Heat Transfer in the Environment:
Development and Use of Fiber-Optic Distributed Temperature Sensing

629
known-temperature sections should bracket the expected observations in the corresponding
environment. If possible, the fiber-optic cable should have a loop to return the cable to the
instrument (see Suárez et al. (2011) for more details about calibration procedures). This
permits the DTS instrument to interrogate the fiber-optic from each end, i.e., allowing
single- or double-ended measurements. Single-ended measurements refer to temperatures
estimated from light transmission in only one direction along the optical fiber. These
measurements assume a uniform rate of differential attenuation (Δα) over the entire fiber,
and provide greater precision near the instrument, degrading with distance because of the
energy loss along the fiber length. Double-ended measurements refer to temperatures
estimated from light transmission in both directions along the optical fiber. In these
measurements, the temperature is estimated using single-ended measurements made from
each end of the fiber, and can account for spatial variation in the differential attenuation of
the anti-Stokes and Stokes backscattered signals, which typically occurs in strained fibers.
Double-ended measurement results in a signal noise more evenly distributed across the
entire length of the optical fiber, but uniformly greater than that obtained in a single-ended
measurement (Tyler et al., 2009b; Suárez et al., 2011). Single-ended calibrations are
encouraged for short cables (i.e., smaller than 1 or 2 km) since they provide more precision
near the instrument. However, sometimes strains or sharp bends in the deployed fiber-optic
cable yields large localized losses in the Stokes and anti-Stokes signals, which decrease the
magnitude of the signals and add noise to the temperature data. Because these localized
losses cannot be handled adequately by a single uniform value of the differential
attenuation, further calibration is sometimes required to translate the scattered Raman
signals into usable temperature data. In these cases, double-ended measurements are
recommended because they allow the calculation of the differential attenuation along the
entire length of the cable, and are much better able to handle the step losses introduced by
strains and bends.
4.4 Operating conditions

In the environment, heat transfer mechanisms are combined in a variety of ways and span
spatial scales that range from millimeters to kilometers. This extremely wide spatial scaling
has been a barrier that limits observation, description, and modeling of environmental
processes. The introduction of fiber-optic DTS has contributed to fill the gap between these
two disparate scales. Fiber-optic DTS has proven effective to precisely observe temperatures
at thousands of locations at the same time, with no issues of bias, and avoiding variability
due to use of different sensors.
In this work, we have shown some of the environmental applications that have benefited
from DTS methods. For instance, using fiber-optic DTS provides the first and only reliable
method in which the spatial variability of snowpack temperatures can easily and remotely
be measured. Measurement of both vertical and horizontal gradients and their spatial
variability may provide important insights into snowpack dynamics, melting and avalanche
susceptibility. DTS methods also have improved thermal measurements in natural and
managed aquatic systems. For example, the hydrodynamic regimes in Devils Hole were
observed at resolutions smaller than 0.1 °C, allowing observation of temperature gradients
as small as 0.003 °C m
-1
. This resolution allowed the examination of seasonal oxygen and
nutrient distribution in the water column. In salt-gradient solar ponds, this temperature
resolution allowed observation of both mixing and stratification, which is important for
pond efficiency. In both Devils Hole and the solar pond, fiber-optic DTS provided high-
resolution thermal measurements without disturbance of the water column. DTS methods
also have been successfully utilized in other environments such as in atmosphere, streams,
boreholes, and in many applications to understand the interdependence between
groundwater and surface water. Novel extensions of DTS methods include spatially
distributed soil moisture estimation, detection of illicit connections in storm water sewers,
and there are many more to come in the near future, especially because the technology is
growing and improving the spatial and temporal resolutions of DTS instruments, which will
open new opportunities for environmental observations.
6. Acknowledgement

Builtjes, P.J.H. (2001). Major twentieth century milestones in air pollution modelling and its
Applications, In: Air Pollution Modeling and its Applications XIV, Gryning, S.E. &
Schiermeier (eds.), Springer, pp.3-16, Kluwer Academic/Plenum Publishers, ISBN
0306465345, New York
Campbell, G.S. (1985). Soil physics with BASIC: transport models for soil-plant systems (3rd
Edition), Elsevier, ISBN 9780444425577, New York, USA
Dakin, J.P., Pratt, D.J., Bibby, G.W. & Ross, J.N. (1985). Distributed optical fiber Raman
temperature sensor using a semiconductor light-source and detector. Electronics
Letters, Vol.21, No.13, (1985), pp. 569-570, ISSN 0013-5194
Dürrenmatt, D.J. & Wanner, O. (2008). Simulation of the wastewater temperature in sewers
with TEMPTEST. Water Science and Technology, Vol.57, No.11, (2008), pp.1809-1815,
ISSN 0273-1223
Eichinger, W.E., Cooper, D.I., Parlange, M. & Katul, G. (1993). The Application of a
Scanning, Water Raman-Lidar as a Probe of the Atmospheric Boundary Layer. IEEE
Transactions on Geoscience and Remote Sensing, Vol.31, No.1, (January 1993), pp.70-79,
ISSN 0196-2892
Farahani, M.A. & Gogolla, T. (1999). Spontaneous Raman Scattering in Optical Fibers with
Modulated Probe Light for Distributed Temperature Raman Remote Sensing.
Journal of Lightwave Technology, Vol.17, No.8, (August 1999), pp.1379-1391, ISSN
0733-8724
Förster, A., Schrötter, J., Merriam, D.F. & Blackwell, D. (1997). Application of optical-fiber
temperature logging—An example in a sedimentary environment. Geophysics,
Vol.62, No.4, (July-August 1997), pp.1107-1113, ISSN 0016-8033
Fridleifsson, I.B., Bertani, R., Huenges, E., Lund, J.W., Ragnarsson, A. & Rybach, L. (2008).
The possible role and contribution of geothermal energy to the mitigation of

Developments in Heat Transfer

632
climate change, In: O. Hohmeyer and T. Trittin (Eds.) IPCC Scoping Meeting on

regime of double-diffusive convection. Progress in Oceanography, Vol.86, No.3-4,
(March 2003), pp. 461-481, ISSN 0079-6611
Keller, C.A., Huwald, H., Vollmer, M.K., Wenger, A., Hill, M., Parlange, M.B. & Reimann, S.
(2011). Fiber optic distributed temperature sensing for the determination of the
nocturnal atmospheric boundary layer height. Atmospheric Measurement Techniques,
Vol.4, No.2, (2011), pp. 143-149, ISSN 1867-1381
Kersey, A.D. (2000). Optical fiber sensors for permanent downwell monitoring applications
in the oil and gas industry. IEICE Transactions on Electronics, Vol.E83c, No.3, (March
2000), pp. 400-404, ISSN 0916-8524
Kumar, A. & Kishore, V. (1999). Construction and operational experience of a 6000 m2 solar
pond at Kutch, India. Solar Energy, Vol.65, No.4, (March 1999), pp. 237-249, ISSN
0038-092X
Kurashima, T., Horiguchi, T. & Tateda, M. (1990). Distributed-temperature sensing using
stimulated Brillouin scattering in optical silica fibers. Optics Letters, Vol.15, No.18,
(1990), pp. 1038-1040, ISSN 0146-9592
Heat Transfer in the Environment:
Development and Use of Fiber-Optic Distributed Temperature Sensing

633
Lachenbruch, A.H. (1959). Periodic heat flow in a stratified medium with applications to
permafrost problems. U.S. Geological Survey Bulletin, 1083-A, 36 pp.
Lean, J. & Rind, D. (1998). Climate Forcing by Changing Solar Radiation. Journal of Climate,
Vol.11, No.12, (December 1998), pp. 3069-3094, ISSN 0894-8755
Lee, K.K.M., Steinle-Neumann, G. & Akber-Knutson, S. (2009). Ab initio predictions of
potassium partitioning between Fe and Al-bearing MgSiO
3
perovskite and post-
perovskite. Physics of the Earth and Planetary Interiors, Vol.174, No.1-4, (May 2009),
pp. 247-253, ISSN 0031-9201
Lema, S.C. & Nevitt, G.A. (2006). Testing an ecophysiological mechanism of morphological

Otto, R.G. & Gerking, S.D. (1973). Heat tolerance of a Death Valley pupfish (genus
Cyprinodon). Physiological Zoology, Vol.46, No.1, (January 1973), pp. 43-49, ISSN
0031-935X
Painter, T.H., Donahue, D., Dozier, J., Li, W., Kattelmann, R., Dawson, D., Davis, R.E., Fiori,
J., Harrington, B. & Pugner, P. (2000). The Mammoth Mountain cooperative snow
study site: data acquisition, management, and dissemination. Proceedings of the

Developments in Heat Transfer

634
International Snow Science Workshop, Vol. ISSW2000, pp. 447-451, Big Sky, Montana,
USA, October 2000
Rabl, A. & Nielsen, C. (1975). Solar ponds for space heating. Solar Energy, Vol.17, No.1,
(April 1975), pp. 1-12, ISSN 0038-092X
Riggs, A. & Deacon, J.E. (2002). Connectivity in Desert Aquatic Ecosystems: The Devils Hole
Story, Proceedings of Spring-fed wetlands: important scientific and cultural resources of
the intermountain region, DHS Publication No. 41210, Las Vegas, Nevada, USA, May
2002, available from:
Robinson, D.A., Campbell, C.S., Hopmans, J.W., Hornbuckle, B.K., Jones, S.B., Knight, R.,
Ogden, F., Selker, J.S. & Wendroth, O. (2008). Soil moisture measurement for
ecological and hydrological watershed-scale observatories: a review. Vadose Zone
Journal. Vol.7, No.1, (February 2008), pp. 358-389, ISSN 1539-1663
Rogers, A. (1999). Distributed optical-fibre sensing. Measurement Science and Technology,
Vol.10, No.8, (August 1999), pp. R75-R99, ISSN 0957-0233
Roth, T.R., Westhoff, M.C., Huwald, H., Huff, J.A., Rubin, J.F.,Barrenetxea, G., Vetterli, M.,
Parriaux, A., Selker, J.S. & Parlange, M.B. (2010). Stream Temperature Response to
Three Riparian Vegetation Scenarios by Use of a Distributed Temperature
Validated Model. Environmental Science and Technology, Vol.44, No.6, (February
2010), pp. 2072–2078, ISSN 0013-936X
Sayde, C., Gregory, C., Gil-Rodriguez, M., Tufillaro, N., Tyler, S.W., van de Diesen, N.C.,

using Passive Distributed Temperature Sensing. Water Resources Research, Vol.42,
No.W03534, (2010), 12 pp., ISSN 0043-1397
Suárez, F. (2010). Salt-gradient solar ponds for renewable energy, desalination and reclamation of
terminal lakes. Ph. D. Thesis, University of Nevada, Reno, 195 pp.
Suárez, F., Childress, A.E. & Tyler, S.W. (2010a). Temperature evolution of an experimental
salt-gradient solar pond. Journal of Water and Climate Change, Vol.1, No.4, (2010), pp.
246-250, ISSN 2040-2244.
Suárez, F., Tyler, S.W. & Childress, A.E. (2010b). A fully coupled transient double-diffusive
convective model for salt-gradient solar ponds. International Journal of Heat and Mass
Transfer, Vol.53, No.9-10, (April 2010), pp. 1718-1730, ISSN 0017-9310.
Suárez, F., Tyler, S.W. & Childress, A.E. (2010c). A theoretical study of a direct contact
membrane distillation system coupled to a salt-gradient solar pond for terminal
lakes reclamation. Water Research, Vol.44, No.15, (August 2010), pp. 4601-4615, ISSN
0043-1354.
Suárez, F., Aravena, J.E., Hausner, M.B., Childress, A.E. & Tyler, S.W. (2011). Assessment of
a vertical high-resolution distributed-temperature-sensing system in a shallow
thermohaline environment. Hydrology and Earth System Sciences, Vol.15, No.3,
(March 2011), pp. 1081-1093, ISSN 1027-5606
Tyler, S.W., Burak, S.A., Mcnamara, J.P., Lamontagne, A., Selker, J.S. & Dozier, J. (2008).
Spatially distributed temperatures at the base of two mountain snowpacks
measured with fiber-optic sensors. Journal of Glaciology, Vol.54, No.187, (December
2008), pp. 673-679, ISSN 0022-1430
Tyler, S.W. & Selker, J.S. (2009). New user facility for environmental sensing, EOS,
Transactions, American Geophysical Union, Vol.90, No.50, (December 2009), pp. 483,
ISSN 0096-3941
Tyler, S.W., Hausner, M.B., Suárez, F. & Selker, J.S. (2009a). Closing the energy budget:
advances in assessing heat fluxes into shallow lakes and ponds, EOS, Transactions,
American Geophysical Union, Vol.90, No.52 (Fall Meet. Suppl.), ISSN 0096-3941, San
Francisco, California, USA, December 2009
Tyler, S.W., Selker, J.S., Hausner, M.B., Hatch, C.E., Torgersen, T., Thodal, C.E. & Schladow,

in power cables. Sensors and Actuators A: Physical, Vol.125, No.2, (January 2006), pp.
148-155, ISSN 0924-4247
32
Prandtl Number Effect on Heat Transfer
Degradation in MHD Turbulent Shear
Flows by Means of High-Resolution DNS
Yoshinobu Yamamoto and Tomoaki Kunugi
Department of Nuclear Engineering, Kyoto University
Japan

1. Introduction
Estimation of the heat transfer degradation effected by Magneto-Hydro-Dynamics (MHD)
forces is one of the key issues of the fusion reactor designs utilized molten salt coolant.
FLiBe which is the molten salt mixture of LiF and BeF, is one of the coolant candidates in the
first wall and blanket of the fusion reactors, and has several advantages which are little
MHD pressure loss, good chemical stability, less solubility of tritium and so on. In contrast,
heat transfer degradation for the high Prandtl number, (Pr=
ν
/
α
, Prandtl number,
ν
is the
kinetic viscosity,
α
is the thermal diffusivity) characteristics caused by the low thermal
diffusivity and high viscosity (Sagara et al, 1995), was one of the issues of concern.
MHD turbulent wall-bounded flows have been investigated extensively by both
experimental and numerical studies (Blum, 1967, Reed & Lykoudis, 1978, Simomura, 1991,
Lee & Choi, 2001, Satake et al., 2006, Boeck et al, 2007, etc.) and much important information

L
z
=4h
L
y
=2h
Flow
L
x
=8h
Bottom wall
Top wall
x
y
z
θ
top

B
y


x
,N
y
,M
z
)
Resolution
Δ
x
+
,
Δ
y
+
,
Δ
z
+

(temperature)
CASE1
(Lithium)
0,8,10,12 0.025
CASE2
(KOH)
0,6,8,10,12 5.7
72,182,72 16,7,0.25-2.0,8.3
CASE2
‘


y
2h(
σ
/
ρν
)
1/2
, B
y

: wall-normal magnetic flux density,
σ
: electrical conductivity,
ρ
:
density ) was also limited around 12 in Re
τ
=150 (Lee & Choi, 2001, Yamamoto et al., 2008).
Numerical conditions are tableted in Table 1. Here, N
x
(
Δ
x)

,N
y
(
Δ
y), and N
z

x
u

(1)

**
**2**
*
1
,
ij
ii
i
ii
j
k
j
lm l m k
jijj j
uu
upu
F
BuBB
tx x xx x
ϕ
σ
δν ε
ρρ ρ
⎛⎞
∂

∂
(3)

**
*2*
.
j
jjj
u
tx xx
θ
θθ
α
∂
∂∂
+=
∂∂ ∂∂
(4)
Here u
i
and x
i
are the streamwise (i=1), the vertical (i=2) and the spanwise (i=3) velocity and
direction, respectively. t is time, F
i
is the i-th competent mean pressure gradient, p is the
pressure,
φ
is the electric potential, B
i

periodic condition imposed on the horizontal directions. Total electric current in the
spanwise flow domain is kept zero.
3.2 Numerical procedures
A hybrid Fourier spectral and the second-order central differencing method (Yamamoto et
al, 2009) is used for the computations. The spectral method is used to compute the spatial
discretization in the stream (x) and spanwise (z) directions. Nonlinear terms are computed
with 1.5 times finer grids in horizontal (x and z) directions to remove the aliasing errors
(Padding method). The derivative in the wall normal (y) direction is computed by a second-
order finite difference scheme at the staggered grid arrangement (Satake et al, 2006). Time
integration methods of the governing equations are the 3rd-order Runge-Kutta scheme for
the convection terms, the Crank-Nicolson scheme for the viscous terms and the Euler
Implicit scheme for the pressure terms, respectively. The Helmholtz equation for the viscous
(diffusion) terms and the Poisson equations of the pressure and the electrical potential are
solved by a Tri-Diagonal Matrix Algorithm, TDMA in Fourier space.
In DNS of the flow field, the Kolmogorov length scale has to be resolved. On the other
hands, the length scales of the high-Pr temperature field are smaller than the smallest length
scales of the velocity fields (Batchelor, 1959). To reduce the numerical costs in DNS of the

Developments in Heat Transfer

640
high-Pr fluids, a different number of grid resolutions in the horizontal direction for velocity
and temperature fields is adapted. In computing the temperature convection terms in (4)
pseudo-spectrally, the grid points of velocities were expanded to the same grid points of the
high-Pr temperature, as follow,

(,,) /, /
(,,) , / , /.
0otherwise
xzx xxz zz

10
0
10
1
10
-6
10
-5
10
-4
10
-3
10
-2
10
-1
k
x
h
E
uu
+
y
+
=149
Ha= 0
Ha= 8
Ha=12
0
50 100 150

study, we investigated the grid dependency effects on the higher-order statistics such as the
energy dissipation (=
ε) and temperature energy dissipation (=ε
θ
).

10
1
10
2
0
0.2
0.4
0.6
0.8
ε
θ
+
y
+
Δx
+
=2.8
Δx
+
=16.8
Δx
+
=20.0
Pr=5.0, Ha=0

=16.7, and
Δ
z
+
=8.3 in this medium high-Pr fluid.
4.2 High-Pr case
For Pr=25, DNS in Re
τ
=180 were conducted by means of a hierarchical algorithm in which
only the scalar fields were solved on the grid dictated by the Batchelor scale (Schwertfirm
&Manhart, 2007). However, the validation by using the different resolution for flow and
high-Pr temperature field has not been reviewed yet. In this study, the adequacy of DNS by
using a different resolution for flow and high-Pr temperature field is verified compared
with DNS data fully-resolved the Batchelor length scale in the same grid size for flow and
temperature. (a) (b) (c)
Fig. 4. Flow visualization, CASE3, Ha=0, Pr=25, y
+
=149. (a) streamwise turbulent velocity,
-2 (black) < u
+
< 2.0 (white), (b) turbulent temperature (coarse grid), -0.15 (black) <
θ
/
Δθ
<0.15
(white) (c) turbulent temperature (fine grid), -0.15(black)<
θ

spectra profile cannot be observed in this high-Pr temperature field. This indicates that the
high wave-number velocity fluctuations less than the Kolmogorov scale can be ignored in a
high-Pr passive scalar transport. As a consequence, we verify the adequacy of DNS by using
the different resolution for flow and high-Pr temperature field and numerical cost in DNS of
high-Pr fluids can be substantially reduced.

10
1
10
2
0
1
2
3
ε
θ
+
y
+
Δx
+
=1.9
Δx
+
=16.8 (temp. field Δx
+
=3.8)
Δx
+
=8.3

+
, CASE3'
E
θθ
/Δθ
2
, CASE3'
E
uu
+
, CASE3
E
θθ
/Δθ
2
, CASE3

(a) (b)
Fig. 5. Grid dependency and validation of different grid resolution for flow and high-Pr
temperature field. (a) Temperature dissipation, (b) Streamwise energy spectra, streamwise
velocity and temperature
5. MHD pressure loss and heat transfer
In this study, the friction drag confident (Cf) and Nusselt number (Nu) at the wall were
expressed by
Cf=2u
τ
2
/U
b
2

with increase of Ha; MHD pressure loss is less than the turbulent drag reduction effected by
MHD. Therefore, all MHD cases of this study might be considered in a turbulent-laminar
transition status. We need the DNS data in more higher Re to discuss the general
relationships between MHD pressure loss and MHD turbulent drag reduction in turbulent
condition.
Figure 6-(b) shows the Nusselt number as a function of N, where the Nusselt number were
also normalized by that in Ha=0. Maximum heat transfer degradation in the low-Pr fluid
was no more than 5% of the non-MHD condition. The usability of a low-Pr fluid was no
doubt about heat transfer, however, Ha of Lithium was 700 times as large as one of FLiBe in
the same Reynolds number (Re) and magnetic flux density (B
y
) conditions.

0 0.01
0.02 0.03
0.9
0.95
1
1.05
N=Ha
2
/Re
b
Cf/Cf
Ha=0
0 0.01 0.02 0.03
0.5
0.6
0.7
0.8

Nu/Nu
Ha=0
Pr=5.7
δ
Ha=0
/δ , P r = 2 5
Nu/Nu
Ha=0
Pr=25

Fig. 7. Thermal viscosity thickness and Nusselt number as a function N
Figure 7 shows the thermal viscosity thickness (
δ) and Nusselt number as a function of Ha in
the high-Pr fluids, where thermal viscosity thickness was defined as

Developments in Heat Transfer

644
δ=y
+
at Θ
+
=0.99Pry
+
. (8)
Thermal viscosity thickness was normalized by those in Ha=0. Heat transfer degradation
was strongly correlated with change of the thermal viscosity thickness without depending
on Pr.
6. Turbulence statistics
Figure 8 shows the profiles of temperature turbulent intensities for Pr=5.7 and 25. With

10
1
10
2
0
10
20
30
40
y
+
θ
rms
+
Pr=25.0
Ha=0
Ha=8.0
Ha=12.0

(a) Pr=5.7, (b) Pr=25,
Fig. 8. Turbulent temperature profiles

10
0
10
1
10
2
-5
0


645
10
0
10
1
10
2
-0.1
-0.05
0
0.05
0.1
Gain Loss
y
+
Production
TPG
Turbulent diff.
Viscous diff.
ε
θv
Residual
Budget of vθ
10
0
10
1
10
2

v
K
v
yy x
y
θ
θ
ε
θθ θ
θαα
⎛⎞
∂Θ ∂ ∂
∂
⎜⎟
=− − + −
⎜⎟
∂∂ ∂
∂
⎝⎠
 


 
, (9)

()
Production Turbulent diff. Temp.Press-Grad.
Viscous diff.
Dissipation
0

) term. Predominance of the diffusion terms in the high-Pr fluids (Pr>10) was
confirmed in the previous DNS (Schwertfirm &Manhart, 2007). In Ha=12, predominance of
diffusion terms was observed more clearly as shown in Fig. 9-(b). As well as turbulent
temperature energy, turbulent diffusion term in Fig. 10-(b) was dominant at y
+
=15-30 in
Ha=12, however, the predominance of viscous diffusion term was indistinct. Compared
with no-MHD case in Fig. 9-(a), the damping of turbulent diffusion term was small but the
others were suppressed by the MHD effects; effects of turbulent diffusion on the MHD heat
transfer were relatively larger with increase of Ha. These indicate that a sensitive model of
the turbulent diffusion would be required in the prediction of MHD heat transfer in high-Pr
fluids.
Figure 11 shows the turbulent Prandtl number (Pr
T
) profiles for Pr=5.7 and 25. Turbulent
Prandtl number was defined as

Pr /
T
uv v
θ
= . (11)

Developments in Heat Transfer

646
10
0
10
1

+
Pr
T

(a)Pr=5.7, (b) Pr=25,
Fig. 11. Turbulent Prandtl number profiles
Na & Hanratty, 2000 and Schwertfirm & Manhart, 2007 pointed out that turbulent Prandtl
number close to the wall increases with increase of Pr. The turbulent Prandtl number
profiles in the non-MHD case were good agreements with the results of Schwertfirm &
Manhart, 2007, however, profiles in MHD case was decreased close to the wall for Pr=5.7
and 25 with increase of Ha. In Ha=12, the values of the turbulent Prandtl number in the
vicinity of the wall fell into 1 for Pr=5.7 and 25. It was suggested that there was no MHD
terms in balance of the heat transfer equation; turbulent effect on heat transfer might exceed
that on momentum transfer as the limiting case of a turbulent-laminar transition status in
Ha=12.
Figure 12 shows the time scale ratio for Pr=5.7 and 25. In non-MHD flow, time scale ratio
had the weak peak at the buffer region for Pr=25 and 49 (Schwertfirm & Manhart, 2007
pointed out that). Time scale ratio profiles in MHD cases clearly had the peak in increase of
Ha for Pr=5.7 and 25. At the buffer region, MHD effects on heat transfer might to be
corresponded to the heat transfer in a higher-Pr fluid as shown in Figs. 9 and 12. However,
these close to the wall might act on like a lower-Pr fluid as shown in Fig. 11. 10
0
10
1
10
2
0

θ
/ε
θ
)/(Pr*K/ε)

(a) Pr=5.7, (b) Pr=25,
Fig. 12. Time scale ratio profiles
Prandtl Number Effect on Heat Transfer Degradation in
MHD Turbulent Shear Flows by Means of High-Resolution DNS

647
Since both turbulent Prandtl number and time scale ratio were one of the dominant
parameters in turbulent heat transfer modeling, change of profiles in increase of Ha might
be caused the aggravation of the prediction accuracy.
7. Conclusion
In this study, direct numerical simulation of MHD turbulent channel flow for Prandtl
number up to Pr=25 were performed. The adequacy of the present DNS data was verified by
comparison with the DNS data fully-resolved the Batchelor length scale. As the results, the
MHD turbulent heat transfer characteristics in Pr=25 were reported for the first time.
Maximum heat transfer degradation in the low-Pr fluid was no more than 5% of the non-
MHD condition. On the other hands, heat transfer degradation in the high-Pr fluids (Pr=5.7
and 25) reached up to 30%. The similarity of heat transfer degradation in high-Pr MHD
flows seemed be existed.
On the MHD heat transfer in high-Pr fluids, effects of turbulent diffusion were relatively
larger. Turbulent Prandtl number and time scale ratio were considerably changed with
increase of Ha.
The scaling of MHD heat transfer in high-Pr fluids was not understood yet. For the high-Ha
and Re
τ
condition (Ha>5, Re

648
Reed, C.B. & Lykoudis, P.S., (1978), The effect of a transverse magnetic field on shear
turbulence Journal of Fluid Mechanics, Vol.89, pp.147-171.
Sagara. A., Motojima, O., Watanabe, K., Imagawa, S., Yamanishi, H., Mitarai, O., Sato, T.,
Chikaraishi, H. and FFHR Group, Design and development of the Flibe blanket for
helical-type fusion reactor FFHR, Fusion Engineering and Design, Vol.29, pp.51-56.
Satake, S., Kunugi, T., Takase, T., and Ose, Y., (2006), Direct numerical simulation of
turbulent channel flow under a uniform magnetic field for large-scale structures at
high Reynolds number, Physics of Fluids, Vol.18, 125106.
Schwertfirm, F. & Manhart, M., (2007), DNS of passive scalar transport in turbulent channel
flow
at high Schmidt numbers, International Journal of Heat and Fluid Flow, Vol.28,
pp. 1204–1214.
Simomura Y., (1991), Large eddy simulation of magnetohydrodynamic turbulent channel
flows under a uniform magnetic field, Physics of Fluids A 3, pp.3098-3106.
Yamamoto, Y.,Kunugi, T., Satake, S., and Smolentsev, S. (2008), DNS and k–ε model
simulation of MHD turbulent channel flows with heat transfer, Fusion Engineering
and Design, Vol.83, pp.1309-1312.
Yokomine, T., Takeuchi, J., Nakaharai, H., Satake, S., Kunugi, T., Morley, N.B., and M. A.
Abdou, M.A., (2007), Experimental investigation of turbulent heat transfer of high
Prandlt number fluid flow under strong magnetic field, Fusion Science and
Technology, Vol.52, pp.625-629.
33
Effective Method of
Microcapsules Production for Smart Fabrics
Luz Sánchez-Silva, Paula Sánchez and Juan F. Rodríguez
Department of Chemical Engineering/University of Castilla-La Mancha
Spain

1. Introduction

complete phase separation into capsules are obtained.

Developments in Heat Transfer

650
Well-known PCM are linear chain hydrocarbons known as paraffin waxes (or n-alkanes),
hydrated salts, polyethylene glycols (PEGs), fatty acids and mixture or eutectics of organic
and non-organic compounds. PCM materials absorb energy during the heating process as
phase change takes place and release energy to the environment in the phase change range
during a reverse cooling process.
The required properties for a phase change materials depend on their specific application in
textile fields. A wide spectrum of phase change materials are available with different heat
storage capacity and phase change temperature. Different types of commercial PCMs can be
encapsulated by means of suspension polymerization process. Rubitherm
®
RT20,
Rubitherm
®
RT27, Rubitherm
®
RT31, Petrepar
®
n-C14 and Petrepar
®
n-C-13 have
demonstrate their capability to be encapsulated and their thermal abilities to absorb and
release energy. Their physical and chemical properties make them very attractive for
thermal storage.
Thermal properties, air permeability, moisture vapour permeability and moisture regain of
materials also influence the heat balance of the body and, consequently, affect clothing

carried out. Furthermore, the influence of different coating formulations and mass ratio of
microcapsules to coating formulation were evaluated in order to obtain an adequate textile

Effective Method of Microcapsules Production for Smart Fabrics

651
with thermo-regulating properties (Sánchez et al., 2010). The coating fabric with 35 wt.% of
microcapsules added related to commercial coating binder (WST SUPERMOR
®
) showed a
energy storage capacity of 7.6 J g
−1
, a high durability and an adequate stability after
washing, rub fastness and ironing treatments. A difference of 8.8°C for 6 s was observed for
textiles with thermo-regulating properties in comparison with a coated one without
microcapsules. The different application areas of textiles with thermo-regulating properties
imply the fixation to very different substrates. In this sense, there are few references in the
literature studying the influence of the kind of textile on the fixation of microcapsules (Koo
et al., 2009). In addition, the PCM microcapsules incorporation could degrade the original
functionalities of the textile such as soft touch, vapor or moisture permeability and wearing
comfort.
The aim of this work was to investigate the production of textiles with thermo-regulating
properties by using PCM microcapsules and a coating technique. The influence of the type
of used PCM on the heat capacity of microcapsules, the particle size distribution (PSD) and
the microcapsules yield of each experiment was studied. On the other hand, different type of
textile substrates depending on the field of their textile applications (apparel, blankets,
insulation, protective clothing) were evaluated. Furthermore, a study of thermoregulatory
effect of the coating fabrics produced was carried out using an infrared thermography
camera. Thermal properties of textile samples were examined by Differential Scanning
Calorimetry (DSC). Furthermore, Environmental Scanning Electron Microscopy (ESEM) and

2.2 Preparation of textiles with thermo-regulating properties
Microcapsules were fixed into seven fabrics by means of a coating technique, using a
motorized film applicator from Elcometer model 4340 according with ASTM D-823C (ASTM
D-823-C, 1997). WST SUPERMOR
®
(supplied by Minerva Color Ltd.) were used as
commercial coating binder. In a previous study (Sánchez et al., 2010), this binder was
selected due to allow an efficient fixation of the PCM microcapsules on the fabrics. Every
sample had 200 mm of wide and 290 mm of length due to requirements of the motorized
film applicator.

Developments in Heat Transfer

652
The coating formulation consisted of WST SUPERMOR
®
commercial binder and
Rubitherm
®
RT31 microcapsules (35 wt. % of the coating mixture).
The textile substrate was set on the motorized film applicator surface assuring the fabric
with clips. In this study, the thickness selection of the coating layer was 0.1 mm to obtain a
high thermal storage. The position of the motorized film applicator and the selection
thickness was carried out manually. A dragging speed of 5 mm s
-1
was chosen to allow a
homogeneous coating along the film applicator.
Finally, the coated fabric was cured at 95 ºC for 11 minutes.
2.3 Characterization
2.3.1 Differential Scanning Calorimetry (DSC)

The temperature distributions of the coated textiles with thermo-regulating properties were
evaluated by means of an infrared and visible camera Fluke Ti25. This dispositive allows to
obtain thermal and visual images in the range of temperatures from -20ºC to 250ºC with a
precision of ±2 ºC. The screen was observed from a distance of 30 cm at 24ºC. Images were
downloaded using Fluke SmartViewTM software for analysis. The coated fabrics were pre-
heated at 60ºC, time considered as zero, and then cooled to room temperature.

Effective Method of Microcapsules Production for Smart Fabrics

653
Thermal human comfort in summer conditions was tested, recording images from 25ºC to
outside temperature (35ºC), comparing a reference textile with a prototype textile with
thermo-regulating properties in contact with the body (shoulders in this specific case).
3. Results
3.1 Microencapsulation of different type of phase change materials (PCM)
The required properties for a phase change materials depend on their specific application in
textile fields. A wide spectrum of phase change material is available with different heat
storage capacity and phase change temperature. In this study different type of commercial
PCM were assayed in order to know what PCM materials are suitable to be encapsulated by
means of suspension polymerization. Thus, Rubitherm
®
RT20, Rubitherm
®
RT27,
Rubitherm
®
RT31, Petrepar
®
n-C14 and Petrepar
®

RT31 268 199.3 31 2.07
Rubitherm
®
RT27 258 214.6 28 1.64
Rubitherm
®
RT20 244 177.7 22 1.52
Petrepar
®
n-C14 198.4 225.0 3-7 0.99
Petrepar
®
n-C13 184.4 134.4 (-7)-(-5) 0.88
Table 1. Properties of different types of PCMs investigated
Figure 1 shows the particle size distributions (PSDs) in volume (Figure 1a) and in number
(Figure 1b) of microcapsules obtained after the polymerization process using these PCM
materials. It can be seen from Figure 1a that Petrepar
®
n-C14 and n-C13 exhibit bimodal
PSDs with particles sizes smaller than 115 μm. However, PRS® paraffin wax, Rubitherm
®

RT31, RT27 and RT20 shows unimodal PSDs ranging in the interval between 149 to 251 μm.
In all experiments a big difference between the average particle size in volume and in
number was observed due to the heterogeneous sizes of obtained microcapsules. This
behaviour was reported in previous works (Sánchez et al., 2007; Sánchez et al., 2008a;
Sánchez et al., 2008b).
Table 2 reports average diameters (dp
0.5
) in volume and in number, storage energy