) to the output natural frequencies in the
macro and micro mechanical analyses. For stochasticity in macro-mechanical material properties, it is
observed that mass density, longitudinal Young’s modulus and transverse Young’s modulus (in
decreasing order of sensitivity) are most sensitive to the first three natural frequencies, while the shear
31
(a)
(b)
(c)
Fig. 12 Probability density function (pdf) plots for natural frequencies considering different ply
orientation angle (
) for stochasticity in micro-mechanical properties (
C
micro
g
)
moduli and Poisson’s ratio are comparatively less sensitive. For the micro-mechanical analysis, it is found
that the most sensitive system parameters (stochastic input) according to decreasing order of sensitivity
are mass density of fibre, longitudinal Young’s modulus of fibre and volume fraction, while transverse
Young’s modulus of fibre, Poisson’s ratio of fibre and shear modulus of fibre are the least sensitive
parameters. Two different forms of analyses, as carried out here considering macro and micro mechanical
material properties, render an in-depth understanding regarding the relative influence of various stochastic
input parameters (source-uncertainty). For example, the macromechanical analysis (refer to figure 15(a))
shows that mass density of composites is the most sensitive parameter for low frequency vibration modes;
however, the micromechanical analysis (refer to figure 15(b)) provides information in more depth
showing that mass density of fibre is the most sensitive parameter. Outcomes of such sensitivity analyses
serve as an important guideline for efficient uncertainty quantification (including dimensionality
reduction) and subsequent analysis/ design and quality control of input parameters.
32
CCCC
SSSS
Fundamental natural frequency
(a)
(d)
Second natural frequency
(b)
(e)
Third natural frequency
(c)
(f)
Fig. 13 (a-c) Probability density function (pdf) plots of natural frequencies for micro mechanical analysis
(
C
micro
g
) considering clamped boundary condition (CCCC) (d-f) Probability density function (pdf) plots of
natural frequencies for micro mechanical analysis (
C
micro
g
) considering simply supported boundary
condition (SSSS)
33
(a)
(b)
(c)
Fig. 14 Probability density function (pdf) plots of natural frequencies for different values of aspect ratios
(AR) considering micro-mechanical properties (
C
micro
g
)
The random field of the stochastic micro and macro mechanical material properties depend on the
correlation length considered in the analysis. We have investigated the effect of correlation length on the
first three modes of vibration. Depending on the value of correlation length, the random field of all the
micro and macro mechanical properties would vary; representative plots are presented in figure 16
showing the spatial distribution of micro and macro mechanical material properties concerning the
longitudinal Young’s modulus for a single random realization considering different values of correlation
length. It can be noted that two extreme cases can be realized when the correlation length tends to the
upper and lower limits. The system becomes a randomly homogenous system (analogous to the random
variable based approach) when the correlation length is very high, while it becomes an uncorrelated
randomly inhomogeneous system when the correlation length is very low. The typical probability
distribution of a representative micromechanical property (E1f) and a macromechanical property (E1) for a
randomly chosen SRVE having three different laminae is shown in figure 17 considering a correlation
34
(a)
(b)
Fig. 15 (a) Relative coefficient of variation (RCOV) for the natural frequencies considering macro-
mechanical material properties (b) Relative coefficient of variation (RCOV) for the natural frequencies
considering micro-mechanical material properties
length of 1/50. The effect of correlation length on the probability density function plots of the first three
natural frequencies is presented in figure 18, wherein a clear difference is noticed between the lower and
higher values of correlation lengths. This, in turn indicates the difference in the probabilistic
characteristics of the natural frequencies corresponding to random field (lower values of correlation
length) and random variable (higher value of correlation length) approach. The figure shows that the
mean and response bound decrease in case of the random field based modelling of source-uncertainty
compared to the random variable based modelling.
35
CoL = 1
CoL = 1/20
CoL = 1/50
CoL = 1/100
CoL = 1/200
E1f
(a)
(b)
(c)
(d)
(e)
Em
(f)
(g)
(h)
(i)
(j)
Vf
(k)
(l)
(m)
(n)
(o)
E1
(p)
(q)
(r)
(s)
(t)
Fig. 16 Representative plots showing the spatial distribution of micro and macro mechanical properties concerning the longitudinal Young’s modulus for a random
realization considering different values of correlation length (CoL). Here X and Y axes in the figures are the two spatial directions of a lamina, while the Z axis shows
the value of corresponding material property.
36
(a)
(b)
Fig. 17 Probability distribution of a representative micromechanical property (E1f) and a
macromechanical property (E1) for a randomly chosen SRVE having three different laminae considering a
correlation length of 1/50
(a)
(b)
(c)
Fig. 18 Effect of correlation length (CoL) on the probability distribution of the first three natural
frequencies of a composite laminate
The effect of stochasticity in micro and macro mechanical material properties are studied on the
vibration mode shapes considering two different boundary conditions (SSSS and CCCC). The results are
presented in figure 19 for first three modes of vibration. Stochastic mode shapes for first three modes of
37
Fig. 19 Stochastic modeshapes and representative probability distribution of the normalized eigenvectors
38
vibration considering a single random realization are presented in case of: a clamped (CCCC) composite
plate with stochasticity in the macromechanical (
C
macro
g
) properties (refer to figure 19(a c)), a simply
supported (SSSS) composite plate with stochasticity in the macromechanical (
C
macro
g
) properties (refer to
figure 19(d f)), a clamped (CCCC) composite plate with stochasticity in the micromechanical (
C
micro
g
)
properties (refer to figure 19(g i)) and a clamped (CCCC) composite plate with stochasticity in the
micromechanical (
C
micro
g
) properties (refer to figure 19(j l)). From the mode shapes presented in figure
19(a l), it can be observed that the basic global pattern of the stochastic mode shapes remains similar to
the corresponding deterministic case. However, the value of normalized eigenvectors becomes stochastic
in nature for each of the elements in the composite plate. Probability distribution of the normalized
eigenvectors of first three vibration modes for the elements indicated in figure 19(o) are shown
considering a clamped (CCCC) boundary condition (refer to figure 19(m)) and a simply supported (SSSS)
boundary condition (refer to figure 19(n)). The results for micro and macro mechanical analyses are
shown using lighter and darker shades of respective colours indicated in figure 19(o). It can be noticed
that the probability density function plots depend significantly on the type of analysis (micro and macro
mechanical) and location of the element under consideration.
5.2. Stochastic stability analysis
5.2.1. Validation and convergence study
The FE code and the surrogate model are validated first for analysing the buckling loads similar to
the case of dynamic analysis as discussed in section 5.1.1. The results of convergence study and
validation of the finite element code of a composite plate is furnished in Table 4, wherein the non-
dimensional first buckling load is validated with the results available in scientific literature. Based on the
results presented in Table 4, a mesh size of 9
9 is found to be adequate for the finite element model. The
optimum number of samples (drawn from Sobol sequence) to form the surrogate models of buckling
loads are decided based on the comparative performance (four different statistical parameters: minimum
value, maximum value, mean value and standard deviation) with respect to direct MCS. The results of
convergence study for macro and micro mechanical analyses showing the values of absolute error with
39
Table 4 The convergence study of uniaxial buckling load of four layer
(0 /90 /90 /0 )
o o o o
simply
supported SSSS rectangular laminates
23
2
( ( ), 0, 0)
xx xy yy
N N b E h N N  
t/b
Mesh
Buckling load
Present FEM
Reference results
0.001
5×5
23.3511
23.2928 (Neves and Ferreira (2016))
23.463 (Liew and Huang (2003))
6×6
23.3217
7×7
23.3190
8×8
23.2940
9×9
23.2927
10×10
23.2885
(a) Maximum
(b) Minimum
(c) Mean
(d) Standard Deviation
Fig. 20 Absolute percentage error in minimum value, maximum value, mean value and standard deviation
with respect to direct MCS for macro-mechanical analysis considering a coefficient of variation of 0.6 in
the stochastic input parameters (
C
macro
g
)
40
(a) Maximum
(b) Minimum
(c) Mean
(d) Standard Deviation
Fig. 21 Absolute percentage error in minimum value, maximum value, mean value and standard deviation
with respect to direct MCS for micro-mechanical analysis considering a coefficient of variation of 0.6 in
the stochastic input parameters (
C
micro
g
)
different sample size are presented in figure 20 and 21. From the figures it is evident that a sample size of
1024 provides reasonably accurate results for predicting the buckling loads. To further examine the
prediction capabilities of the surrogate models, scatter plots are presented for the macro and micro
mechanical analyses in figures 22(a-c) and 23(a-c), respectively. Negligible deviation of the sample
points from the diagonal line corresponding to the sample size of 1024 indicates the accuracy of
prediction. The comparative probability density function plots on the basis of surrogate based MCS
corresponding to the sample size of 1024 and direct MCS are presented for the macro and micro
mechanical analyses in figures 22(d-f) and 23(d-f), respectively. A good agreement between the
probabilistic descriptions of natural frequencies corroborates the accurate prediction capability of the
surrogate models for further analyses.
In this section, we have presented the results of stochastic stability analysis considering the first
three buckling modes for the sake of completeness (as presented in previous scientific literatures like Patel
41
First buckling load
(a)
(d)
Second buckling load
(b)
(e)
Third buckling load
(c)
(f)
Fig. 22 (a-c) Scatter plots for HDMR based analysis (normalized on the basis of respective deterministic
values) for various sample sizes of Sobol sequence considering the stochasticity in macro-mechanical
attributes (
C
macro
g
) with respect to direct MCS (with coefficient of variation of 0.6 in the stochastic input
parameters) ; (d-f) Probability density function (pdf) of buckling loads (normalized with respect to the
corresponding deterministic values) for macro-mechanical attributes (
C
macro
g
) obtained by direct MCS and
HDMR model based on a sample size of 1024 (with coefficient of variation of 0.6 in the stochastic input
parameters)
42
First buckling load
(a)
(d)
Second buckling load
(b)
(e)
Third buckling load
(c)
(f)
Fig. 23 (a-c) Scatter plots for HDMR based analysis (normalized on the basis of respective deterministic
values) for various sample sizes of Sobol sequence considering the stochasticity in micro-mechanical
attributes (
C
micro
g
) with respect to direct MCS (with coefficient of variation of 0.6 in the stochastic input
parameters) ; (d-f) Probability density function (pdf) of buckling loads (normalized with respect to the
corresponding deterministic values) for micro-mechanical attributes (
C
micro
g
) obtained by direct MCS and
HDMR model based on a sample size of 1024 (with coefficient of variation of 0.6 in the stochastic input
parameters)
43
(a)
(b)
(c)
Fig. 24 Probability density function (pdf) plots for macro (
C
macro
g
) and micro (
C
micro
g
) mechanical analyses
of first three buckling loads considering different degree of stochasticity (
). Normalized results are used
with respect to the corresponding deterministic values for plotting the pdfs. The bar plots in the inset
indicate the percentage increase of stochastic bounds for a micromechanical analysis with respect to the
macromechanical analysis.
and Sheikh (2016)). Besides that, in case of stochastic stability analysis, the buckling loads are found to
have a response bound (depending on the degree of stochasticity) with respect to the corresponding
deterministic values. Thus there exists a possibility of overlap in the stochastic responses of the buckling
loads corresponding to different modes of buckling resulting in a non-unique critical buckling mode with
the minimum value of buckling load. For this reason, it is essential to consider higher buckling modes in
case of stochasticity in the system parameters.
5.2.2. Results of stochastic stability analysis
Having the finite element model and the surrogate model validated, as shown in the preceding
subsection, stochastic results are presented in this subsection for the first three modes of buckling for a