C. J. Ho
Department of Mechanical Engineering
National Cheng Kung University
Tainan, Taiwan 70101, ROC
1
Functional(Intelligent) Media
Functional medium may be classified into:
a. One that has an ability to adapt its functionality in terms of its properties (chemical, electrical, magnetic, mechanical, or thermal) in response to a change in external stimuli in the surrounding environment.
b. One that transforms energy from one form to another by means of its behaviors:
•
•
•
•
Photovoltaic
Thermoelectric
Piezoelectric
Photo‐luminescent
2
Functional(Intelligent) Media
Multidisciplinary research interests for
engineering applications including:
¾ Structural health monitoring
¾ Smart manufacturing
¾ Active fluid, thermal, vibration, deformation control
¾ Intelligent sensory actuator
¾ Energy conversion and management
¾ Biomedical material
3
Functional (Complex/Multiphase) Fluids
• A mixture of different substances or different phases of matter (solid, liquid, or gas) in coexistence.
• Functional fluids may appear as:
¾ Dispersed regimes (i.e. not materially connected), e.g. particle, droplet or bubbly flows
¾ Non‐dispersed regimes, e.g. flows through a porous medium
4
Examples of Functional Fluids
y Electro‐Rheological (ER) fluids
y Magneto‐Rheological (MR) fluids
y Photo‐Rheological (PR) fluids
y Magnetic fluids
y Viscoelastic fluids
y Viscoplastic fluids
y Thermal fluids
y Others
5
Functional Thermal Fluids
High‐density thermal energy Wide temperature range Heat transfer enhancement High heat capacity Functional Thermal Fluids Down‐sizing of heat exchanger Flow drag reduction Reduction in heat loss High‐speed transport of high‐density thermal energy Classification of Functional Thermal Fluids
Functional Thermal Fluids
Sensible‐heat Fluids
Latent‐heat Fluids
e.g.
e.g.
Coolants, Refrigerants
Solid‐liquid phase change materials (PCM)
Absorbent of absorption refrigeration system
Aqueous polymer solution Vapor‐liquid phase change materials
Aqueous surfactant solution
Nanofluids
7
Nanofluids ‐
suspensions of nanoparticles in base fluids
„
Conventional suspensions that contain mm‐ or μm‐
sized particles do not work with the emerging “miniaturized” technologies because they can clog the tiny channels of these devices.
„
Modern nanotechnology provides opportunities to produce nanoparticles of various materials.
„
Argonne National Lab (S. U. S. Choi’s team, 1995) developed the novel concept of nanofluids.
8
Materials for Nanoparticles and Base Fluids
Nanoparticles :
„
Oxide ceramics – Al2O3, CuO, TiO2
Base fluids :
„
Water
„
Ethylene‐ or tri‐ethylene‐
glycols and other coolants
„
Metal carbides – SiC
„
Nitrides – AlN, SiN
„
Oil and other lubricants
„
Metals – Ag, Au, Cu, Fe
„
Bio‐fluids
„
Nonmetals – Graphite, carbon nanotubes
„
Polymer solutions
„
Other common fluids
„
Layered – Al + Al2O3, Cu + C
„
PCM – S/S
„
Functionalized nanoparticles
9
Nanofluids in Nature and Industry
„
„
„
„
Nature is full of nanofluids, like blood, a complex biological nanofluid where different nanoparticles (at molecular level) accomplish different functions
Many natural processes in biosphere and atmosphere
include wide spectrum of mixtures of nanoparticles with different fluids
Many mining and manufacturing processes leave waste products which consist of mixtures of nanoparticles with fluids
A wide range of self‐assembly mechanisms for nano‐
structures start from a suspension of nanoparticles in fluid
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Applications of Nanofluids
„
„
„
„
„
„
„
„
Heat‐transfer nanofluids
Tribological nanofluids
Surfactant and coating nanofluids
Chemical nanofluids
Process/extraction nanofluids
Environmental (pollution cleaning) nanofluids
Bio‐ and pharmaceutical‐nanofluids
Medical nanofluids
(drug delivery and functional tissue‐cell interaction)
11
Attracting Features of Heat Transfer Nanofluids
z
Abnormally increased thermal conductivity at low nanoparticle concentrations
z
Strong temperature‐dependent thermal conductivity
z
Non‐linear increase in thermal conductivity with nanoparticle concentration
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Enhanced Nanofluid Thermal Conductivity
Comparison of experimental data on thermal conductivity of nanofluids, Int.
J. Thermal Sciences, vol. 46, pp. 1-19, 2007.
13
Thermal Conductivity Ratio knf/kbase
Nonlinear Increase in Conductivity with Nanotube Loadings
1.08
1.06
1.04
1.02
1.00
0.0
0.4
0.8
1.2
Volume Fraction [%]
Measured and predicted thermal conductivity enhancement for nanotube-in-oil
nanofluids. Appl. Phys. Lett. 79, 2252, 2001.
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Temperature‐Dependent Conductivity
1 .3
A l 2 O 3 (1 % )
A l 2 O 3 (4 % )
Thermal conductivity ratio λ/λ
wate
1 .2 5
1 .2
1 .1 5
1 .1
1 .0 5
1
0
10
20
30
40
50
60
T e m p e ra tu re (　C)
Temperature dependence of thermal conductivity enhancement for Al2O3-in-water nanofluids
J. Heat Transfer, 125, 567, 2003.
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Disparity between Model Predictions and Experimental Data on Thermal Conductivity for Nanofluids
Comparison between selected theoretical predictions and experimental data on
thermal conductivity for Al2O3-water nanofluids. Int. J. Thermal Sciences, vol. 46,
pp. 1-19, 2007.
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Possible Mechanisms for Enhanced Thermal Conductivity of Nanofluids
Enhancement of thermal conductivity due to (a) nano-layer of liquid structure at
liquid-particle interface; (b) ballistic & diffusive phonon transport in a solid particle;
(c) highly conducting clusters. Int. J. Heat Mass Transfer, vol. 45, pp. 855-863, 2002.
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Normalized conductivity (keff/kBF)
Brownian Motion of Nanoparticles
Water+ Cu
(6nm)
1.8
1.6
1.4
Water+ Al2O3
1.2
(38.4nm)
1.0
300
305
310
315
320
325
Temperature (K)
Temperature-dependent thermal conductivities of nanofluids at a fixed concentration of 1
vol.%, normalized to the thermal conductivity of the base fluid. Appl. Phys. Lett. , 84, 4316,
2004.
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Applications of Heat Transfer Nanofluids
„
Efficient flow and lubrication, cooling and heating in new and
critical applications, like electronics, nuclear and biomedical
instrumentation and equipments, transportation and industrial
cooling, e.g. cooling densely packed integrated circuits at the small scale to heat transfer in nuclear reactors at the large scale.
„
Thermal management in various critical applications, as well as
environmental control and cleanup, bio-medical applications, and
directed self-assembly of nanostructures, which usually starts
from a suspension of nanoparticles in fluid.
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Heat Transfer Performance of Al2O3‐Water Nanofluid in a Micro‐Channel Heat Sink
Dc (μm)
Dbase (mm)
Hch (μm)
Lch (mm)
Wch (μm)
Wfin (μm)
10
24
800
50
283
300
20
Experimental Loop for Heat Transfer Performance of a Micro‐Channel Heat Sink
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Heat Transfer Efficacy of Nanofluid for Simultaneous Developing Flow in a Channel
The average Nusselt number for the simultaneous developing flow in
an isothermal channel can be evaluated by a correlation due to Seider
and Tate as
Nu D = 1.86(
Re D Pr 1/ 3 μ 0.14
) ( )
μs
Lch / Dh
Assuming (μ/μs) ≈ 1, a relation of the average heat transfer coefficient
with the relevant thermophysical properties of the fluid as well as the
characteristic lengths of the channel can then be expressed as
h ~ m 1/ 3c1/p 3 k 2 / 3 Dh−1 L−ch1/ 3
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Thermophysical Properties of Nanoparticle, Base fluid, and Nanofluid at 30°C
Properties
Nanofluid (alumina-water)
Nanoparticle
Base fluid
(alumina)
(water)
φ = 1 vol.%
2 vol.%
3600
995.1
1021.1
1047.2
4.144 (Eq. (3a))
4.110 (Eq. (3a))
4.058 (Eq. (3b))
3.943 (Eq. (3b))
0.635
0.654
ρ (kg/m3)
c p (kJ/kg⋅K)
0.765
k (W/m⋅K)
4.178
36.0
0.620
μnf ×103 (N⋅s/m2)
φ
T = 20°C
25°C
30°C
35°C
40°C
0 vol.%
0.9590
0.8550
0.7690
0.6950
0.6310
1 vol.%
1.0040
0.8955
0.8062
0.7483
0.6507
2 vol.%
1.0930
1.0750
0.9689
0.8750
0.7947
23
Enhanced Nusselt Number of Nanofluid
1.8
25
20
ϕ
(Vol.%)
0
Symbols
A
1.7
B
1.6
1
2
1.5
15
1.4
hnf / hbf1.
Nu
|
1.3
10
1.2
1.1
1 vol. %
2 vol. %
1
5
200
500
Re
1000
1500
2000
0.9
200
400
600
800
1000
1200
1400
1600
Re( ρbf / ρ nf )( μnf / μbf )
24
1800
Hydraulic & Thermal Performances of Al2O3‐Water
Nanofluid in a Micro‐Channel Heat Sink
0.1
1.0
φ (%)
Present work
Lee & Mudawar [14]
0.08
Ritd (K/W)
0
1
2
ϕ
(Vol.%)
0
1
2
0.06
Symbols
A
B
0.04
0.02
0.1
f
0
Rlm (K/W)
16/Re
0.01
60
Re
500
1000
1500 2000
0.04
0.02
0
0
0.2
0.4
0.6
0.8
P(W)
Ritd =
(Tw − Tin )
qf
Rlm =
ΔTlm
qf
25
Solid-Liquid Phase Change Material
(PCM) Suspension
26
Latent Heat Functional Thermal Fluids & Their
Applications
Latent Heat Functional Thermal
Fluids
„
Micro-emulsion/microencapsulated PCM (paraffin wax)
suspensions
„
Ice slurry
„
Clathrate slurry (host gas or
liquid, refrigerant)
Applications
Thermal management
„ Air conditioning
„
Cooling
„ Refrigeration
„
„
Air conditioning
27
PCM (Phase Change Material) Suspension
y Suspensions of micro‐ or nano‐sized particles of solid‐liquid phase change material (PCM), by means of emulsion and/or micro‐encapsulation
techniques, in a suspending fluid such as water, ethylene glycol.
y Concept originated in the early 1980 for convective heat transfer enhancement.
y Functional thermal fluid of dual capability of sensible and latent heat transport.
28
Thermally Developing Forced Convection in a Circular Duct
PCM Particles
Liquid
Solid
Suspending
Fluid
r+
PCM Suspension
0
Flow
lu+
Adiabatic
ri+
x+
lh+
ld+
qh′′
Adiabatic
29
Experimental Set-Up
30
Heat Transfer Efficacy of PCM Suspension for Thermal Developing Flow in a Channel
The average Nusselt number for the thermal developing flow in a
channel can be evaluated by a correlation of the form:
Nu = C (
Re Pr 1/ 3
)
+
+
lh / 2ri
A relation of the average heat transfer coefficient with the relevant
thermophysical properties of the fluid as well as the characteristic
lengths of the channel can then be expressed as
h ~ Q 1/ 3 ρ 1/ 3c1/p 3 k 2 / 3 (ri + ) −1 (lh+ ) −1/ 3
31
Thermophysical Properties of PCM particles, Base fluid, and Suspensions at 30°C
Description/Compo
sition
Density
(kg/m3)
Specific heat
(J/kg⋅K)
Thermal
conductivity
(W/m⋅K)
Dynamic viscosity
(kg/m⋅s)
Pure water
997
4180
0.62
8×10-4
n-Eicosane
856(solid)
778(liquid)
2210
0.15 (solid)
0.35 (liquid)
-
Urea-formaldehyde
1500
1672
0.42
5×10-5
MEPCM particle
961.4
2135.7
0.2
-
2% suspension
991
4129
0.608
9.3×10-3
5% suspension
982
4061
0.592
1.05×10-3
10% suspension
970
3941
0.567
1.26×10-3
32
Model Prediction vs. Experimental Data for Outer Wall Temperature
o
*
Re = 200 , Ste = 0.1 , θin = - 0.07 , cv = 10 %
1
46
Experiment [ 5 ] ( tw = 0.52 )
Present prediction ( tw = 0.52 , k*wf = 648.2 )
Present prediction ( tw = 0 )
Prediction [ 5 ] ( tw = 0 )
Prediction [ 6 ] ( tw = 0 )
0.8
qh = 20W , Tin ≈ 33.5 C , m ≈ 60 g/min
44
cm ( % )
Water
1%
5%
experiment prediction
42
θi-θin
Tw,o ( oC )
0.6
0.4
40
38
36
0.2
34
0
0
0.02
0.04
+
0.06
+
i
x / ( r × Pef )
0.08
0.1
0.12
32
-10
0
10
20
X (cm)
30
40
33
Bulk Temperature θb & Mean Particle Melted Fraction ξb
34
Duct Wall Temperature θw
2
1.5
Pef = 1000
cv = 2%
cv = 5%
cv = 10%
0.5
*
Ste = 0.5
Ste* = 0.5
0.4
cv = 0 %
Ste* = 0.1
Ste* = 0.1
1
Ste* = 0.01
θw
θw
0.6
cv = 2%
cv = 5%
cv = 10%
Pef = 100
cv = 0 %
0.3
0.2
0.5
0.1
0
*
Ste = 0.01
0
0
0.1
+
0.2
+
i
x / ( r × Pef )
0.3
0.4
0
0.01
+
0.02
+
i
0.03
0.04
x / ( r × Pef )
35
Local Nusselt Number Nub
50
60
Pef = 100
cv = 2%
cv = 5%
cv = 10%
40
Pef = 1000
cv = 2%
cv = 5%
cv = 10%
50
*
40
Ste* = 0.1
*
30
Nub
Ste = 0.5
*
Nub
Ste = 0.5
Ste = 0.1
30
*
Ste = 0.01
20
Ste* = 0.01
10
0
20
10
cv = 0 %
( 4.36 )
x+ / ( r+i× Pef )
cv = 0 %
( 4.36 )
0
0.1
0.2
+
+
i
0.01
0.02
x / ( r × Pef )
36
Sensible & Latent Heat Transport Fractions
1
1
cv = 2%
cv = 5%
cv = 10%
*
Ste = 0.1
1
0.8
0.8
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.2
0.2
0.2
0.2
101
102
Pef
103
*
*
0
100
*
0.8
( qh,conv )sen
0.8
0
104
0
100
101
102
Pef
103
0
104
*
qh*,conv
N
Total convection
heat transfer rate
1
1 ρ pf cv
[(θb ( x = lh ) − θ b ( x = 0)] +
[ξ b (x = lh ) - ξ b (x = 0)]
=
*
*
4lh
4lh ρbf Ste
Sensible heat transport fraction
(q*h,conv ) sen
( qh,conv )lat
*
Ste = 0.01
( qh,conv )lat
( q*h,conv )sen
1
cv = 2%
cv = 5%
cv = 10%
Latent heat transport fraction
(q*h,conv )lat
37
Heat Transfer Enhancement Effectiveness
4
3.5
3
cv = 0 %
cv = 2 %
cv = 5 %
cv = 10 %
3
Ste* = 0.01
2.5
*
Ste = 0.1
cv = 0 %
cv = 2 %
cv = 5 %
cv = 10 %
2
εh
εh
2.5
2
1.5
1.5
1
1
0.5
100
101
εh = (
102
Pef
103
h
Nu b *
)(k bf ) = b
Nu f
hf
104
0.5
100
101
102
Pef
103
104
(Heat transfer enhancement effectiveness)
38
Heat Transfer/Pumping Power Performance Index
3
3
Pef = 10
Pef = 100
Pef = 1000
2.5
2.5
2
Ste* = 0.01
1.5
*
Ste = 0.01
εh / εp
εh / εp
2
cv = 2 %
cv = 5 %
cv = 10 %
*
Ste = 0.1
1
1.5
1
Ste* = 0.1
0.5
0
0.5
0
0.02
0.04
0.06
cv
0.08
0.1
h
Nu b *
εh = (
)(k bf ) = b
Nu f
hf
0.12
0
100
εp =
101
102
Pef
103
104
[ Δp( m /ρ )]b
(Pumping power ratio)
[ Δp( m /ρ )] f
39
Uncertainties and Challenges for Applications of Functional Thermal Fluids:
„
Disparities exist between experimental results from different studies concerning heat transfer efficacy of using nanofluids for various heat transfer configurations or the data of thermophysical properties for the functional thermal fluids.
„
Stability of nanoparticles/PCM suspension, including clustering, agglomeration, clogging, and so on.
„
Necessity of developing standardized method for experimental formulation of various functional thermal fluids.
„
Development of theoretical models and experimental methods for characterizing nanoscale structure and dynamics functional thermal fluids in the laboratory and in nature.
„
Further understanding of heat and mass transport phenomena and thus development of theoretical models for functional thermal fluid flows including physical and chemical interactions between nanoparticles/PCM and base‐fluids.
„
Identifying new and unique applications for functional thermal fluids.
40
Thanks for Your Attentions
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