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International Journal of Computer and Electrical Engineering, Vol. 5, No. 3, June 2013
291DOI: 10.7763/IJCEE.2013.V5.715
Abstract—A heat pipe optimization is known as a difficult
one because design variables have nonlinear interaction one
another and multiple constraints are involved. it is unsuitable to
Gradient-based methods. The object of present study is to
optimize the design variables of the heat pipe for a space
application using an evolutionary method.
In this study, Particle Swarm Optimization(PSO) method,
simple heuristic search method, is used to estimate variables
and improve search efficiency. The heat pipe configuration is
optimized regarding to seven parameters, such as diameter of
vapor core, thickness of wick, etc., and eighteen constraints
including operational, dimensional, and structural ones.
To verify the performance of the PSO method, a minimum
total mass estimation and searching efficiency are compared
with results obtained by generalized extremal
optimization(GEO). It is proven that PSO found optimized
solutions effectively than GEO for simultaneous estimation of
multi-parameters.
Index Terms—Optimization design, PSO (Particle Swarm
Optimization), heat pipe, mesh wick.
I. INTRODUCTION
Heat pipe, simple tube-shaped heat transfer device, is
using for cooling device of micro-semiconductor to huge oil
pipeline due to high heat transfer efficiency, light weight and
setup simplicity[1], Lately, it is important to decrease
manufacturing cost by design optimization for special
environment like satellite, space shuttle, and so on[2].
It is not suitable to optimize heat pipe configuration by
gradient-based method because nonlinear equation should be
solved for heat pipe design and several constraints should be
considered on heat pipe shape. Instead, stochastic algorithm
is effective for solving nonlinear problem like the heat pipe
design optimization[3]. Chengbin Zhang, et al. optimized
wick shape of heat pipe using NPGA(Niched Pareto Genetic
Algorithmes) which is modified GA(Genetic Algorithm)[4].
Fabiano L. S. et al. used GEO(Generalized Extremal
Optimization), one of stochastic algorithms, to estimate
minimum mass of mesh wick heat pipe[5].
In this study, for increasing searching efficiency,
PSO(Particle Swarm Optimization) method is applied to
obtain configuration variable for minimizing mass of heat
pipe. Optimization procedure is performed to investigate
multiple design factors, those determine shape of heat pipe
using methanol for working fluid with mesh type wick.
II. PARTICLE SWARM OPTIMIZATION
It is mimicked that bird flock find new area for nesting to
develop PSO(Particle Swarm Optimization) algorithm by
Kennedy and Eberhart[6]. PSO modifies its existing solution
referring personal best value and global best value, otherwise
Genetic Algorithm discards existing solution after creating
new value from existing value. This PSO algorithm is carried
out by below procedure.
1) Generating initial value of particles randomly in limit of
range of solution
2) Renewing velocity vector of each particle
1 1 1 2 2
i i i i g i
k k k k k kv wv c r p x c r p x (1)
3) Renewing value of each particle
1 1
i i i
k k kx x v (2)
x is position(value) of a particle. v is velocity vector of a
particle. Superscript is number of particle and subscript
means iteration step. p is best value up to now. Superscript g
represents the whole particles, swarm (global). Coefficient w,
c1 and c2 are inertia factor, self-confidence factor and swarm
confidence factor, respectively. Those determine how much
are each term considered. r1 and r2 is random value in 0 to 1
changing influence of personal best value and global best
value at each step. Therefore, vik+1, new velocity of i-th
particle, reflects existing velocity vik and distance (difference)
between xik, its existing position, and personal best value pi
k
and global best value pgk respectively. New position value of
particle is calculated by sum of existing position value xik and
new velocity vector vik+1. After renewing value of a particle,
personal best value pik and global best value pg
k are renewed
by comparing new and old value. Step 2) and 3) are repeated
while renewed pgk satisfy given condition of solution.
Genetic algorithm is relatively complex because it should
realize selection, crossing and mutation. As same reason, it
takes long time to find optimum solution. However, PSO is
composed by just two equations which are tracking a particle
that approach close to real solution, so it is simple and
effective to search the value [7].
III. HEAT PIPE
Heat pipe is confined pipe filled with working fluid. Wick
is installed in the pipe that liquid can flow in the wick.
Because heat is transferred by latent heat of working fluid,
heat pipe can transfer much more heat than normal metal pipe
or beam. Heat pipe has advantage to separate high
A Study on Heat Pipe Optimization Using PSO
Kwonho Kim, Kyun Ho Lee, and Seung Wook Baek
Manuscript received October 19, 2012; revised November 24, 2012
Kwonho Kim and Sung Wook Baek are with the Aerospace engineering,
KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea
(e-mail: [email protected], [email protected]).
Kyun Ho Lee is with the Department of Aerospace Engineering, Sejong
University, 98 Gunja-dong, Gwangjin-gu, Seoul 143-747, Republic of
Korea(e-mail: [email protected]).
temperature part and low temperature part due to its
tube-shape. Also, it is simple to install heat pipe despite
limitation of installing space.
Usual heat pipe contacts its each end with high
temperature part and low temperature part. When heat pipe is
heated up at high temperature part, in other words, evaporator
part, working fluid in wick evaporates and pressurizes heat
pipe end of high temperature part. As the result, vaporized
working fluid moves on to low temperature part that is also
called condenser part. At condenser part, working fluid
condenses with putting the heat out. Liquefied working fluid
returns to evaporator part by capillary effect. The heat is
transferred from high temperature part to low temperature
part in this recurrent process.
In this research, seven configuration variables are
optimized to minimize mass of heat pipe that can transfer
desired heat loads with given environment temperature. The
seven configuration variables are mesh number of wick (N),
diameter of wick wire(d), vapor core diameter(dv), thickness
of wick(tw), length of evaporator section(Le), length of
condenser section(Lc) and thickness of pipe wall(tt). Length
of adiabatic section is supposed 0.5m to compare with result
of previous research. In optimization procedure, total mass of
heat pipe is set for objective function. The total mass is sum
of mass of container(mcont), wick(mwd), liquid in wick(mwl)
and vapor in vapor core(mvapor).
total cont wd wl vaporm m m m m (3)
When desired heat load (Q) and temperature of condenser
section are given, seven configuration variables have
eighteen constraints [8].
1: ,c g
c c
l v eff
P PG Q Q Q
F F L
(4)
,min ,max2: so so soG T T T (5)
2 23: ,
ln
e e vb b c
v i v n
L k TG Q Q Q P
d d r
(6)
0.52
,
4 : ,4 2
v ve e
r h
dG Q Q Q
r
(7)
4
5 : ,256
v v vv v
v eff
d PG Q Q Q
L
(8)
3
86 : 0.2,v v
v v v v
QG M M
d R T (9)
47 : Re 2300,Rev v
v v
QG
d (10)
8:0.0001 0.9999G (11)
9 : 2 wG d t (12)
10:314 15000G N (13)
3 311:0.025 10 1.0 10G d (14)
3 312:5.0 10 80.0 10vG d (15)
3 313:0.05 10 10.0 10wG t (16)
3 314:50.0 10 400.0 10eG L (17)
3 315:50.0 10 400.0 10cG L (18)
3 316:0.3 10 3.0 10tG t (19)
2 2
2 217 :
4
o i ts
o i
P d d uG
d d
(20)
3 3
3 3
218 :
42
o i ts
o i
P d d uG
d d
(21)
G1 to G7, constraints caused by operation characteristic of
heat pipe are called operational limit (8). G8 to G16 are
dimensional limit which occur by limitation of installing
space. Last two conditions are structural limitation to prevent
design that would lead to a burst of the tube
IV. RESULT
In this research, optimization is conducted for stainless
steel (SS304) heat pipe using methanol working fluid.
Methanol properties are assumed to dependent on the
operating temperature of the heat pipe, and data from Dunn
and Reay were used to obtain interpolation curves [9]. The
temperature of low temperature part goes from -15℃ to 30
℃ with step 15℃. Desired heat load is set from 25W to
100W with steps of 25W.
Fig. 1. Total mass of HP as a fuction of Q to Tsi = -15.0℃, 0.0℃
Fig. 2. Total mass of HP as a fuction of Q to Tsi = 15.0℃, 30.0℃
International Journal of Computer and Electrical Engineering, Vol. 5, No. 3, June 2013
292
International Journal of Computer and Electrical Engineering, Vol. 5, No. 3, June 2013
293
TABLE I: VALUE FOR DESIGN VARIABLE FOR THE CONDITION: TSI = 0.0℃,
Q=25.0W
mtotal N d 10-3 dv 10-3
PSO 0.032 314 0.025 6.1
GEO 0.035 315 0.025 6.4
tw 10-3 Le 10-3 Lc 10-3 tt 10-3
PSO 0.19 57.1 50 0.3 GEO 0.21 71.9 50.3 0.3
Result of optimization by PSO is compared with result of
previous optimization research that is performed by GEO for
each condition. Those are noted on Fig. 3 and 4. From those
figures, it can be seen that total mass of heat pipe increase
with desired heat load increasing for same temperature of
condenser section. It is also come out that optimized mass by
PSO is lighter than design mass from GEO. For the case of
Tsi=15℃, Q
=25W, the mass from PSO is as 16% as lighter then mass get
by GEO. On average, PSO method estimates 10% lighter
heat pipe then GEO method. Seven configuration values are
calculated by PSO and GEO is addressed at table 1. It is
easily shown that two results has difference in dv, tw and Le.
From that result, it is verified that PSO produces more
optimized mass then GEO by estimating optimum value of
design variables.
Lastly, In Fig. 5, the variation of total mass as a function of
Number of function evaluation is shown for the PSO and
GEO. It can be seen that value of PSO comes close to
optimum value more quickly than GEO, especially, at the
early stage of optimization process. It means the PSO is more
efficient than the GEO on searching for the optimum design.
V. CONCLUSION
In this paper, by optimization technique, value of design
variables of heat pipe are estimated to minimize total mass of
heat pipe with maintaining proper heat transfer performance
of it. PSO is applied to search optimum value efficiently
considering multiple constraints and nonlinear equations
simultaneously. Total seven configuration variable and
eighteen constraints are considered. it is drawn a below
conclusions to compare result with previous research.
Fig. 3. Minimum total HP mass as a fuction of NFE at Tsi = 0.0℃, Q=25W
1) As a result of applying PSO method, it is obtained
design value of the heat pipe that is as 10~15% as light
then GEO.
2) PSO has better performance to estimate optimum value
then GEO. Especially, PSO quickly comes close to optimum
value at early stage of calculation.
It is made a judgment that it is useful to estimate optimum
design value of heat pipe configuration effectively by PSO in
place of GEO.
REFERENCES
[1] Y. S. Lee, “Design and Application of the heat pipe”, Air-conditioning
and refrigeration engineering, vol ,26, no.1, pp34-45, 1997
[2] J.H. Boo, “열수송용 히트파이프,” Journal of the KARSE, vol. 16, no.
11, pp. 48-66, 1999.
[3] M. J. Colaco, M. R. B. Orlande, and G. S. Dulikravich, “Inverse and
Optimization Problems in Heat Transfer,” J. of the Braz. Soc. Of Mech.
Sci. & Eng., vol. 28, no. 1, pp. 1-24, 2006.
[4] C. Zhang, Y. Chen, M. Shi, G. P. Peterson, “Optimization of heat pipe
with axial “Ω”-Shaped Micro Grooves Based on a Niched Pareto
Genetic Algorithm(NPGA),” Applied Thermal Engineering, vol. 29,
no. 16, pp. 3340-3345, 2009.
[5] F. L. D. Sousa, F. M. Ramos, and V. V. Vlassov, “Heat Pipe Design
Through Generalized Extremal Optimization,” Heat Transfer
Engineering, vol. 25, no. 7, pp. 34-45, 2004.
[6] J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” in Proc.
of the IEEE Int. Conf. Neural Networks, Perth, Australia, 1995, pp.
1952-1945
[7] K. H. Lee, S. W. Baek, and K. W. Kim, “Inverse radiation analysis
using repulsive particle swarm optimization algorithm,” International
Journal of Heat and Mass Transfer, vol. 51, pp. 2772-2783, 2008.
[8] S. W. Chi, Heat Pipe Theory and Practice, A Sourcebook, New York :
McGraw-Hill Book Company, 1976, pp 33-95
[9] P. Dunn and D. A. Reay, Heat Pipes, New York: Pergamon Press, 1976,
pp. 272-277.
Kwonho Kim received a B.S. in Aerospace Engineering
from the KAIST, Daejeon, S. Korea, in 2011. His
research interests include optimized design by inverse
analysis and hybrid rocket.
He currently takes a course for master degree in aerospace
engineering from the KAIST.
Kyun Ho Lee received a B.S. in Mechanical
Engineering from Yonsei University, Seoul, S. Korea, in
1998, M.Sc. in same major and university, in 2000, Ph.D.
in Aerospace Engineering in KAIST, Daejeon, S. Korea,
in 2009. His research interests include space propulsion,
combustion and inverse analysis in heat transfer. He is
currently an assistant professor of Department of
Aerospace Engineering at Sejong University.
Seung Wook Baek received a B.S. in Mechanical
Engineering from Seoul National University, Seoul, S.
Korea, in 1978, a M.Sc. in same major from same
university, in 1981 and a Ph. D. in Aerospace Engineering,
from University of Michigan, Michigan, in 1985. His
Research interests include with combustion and radiation
phenomena.
He is a Professor in Aerospace Engineering in KAIST,
Daejeon, S. Korea, from 1989 to now on. He also work in Guest researcher in
NIST and NRL.
Prof. Baek is in AIAA senior membership. He also participate in
KSME(Korean Sociey of Mechanical Engineers) and KSAS(Korean Society
for Aeronautical and Space Science)