Experimental and simulation approach of cooling system in 3-phase inverter using extended surface

International Journal of Power Electronics and Drive Systems (IJPEDS)
Vol. 13, No. 4, December 2022, pp. 2313~2323
ISSN: 2088-8694, DOI: 10.11591/ijpeds.v13.i4.pp2313-2323  2313
Journal homepage: http://ijpeds.iaescore.com
Experimental and simulation approach of cooling system in
3-phase inverter using extended surface
Agus Mukhlisin1,2
, Prisma Megantoro1
, Estiko Rijanto3
, I Nyoman Sutantra2
, Lilik Jamilatul Awalin1
,
Yoga Uta Nugraha1
, Indra Sidharta2
1
Electrical Engineering Department, Faculty of Advanced Technology and Multidiscipline, Airlangga University, Surabaya, Indonesia
2
Mechanical Engineering Department, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
3
Research Center for Smart Mechatronics, National Research and Innovation Agency (BRIN), Jakarta Pusat, Indonesia
Article Info ABSTRACT
Article history:
Received Apr 19, 2022
Revised Aug 1, 2022
Accepted Aug 17, 2022
Overheating is a failure mode that significantly affects the reliability of
electronic devices. All electronic devices, including a 3-phase inverter
driving a traction motor, produce heat dissipation. Heat dissipation needs to
be controlled with cooling to prevent overheating. Overheating can be
avoided by increasing cooling or reducing heat dissipation. Heat dissipation
in the 3-phase inverter is caused by the internal resistance of the metal–
oxide–semiconductor field-effect transistor (MOSFET), switching loss, and
other factors. Cooling for the 3-phase inverter can use water coolant or air
coolant. The cooling system is based on the amount of heat dissipation
produced. Cooling of a 3-phase inverter can use air coolant with the addition
of an extended surface area in the heat sink. The heat sink uses aluminum
material, often called pin fin. There are kinds of aluminum available in the
market. We calculated heat generation based on the MOSFET's internal
resistance, switching loss, and other factors. We validated the simulation
results experimentally using a thermal camera. Thus, we could find an
optimal number, dimensions, and aluminum type of fin for the cooling
system in the 3-phase inverter.
Keywords:
3-phase inverter
Cooling system
Extended surface
Heat dissipation
Heat sinks
This is an open access article under the CC BY-SA license.
Corresponding Author:
Agus Mukhlisin
Department of Electrical Engineering, Faculty of Advanced Technology and Multidiscipline
Airlangga University
Gedung Kuliah Bersama, Campus C, Mulyorejo, Surabaya 60115, Indonesia
Email: agus.mukhlisin@ftmm.unair.ac.id
1. INTRODUCTION
Electronic component reliability is influenced by a cooling system [1]. Each electronic component
will produce heat dissipation, including the 3-phase inverter for a motor controller. Heat dissipation needs to
be controlled with a cooling system to prevent overheating. It can also be done by reducing the amount of
heat dissipation produced, such as using the right switching strategy [2]–[4], using field-oriented control
(FOC) [5], [6], using intelligent control [7]–[11], and the others. The most dominant heat source in the motor
controller is losses in the 3-phase inverter. Part of the 3-phase inverter that will produce heat is the metal–
oxide–semiconductor field-effect transistor (MOSFET).
One of the MOSFET failure modes is overheating. The heat generated by MOSFETs must be
transferred quickly to the cooling media with intelligent cooling using a fan [12], thermoelectric cooling [13],
others so that the temperature of the MOSFET does not rise above its ability limit. Heat transfer can occur in
several ways, including the following: conduction, convection, and radiation [14]–[16]. This paper will

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Int J Pow Elec & Dri Syst, Vol. 13, No. 4, December 2022: 2313-2323
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discuss the MOSFET cooling method using an extended surface (pin fin). The extended surface area of heat
transfer improves the thermal efficiency [17], [18].
Computational fluid dynamic (CFD) simulation can be used to obtain a cooling method that suits your
needs [19], [20]. This method can help to analyze the cooling method without spending time doing
experiments [21]. The numerical method can show the temperature distribution and how much heat must be
cooled [22]. If the final design has been obtained, this design can be used as a reference in making a prototype.
This work presents optimized heat transfer from MOSFET in a 3-phase inverter by using extended surfaces with
some pin fins and dimensions. The calculation of heat loss in the 3-phase inverter is used as the input in the
simulation. The simulation result is validated with the experimental results. The validation is carried out based
on the temperature distribution and the temperature's value at the same point. If the error tolerance that occurs
does not exceed 5%, we consider that an accurate simulation is obtained. Therefore, it can be used to optimize
the number of fins and the dimensions of the fins. Our target is that the 3-phase inverter can operate normally at
a power of 30 kW.
2. METHOD
First, we performed a heat loss calculation in 3-phase inverter for parameter input in the simulation.
Next, the simulation results were validated with the experimental results, where the temperature distribution
was obtained using a thermal camera. After the simulation results are validated, it is possible to optimize the
heat sink according to the heat loss. As a result, the optimal heat sink configuration is obtained with the
controller not overheating.
Heat losses can be seen in the power flow diagram in Figure 1. Based on Figure 1, the 3-phase
inverter losses consist of conduction loss pulse-code modulation PCM, switching loss part submission
warrant PSW, dead time loss PD, and gate charge loss PG, as given in (1) to (4) [23], [24].
𝑃𝐶𝑀 =
1
𝑇𝑆𝑊
∫ 𝑝𝐶𝑀(𝑡)𝑑𝑡
𝑇𝑆𝑊
0
=
1
𝑇𝑆𝑊
∫ 𝑅𝐷𝑆𝑜𝑛. 𝑖𝐷
2(𝑡)𝑑𝑡 = 𝑅𝐷𝑆𝑜𝑛. 𝐼𝐷𝑟𝑚𝑠
2
𝑇𝑆𝑊
0
(1)
𝑃𝑆𝑊 =
1
2
𝑉𝐼𝑁. 𝐼𝑂. (𝑡𝑟 + 𝑡𝑓). 𝑓𝑆𝑊 (2)
𝑃𝐷 = 𝑉𝐷. 𝐼𝑂. (𝑡𝐷𝑟 + 𝑡𝐷𝑓). 𝑓𝑠𝑤 (3)
𝑃𝐺 = (𝑄𝑔−𝐻 + 𝑄𝑔−𝐿). 𝑉
𝑔𝑠. 𝑓𝑠𝑤 (4)
The symbol 𝑅𝐷𝑆𝑜𝑛 is the MOSFET drain-source internal resistance (Ω), TSW is switching time (s), iD is the
current flowing in drain (A), and IDrms is the current flowing from source to drain (A). Vin and Io represent the
battery voltage (V) and the output current (A). tr is the rise time of the MOSFET (s), tf is the fall time of the
MOSFET (s), and fSW is the switching frequency (Hz). VD, tDr, and tDf denote the low-side MOSFET – diode
forward-voltage (Volt), dead-time at rising (s), and dead-time at falling (s). Vgs is the gate drive voltage of the
MOSFET (V), Qg – H is the high side MOSFET gate electric charge (C), and Qg – L is the low side MOSFET
gate electric charge (C). This controller uses the type of MOSFET IRFB4115 with the specifications shown
in Figure 2.
Figure 1. Power flow 3-phase inverter Figure 2. MOSFET IRFB4115 specification [25]

Int J Pow Elec & Dri Syst ISSN: 2088-8694 
Experimental and simulation approach of cooling system in 3-phase inverter … (Agus Mukhlisin)
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This controller has the following target specifications; i) maximum voltage operation 150 VDC, ii)
maximum power 30 KW, and iii) using an air-cooling system. We first made a simplified 3D model of the 3-
phase inverter for numerical analysis using CFD software, as shown in Figure 3. The heat source on the 3-
phase inverter is the MOSFET component. So that heat transfer can be analyzed with thermal resistance as
shown in Figure 4.
Figure 3. Simplification of 3D model of the 3-phase inverter
Figure 4. Thermal resistance model controller
The heat source from the MOSFET moves either up to the controller cover or down to the heat sink.
The heat transfer downward will be more dominant than upward. The controller cover traps the air contained
inside the controller container, so it does not allow air circulation. The heat transmitted downward propagates
through the thermal pad.
A thermal pad is a component that serves as an insulator between the base metal of the MOSFET
and the heat sink. The use of the thermal pad must have good thermal conductivity as well as a good
insulator. The motor controller failure can also be caused by improper thermal pad selection. If the heat
generated by the MOSFET is not transferred to the heat sink quickly, the MOSFET will experience
overheating, resulting in failure. Similarly, if the thermal pad cannot isolate the metal base of the MOSFET, a
short circuit will occur, and the controller will fail. Generally, the thermal pad uses silicone material. The
heat transfer then continues to the heat sink fin by conduction. The equation of heat transfer by conduction is
given in [14], [16]:
𝑞𝑐𝑜𝑛𝑑𝑢𝑐𝑡𝑖𝑜𝑛 = 𝑘. 𝐴. ∆𝑇 (5)
The type of heatsink material affects the quality of heat transfer: the more significant the heat sink material's
thermal conductivity (k), the more remarkable its heat propagation ability. Our heat sink uses 1060 series

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aluminum material, often found in the local market. Convection sends the heat transfer into the air around the
heat sink fin. The equation of heat transfer by convection is given by (6) to (8) [14], [16]:
𝑞𝑐𝑜𝑛𝑣𝑒𝑐𝑡𝑖𝑜𝑛 = ℎ. 𝐴. ∆𝑇 (6)
𝑁𝑢 =
ℎ.𝐿
𝑘𝑓
= 𝑓(𝑅𝑒𝐿, Pr) (7)
𝑅𝑒𝐿 =
𝜌.𝑉.𝐿
𝜇
(8)
The symbols h and A represent the convection coefficient (W/m2
K) and the heat transfer area (m2
). L
is the length of material (meters), kf is the thermal conductivity of the fluid (W/mK), and Nu is the Nusselt
number. Re and Pr denote Reynold’s number and Prandtl’s number. ρ is the density of air (kg/m3
), V is the
velocity of air (m/s), and μ is the friction coefficient.
Convection heat transfer can be optimized by increasing the convection coefficient and cooling area.
Increasing the convection coefficient can be done by increasing the speed of air flowing at the fin heat sink,
for example, by adding a fan, directing the air with a guide fan so that air can be directed precisely at the
heatsink. The cooling area can be enlarged by using variations in dimension and the number of fins on the
heatsink. The quality of the simulation results is determined by the dimensions and meshing used. Our
meshing uses a curvature-based parameter mesh with total nodes of 615,925, using Jacobian points by 4
points. Table 1 lists material types used in the motor controller cooling simulation. Figure 5 shows the
meshing model.
Table 1. Material types for cooling simulation
Component Materials Thermal conductivity (W/mK)
Fin heat sink Aluminum 1060 200
Base plat Aluminum 1060 200
Thermal pad Silicone 3.2
MOSFET plastic body Plastic Silicone 124
MOSFET base metal Aluminum 5052 137
Figure 5. Meshing model
Connection contact set using bounded type. The contact that needs to be defined is between
MOSFET with the thermal pad and the thermal pad with the heat sink. This step ensures that the connections
between different materials are perfectly connected. We conducted numerical simulation using CFD with the
following conditions:
− Surrounding temperature: 28 o
C
− Controlled variable: Convection coefficient (h)
− The source of the heat loss: the MOSFET
First, we calculated heat loss when the motor controller is under a 1.5 kW load as show in Table 2.
Afterward, the simulation can be run, and temperature distribution can be obtained. Simulation results will be
validated using experimental results. Experiments with a maximum load of 30 kW continuously (30 minutes)
cannot be done because the dynamometer will overheat. So, the experiment was carried out with a continues
load of 1.5 kW. Furthermore, the results of these experiments can be used to validate the simulation results.
The validated simulation results can then be used to test the maximum loading, so that the temperature

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distribution is obtained when the maximum loading to obtain the temperature distribution under the
maximum loading. This simulation can also be used to optimize the fin on the heat sink so that cooling is
obtained as needed with minimum fin dimensions.
Figure 6 shows the experimental setup with (a) schema of experiment and (b) installed equipment in
this paper. It is equipped with an eddy current dynamometer paired with a 30 KW motor. The temperature
sensor uses a 10K thermistor recorded in a datalogger (USB to CAN Datalogger). An oscilloscope with four
channels is used to measure voltage and current, both input and output. A thermal camera is used to obtain
the temperature distribution.
Table 2. Calculated heat loss in the motor controller under 1.5 kW load
Type Heat Loss Parameter Value
Conduction Loss RDS(on)= 11 mΩ; ID = 8.15 A; Vin = 80.34 V;
𝑡𝑟=73 ns; 𝑡𝑓=39 ns;𝑓𝑆𝑊=20kHz;
td(on) = 18 ns; td(off) =41ns; Vgs = 23V; Qg =120 nC.
0.617 W
Switching Loss 0.733 W
Deadtime Loss 0.772 W
Gate Charge Loss 0.055 W
Total Heat Loss 2.177 W
(a) (b)
Figure 6. Experiment setup (a) schema and (b) equipment
3. RESULTS AND DISCUSSION
3.1. Simulation validation through experiment
The load was kept constant at 1.5 kW with a motor speed of 950 rpm and variations in wind speed.
We changed wind speed by controlling a fan power to obtain speed 1, speed 2, and speed 3. CAN to USB
datalogger was used to obtain data, among others: temperature of heat sink, phase current, battery current,
battery voltage, and the motor speed. One-time data collection took 35 minutes or until the heat sink
temperature got steady. The result of the experiment is shown in Figure 7.
Figure 7. Motor speed and heat sink temperature

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The heat sink temperatures in Figure 7 were measured using a thermistor sensor. Yellow, red, blue,
and green line curves are the heat sink temperatures with variations in wind speed 1, speed 2, speed 3, and
natural convection. Each line's steady-state temperature is yellow at 33.37 o
C, red at 33.96 o
C, blue at
34.92 o
C, and green at 39.72 o
C. The greater wind speed yields a lower temperature of the heat sink. This is
in accordance with (6), (7), and (8). The trend line that occurs tends to be steady at a specific time. This
indicates that the cooling is sufficient so that overheating will not occur. The temperature distribution can be
captured in this steady state with a thermal camera.
Figures 8 and 9 show the temperature distribution results obtained from the simulation and
experiment. Figure 8 shows the temperature distribution using h=40 W/m2
K, both experimentally and in
simulation results. Figure 8(a) shows the simulation results where the thermistor sensor laying point shows a
temperature of 33.1 o
C. While Figure 8(b) is a photo taken by the thermal camera where the location of the
thermistor shows the value of 33.3 o
C. These results indicate that the simulation results have a 0.6% error.
The temperature distribution in general also has similarities. Under continuous loading of 1.5 kW, the highest
temperature occurs in the DC-DC converter, yielding 37 o
C.
(a) (b)
Figure 8. Temperature distribution with h=40 W/m2
K (a) simulation result and (b) experiment result
Figure 9 shows the temperature distribution using h=33 W/m2
K for both experimental and
simulation results. Figure 9 (a) shows the simulation results where the thermistor sensor laying point shows a
temperature of 34.3 o
C. In comparison, Figure 9 (b) is a photo taken by the thermal camera where the
location of the thermistor shows the value of 34.9 o
C. The result indicates that the simulation results have a
1.75% error. Based on the results shown in Figures 8 and 9, the simulation results can be accepted with an
error tolerance of ± 1.75%. Furthermore, the simulation method can be used to evaluate the maximum
loading of the 3-phase inverter, i.e., with a load of 30 kW. Table 3 summarizes the data obtained from the
simulation and experimental results.
(a) (b)
Figure 9. Temperature distribution with h=33 W/m2
K (a) simulation result and (b) experiment result

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Table 3. Comparison of simulation with experiment results
Coefficient of Convection Experiment Simulation Percentage of Error
h= 33 W/ m2
K 34.9 o
C 34.3 o
C 1.75 %
h= 40 W/ m2
K 33.1 o
C 33.3 o
C 0.6 %
3.2. Optimization of the heat sink under maximum load
We calculated heat loss produced by each MOSFET when the 3-phase inverter operates under the
maximum load 30 kW. Table 4 shows the parameter values and the heat loss values. Since we have 48
MOSFETs, the total heat loss is 1,048.27 Watts. We used this heat loss value as the input to our CFC model
and conducted a numerical simulation. Figure 10 shows the temperature distribution of the heat sink under a
maximum load of 30 kW. The maximum temperature is 98.9 o
C, so the controller does not overheat. The
temperature where the MOSFET is situated shows a value of 38.5 o
C. Since the temperature is significantly
lower than the critical temperature of the MOSFET, the heat sink fin is not required in this area.
Table 4. Heat loss of each MOSFET in the motor controller under 30 kW Load
Type Heat Loss Parameter Value
Conduction Loss RDS(on)= 11 mΩ; ID = 65.16 A; Vin = 80.34 V;
𝑡𝑟=73 ns; 𝑡𝑓=39 ns;𝑓𝑆𝑊=20kHz;
td(on) = 18 ns; td(off) =41ns; Vgs = 23V; Qg =120 nC.
14.491 W
Switching Loss 3.551 W
Deadtime Loss 3.742 W
Gate Charge Loss 0.055 W
Total Heat Loss 21.839 W
Figure 10. Simulation result under maximum load 30 kW
Furthermore, we optimized the number and length of the heat sink fins with maximum thermal load
and h=60 W/m2
K. The original length of ach heat sink fin is 20 mm. Figure 11 shows the simulation results.
Figure 11(a) shows the temperature distribution using 15 pieces of heat sink fins. The highest temperature of
the MOSFET is 103 o
C. The temperature is lower than the critical operation temperature of the MOSFET,
which is 125 o
C. Figure 11(b) shows the temperature distribution using 12 pieces of heat sink fins. The
highest temperature of the MOSFET is 107 o
C. The result indicates that reducing of fin number to 12 pieces
yielding maximum temperature that is lower than the critical temperature of the MOSFET. Further
optimization is carried out by reducing the fin length to 12 mm and yielding the temperature distribution
shown in Figure 11(c). The maximum temperature of the MOSFET in this condition is 117 o
C, which is
lower than the critical temperature of MOSFET. However, there is an increase in temperature compared to
the result of 12 pieces 20 mm fin. Therefore, the controller under a maximum load of 30 kW is optimally
cooled using the configuration of 12 pieces of fins with 12 mm length, following Figure 11(c).

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(a)
(b)
(c)
Figure 11. Optimized heat sink in the number and length of the fins: (a) 15 pieces of 20 mm long,
(b) 12 pieces of 20 mm long, and (c) 12 pieces of 12 mm long

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4. CONCLUSION
Thermal analysis of the controller cooling system was carried out using modeling and simulation.
The simulation was validated experimentally with an error of ± 1.75%. Furthermore, the simulation method
can be used to optimize the cooling design for a maximum loading of 30 kW. The results indicate that the
controller does not experience overheating, as demonstrated by the MOSFET temperature. Further
optimization, through simulation was carried out in order to reduce the size and the number of the fin. The
simulation result of the optimized heat sink suggests that the maximum temperature of the MOSFET is lower
than its critical temperature. Hence, the controller did not experience overheating.
ACKNOWLEDGEMENTS
Author thanks to Faculty of Advanced Technology and Multidiscipline, Airlangga University and
Lembaga Pengelola Dana Pendidikan (LPDP) in most cases, sponsor and financial support
acknowledgments. This program support Sustainable Development Goals (SDGs) for affordable and clean
energy also goal 9 for Industry, Innovation and Infrastructure.
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BIOGRAPHIES OF AUTHORS
Agus Mukhlisin is a lecturer in Electrical Engineering Department at Faculty of
Advanced Technology and Multidiscipline, Airlangga University, Surabaya, Indonesia. He
received a Bachelor’s degree and Master’s degree from Institute Technology of Sepuluh
November, Surabaya, Indonesia in 2015 and 2017. He is currently pursuing Doctoral Program
in Institute Technology of Sepuluh November, Surabaya, Indonesia. His Dissertation Topic
was about Intelligent Vector Control in Motor Controller. His research interests include
System and Control of Electric Vehicle, Battery Management System, Charging Station, and
Dynamic System Simulation. He can be contacted at email: agus.mukhlisin@ftmm.unair.ac.id.
Prisma Megantoro is a lecturer in Electrical & Electronic Engineering
Department, Faculty of Advanced Technology and Multidiscipline, Airlangga University,
Surabaya, Indonesia. He received the bachelor degree and master degree from Universitas
Gadjah Mada, Yogyakarta, Indonesia in 2014 and 2018. His current research is focused on
solar photovoltaic technology, embedded system, and internet of things. He can be contacted
at email: prisma.megantoro@ftmm.unair.ac.id.
Estiko Rijanto has worked for Research Center for Smart Mechatronics - the
National Research and Innovation Agency (BRIN) - Indonesia since 2021. From 2002 to 2021,
he worked at Research Center for Electrical Power and Mechatronics, Indonesian Institute of
Sciences (LIPI). In 1987 he enrolled at Institut Teknologi Bandung (ITB). He completed his
B.Eng. degree at Tokyo University of Agriculture and Technology (TUAT), Tokyo - Japan, in
1993. He received M. Eng. and Dr. Eng. degrees from the same university in 1995 and 1998.
In 2000, he completed his book on “Robust Control: Theory for Application” during his post-
doctoral program at TUAT-VBL. In 2013, he was inaugurated as the Research Professor on
Applied Control Systems by LIPI after presenting his book “Integration of control and
information systems for industrial competitiveness,”. His research interests include control
systems and their applications for mechatronics, electric vehicles, power generations,
renewable energies, and battery management systems. He can be contacted at email:
esti003@brin.go.id.
I Nyoman Sutantra is a lecturer in Mechanical Engineering Department, Institut
Teknologi Sepuluh November, Surabaya, Indonesia. He received a Bachelor’s degree from
Institute Technology of Sepuluh November, Surabaya, Indonesia. He received a Master and
Doctoral degree from The University of Wisconsin – Madison in mechanical department. He
was inaugurated the Professor on mechanical engineering. His research interests include
automotive technology, electric vehicles, hybrid vehicle and steering control. He can be
contacted at email: tantra@me.its.ac.id.

2323
Lilik Jamilatul Awalin is a lecturer in Electrical & Electronic Engineering
Department, Faculty of Advanced Technology and Multidiscipline, Airlangga University,
Surabaya, Indonesia. She received the bachelor degree from electrical engineering, Widya
Gama Malang, Indonesia in 1977. She received the master degree from Institute Technology
of Sepuluh November, Surabaya, Indonesia in 2004. Finally, she gets the doctoral degree from
University of Malaya, Malaysia in 2014. Her current research is focused on fault localization,
smart grid technology, transmission and distribution network, load shedding, coordination
protection and optimization. She can be contacted at email: lilik.j.a@ftmm.unair.ac.id.
Yoga Uta Nugraha is as a lecturer at Electrical Engineering program in the
Faculty of Advanced Technology and Multidicipline, Universitas Airlangga, Surabaya,
Indonesia. He was born in Sukoharjo, Central of Java. He is a Doctoral candidate in Electrical
Engineering Department of Sepuluh Nopember Institute of Technology Surabaya. He has a
Bachelor (2015) and Master (2020) of Electrical Engineering from Sepuluh Nopember
Institute of Technology Surabaya. His current research is focused on integration on Electric
Vehicle, electric machine, permanent magnet motor, new and renewable energy. He can be
contacted at email: yoga.uta.n@ftmm.unair.ac.id.
Indra Sidharta is a lecturer in Mechanical Engineering Department, Institut
Teknologi Sepuluh November, Surabaya, Indonesia. He received a Bachelor’s degree from
Institute Technology of Sepuluh November, Surabaya, Indonesia in 2004. He received a
Master degree from Hamburg University of Technology in field Materials Science, 2010. His
current research is focused on metal matrix composite, metallurgy, materials of battery. He
can be contacted at email: indra.sidharta@gmail.com.

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