Enhanced Flow Boiling Heat Transfer in Microchannels
with Structured Surfaces at Varied Mass Flow Rates
by
David Bian
SUBMITTED TO THE DEPARTMENT OF MECHANICAL ENGINEERING IN
PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
BACHELOR OF SCIENCE IN MECHANICAL ENGINEERING
ARCHWEB
AT THE
MASSACHUSETTS INS'TIT(UTE
OF TECHNOLOLGY
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
JUN 2 4 2015
June 2015
LIBRARIES
@ 2015 David Bian. All rights reserved.
The author hereby grants MIT permission to reproduce and to
distribute publicly paper and electronic copies of this thesis document
in whole or in part in any medium now known or hereafter created.
Signature of Author:_
Signature redacted
____
Department of Mechanical Engineering
May 22, 2015
Certified by:
Signature redacted
61
10
Accepted by:
V
velyn N. Wang
Professor of Mechanical Engineering
Thesis Supervisor
Signature redacted
Anette Hosoi
Professor of Mechanical Engineering
Undergraduate Officer
......... .
.....
.....
Enhanced Flow Boiling Heat Transfer in Microchannels
with Structured Surfaces at Varied Mass Flow Rates
by
David Bian
Submitted to the Department of Mechanical Engineering on
May 22, 2015 in partial fulfillment of the requirements for the
degree of Bachelor of Science in Mechanical Engineering
Abstract
This thesis investigates the role of mass flux on flow boiling heat transfer in microchannels with surface micropillar arrays. The motivation for this investigation was to determine
the general trends of the optimal micropillar array geometry in terms of its heat transfer
capabilities. The experiment was conducted with three microchannels: a flat surface microchannel, a sample called the 5-15 (height h = 25 pm, diameter d = 5 pm, and pitch 1 =
15 jm) and a sample called the 10-40 (height h = 25 jm, diameter d = 10 jm, and pitch
1 = 40 jm). The structured surface microchannels, due to their capillary pressure-induced
wicking capabilities, exhibited less temperature rise and pressure drop fluctuations at high
heat fluxes. Furthermore, it was verified that the critical heat flux value of all microchannels
increased with mass flux. In addition, it was concluded that at lower mass fluxes, the relative
percentage heat transfer enhancement of the structured surface microchannels over the flat
surface microchannel was greater. The trend observed suggests that denser samples are better at lower mass fluxes. However, if a sample is too dense, there may be too much viscous
drag. Thus, an optimal balance between capillary force and viscous drag must be found
in order to determine the optimum micropillar array geometry and density for maximizing
the critical heat flux value. Finally, for a given mass flux, the pressure drop across every
microchannel was approximately equal at all heat fluxes. This implies that no additional
power consumption is required to pump a particular mass flux through a structured surface
microchannel than a flat surface microchannel, though there is certainly additional power
required to increase the mass flux. This work provides insights into the roles of both the
micropillar array surface structures and the mass fluxes on the heat transfer performance of
flow boiling in microchannels. The results and observations of this experiment may prove
helpful in guiding future work in an attempt to optimize microchannels for heat transfer
applications in electronics.
Thesis Supervisor: Evelyn N. Wang
Title: Professor of Mechanical Engineering
Acknowledgements
I would like to extend my gratitude to Professor Evelyn Wang, who graciously allowed me
into the Device Research Laboratory to pursue a senior thesis project, and for the general
friendliness and understanding of the rest of the laboratory members.
My most enthusiastic thank you of all to Yangying Zhu and Dr. Dion Savio Antao for providing me with such a wonderful thesis topic, their patience and support throughout the
project, their constant willingness to guide me through the physical concepts and experimental procedure, their belief in my ability to conduct experiments, analyze data, and draw
conclusions on my own, and finally, for their forgiveness for breaking their precious samples.
Contents
1
2
Introduction
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10
10
1.2
1.3
11
12
Experiment Motivation and Summary . . . . . . . . . . . . . . . . . . . . . .
Structure of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Experimental Details
2.1 Microchannel Samples
2.1.1
2.1.2
2.1.3
2.2
2.3
2.4
2.5
3
4
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
13
13
Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Fabrication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RTD Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
13
16
18
Experimental Setup . . . . . . . . .
Experimental Preparation . . . . .
Experimental Procedure . . . . . .
Measurements and Data Processing
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21
22
24
24
Results and Discussion
26
3.1
Flow Instability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
26
3.2
3.3
Heat Transfer Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Pressure Drop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
29
34
Conclusion
37
5.1
5.2
Works Cited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
39
39
41
5.2.1
MATLAB Script for Processing Experimental Data . . . . . . . . . .
41
5.2.2
LabVIEW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
46
5
6
List of Figures
2.1
2.2
Schematic of a generic operating microchannel with micropillar structures
on the bottom surface, an inlet, an outlet, and a heater indicated by heat
generation are shown. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Graph of how capillarity varies the with structural pillar geometry. Adapted
from Xiao et al. [13]
2.3
2.4
2.5
2.6
3.1
3.2
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Fabrication process of the microchannel. (a) Micropillars of 25 im height
were etched in Si using Deep Reactive Ion Etching (DRIE). (b) A Si wafer
was etched through using DRIE to define the channel. (c) The two wafers
from the first two steps were bonded together using Si-Si fusion bonding. (d)
Inlet and outlet ports were laser-drilled on a Pyrex glass wafer. (e) The Si
layers were bonded using direct Si-Si bonding. A silicon dioxide (Si02) layer
was thermally grown on the Si surface. The Pyrex layer was bonded to the
top Si using anodic bonding. (f) A Pt layer was deposited on the backside
of the microchannel using E-beam evaporation and patterned to form heater
and temperature sensors. From Zhu et al. [9] . . . . . . . . . . . . . . . . . .
Images of a representative fabricated microchannel with micropillar arrays.
Optical images of the (a) front and (b) back side of a sample. (c) A crosssectional image as well as scanning electron micrograph of the micropillars on
the channel bottom surface. From Zhu et al. [9] . . . . . . . . . . . . . . . .
Measured data and the linear fit of the resistance of the RTDs with temperature to determine calibration constants oz and 3 for the linear fit equation
14
15
17
18
T =aR+.....................................
20
Schematic of the Experimental Setup. "P", "T", and "M" indicate pressure
transducers, thermocouples, and a flow meter, respectively. The blue lines
indicate fluid flow, red lines indicate measurements, green lines indicate data
transfer and recording, and gray lines indicate physical connections. . . . . .
22
Temporal data comparison of temperature and pressure drop for each microchannel sample with a 150 kg/m 2 s mass flux at a range of representative
heat fluxes. The uncertainties of the temperature and pressure drop measurements were approximately 6.5'C and 0.45 kPa, respectively. . . . . . . .
Temporal data of temperature and pressure drop for each microchannel sample with a 300 kg/m 2 s mass flux at a range of representative heat fluxes.
The uncertainties of the temperature and pressure drop measurements were
approximately 3.5'C and 0.22 kPa, respectively. . . . . . . . . . . . . . .
7
27
28
....... ... ... .. ... .. ..46
3.3
3.4
3.5
3.6
5.1
Temporal data of temperature and pressure drop for each microchannel sample with a 500 kg/m 2 s mass flux at a range of representative heat fluxes.
The uncertainties of the temperature and pressure drop measurements were
approximately 0.3'C and 0.28 kPa, respectively. . . . . . . . . . . . . . .
Boiling curves for all the flow rates for each microchannel. The effects of varying mass flux can be clearly observed for each microchannel. The initial slope
indicates heating single phase flow, whereas the sudden jump back indicates
the onset of nucleate boiling. The following upwards slope is heating of the
two phase flow, and finally, the critical heat flux is indicated by when the
slope changes significantly from the two phase flow slope. Note: the 10-40
sample broke during the experiment at 500 kg/m 2 s, and since there was no
slope change observed, the critical heat flux was not reached and is therefore
unknown. The error bars were approximately 1% for the heat flux q" and
ranged from 2.0'C to 6.7'C for the temperature rise. . . . . . . . . . . .
Boiling curves for all the microchannels at each mass flux. Differences between
the microchannels can be clearly observed at each mass flux. The initial slope
indicates heating single phase flow, whereas the sudden jump back indicates
the onset of nucleate boiling. The following upwards slope is heating of the
two phase flow, and finally, the critical heat flux is indicated by when the
slope changes significantly from the two phase flow slope. Note: the 10-40
sample broke during the experiment at 500 kg/m2 s, and since there was no
slope change observed, the critical heat flux was not reached and is therefore
unknown. The error bars were approximately 1% for the heat flux q" and
ranged from 2.0'C to t6.7'C for the temperature rise. . . . . . . . . . . .
Boiling curves for each flow rate as well as each microchannel. Differences
between the microchannels can be clearly observed at each mass flux. The
error bars were approximately +1% for the heat flux q" and ranged from
0.36 kPa to 0.60 kPa for the pressure drop. . . . . . . . . . . . . . . . . .
Screenshot of the front panel of the LabVIEW file used to monitor the entire
. .. . . . . . . . . .. .. .
system loop.
8
29
31
32
35
List of Tables
2.1
2.2
2.3
3.1
Geometric parameters of tested structured surface microchannels . . . . . . .
At a given temperature, the measured temperature and resistance of each RTD
was recorded and averaged in order to calibrate the flat surface microchannel
sample. The same process was used to calibrate the other microchannels as
14
well.
19
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Calibration constants of the microchannel samples that linearly relate the
resistance of the RTDs to their temperatures, where ais the slope with units
'C/Qand Pis the intercept with units 'C. (a) Flat Sample. (b) 5-15 Sample.
(c) 10-40 Sam ple. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
21
Critical heat flux values of each microchannel at each flow rate as well as
their critical heat flux enhancement percentage relative to the flat surface
microchannel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
33
9
Chapter 1
Introduction
Background
1.1
Moore's Law projects that the density of transistors in an integrated circuit will double every
two years. The limits of heat dissipation have recently been unable to match the microprocessor power trends enabled by transistor density, partially due to the lack of affordable,
high performance thermal management options [1]. As a result, multi-core processors have
been developed to produce the required power because conventional thermal solutions consisting of a fin set and fan combination are unable to dissipate heat fluxes approaching 100
W/cm2 [2].
Furthermore, power electronic devices, concentrated photovoltaics, and laser
diodes require much more power and generate heat fluxes on the order of and greater than
1000 W/cm 2 [1].
The high density power electronic devices of today have increased the demand for high
density heat transfer technology.
Two-phase thermal management systems such as heat
pipes and vapor chambers first emerged because they utilized the latent heat of vaporization
of fluids to successfully transport and dissipate heat fluxes up to 100 W/cm 2 [3]. As the heat
fluxes increase with the density of transistors, the heat dissipation capabilities of devices
must improve. Conventional thermal solutions such as a fin set and fan combination utilize
conduction and forced convection. Other alternative and better methods utilize two-phase
systems, which include pool boiling, spray cooling, and flow boiling using refrigerants or
Pool boiling with water has demonstrated critical heat flux (CHF) values of 100
water.
W/cm
2
on smooth surfaces [4] and 250 W/cm 2 with the micro and nanoscale structured
surfaces [5]. The method with the most potential is known as jet impingement, which has a
predicted CHF of up to 100,000 W/cm 2 [6]. However, this method has been known to cause
major instabilities and large variations in temperature, which is unacceptable for electronics
10
cooling applications. On the other hand, flow boiling with water has the potential to achieve
CHF values up to 1000 W/cm 2 [7,8].
In fact, Zhu et al. opted to explore the solution of using water in flow boiling in two-phase
microchannel heat sinks due to their extremely compact form factor [9].
The design and
use of two-phase microchannels present two main challenges. This includes maximizing the
critical heat flux as well as minimizing the flow instabilities. Vapor bubbles form and expand
along the channel because the cross-sectional length scale is smaller than the capillary length
of water. These bubbles cause a large pressure build up until the pressure gradient forces the
bubble away. However, as the vapor bubble expands, the channel begins to dry, a process
known as dryout. Once dry, the heat dissipation performance of the microchannel plummets
and the surface temperature of the channel spikes.
Previous studies have shown that microstructured surfaces have been able to delay dryout
through capillary wicking. For example, Li et al. [10] and Yang et al. [11,12] both used
silicon nanowire-coated channel surfaces to improve heat transfer performance. The critical
heat flux enhancement is hypothesized to be the result of the wicking capabilities of the
hydrophilic nanowires which assist in preventing dryout. Additionally, the structures and
fluid flow may serve a similar purpose to a micro-scale fin set and fan combination, with the
structures spreading the heat flux across a larger surface area to be taken away by the fluid
flow in the channel.
1.2
Experiment Motivation and Summary
Microchannels with structured surfaces have been shown to enhance flow boiling heat transfer
capabilities.
However, the exact effects of the structured surface geometry on the flow
instability and heat transfer performance are not well-studied.
Previous research, such as that done by Zhu et al. [9], has examined structured surfaces
in the form of micropillar arrays. Their work has found a near-optimal geometry for the
micropillar arrays at a particular mass flux rate, 300 kg/m 2 s through the microchannel.
While Zhu et al. examined how heat transfer performance varied with geometry at a constant
mass flow rate [9], this thesis examines the additional dimension of varying the mass flow rate.
The experiment considers the performance of three particular microchannels, one flat surface
and two structured surfaces at different mass flow rates, 150 kg/m 2 s and 500 kg/m 2 s. The
author emphasizes that the microchannels were provided by Zhu et al. [9] for the purpose
of conducting these experiments.
The goal of doing so is to observe how the performance of each microchannel varies with
11
mass flow rate, as well as the performance of each microchannel relative to one another.
This information may reveal a relationship between optimum heat transfer performance,
geometry, and mass flow rate in the microchannels that will be crucial to the future of
characterizing of flowing boiling heat transfer at the micro-scale.
1.3
Structure of Thesis
This thesis is structured as follows:
Chapter 1 provides an overarching background and structure of this thesis.
Chapter 2 describes all experimental details regarding the microchannel samples with structured surfaces, the experimental setup and preparation, and the experimental procedure.
Chapter 3 discusses the qualitative and quantitative results to compare the heat transfer
performance and flow stability for each microchannel sample at each mass flux.
Chapter 4 summarizes the results obtained from the experiments, including how the mass
flux affects flow stability, heat transfer characteristics, and pressure drop across the microchannel. It concludes by proposing potential directions for future research.
Chapter 5 is a bibliography of all works referenced in the writing of this thesis followed
by an appendix of MATLAB scripts and a LabVIEW diagram utilized in the experimental
setup and data analysis.
12
Chapter 2
Experimental Details
2.1
2.1.1
Microchannel Samples
Design
The microchannels used in this experiment came from Zhu et al.'s experiments [9] and are 10
mm long with cross-sectional dimensions of 500 im x 500 4m. On the bottom surface of each
structured surface microchannel is an array of micropillars. In addition, there is a thin-film
metal heater 9 mm wide x 350 4m long integrated directly below the microchannel that
provides Joule heating. There are also four resistance temperature detectors (RTDs) along
the length of the microchannel; RTD1 and RTD2 are positioned at the inlet of the channel,
RTD3 is positioned directly at the midpoint of the microchannel as well as the heater, and
RTD4 is positioned at the outlet of the channel. Figure 2.1 depicts a fully labeled operating
microchannel from a cross-sectional side view.
13
outlet
inlet
heat generation
bottom surface,
Figure 2.1: Schematic of a generic operating microchannel with micropillar structures on the
shown.
are
an inlet, an outlet, and a heater indicated by heat generation
The three specific microchannels utilized for this thesis were: a flat sample (smooth surface,
= 5
no pillar array), a sample henceforth called the 5-15 (height h = 25 ym, diameter d
im,
yim, and pitch 1 = 15 ym) and a sample henceforth called the 10-40 (height h = 25
2.1.
Table
in
viewed
diameter d = 10 ym, and pitch 1 = 40 ym), whose geometries can be
These cover the relative cases of smooth surface, dense pillar array, and sparse pillar array
microchannels, respectively.
25
5
15
5
0.33
Table 2.1: Geometric parameters of tested structured surface microchannels
The flat surface microchannel was used as a control, but the length scales for the microscale pillars of 5-25 pm were originally chosen for Zhu et al.'s experiments for the following
reasons: (1) Fabricating structures of those sizes on silicon are standard, well-controlled
etching processes that present no complications. (2) Capillary pressure that can be generated
the
at this length scale with moderate mass fluxes is on the same order of magnitude as
wicking.
pressure drop across the microchannel, and can therefore effectively promote liquid
as liquid
(3) The pillars' structural integrity are robust and will not deform appreciably
the
evaporates. The specific selection of the geometric parameters was based on balancing
14
maximization of capillary driving pressure and minimizing viscous drag resistance to flow.
A model created by Xiao et al., depicted in Figure 2.2, shows how capillarity of the pillar
array varies with its geometric parameters, specifically the spacing 1.
0.1 A
large viscous drag
0.12
4A
0
0
0.1
-
-
..
0.08
-i/d=4
Wd=6
40
0.06
-o
-+Wd=10
/
-d
0~
-*Wd=infinity
0.04
Cr
0.2Q
p
in dequate
apillarity
I
___I
.1
0.15
0.2
0.25
i
0.35
0.3
d/1 Ratio
I
I
I
I
0.4
0.45
0.5
0.55
Figure 2.2: Graph of how capillarity varies the with structural pillar geometry. Adapted from Xiao et al.
[13]
As the labels indicate, a low d/1 ratio translates to a very low capillary pressure induced by
the pillars. On the other hand, too large a d/1 ratio induces large viscous drag forces, thereby
inhibiting the flow. Therefore, the structured surface microchannels fabricated primarily fell
into the middle range: d/l = 0.25-0.5.
Flow boiling using water in microchannel heat sinks generally operate in the annular flow
regime not only because of the capillary length of water, which is greater than 2.5 mm,
but also because of the high vapor quality of the fluid at high heat fluxes. The uniform
micropillar arrays at the bottom surfaces of the microchannels are designed for the purpose
of enhancing and sustaining stable liquid film evaporation through the mechanism of capillary
flow. This was done because only the bottom surface is heated and therefore the most likely
15
to experience dryout. While the bottom surface is structured, the side walls of a microchannel
have a roughness of 1-20 im in order to facilitate nucleation. By avoiding nucleation from
the heated bottom surface, the possibility of dryout on the structured surface is mitigated.
This decoupling of film evaporation from the heated surface and the nucleation from the side
walls enables the microchannels to dissipate high heat fluxes while maintaining stable heat
transfer performance.
2.1.2
Fabrication
Standard silicon microelectromechanical systems (MEMS) fabrication processes were used
to create the microchannel samples and are summarized in Figure 2.3. The pillar arrays, all
with heights of 25 tm, were etched out of a 500
rm thick silicon wafer using deep reactive
ion etching (DRIE) to the channel's bottom surface (Fig. 2.3a). Another silicon wafer of the
same thickness was etched through using DRIE to create the channel's side walls (Fig. 2.3b).
The two wafers from the first two steps were bonded together using Si-Si fusion bonding
(Fig. 2.3c). The inlet and outlet ports were laser drilled through a Pyrex wafer (Fig. 2.3d).
Si-Si bonding was then used to bond the two silicon wafers together. A 1 tm hydrophilic,
silicon dioxide coating was thermally grown on the channel walls, as well as on the back side
as an electrical insulation layer. The Pyrex wafer was then bonded to the silicon wafers using
anodic bonding to enclose the microchannel and facilitate observation of the flow (Fig. 2.3e).
Finally, a ~170 nm thick layer of platinum was deposited on the back side of the channel with
E-beam evaporation and patterned by the lift-off technique to serve as the heater and RTDs
(Fig. 2.3f).
The fabrication process was the same for the flat samples with the exception
that step (a) requires no etching.
16
(ab1(e)
(b)
(c)
ff
(d)
Figure 2.3: Fabrication process of the microchannel. (a) Micropillars of 25 pm height were etched in Si using
Deep Reactive Ion Etching (DRIE). (b) A Si wafer was etched through using DRIE to define the channel. (c)
The two wafers from the first two steps were bonded together using Si-Si fusion bonding. (d) Inlet and outlet
ports were laser-drilled on a Pyrex glass wafer. (e) The Si layers were bonded using direct Si-Si bonding. A
silicon dioxide (Si02) layer was thermally grown on the Si surface. The Pyrex layer was bonded to the top
Si using anodic bonding. (f) A Pt layer was deposited on the backside of the microchannel using E-beam
evaporation and patterned to form heater and temperature sensors. From Zhu et al. [9]
Figure 2.4 displays a comprehensive set of images of the microchannel. Fig. 2.4a and 2.4b
show the full front and back sides of a fabricated microchannel sample. The trapezoidal air
chambers next to the center microchannel in Fig. 2.4a were included for thermal insulation
to reduce heat loss through the sides of the microchannel. A cross-sectional image as well as
scanning electron micrograph of the micropillars on the channel bottom surface are shown
in Fig. 2.4c.
17
0
3
30mm
RTDs
Pt heater
(C)
Thermal
insulation
chamber
Figure 2.4: Images of a representative fabricated microchannel with micropillar arrays. Optical images of the
(a) front and (b) back side of a sample. (c) A cross-sectional image as well as scanning electron micrograph
of the micropillars on the channel bottom surface. From Zhu et al. [9]
2.1.3
RTD Calibration
The temperature of the microchannels are obtained by measuring the resistance of each RTD,
which is a function of the temperature. The relationship between the temperature and the
resistance of each RTD acquired by calibration, which is important because they are not all
perfectly identical, despite having the same fabrication process. The microchannel sample
was secured in a fixture created for the samples. The purpose of the fixture is to provide
electrical contact to the RTDs on the sample and is connected to an external electrical circuit,
from which the RTD voltages were measured and their resistances calculated. The entire
fixture and sample were then placed in a controlled thermal chamber along with a separate
thermocouple to measure the internal temperature of the chamber. The temperature of the
0
0
thermal chamber was set to 25'C, 50 C, 75 C, and 100'C. Two 30-second sets of raw voltage
data from the thermocouple and the four RTDs on the sample were recorded and afterwards
converted into temperature and four resistance values, respectively. These values were then
averaged, and a sample of the averaged data for the flat sample calibration is shown below
in Table 2.2.
18
102.27
310.10
302.69
304.35
300.25
Table 2.2: At a given temperature, the measured temperature and resistance of each RTD was recorded and
averaged in order to calibrate the flat surface microchannel sample. The same process was used to calibrate
the other microchannels as well.
The data was then plotted, and the trend appeared linear, which was expected because the
RTDs are made of platinum, whose resistance varies linearly with temperature at relatively
low temperatures. The plot is shown in Figure 2.5.
19
120
Expe i--ntal Data RR12
Expe ri nental Data
A Expe ri Tental Data R3
x Experi Tiental Data R41
Linear (Experimental lData RI)
- Linear (Experimental 'Oata R2)
Linear (Experimental D~ata R3)
Linear (Experimental Oata R4)
100
0~
+
*
*
y = 1.7224x 419.13 y = 1.7 )73x - 417.4
y
15867x-374.
C
y
1.6766x - 417 71
801
Q
=
60
a.
EGD
I-
40
20
1
4
0
250
260
270
280
290
300
310
320
Resistance (D)
Figure 2.5: Measured data and the linear fit of the resistance of the RTDs with temperature to determine
calibration constants a and 0 for the linear fit equation T = aR + 0
Therefore, the governing equation decided as the best fit for the data was a line equation
depicted by Equation 2.1.
T = aR+
(2.1)
As Equation 2.1 implies, the slope of each best fit line is the a value for the RTD, while the
temperature, or y-axis, intercept, is the 0 value. The list of the values for each microchannel
sample and their RTDs are shown in Table 2.3
20
(a)
(b)
(C)
-
(Q)
R2 (0)
R3 (0)
R4 (0)
31.93
-351.95
-349.15
-352.93
R 1 (0)
R2 (Q)
R3 (0)
R4 (n)
-361.98
-363.79
R
10-40
0
33.8-363.18
Table 2.3: Calibration constants of the microchannel samples that linearly relate the resistance of the RTDs
to their temperatures, where otis the slope with units 'C/Qand Pis the intercept with units 'C. (a) Flat
Sample. (b) 5-15 Sample. (c) 10-40 Sample.
2.2
Experimental Setup
The flow boiling test setup is a closed loop system, a schematic of which is shown in Figure 2.6. The water reservoir was heated, maintaining degassed water at saturation temperature and pressure. The saturated water leaves the reservoir and flows through tubing that is
fed into a peristaltic pump, continues through a flow meter, and through a heated segment
(called the pre-heater) of the loop which heats the water closer to saturation temperature.
The water then passes a needle valve and enters another heated portion (called the postheater) of the loop which heats the water to near boiling
-90-95'C. The flow then enters
the microchannel, which is being heated through its built-in resistive element. The channel
can be observed through an inverted microscope as well as an attached high speed camera,
which can capture flow boiling videos. The tubing at the outlet of the microchannel leads
back into the reservoir to complete the closed loop.
21
DAQ Card
T
P
Power Supply
P
T
Test Fixture with
Microchannel Sample
Post-Heater
Needle
High Speed
Inverted
Valve
Camera
Microscope
thDgsd
with DegassedT
ReservoirT
Liquid
Peristaltic Pulve
Pre-Heater
M
transducers, thermoFigure 2.6: Schematic of the Experimental Setup. "P", "T", and "M" indicate pressure
measurements,
indicate
lines
red
flow,
fluid
indicate
lines
blue
The
couples, and a flow meter, respectively.
connections.
physical
indicate
lines
gray
and
recording,
and
transfer
data
indicate
green lines
transAdditionally, sensors were placed throughout the loop: 7 thermocouples, 3 pressure
on
ducers, and a flow meter. As mentioned in Chapter 2, there are the four thermocouples
the fixture of the microchannel sample. Additionally, there is one thermocouple and pressure
transducer each in the reservoir, at the inlet of the microchannel, and at the outlet. The
single flow meter lies just downstream of the peristaltic pump. The sensors are all connected
to a data acquisition (DAQ) card. The sensors and heaters also each have their own variable
power supply.
2.3
Experimental Preparation
The reservoir was made ~90% full of deionized water by adding deionized water through
cona valve using a syringe. Deionized water was used to minimize particulates and other
before
taminants that could affect the results of the experiment. The valve had to be closed
detaching the syringe, in that order, to prevent outside contaminants from being sucked into
the reservoir, as the reservoir pressure is generally less than atmospheric pressure at room
all
temperature. To prevent unintentional contamination or flow, the reservoir valves should
above
be closed before continuing. The water level was maintained throughout experiments
80% capacity.
were
Using the LabVIEW program "flow boiling DP test," the reservoir pressure readings
22
monitored to ensure safety. The reservoir's PID controller was turned on and set to 102'C,
and its heater was turned on to 45 V in order to ensure adequate power to heat the reservoir.
While the reservoir was heating up, the sample was cleaned, again to remove any contamination amassed through previous experiments or obtained during storage between experiments.
Clean tweezers were used to hold the edge of the sample, and then, while ensuring the back
side did not get wet, acetone, isopropyl alcohol, and water were flowed through the inlet
to the outlet of the channel in that order for about a minute each to dissolve any possible
organic contaminants.
Then the air gun was used to blow dry the sample for a minute,
making sure to aim at the inlet so that all the liquids were flushed from channel through
the outlet. To further clean the sample, it was laid face up inside a plasma cleaner so the
inlet and outlet ports would be exposed to the plasma gas. The plasma cleaner valve was
then closed, the pump was turned on, and the oxygen valve was opened until the pressure
reading stabilized. The power switch was turned to "HIGH" and the purple plasma gas was
seen inside the chamber. After 10 minutes of plasma cleaning, everything was turned off,
and the valve was slowly opened to prevent outside contamination from being sucked into
the chamber.
After the sample was removed from the plasma cleaner, it was placed in the plastic fixture
that secures the sample in the loop. The heater pads were lined up with the pogo pins on
the fixture cover; incorrect orientation of the sample could result in burning it. The sample
was then secured in the fixture by tightening the bolts such that they could be loosened with
the slightest effort.
Once the reservoir's measured temperature approached 100 C, the heater was turned down
to ~35 V. Once the reservoir water temperature reached 101'C, the vent valve at the top
of the reservoir was opened and kept open to degas the reservoir and prevent the pressure
inside from getting too high.
The resistance temperature detector (RTD) power supply
was turned on to 2 V and the microchannel heater power supply was turned on. Then a
different LabVIEW program "Flow boiling input power control new 2," which displays all
the measurements for the experiment (temperatures, pressures, mass flow rate, heat flux,
and resistance), was opened and run. Monitoring the values here were crucial to ensuring
that the system did not overheat or overpressurize. The calibration constants, the alpha and
beta values, for each RTD were entered to the corresponding value for the sample in the loop
in order to obtain accurate temperature data. In addition, the heater resistance was set to
300 Qand the maximum heater voltage to 2 V. Then, the preheater and postheater were set
to the appropriate voltages such that at steady state, which takes 30-60 minutes to achieve
depending on the flow rate, the inlet water temperature is between ~90-95'C. The needle
valve was also set such that there was approximately a 10 kPa pressure rise after the pump.
23
2.4
Experimental Procedure
Once the inlet water temperature was stable between ~90-95'C, the experiment could begin.
The LabVIEW program was stopped and rerun before saving a new data file; otherwise, the
data would be saved to the older file. For each data set, the program was run, the desired
heat flux specified, and the maximum heater voltage increased as necessary such that the
actual heater voltage was slightly below the maximum heater voltage and the actual heat flux
matched the desired value. Once the measurements, such as temperature, reached steady
state, data was recorded for at least 2 minutes, the recording was stopped, and finally the
program was stopped. Notes of interesting and benchmark phenomena, such as the onset of
two-phase flow, were recorded by hand in a laboratory notebook.
During the experiment, high speed videos were taken in order to document interesting and
benchmark phenomena in the flow boiling process throughout the experiment. Video was
only taken while data was being recorded, so as to match up the data with the video for
future reference.
To do so accurately, the camera's clock had to be synchronized with the
computer's clock. Then video would be taken for a few seconds, after which only a selected,
representative fraction of that video would be saved.
Once the system appeared to have achieved its critical heat flux or if the measured temperature of the microchannel consistently rose above ~200'C the experiment was stopped in
order to protect the samples. At this point, the desired heat flux and the maximum heater
voltage settings were gradually decreased to zero both to ensure that the actual heat flux
becomes zero and to prevent the sample from cooling too quickly and causing a thermal
fracture, damaging the sample. Then, the preheater, postheater, and reservoir heater were
turned to 0 V and turned off.
fllr wr
The pump was turned off, and then all the valves of the
osed. Once it was verified that the temperatures in the microchannel were
dropping, then the heater power could be turned off and the LabVIEW program could be
stopped and closed.
2.5
Measurements and Data Processing
By following the procedure above, the resistances of the RTDs were measured. The temperatures throughout the loop were measured, excluding those in the test fixture. The pressure
sensors at the inlet and outlet of the microchannel measured the inlet and outlet pressures.
Finally, the mass flux was measured by the flow meter after the pump.
Instrument error included the temperature measurements from the RTDs (2'C), pressure
24
___ - - - 1.. -11 _11
measurements from the pressure transducers ( 0.3 kPa), instantaneous mass flux measurements from the flow meter ( 1 mL/min, or approximately 70 kg/m 2 s), and voltage and
current measurements from the power supply ( 0.4 mA and
0.06 V, respectively).
The quantities of interest were the input heat flux from the heater, the temperature rise of
the fluid, and the pressure drop across the channel. The equations for calculating each of
the values are given below in Equations 2.2, 2.3, and 2.5, respectively.
q"=
I1AAV
(2.2)
Aheater
where I and AV are the input current and voltage from the power supply.
AT = T3 - Tsat
(2.3)
where T 3 is the temperature of RTD3, calculated using the resistance of RTD3 in Equation 2.1
along with its calibration constants a and 3. Tat is a function shown below in Equation 2.4
that varies with the average of the inlet and outlet pressures, defined here as Psat.
Tsat = 0.00000502877Psat - 0.00266178sat + 0.661681943Psat + 55.02536145
(2.4)
This relationship is important to include because saturation temperature changes with the
pressure of the environment and it is upon this value that the temperature rise is based.
APb
Pinlet - Pouthet
The above equations produced the results discussed in the following section.
25
(2.5)
Chapter 3
Results and Discussion
3.1
Flow Instability
During the heated flow through the microchannels, flow instabilities in the form of temperature and pressure fluctuations could be observed in time. The heater surface temperature
and the pressure drop across the entire channel were measured.
Every sample (flat, 5-15, and 10-40) exhibited increasingly large oscillations with increasing heat fluxes for the lower mass fluxes 150 kg/m 2 s and 300 kg/m 2 s. For the 150 and 300
kg/m 2 s mass fluxes, the flat sample exhibited much greater temperature and pressure fluctuations than the structured samples due to more prolonged periodic dryout. These temporal
fluctuations can be observed in the figures below, in which the heater surface temperature
and the pressure drop across the channel were plotted for each sample at each mass flux for
a representative range of heat fluxes.
The following results are in reference to experiments performed at the mass flux of 150
kg/m 2 s, during which a representative range of heat fluxes was examined after two phase flow
began, 15 W/cm 2 to 431 W/cm 2 , and these temporal results can be observed in Figure 3.1.
At the lower heat fluxes, 150 W/cm 2 and 264 W/cm 2 , before the critical heat flux of the
flat sample was reached, the flat sample experienced temperature fluctuations of
t10'C and pressure drop fluctuations of t0.7 kPa to
temperature fluctuations of
6'C to
1.0 kPa. The 5-15 sample experienced
8'C and pressure drop fluctuations of
0.6 kPa to
0.8 kPa. The 10-40 sample experienced temperature fluctuations of t0.3'C to
and pressure drop fluctuations of
0.3 kPa.
7'C to
2.5'C
2
At the higher heat fluxes, 357 W/cm and
431 W/cm 2 , after the critical heat flux of the flat sample was reached, the 5-15 sample
experienced temperature fluctuations of
0.6 kPa to
10'C to
130 C and pressure drop fluctuations of
0.8 kPa. The 10-40 sample experienced temperature fluctuations of
26
5'C to
8'C and pressure drop fluctuations of
150 kg/m^2*s,
0.6 kPa.
153 Wlcm^2
15) k/m^2-i
153 W/cme2
150 kg/n^2
264 W/cm^2
Time (s)
150 kgje
2*
-264 Wckm2
TimeW
150 kgirn
Pme
i5o
kgim"2~i
1I0 kg/me2*,
, -357 Wfcm2
-357 W/cm^2
me
(S)
150 kg/m"2
431 WAw^2
(S)
-431 W/UnI2
kNAn
Figure 3.1: Temporal data comparison of temperature and pressure drop for each microchannel sample with
a 150 kg/m 2 s mass flux at a range of representative heat fluxes. The uncertainties of the temperature and
pressure drop measurements were approximately 6.5'C and 0.45 kPa, respectively.
2
For the experiments performed at the mass flux of 300 kg/m s, a representative range of
2
2
heat fluxes was examined after two phase flow began, 152 W/cm to 640 W/cm , and these
temporal results can be observed in Figure 3.2. The flat sample experienced temperature
fluctuations of 5'C to 20'C and pressure drop fluctuations of 0.3 kPa to 1.0 kPa.
The 5-15 sample experienced temperature fluctuations of 5'C to 7'C and pressure drop
fluctuations of 0.6. The 10-40 sample experienced temperature fluctuations of 4'C to
6'C and pressure drop fluctuations of
0.2 kPa to +1.0 kPa.
27
-A
MM
300 kg/^2i.
W/Cm^2
~152
300 kg/m^2s
12 W/CMr2
30Okg/m2*s,
376 W/cm^2
300 kg/rn~2~,
ii23 WIcm'Z
Time is)
-'376 W/cm-'2
~00 k~Im~2
Time fs}
300 kg/m^P2s.
523 W/Cm'2
w~
300 kg/m
2s
640
Time
W/m
300 kg/m^2% -640 W/cm2
2
Is
2
with a 300 kg/m s
Figure 3.2: Temporal data of temperature and pressure drop for each microchannel sample
and pressure drop
mass flux at a range of representative heat fluxes. The uncertainties of the temperature
respectively.
kPa,
0.22
measurements were approximately 3.5*C and
2
in order to
The experiments performed at the mass flux of 500 kg/m s were more averaged
system. The
filter the non-physical measurements such as noise or slight vibrations in the
153 W/cm 2
representative range of heat fluxes examined after two phase flow began was
with the
to 859 W/cm 2 , and these temporal results can be observed in Figure 3.3. Even
is suspected
additional averaging, the 5-15 sample exhibited more pressure fluctuation. This
from a
to be due the relatively dense spacing of 5-15. The vapor shear stress resulting
drop across
combination of the high mass flux and dense pillar spacing causes the pressure
the channel to increase dramatically.
28
/cm^2Is,
600 kg/mc2~,
"153 W/cm12
!Oh?_L
A
-A
163 V1/cm~2
- A .A
-V
-
600
Time
500 kg/m
2 '524
W/cm 2
S00
kg/m^2*,
(s)
524 W/cc 2
Tmie fs
5,00
-'c~2
746 WAITnc2
20* 41 SN/b 09
OmAA
OO#
-4W1
500 kgm2*,-5 W/c'
T fme1p
2
Figure 3.3: Temporal data of temperature and pressure drop for each microchannel sample with a 500 kg/m s
drop
mass flux at a range of representative heat fluxes. The uncertainties of the temperature and pressure
0
measurements were approximately 0.3 C and 0.28 kPa, respectively.
3.2
Heat Transfer Performance
In addition to flow instabilities, the time-averaged heat transfer performance of the microchannels were also considered. In general, it was observed that as the heat flux q" increases, the temperature rise AT initially increases relatively linearly, then suddenly drops,
begins to rise relatively linearly again, and then begins to increase enormously compared
to the heat flux. The initial increase is due to heating single phase flow, during which the
water simply increases in temperature with the heat flux towards the boiling point. The
29
sudden drop indicates the onset of nucleate boiling, when the heat transfer coefficient increases substantially due to the slight mixing that occurs when bubbles separate from their
nucleation sites. The subsequent rise is the continual heating of two phase flow. Finally,
massive temperature rises with little heat flux increase indicates that the critical heat flux
has been surpassed. The critical heat flux is the point just before the heat transfer of the
microchannel suddenly becomes inefficient, meaning that a small increase in heat flux will
cause the temperature to rise dramatically. This transition occurs due to a dynamic phenomenon (not captured because of the time-averaged nature of the data) known as dryout.
Dryout is a condition in which the liquid on the microchannel surface has evaporated away,
thus causing the heat transfer coefficient to drop, which explains the inefficiency.
Boiling curves were obtained by plotting the heat flux q" against the time-averaged temperature rise of the heater (RTD3) AT for each flow rate and microchannel investigated. The
boiling curves for all the flow rates for each microchannel can be observed in Figure 3.4 and
all the microchannels at each mass flux can be observed in Figure 3.5.
30
--
-I---'
-
-
-
--
-
- -
-
-
Boiling Curve, Flat
1200
2N
-
150, FIat
- -
300, Flat
500, Flat
-
-
1000
800
600
X<
400
20
r
0
10
0
60
50
40
30
20
70
AT (*C)
Boiling Curve, 5-15
1200
-e-300, S-15
--
150, 5-15
-0- 500, 5.15
1000
800
600
200
II0
0
s0
40
30
20
10
60
70
80
60
70
80
AT (*C)
Boiling Curve, 10-40
1200
--
150,
A
10 40
300, 10-40
-
-500,
10 40
1000
800
600
X
400
200
I
0
0
10
20
40
30
50
AT (*C)
Figure 3.4: Boiling curves for all the flow rates for each microchannel. The effects of varying mass flux can
be clearly observed for each microchannel. The initial slope indicates heating single phase flow, whereas the
sudden jump back indicates the onset of nucleate boiling. The following upwards slope is heating of the two
phase flow, and finally, the critical heat flux is indicated by when the slope changes significantly from the
2
two phase flow slope. Note: the 10-40 sample broke during the experiment at 500 kg/m s, and since there
was no slope change observed, the critical heat flux was not reached and is therefore unknown. The error
bars were approximately 1% for the heat flux q" and ranged from 2.0'C to 6.7'C for the temperature
rise.
31
--
. "
qj
-
-
--- --; -1 -
Boiling Curve, 150 kg/mA2*s
600
S-150,Flat
-0-150,1-40
-4-150,5-15
50c)
U
400
300
10
0
50
40
30
20
60
70
AT (*C)
(U
Boiling Curve, 300 kg/m^2*s
1200
-A
300, fat
-i-
-h-- 300, 1-4
300, 5-15
1000
00
2-0
E
10
0
U
50
40
30
20
60
70
80
60
70
80
AT (*C)
Boiling Curve, 500 kg/mA2*s
1200
-
500, lat
-U-
500,
S-15
500, 10-40
-
1000
800
600
X
400
200
0
0
10
20
40
30
50
AT (*C)
Figure 3.5: Boiling curves for all the microchannels at each mass flux. Differences between the microchannels
can be clearly observed at each mass flux. The initial slope indicates heating single phase flow, whereas the
sudden jump back indicates the onset of nucleate boiling. The following upwards slope is heating of the two
from the
phase flow, and finally, the critical heat flux is indicated by when the slope changes significantly
2
two phase flow slope. Note: the 10-40 sample broke during the experiment at 500 kg/m s, and since there
was no slope change observed, the critical heat flux was not reached and is therefore unknown. The error
0
0
bars were approximately 1% for the heat flux q" and ranged from t2.0 C to t6.7 C for the temperature
rise.
32
For a given microchannel, Figure 3.4 clearly shows that critical heat flux increased with the
mass flux, as expected.
The structured surface microchannels exhibited an increased heat transfer coefficient due
to the fact that evaporation is dominant in the annular flow regime and the structures
facilitate a stable liquid thin film. For each mass flux, it seems that the 10-40 structured
surface microchannel exhibited the greatest critical heat flux value, followed by the 5-15,
and then the flat surface. Unfortunately however, the 10-40 sample broke during the 500
kg/M2s experiment, so the critical heat flux value at that flow rate could not be determined.
Table 3.1 displays the critical heat flux values of each microchannel at each flow rate as well
as their critical heat flux enhancement percentage relative to the flat surface microchannel.
Flo
1 50
Rte\amle
Flat (W/Cm,2 , %)
5-15 (W/cm,2, %)
10-40 (W/cm,2, %)
kg/mS
300 kg/i-s
762
612
24%
969
58%
Table 3.1: Critical heat flux values of each microchannel at each flow rate as well as their critical heat flux
enhancement percentage relative to the flat surface microchannel.
The critical heat flux enhancement due to structured surface microchannels relative to the
flat surface microchannel decreased with increasing mass flux. This effect can be explained
by the fact that capillary pressure, given by Equation 3.1, assists in prolonging dryout, is
independent of mass flux, and is dependent on the spacing between pillars.
r
Pcap
(3.1)
where o is the surface tension of the fluid, and r is the radius of curvature of the liquid-vapor
interface whose lower bound is the pillar spacing, which in turn limits the magnitude of the
capillary pressure Pap. Therefore, because the pressure driving the flow increases with mass
flux, it eventually dominates the effect of capillary flow.
At the lower mass fluxes, the 10-40 microchannel achieved greater critical heat flux values
2
than the 5-15. However, at the lowest mass flux of 150 kg/m s, the critical heat flux values
for the 5-15 and the 10-40 are nearly equal. The trend suggests that perhaps at even lower
mass fluxes, the critical heat flux achieved by the 5-15 may surpass that of the 10-40. This
is because the 5-15 has a denser micropillar array due to its smaller spacing than the 10-40.
33
A smaller spacing would cause there to be greater capillary pressure, which is independent
of mass flux. Therefore, the effect of capillary pressure would be dominant at lower mass
fluxes, making denser micropillar arrays such as the 5-15 more beneficial than sparser arrays
such as the 10-40 at lower mass fluxes.
Based on these trends and observations, the lower the mass flux, the denser the micropillar
array should be to maximize the critical heat flux value.
3.3
Pressure Drop
While it has been shown that the heat transfer performance can be improved by adding
structures to microchannel surfaces, the pumping costs must also be determined in order to
accurately characterize the effectiveness of surface structures. Figure 3.6 shows the timeaveraged pressure drop across the microchannels as a function of heat flux. For each mass
flow rate, the pressure drop across all the microchannels were nearly the same, implying that
the expected additional pressure drop introduced by the surface structures was practically
negligible.
34
-- --------I'll-........
---
i-
Pressure Drop, 150 kg/mA2*s
6
-o-
150, Flat
-0
150, 5-15
-
150,
10-40
5
4
CL
0~
1
01T
50
0
200
150
100
250
350
300
400
450
500
Heat Flux q" (W/cmA2)
Pressure Drop, 300 kg/mA2*s
18
16
-r-300,
300 Flat
--
5-1S
-R-300,
10-40
14
12
CL
10
8
4
2
0
600
400
200
0
800
1000
1200
1000
1200
Heat Flux q" (W/cmA2)
Pressure Drop, 500 kg/mA2*s
25
-0
500, Flat
-M- 5(0, 5 15
--
500, 1040
20
CU
1
CL 10
5
0
0
200
600
400
800
Heat Flux q" (W/cmA2)
Figure 3.6: Boiling curves for each flow rate as well as each microchannel. Differences between the mi1% for the heat
crochannels can be clearly observed at each mass flux. The error bars were approximately
flux q" and ranged from 0.36 kPa to 0.60 kPa for the pressure drop.
35
This could have been predicted by the fact that there is a 500
im x 500 im cross-section
above the structures in the structured microchannels, the same size as the entire cross-section
of the flat surface microchannel. Therefore, at heat fluxes during which the structures are
completely immersed in liquid, there should not be a noticeable pressure drop difference
between each microchannel.
Additionally, at heat fluxes during which there is insufficient
liquid supply, the liquid-vapor interface forms menisci between the structures and generates
capillary flow, increasing the flow rate. This capillary flow also does not require a larger
pressure drop occur.
Finally, comparing single microchannels at different mass fluxes, it can be observed that the
pressure drop across the microchannels are comparable at 150 and 300 kg/m 2s, but that
there is a noticeable increase at 500 kg/m 2 s. This could potentially be explained by the fact
that at 500 kg/m 2 s, the viscous resistance has increased greatly and thus requires a much
larger pressure head to propel the flow through the microchannel.
36
Chapter 4
Conclusion
In an effort to find potential thermal solutions for newer and higher power density electronics,
it has been demonstrated that utilizing two-phase microchannels with micropillar arrays are
a viable future solution for some high power applications. These surface structures reduced
the flow boiling instability because they enhanced the annular flow while the flat surface
microchannel experienced more instabilities such as temperature spikes and pressure drop
fluctuations due to more frequent dryout. Furthermore, because surface structures decreased
the instances of dryout in microchannels, the structured surface microchannels were able to
achieve greater critical heat flux values than the flat surface.
In examining the heat transfer capabilities of the microchannels at various mass fluxes, it
was verified that the critical heat flux value of all microchannels increased with mass flux.
In addition, it was concluded that at lower mass fluxes, the relative percentage heat transfer
enhancement of the structured surface microchannels over the flat surface microchannel was
greater. The trend observed suggests that denser samples are better at lower mass fluxes.
However, if a sample is too dense, there may be too much viscous drag and thus an optimal
balance between capillary force and viscous drag must be found in order to determine the
optimum micropillar array geometry and density.
Finally, for a given mass flux, the pressure drop across every microchannel was approximately
equal at all heat fluxes. This implies that no additional power consumption is required to
pump a particular mass flux through a structured surface microchannel than a flat surface
microchannel.
This work provides insights into the roles of both the micropillar array surface structures and
the mass fluxes on the heat transfer performance of two-phase microchannels. The results
and observations of this experiment may prove helpful in guiding future work in an attempt
to optimize microchannels for heat transfer applications in electronics.
37
While this work is a preliminary examination of the role of mass flux on the heat transfer
properties, there are many more opportunities to further characterize flow boiling in structured surface microchannels.
A few examples of future work include:
(1) Examining the
relationship between micropillar arrays with the same dl ratio. This study would aim to
determine how and under what circumstances that microchannels with the same ratio but
different diameter pillars differ.
(2) Examining how the height of the micropillars given
a constant cross-section affects the heat transfer and wicking properties of the microchannel. (3) Examining the performance of other common coolants that boil near the operating
temperature of high power electronics in structured surface microchannels.
38
Chapter 5
5.1
Works Cited
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Y. Zhu, D.S. Antao, K.-H. Chu, T.J. Hendricks, E.N. Wang, "Enhanced Flow Boiling Heat
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D. Li, G. S. Wu, W. Wang, Y. D. Wang, D. Liu, D. C. Zhang, Y. F. Chen, G. P. Peterson,
and R. Yang, "Enhancing Flow Boiling Heat Transfer in Microchannels for Thermal Management with Monolithically-Integrated Silicon Nanowires," Nano Lett., vol. 12, no. 7, pp.
3385-3390, Jul. 2012.
39
F. Yang, X. Dai, Y. Poles, P. Cheng, J. Khan, and C. Li, "Flow boiling phenomena in a single
annular flow regime in microchannels (I): Characterization of flow boiling heat transfer," Int.
J. Heat Mass Transf., vol. 68, pp. 703-715, Jan. 2014.
F. Yang, X. Dai, Y. Peles, P. Cheng, J. Khan, and C. Li, "Flow boiling phenomena in a single
annular flow regime in microchannels (II): Reduced pressure drop and enhanced critical heat
flux," Int. J. Heat Mass Transf., vol. 68, pp. 716-724, Jan. 2014.
R. Xiao, R. Enright, and E. N. Wang, "Prediction and Optimization of Liquid Propagation
in Micropillar Arrays," Langmuir, vol. 26, no. 19, pp. 15070-15075, Oct. 2010.
40
5.2
5.2.1
clear
clC
Appendix
MATLAB Script for Processing Experimental Data
all
(V)
)
(
% Read all the raw data files
i-min0 = 4;
i-maxO = 29;
i-minl = 4;
i-maxl = 29;
i-min2 = 5;
i-max2 = 29;
for i = i_min0:i_max0
File = [ ' TestData ' , sprintf ('%02d' , i ) , '.lvm ']
23, 0);
Data{i} = dlmread(File '\t',
Data0
= Data{i};
% time (s)
= Data0(: ,1);
t{i}
% Calculated Uset f rom heater res istance
= Data0(:,2);
U-cal{i}
% Actual Uset (V)
= DataO(: ,3);
U-set { i}
% Heater resistance (Ohm)
R-heater{i} = Data0(:,4);
% power (W)
DataO(: ,5);
Pw{ i}
C) Inlet
% RTD temperature
Data0(: ,6);
T1{ i}
C) before the heater
% RTD temperature
= Data0(: ,7);
T2{i}
C) heater cent er
% RTD temperature
= DataO(: ,8);
T3{i}
C) after the h eater
% RTD temperature
= DataO (:,9);
T4{ i}
= Data0 (:,10); % Reservoir pressur e (kPa)
Pt{i}
% Inlet pressure (b Pa)
Data0(: ,11);
Pi{i}
kPa)
Po{i}
= Data0(: ,12);
% Outlet pressure
% Flow rate (mL/mir
= Data0(: ,13);
G{i}
% Pump pressure (kF a)
DataO (:,14);
Pc{ i }
% Microchannel inle t temperature
= Data0(: ,15);
T-mci{ i}
% Microchannel out [et temperature
= Data0(: ,16);
T-to{i}
end
Optional step to correct temperatures if incorrec t
for i = 1:29
T1{i}= (T1{i}+427.56)/0.8444*1.7308-601.78;
T1{i} =T-{i};
T2{i} = (T2{i}+433.81)/0.8912*1.7998-604.68;
T2{i} = T-2{i};
T-3{i} = (T3{i}+411.06)/0.8638*1.7333-572.48;
T3{i} = T-3{i};
T_4{i} = (T4{i}+447.85)/0.835*1.7515-637.63;
T4{i} = T_4{i};
end
G-ave = 300;
A-itube = pi()*0.003^2;
m-ave = G-ave * A-itube;
T-sc = 10;
Area-heater = 500e-6 * lOe-3;
T.ambient = 22;
% h-itube = 1
%
%
%
%
calibration
constants
Average mass flux (kg/m2s)
Inlet tube cross-section Area
Average mass flux (kg/s)
Subcooling (C)
% (m^2)
% (C)
41
were used
% Boiling curve; HTC; P drop
for i = i-min0:i-max0
Tla(i) = mean (T1{ i });
T2a(i ) = mean(T2{i });
T3a(i) = mean(T3{i});
T4a(i) = mean(T4{i});
= 0.25*T2a(i)+0.5*T3a( i)+0.25*T4a(i );
Ta(i)
Pwa(i) = mean(Pw{i});
h1l(i) = -1.41033e-9*Ta(i)^3
+ 9.50934e-7*Ta(i)^2 ...
- 0.000122382*Ta(i) ...
+ 0.027040238;
loss(i) = h-l(i).*(Ta(i)-T.ambient);
Q{i} = (Pw{i}-loss(i))*0.95/Area-heater/le4;
q(i) = (Pwa( i)-loss( i ))*0.95/ Area-heater/le4;
% assuming 5% extra loss
% accounting for loss as a function
% Ti
average
% T average
% Power average
%
%
%
%
hloss
(W/C)
loss (W)
heat flux (W/cm^2)
Ave heat flux (W/cm^2)
of temperature
dP{i} = Pi{i}-Po{i};
P.mid{i} = 0.5*(Pi{i}+Po{i});
P-mida(i) = mean(P-mid{i});
dPa(i) = mean(dP{i});
T-sat(i) = 0.00000502877*P-mida(i)^3
- 0.00266178*P-mida(i)^2
+ 0.661681943*P-mida(i) ...
+ 55.02536145;
dT(i) = T3a(i)-T-sat(i);
htc(i) = q(i)*1e4/dT(i)/le3;
% Note this will likely underestimate the real
% Pressure drop (kPa)
% center pressure
% average center pressure
% Saturation temperature
%delta T
% HTC (kW/m^2 K)
h
Gal(i) = mean(G{i}); % ml/min
Ga2(i) = Gal( i)/60/956.5/(500e-6)^2;
% kg/m2 s
end
casenumber = 4; % Change casenumber
switch casenumber
case
for
a different
plot
1 % Boiling Curve
figure (1);
h = axes('FontSize ' ,16);
plot (h,dT(i-minO: i-maxO) ,q(i-minO: i-max),'-sb' , 'LineWidth' ,2
'Color' ,[0.8 0 0.2] ...
'MarkerFaceColor ' ,[0.8 0 0. 21
'MarkerSize ' ,6);
xlabel ('{\ it \DeltaT} (^oC) ' ,' FontSize ' ,16 ,'Fontweight ','b')
ylabel('Heat Flux {\itq"} (W/cm^2) ','FontSize' ,16,'Fontweight
ylim ([0 ,1000])
xlim ([0 ,60])
legend (h,'10 -40','Location ','NorthWest')
set (h, 'LineWidth ', 1);
case 2 % Heat Transfer Coefficient
figure (2);
hi = axes ('FontSize ' ,l6);
plot (hl ,q(i-min2 :i-max2) ,htc (imin2:
'Color ' ,[0.8 0 0.2] ...
'MarkerFaceColor ' ,[0.8 0 0. 2], ...
'MarkerSize ' 6);
42
imax2),'-sb','LineWidth
...
','b')
',2
...
at
the center
xlabel ('Heat Flux {\itq"} (W/cm^2)','FontSize ',16, 'Fontweight
y IabeI( '{\ ith}I (kW/m^2-K)' 'FontSize ',16, 'Fontweight',b)
ylim ([0 ,350])
legend (hl, ' Structured 10 -40', 'Location ', 'NorthEast')
set (hl , 'LineWidth ',1);
','b')
case 3 % Pressure Drop
figure (3);
h2 = axes ('FontSize ' ,16);
','LineWidth', 2 ...
plot (h2,q(i-minl : i-maxl) ,dPa(i-minl : i-max),'-sb
'Color' ,[0.8 0 0.2],...
'MarkerFaceColor ' ,[0.8 0 0.2] ...
'MarkerSize ' 6);
xlabel ('Heat Flux {\itq"} (W/cm^2) ','FontSize ' ,16 , 'Fontweight ' , 'b')
ylabel ('{\ it\DeltaP} (kPa) ','FontSize ' ,16, 'Fontweight ' 'b')
ylim ([0 ,14])
xlim ([0 ,900])
legend (h2, ' Structured 10 -40','Location ','NorthWest')
set (h2, 'LineWidth ' , 1);
case 4 % Temporal data,
T3 and dP
temp-data = cell (1,
for
i = 4:length(t)
temp-data{i}
length(t)-3);
[
cell2mat (t(i ))
cell2mat (T3(i ))
end
x1
x2
x3
x4
=
=
=
=
29;
26;
27;
28;
x1 = 28;
x2 = xl + 1;
x3 = x1 + 2;
x4 = x1 + 3;
time-low = 0;
time-high = 200;
temp-low
temp-high
=
0;
155;
p-low
= 0;
p-high = 14;
figure (4);
subplot (4 ,2,1)
plot (tf{xl},T3{x1},'-sb' , 'LineWidth ',1. 5 ,...
'Color' ,[0.8 0 0.2] ...
'MarkerEdgeColor ' ,[0.8 0 0. 2],...
'MarkerFaceColor ' ,[0.8 0 0. 2] ...
'MarkerSize ' ,2);
title
([ 'q"=' sprintf('%0.lf' ,q(xl)) ' W/cm^2'])
xlim ([time-low , time-high])
ylim ([temp-low ,temp-high])
subplot (4 ,2 ,3)
43
,
cell2mat(dP(
i))
plot(t{x2},T3{x2},'-sb','LineWidth',1.5,...
'Color' ,[0 0.65 0.32] ,...
'MarkerEdgeColor ' ,[0 0.65 0.35]
'MarkerFaceColor ' ,[0 0.65 0.35] ...
'MarkerSize ' ,2);
' W/cm^2'])
title
([ 'q"=' sprintf('%0.lf ',q(x2))
xlim ([time-low , time-high])
ylim ([temp-low , temp-high])
ylabel('{Temperature \itT} (oC)','FontSize',12 ,'Fontweight
subplot (4 ,2 ,5)
LineWidth ' ,1.5 ,....
p = plot (t jx3}1,T3{x3}I,'- -','
'Color ' , 'k' ,. ..
'MarkerEdgeColor' ,'k
'MarkerFaceColor' , 'k'
'MarkerSize ' ,2);
title
([ 'q"=' sprintf ('%O.lf ' ,q(x3)) ' W/cm^2'])
xlim ([time.low , time-high])
ylim ([ temp-low , temp.high 1)
subplot (4,2,7)
p = plot (t {x4},T3{x4},'-','LineWidth ' ,1.5 ,....
'Color ', 'b' ...
'b'
'MarkerEdgeColor'
'MarkerFaceColor''b
'MarkerSize ' ,2);
tit le ([ 'q"=' sprint f ('%O.lf ' ,q(x4)) ' W/cm^2']
xlim ([time-low , time.high])
ylim ([temp.low , temphigh])
xlabel ('Time {\ itt } (s ) ' , 'FontSize ' ,12, 'Fontwei ght ' , 'b')
subplot (4,2 ,2)
plot ( t {x1} ,dP{xl},'-sb ' , ' LineWidth ' , 1.5 ,...
'Color ' ,[0.8 0 0.2] ,...
'MarkerEdgeColor ' ,[0.8 0 0. 2], ...
'MarkerFaceColor ' ,[0.8 0 0. 2], ...
'MarkerSize ' ,2);
t it le ([ 'q" =' sprintf ('%0. 1f ' ,q(xl)) ' W/cm ^2']
xlim ([time-low , time-high J)
ylim ([plow , p.high])
subplot (4 ,2 ,4)
plot (t {x2},dP{x2},'-sb ','LineWidth ',1.5 ,...
'Color ' ,[0 0.65 0.32] ...
'MarkerEdgeColor ' , [0 0.65 0. 3 5]
'MarkerFaceColor ' ,[0 0.65 0.35] ...
'MarkerSize ' ,2);
title
([ 'q"=' sprint f ('%O.lf ' ,q(x2)) ' W/cm^2']
xlim ([time-low , time-high])
ylim ([p-low , p.high ])
ylabel ( '{\ it \DeltaP} (kPa) ','FontSize ' ,12 , 'Fontweight
subplot (4,2 ,6)
p = plot (t{x3},dP{x3},'-^','LineWidth ' ,1.5 ...
'Color ' , 'k' ,.
'k'
'MarkerEdgeColor'
'MarkerFaceColor', 'k'
'MarkerSize ' ,2);
t itlIe ([ 'q"=' sprint f ('%0. 1f ' ,(x3))
' W/cm ^2']
44
' ,
'b')
' ,
'b')
)
xlim ([time-low , time-high])
ylim ([p-low , phigh
subplot (4,2,8)
p = plot (t{x4},dP{x4},'-^','LineWidth ' ,1.5 ...
'Color ', 'b' ,...
'MarkerEdgeColor' ,'b
'MarkerFaceColor' , 'b'
'MarkerSize ' 2);
t it Ie ([ 'q" =' sprint f ('%0. 1f ',7q (x4)) ' W/cm ^-2'7j)
xlim ([time-low , time.high])
ylim ([plow , p-high ])
xlabel('Time {\itt} (s)','FontSize ',12,'Fontweight ', 'b')
case
5 % Temporal data,
heat
flux
and inlet/exit
pressure
xI = 29;
to = 0;
x-min = 20;
x-max = 35;
y-minl = 120;
y-maxl = 150;
y-min2 = 1.6;
y-max2 = 2.8;
figure (5);
subplot (1 ,2,1)
plot(t{xl}-tO ,Q{xl},'-sb' ,'LineWidth ',1.5 , ...
'Color ' ,[0.8 0 0.2] ,...
'MarkerEdgeColor ' ,[0.8 0 0. 2] ,...
'MarkerFaceColor ' ,[0.8 0 0. 2] ,...
'MarkerSize ' ,2);
title
(['Structured 10-40, q"=' sprintf('%0.lf' ,q(xl)) ' W/cm^2'])
xlabel ('Time {\itt} (s) ','FontSize' ,12 , 'Fontweight ', 'b')
y labeI ('Heat Flux {\it q"} (W/cm^ 2) ','FontSize ',12 , 'Fontweight ' 'b')
xlim ([x-min ,x-max])
subplot (1 ,2 ,2)
plot (t{x1}-tO ,Pi{xl},'-sb' , 'LineWidth ' ,1.5 ...
'Color ' ,[0 0.65 0.32] ,...
'MarkerEdgeColor ' ,[0 0.65 0. 35], ...
'MarkerFaceColor ',[0 0.65 0. 35] , ...
'MarkerSize ' ,2);
title
(['Structured 10-40, q"=' sprintf('%0.lf' ,q(xl)) ' W/cm^2')
xlim (x.min ,x-max])
hold on,
plot (t{x1}-tO ,Po{x1},'-sb' , 'LineWidth ' ,1.5
'Color ',[0.8 0 0.2],...
'MarkerEdgeColor ' ,[0.8 0 0. 2]
'MarkerFaceColor ' ,[0.8 0 0. 2]
'MarkerSize ',2);
legend ('P in ' , 'P out ')
ylabel('{\itP} (kPa)','FontSize',12,'Fontweight','b')
,12, 'Fontweight ','b')
xlabel('Time {\itt} (s) ','FontSize'
hold off
figure (4);
h = subplot(1,2,1);
45
plot (t {x1}-tO, T3{x},'-sb' , 'LineWidth ' ,1.5
'Color' ,[0.8 0 0.2] ....
'MarkerEdgeColor ' ,[0.8 0 0. 2]
'MarkerFaceColor ' ,[0.8 0 0. 2
'MarkerSize ' 2);
grid minor
' W/cm^2'])
([ 'Structured 10-40, q"=' sprintf('%O.1f ',q(xl))
title
xlim ([x-min,x-max])
ylabel('{\itT_3} (^oC) ','FontSize ' ,12,'Fontweight ','b')
'FontSize ',12, 'Fontweight ','b')
xlabel('Time {\itt} (s)',
set (h, 'LineWidth' ,1);
h=subplot (1 ,2 ,2)
'LineWidth' ,1.5 ...
plot (t{xl}-tO, dP{xl},'-sb
...
0.2]
0
'Color ' ,[0.8
'MarkerEdgeColor ' ,[0.8 0 0. 21 ...
'MarkerFaceColor '40.8 0 0.2]
'MarkerSize ' ,2);
(['Structured 10-40, q"=' sprintf('%0.lf' ,q(xl))
title
xlim ([x-min ,x-max])
ylabel ( '{\ it\DeltaP} (kPa) ','FontSize ' ,12 , 'Fontweight '
xlabel('Time {\itt} (s)' ,'FontSize',12,'Fontweight' ,'b')
set (h, 'LineWidth ' ,2);
W/cm^2'1)
,
'b')
end
5.2.2
LabVIEW
Figure 5.1: Screenshot of the front panel of the LabVIEW file used to monitor the entire system loop.
46