Applications of Nonstandard Finite Difference Methods to Nonlinear Heat Transfer Problems

189
The simple linear diffusion problem in one space variable
x and time
τ
, for
( , ) (0, ) (0, ),xl
τ
∈×∞ is (J. D. Smith, 1985)

2
2
TT
X
κ
τ
∂∂
=
∂
∂
(2.2)
The non-dimensionalizing process is illustrated below with the parabolic heat conduction
equation (2.2).
Work Example 1: (Involves only heat conduction)
The solution of Eq. (2.2) gives the temperature
T at a distance X from one end of a thin
uniform wire after a time .
τ
This assumes the rod is ideally heat insulated along its length
and heat transfers at its ends. Let

⎪
==>
⎪
⎨
=∈
⎪
⎪
=− ∈
⎩
(2.4)
where
1
U and
2
U are the dimensionless forms of
1
T and
2
T , respectively.
In other word we are seeking a numerical solution of
2
2
uu
t
x
∂
∂
=
∂
∂

transfer due to emission and absorption of electromagnetic waves. It usually happens within
the infrared/visible/ultraviolet portion of the spectrum. Some examples are: heating
elements on top of toaster, incandescent filament heats glass bulb and sun heats earth.
Sunlight is a form of radiation that is radiated through space to our planet without the aid of
fluids or solids. The sun transfers heat through 93 million miles of space. There are no solids
like a huge spoon touching the sun and our planet. Thus conduction is not responsible for
bringing heat to Earth. Since there are no fluids like air and water in space, convection is not
responsible for transferring the heat. Therefore, radiation brings heat to our planet.
Heat excites the black surface of the vanes more than it heats the white surface. Black is a
good absorber and a good radiator. Think of black as a large doorway that allows heat to
pass through easily. In contrast, white is a poor absorber and a poor radiator of energy.
White is like a small doorway and will not allow heat to pass easily.
Note that heat transfer problems involve temperature distribution not just temperature.
Heat transfer rates are determined knowing the temperature distribution. While Fourier’s
law of conduction provides the rate of heat transfer related to heat distribution, temperature
distribution in a medium governs with the principle of conservation of energy.
2.3.1 Stefan-Boltzmann radiation law
If a solid with an absolute surface temperature of T is surrounded by a gas at temperature
T
∞
, then heat transfer between the surface of the solid and the surrounding medium will
take place primarily by means of thermal radiation if
TT
∞
− is sufficiently large (P. M.
Jordan, 2003). Mathematically, the rate of heat transfer across the solid-gas interface is given
by the Stefan-Boltzmann radiation law

44
() (),

Mathematically, the rate of heat transfer across the solid-gas interface is given by the
Newton’s law of cooling (H. S. Carslaw & J. C. Jaeger, 1959; R. Siegel & J. H. Howell, 1972)
()(),
s
Tn hATT
κ
∞
∂∂=− −/ (2.6)
Applications of Nonstandard Finite Difference Methods to Nonlinear Heat Transfer Problems

191
where h is the convection heat transfer coefficient and A is cooloing area.
The applications of thermal radiation with/without conduction can be observed in a good
number of science and engineering fields including aerospace engineering/design, power
generation, glass manufacturing and astrophysics (R. Siegel & J. H. Howell, 1972; L. C.
Burmeister, 1993; M. N. Ozisik, 1989; J. C. Jaeger, 1950; E. Battaner, 1996).
In the following Work Examples we consider two problems that involve various heat
transfer properties in a thin finite rod (A. Mohammadi & A. Malek, 2009).
3. Nonlinear heat transfer in a finite thin wire
3.1 Heat transfer involving both conduction and radiation
In the following example we consider a problem that involves both conduction and
radiation and no convection.
Consider a very thin, homogeneous, thermally conducting solid rod of constant cross-
sectional area
,A perimeter ,
p
length l and constant thermal diffusivity 0
κ
> that
occupies the open interval (

(0, ) , ( , ) , 0;
(,0) sin( ), (0,);
XX
TT TT X l
TTTlT
TX T X l X l
τ
κβ τ
τττ
π
∞
⎧
=− − ∈×∞
⎪
==>
⎨
⎪
=∈
⎩
/
(3.1)
where time
τ
is a non-negative variable,
0
p
KA
βκσε
=
/

12
(), (,)(0,1)(0,);
(0,) , (1,) , 0;
( ,0) sin , (0,1);
txx
uu uu xt
utU utU t
ux x x
β
π
∞
⎧
=− − ∈ ×∞
⎪
==>
⎨
⎪
=∈
⎩
(3.3)
where
1
U and
2
U are the dimensionless forms of
1
T and
2
T , respectively.
Heat Transfer - Mathematical Modelling, Numerical Methods and Information Technology

(3.4)
where
τ
the temporal is a non-negative variable,
0
,
p
KA
βκσε
= /
0
,h
p
KA/
ακ
= and
based on physical considerations,
T
is assumed to be nonnegative.
Work Example 3: (Involve conduction, radiation and convection terms)
Using the following dimensionless variables,

232
00
2
0
, = = , ,
, ,
,uTT xX t Tl
p

=− −− − ∈ ×∞
⎪
==>
⎨
⎪
=∈
⎩
(3.6)
where U
1
and U
2
are the dimensionless forms of T
1
and T
2
, respectively.
In the following we propose six nonstandard explicit and implicit schemes for problem (3.6).
Novel heat theory (Microscale)
Tzou (D. Y. Tzou, 1997) has shown that if the scale in one direction is at the microscale (of
order 0.1 micrometer) then the heat flux and temperature gradient occur in this direction at
different times. Thus the heat conduction equations used to describe the microstructure
thermodynamic behavior are:
.
p
T
qQ c
ρ
τ
∂

q
c
TT TT T
T
xy z
Q
Q
ρ
ττ τ
κτ
ττττ
τ
τ
κ
∂∂ ∂ ∂ ∂
+
=∇ + + + +
∂
∂∂∂∂∂∂∂
∂
+
∂
(3.7)
Malek and Momeni-Masuleh in years 2007 and 2008 used various hybrid spectral-FD methods
to solve Eq. (3.7) efficiently. H. Heidari and A. Malek, studied null boundary controllability for
hyperdiffusion equation in year 2009. Heidari, H. Zwart, and Malek, in year 2010 discussed
Applications of Nonstandard Finite Difference Methods to Nonlinear Heat Transfer Problems

193
controllability and stability of the 3D novel heat conduction equation in a submicroscale thin

uu
u
Forward Finite Difference
xx
uu
u
Backward Finite Difference
xx
uu
u
Central Finite Difference
xx
+
−
+−
−
∂
=
∂Δ
−
∂
=
∂Δ
−
∂
=
∂
Δ

The equation

i
j
i
j
uu u uu u uu
t
x
θθ
++++−++−
−−++−−+
=
Δ
Δ

for
01,
θ
≤≤
where
,
(,)
ij
uuix
j
t
=
ΔΔ
for 1, and 1, , in the iNj J xt
=
=− plane. Note that

∑
and
2
sin
t
Ue x
π
π
−
= respectively.
Figs. 1, 2, 3 and 4 display the power of both numerical schemes (Explicit and Crank-
Nicolson) for the calculation of the solution for problems given in Work Example 1.
Heat Transfer - Mathematical Modelling, Numerical Methods and Information Technology

194
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
x
U
h=0.1, k=0.001, r=0.1

difficult, if not impossible, to find a solution of a given differential equation in a reasonably
suitable and unambiguous form, especially if it involves the nonlinear terms. Therefore, it is
important to consider what qualitative information can be obtained about the solutions of
differential equation, particularly nonlinear terms, without actually solving the equations.
4.2 Nonstandard finite difference methods
Nonstandard finite difference methods for the numerical integration of nonlinear
differential equations have been constructed for a wide range of nonlinear dynamical
systems (P. M. Jordan, 2003; W. Dai & S. Su, 2004; H. S. Carslaw & J. C. Jaeger, 1959; R.
Siegel & J. H. Howell, 1972; L. C. Burmeister, 1993). The basic rules and regulations to
construct such schemes (R. E. Mickens, 1994), are:
Regulation 1. To do not face numerical instabilities, the orders of the discrete derivatives
should be equal to the orders of the corresponding derivatives appearing in the differential
equations.
Regulation 2. Discrete representations for derivatives must have nontrivial denominator
functions.
Regulation 3. Nonlinear terms should be replaced by nonlocal discrete representations.
Regulation 4. Any particular properties that hold for the differential equation should also
hold for the nonstandard finite difference scheme, otherwise numerical instability will
happen.
Positivity, boundedness, existence of special solutions and monotonicity are some properties
of particular importance in many engineering problems that usually model with differential
equations. Regulation number four restricts one to force the nonstandard scheme satisfying
properties of differential equation.
In the last two decays, several nonstandard finite difference schemes have been developed
for solving nonlinear partial differential equations by Mickens and his co-authors.
Particularly, Jordan and Dai considered a problem of one-dimensional unsteady heat
conduction in a thin finite rod that is radiating heat across its lateral surface into a medium
of constant temperature. The most fundamental modes of heat transfer are conduction and
thermal radiation. In the former, physical contact is required for heat flow to occur and the
heat flux is given by Fourier’s heat law. In the latter, a body may lose or gain heat without

u
tu
β
β
−
+∞
+
−+ + +Δ
=
+Δ
(4.1)

4
,1,1,
,1
33
1, 1,
(1 2 ) ( ) ( )
,
1()( )2
ij i j i j
ij
ij ij
urruu tu
u
tu u
β
β
−
+∞

+Δ + +
(4.3)
where
2
(),rt x≡Δ Δ/
and
,
(,),
ij
uuix
j
t
=
ΔΔ
x
Δ
is the grid size and t
Δ
is the time
increment. While these three schemes differ in the way of dealing with the nonlinear terms,
truncation errors for all of them are of the order
2
()Ot x
⎡
⎤
Δ+Δ
⎣
⎦
.
Equation (4.3) has better stability property than Eq. (4.1) and (4.2), ( for more details see A.

ij
urruu tutu
u
tu t
βα
βα
−
+∞∞
+
−+ + +Δ +Δ
=
+Δ +Δ
(4.4)

()
(
4
,1,1,
,1
33
1, 1,
(12) ( ) () ()
,
1()( )2()
)
ij i j i j
ij
ij ij
urruu tutu
u

−+ + +Δ + + +Δ
+Δ + + +Δ
(4.6)
4.2 Implicit nonstandard FD schemes
4.2.1 Nonstandard FD implicit schemes for Work Example 2
Finite differencing methods can be employed to solve the system of equations and
determine approximate temperatures at discrete time intervals and nodal points. Problem
(3.3) is solved numerically using the non-standard Crank-Nicholson method. To provide
accuracy, difference approximations are developed at the midpoint of the time increment.
Heat Transfer - Mathematical Modelling, Numerical Methods and Information Technology

198
A second derivative in space is evaluated by an average of two central difference equations,
one evaluated at the present time increment j and the other at the future time increment j+1:

2
1, 1 , 1 1, 1 1, , 1,
22 2
22
1
,
2
() ()
ij ij ij ij ij ij
uuuuuu
u
xx x
−+ + ++ − +
−+ −+
⎛⎞

⎜⎟
Δ
ΔΔ
⎝⎠
(4.8)
Now define

43 3 3 3
, , 1 , 1, 1,
44 2 2
,,,1
, ( ) 2,
( ) ( )( )( ).
ij ij ij i j i j
ij ij ij
uuu u u u
uu uuuuu u
ββ
+−+
∞∞∞+∞
→≡+
−→ + + −
(4.9)
In this study, three nonstandard implicit finite difference schemes are developed as follows
(A. Mohammadi, & A. Malek, 2009)

(
)
3
1, 1 , , 1 1, 1

β
−+ − + + ++
−+∞
−
+++Δ + − =
+− + +Δ
(4.11)
and

(
)
()
22
1, 1 , , , 1 1, 1
22 4
1, , , , 1,
22 ()( )( )
22 ()( ) () ,
i j ij ij ij i j
i j ij ij ij i j
ru r tuuuuu ru
ru r t u u u u u u ru t u
β
ββ
−+ ∞ ∞ + ++
−∞∞∞+∞
−+++Δ++ − =
+−+Δ + + + +Δ
(4.12)
where

−+ + ++
−+∞∞
−
+++Δ +Δ − =
+− + +Δ +Δ
(4.13)

(
)
33
1, 1 1, 1, , 1 1, 1
4
1, , 1,
22 ()( )2 ()
(22) () () ,
()
ij ij ij ij ij
ij ij ij
ru r t u u t u ru
ru r u ru t u t u
βα
βα
−+ − + + ++
−+∞∞
−+++Δ+ +Δ− =
+− + +Δ +Δ
(4.14)
Applications of Nonstandard Finite Difference Methods to Nonlinear Heat Transfer Problems

199

and ,N for 0,j > are
known, these
(1)N
−
equations for 1 1iN
=
− can be written in matrix form as
1, 1
2, 1
2, 1
1, 1
.
.
. .
j
j
Nj
Nj
u
Mr
u
rM r
rM ru
rM
u
+
+
−+
−+
⎡

⎢
⎥
⎢⎥
−
⎢
⎥
⎣⎦
⎣
⎦()
()
()
()
4
0, 0, 1
1,
4
2,
4
2,
4
1,
,,1
0
. .
. .
,
. . .

⎡⎤
⎡⎤
⎢
⎥
⎢⎥
⎢⎥
⎢
⎥
Δ+
⎢⎥
⎢⎥
⎢
⎥
⎢⎥
⎢⎥
⎢
⎥
⎢⎥
⎢⎥
⎢
⎥
⎢⎥
+
⎢⎥
⎢
⎥
⎢⎥
⎢⎥
⎢
⎥

Mrtuuuu
β
∞∞
=++Δ + + (4.17)
and

(
)
22
,,
22 ()( )
ij ij
Qrtuuuuu
β
∞∞∞
=−+Δ + +
(4.18)
i.e.
1
,
jjj
+
=+Au Bu d
where the matrices A and B of order (1)N
−
are as shown in (4.16),
1
j
+
u

+uCuf (4.20)
in which
-1
C=A B
and
jj
=
-1
fAd.
Theorem 4.1: For the scheme (4.12) norm of the error for
j
th time step is less than or equal
to
j
,
0
Ce where
0
e
is the error of the initial values.
Proof : Applying recursively from (4.20) leads to

-1 -1 -2 -2 -1
2
22

.
jjj jjj
j- j- j-1
jj-1j-2

−eu u it follows by Eqs. (4.21) and
(4.22) that
() 1
jj
**
jjj 00 0
=-= - = , j J=euuCuu Ce (4.23)
Hence, for compatible matrix and vector norms,

j
j
j
.≤≤
00
eCe Ce (4.24)
Since the necessary and sufficient condition for the difference equations to be stable when
the solution for the partial differential equation does not increase as
t increases (J. D. Smith,
1985), is
1,≤C in the following theorem we prove it for the scheme (4.12).
Theorem 4.2: The following three statements for the non-standard implicit scheme (4.12)
satisfy
i. Matrix C in Eq. (4.20) is symmetric with real values.
ii.
1<C

iii. The nonstandard implicit scheme (4.12) is unconditionally stable.
Proof (i) From matrix equation (4.16) it is obvious that matrix C is a real tridiagonal matrix.
Since
A and B are both symmetric and commute, matrix C is symmetric with real values,

2cos ( 1)
1, , .
2cos ( 1)
s
Qr sN
sN
Mr sN
π
μ
π
⎛⎞
++
==
⎜⎟
++
⎝⎠
(4.25)
Thus from (4.17) and (4.18) we have

2
2cos ( 1)
() max 1
2cos ( 1)
s
Qr sN
Mr sN
π
ρ
π
++

∞
=
= where
0.02 and 1 5001.xtΔ= Δ= We first chose 2,u
β
∞
=
= figures 5(a) and 6(a) show
temperature profiles obtained based on three schemes for explicit models introduced by (P.
M. Jordan, 2003; W. Dai & S. Su, 2004), and three schemes of this work, respectively. It can
be seen from figure 6(a) that all of our schemes in figure 6(a) are stable while the scheme (1)
in figure 5(a) of Ref. (P. M. Jordan, 2003; W. Dai & S. Su, 2004) is unstable. Fig. 5(a). For
2,u
β
∞
== scheme (1) There explicit nonstandard finite difference scheme
given by Jordan (2003) is plotted in Eq. (4.1) is not stable, while schemes (2) and (3) given in
Eqs. (4.2) and (4.3) are stable.
We then chose
6,u
β
∞
=
=
and the results were plotted in figures 5(b), 5(c) and 6(b). The
solution obtained based on Eq. (4.1) is not convergent as shown in figure 5(b), while the
three implicit schemes of us are stable as shown in figure 6(b).

β
∞
==
schemes (1) and (2), given in Eqs. (4.1) and (4.2), converge but do
not converge to the correct solution. Fig. 5(e). For
20,u
β
∞
==
scheme (3), given in Eq. (4.3), converge to the correct solution.
Heat Transfer - Mathematical Modelling, Numerical Methods and Information Technology

204

Fig. 6(a). For
2,u
β
∞
==
three schemes given by Eqs. (4.10), (4.11) and (4.12) converge to
the correct solution. Fig. 6(b). For
6,u
β
∞

2,u
β
∞
== in Work Example 2 and 2,u
β
∞
=
= 4
α
=
in Work Example 3, where
0.02, 1 5001.xtΔ= Δ=
Figure 7(a) shows the temporal evolution of the temperature profiles corresponding to
initial boundary value problem (3.3) and (3.6), for
2,u
β
∞
=
= and 4
α
=
, where numerical
results for explicit schemes are plotted. It can be seen from figure 7(a) that the solution of
problem without convection term in scheme (1) begun to oscillate, while all of the solution
profiles for problem (3.6) are stable. Fig. 7(a). For
2,u
β


Fig. 7(b). For
2,u
β
∞
== implicit schemes (1), (2) and (3) with convection term
(
)
4
α
= and
without convection term is shown.
Our findings suggest that Regulation 4 is a serious property for a general nonstandard finite
difference scheme because, otherwise it leads to instability. i.e. either the scheme does not
converge or it converges to a wrong solution.
6. References
A. Malek, S. H. Momeni-Masuleh, A mixed collocation-finite difference method for 3D
microscopic heat transport problems. J. Comput. Appl. Math. 217 (2008), no. 1, 137-
147.
A. Mohammadi, A. Malek, Stable non-standard implicit finite difference schemes for non-
linear heat transfer in a thin finite rod. J. Difference Equ. Appl. 15 (2009), no. 7, 719-
728.
D. R. Croft, D. G. Lilly, Heat transfer calculations using finite difference equations. Applied
Science Publishers, 1977.
D. Y. Tzou, Macro-To Micro-Scale Heat Transfer: The Lagging Behavior (Chemical and
Mechanical Engineering Series). Taylor & Francis, 1997.
E. Battaner, Astrophysical Fluid Dynamics, Cambridge University Press, Cambridge, 1996.
G. Ben-Yu, Spectral Methods and Their Applications. World Scientific, 1998.
H. K. Versteeg, W. Malalasekera, An Introduction to Computational Fluid Dynamics: The
Finite Volume Method. Addison-Wesley. 1996.

scheme for the Burgers-Fisher equation. J. Sound Vib. 257 (2002), 791-797.
R. E. Mickens, Advances in the applications of nonstandard finite difference schemes. World
Scientific, London, 2005.
R. Siegel and J. H. Howell, Thermal Radiation Heat Transfer, McGraw-Hill, New York, 1972.
R. W. Lewis, P. Nithiarasu, K. N. Seetharamu, Fundamentals of the Finite Element Method
for Heat and Fluid Flow, John Wiley & Sons Ltd, 2005.
S. H. Momeni-Masuleh, A. Malek, Hybrid pseudospectral-finite difference method for
solving a 3D heat conduction equation in a submicroscale thin film. Numer.
Methods Partial Differential Equations 23 (2007), no. 5, 1139-1148.
S. H. Momeni-Masuleh, A. Malek, Pseudospectral Methods for Thermodynamics of Thin
Films at Nanoscale. African Physical Review, 2007, 35-36.
S. V. Patankar, Numerical Heat Transfer and Fluid Flow (Hemisphere Series on
Computational Methods in Mechanics and Thermal Science). T & F / Routledge,
1980.
W. Dai and S. Su, “A nonstandard finite difference scheme for solving one dimensional
nonlinear heat transfer,” Journal of Difference Equations and Applications 10
(2004), 1025-1032.
Jure Ravnik and Leopold
ˇ
Skerget
University of Maribor, Faculty of Mechanical Engineering
Slovenia
1. Introduction
Development of numerical techniques for simulation of fluid flow and heat transfer has a long
standing tradition. Computational fluid dynamics has evolved to a point where new methods
are needed only for special cases. In this chapter we introduce a Fast Boundary Element
Method (BEM), which enables accurate prediction of vorticity fields. Vorticity field is defined
as a curl of the velocity field and is an important quantity in wall bounded flows. Vorticity
is generated on the walls and diffused and advected into the flow field. Using BEM, we are
able to accurately predict boundary values of vorticity as a part of the nonlinear system of

based on the projection-diffusion method with spatial resolution supplied by polynomial
expansions. Lo et al. (2007) also studied a 3D cubic cavity under five different inclinations ϑ
=
0
o
,15
o
,30
o
,45
o
,60
o
. They used a differential quadrature method to solve the velocity-vorticity
formulation of Navier-Stokes equations employing higher order polynomials to approximate
differential operators. Ravnik et al. (2008) used a combination of single domain and sub
domain BEM to solve the velocity-vorticity formulation of Navier-Stokes equations for fluid
Fast BEM Based Methods
for Heat Transfer Simulation
9
2 Heat Transfer
flow and heat transfer.
Simulations as well as experiments of turbulent flow were also extensively investigated. Hsieh
& Lien (2004) considered numerical modelling of buoyancy-driven turbulent flows in cavities
using RANS approach. 2D DNS was performed by Xin & Qu
´
er
´
e (1995) for an cavity with
aspect ratio 4 up to Rayleigh number, based on the cavity height, 10

below. They investigated convective instability of the flow and heat transfer and reported
that the natural convection of a nanofluid becomes more stable when the volume fraction
of nanoparticles increases. Ho et al. (2008) studied effects on nanofluid heat transfer due to
uncertainties of viscosity and thermal conductivity in a buoyant cavity. They demonstrated
that usage of different models for viscosity and thermal conductivity does indeed have
a significant impact on heat transfer. Natural convection of nanofluids in an inclined
differentially heated square cavity was studied by
¨
Og
¨
ut (2009), using polynomial differential
quadrature method. Stream function-vorticity formulation was used for simulation of
nanofluids in two dimensions by G
¨
umg
¨
um & Tezer-Sezgin (2010).
Forced and mixed convection studies were also performed. Abu-Nada (2008) studied the
application of nanofluids for heat transfer enhancement of separated flows encountered in
a backward facing step. He found that the high heat transfer inside the recirculation zone
depends mainly on thermophysical properties of nanoparticles and that it is independent
of Reynolds number. Mirmasoumi & Behzadmehr (2008) numerically studied the effect of
nanoparticle mean diameter on mixed convection heat transfer of a nanofluid in a horizontal
tube using a two-phase mixture model. They showed that the convective heat transfer
could be significantly increased by using particles with smaller mean diameter. Akbarinia
& Behzadmehr (2007) numerically studied laminar mixed convection of a nanofluid in
horizontal curved tubes. Tiwari & Das (2007) studied heat transfer in a lid-driven differentially
heated square cavity. They reported that the relationship between heat transfer and the
volume fraction of solid particles in a nanofluid is nonlinear. Torii (2010) experimentally
210

Zuni
ˇ
c et al. (2007) and using quadratic
interpolation by Ravnik et al. (2009a) for uncoupled flow problems.
The BEM uses the fundamental solution of the differential operator and the Green’s theorem
to rewrite a partial differential equation into an equivalent boundary integral equation. After
discretization of only the boundary of the problem domain, a fully populated system of
equations emerges. The number of degrees of freedom is equal to the number of boundary
nodes. This reduction of the dimensionality of the problem is a major advantage over the
volume based methods. Fundamental solutions are known for a wide variety of differential
operators (Wrobel, 2002), making BEM applicable for solving a wide range of problems.
Unfortunately, integral equations of nonhomogeneous and nonlinear problems, such as heat
transfer in fluid flow, include a domain term. In this work, we solve the velocity-vorticity
formulation of incompressible Navier-Stokes equations. The formulation joins the Poisson
type kinematics equation with diffusion advection type equations of vorticity and heat
transport. These equations are nonhomogenous and nonlinear. In order to write discrete
systems of linear equations for such equations, matrices of domain integrals must be
evaluated. Such domain matrices, since they are full and unsymmetrical, require a lot of
storage space and algebraic operations with them require a lot of CPU time. Thus the domain
matrices present a bottleneck for any BEM based algorithm effectively limiting the maximal
usable mesh size through their cost in storage and CPU time.
The dual reciprocity BEM (Partridge et al. (1992), Jumarhon et al. (1997)) is one of the
most popular techniques to eliminate the domain integrals. It uses expansion of the
nonhomogenous term in terms of radial basis functions. Several other approaches that
enable construction of data sparse approximations of fully populated matrices are also
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Fast BEM Based Methods for Heat Transfer Simulation
4 Heat Transfer
known. Hackbusch & Nowak (1989) developed a panel clustering method, which also enables
approximate matrix vector multiplications with decreased amount of arithmetical work. A

Navier-Stokes equations by Ravnik et al. (2008; 2009a).
The second part of the algorithm uses fast kernel expansion based single domain BEM. The
method is used to provide a sparse approximation of the fully populated BEM domain
matrices. The storage requirements of the sparse approximations scale linearly with the
number of nodes in the domain, which is a major improvement over the quadratic complexity
of the full BEM matrices. The technique eliminates the storage and CPU time problems
associated with application of BEM on nonhomogenous partial differential equations.
The origins of the method can be found in a fast multipole algorithm (FMM) for particle
simulations developed by Greengard & Rokhlin (1987). The algorithm decreases the amount
of work required to evaluate mutual interaction of particles by reducing the complexity of
the problem from quadratic to linear. Ever since, the method was used by many authors for
a wide variety of problems using different expansion strategies. Recently, Bui et al. (2006)
combined FMM with the Fourier transform to study multiple bubbles dynamics. Gumerov
& Duraiswami (2006) applied the FMM for the biharmonic equation in three dimensions.
The boundary integral Laplace equation was accelerated with FMM by Popov et al. (2003).
In contrast to the contribution of this paper, where the subject of study is the application
of FMM to obtain a sparse approximation of the domain matrix, the majority of work done
by other authors dealt with coupling BEM with FMM for the boundary matrices. Ravnik
et al. (2009b) compared wavelet and fast data sparse approximations for boundary - domain
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Heat Transfer - Mathematical Modelling, Numerical Methods and Information Technology
Fast BEM Based Methods for Heat Transfer Simulation 5
integral equations of Poisson type.
2. Governing equations
In this work, we will present a numerical algorithm and simulation results for heat transfer in
pure fluids and in nanofluids. We present the governing equations for nanofluids, since they
can be, by choosing the correct parameter values, used for pure fluids as well. We assume the
pure fluid and nanofluid to be incompressible. Flow in our simulations is laminar and steady.
Effective properties of the nanofluid are: density ρ
nf


v = −β
nf
(T −T
0
)

g −
1
ρ
nf

∇
p +
μ
nf
ρ
nf
∇
2

v. (2)
We assume that no internal energy sources are present in the fluid. We will not deal with high
velocity flow of highly viscous fluid, hence we will neglect irreversible viscous dissipation.
With this, the internal energy conservation law, written with temperature as the unknown
variable, reads as:
∂T
∂t
+(


μ
nf
=
μ
f
(1 − ϕ)
2.5
. (5)
The effective viscosity is independent of nanoparticle type, thus the differences in heat transfer
between different nanofluids will be caused by heat related physical parameters only. The heat
capacitance of the nanofluid can be expressed as (Khanafer et al., 2003):
(ρc
p
)
nf
=(1 − ϕ)(ρc
p
)
f
+ ϕ(ρc
p
)
s
. (6)
Similarly, the nanofluid thermal expansion coefficient can be written as
(ρβ)
nf
=(1 −
ϕ)(ρβ)
f

f
⎤
⎦
. (7)
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Fast BEM Based Methods for Heat Transfer Simulation