atw Vol. 63 (2018) | Issue 4 ı April
| | Fig. 5.
Validation of numerical model against correlation for P/D =1.25.
4.2 Validation of numerical
model
Since the ultimate test of any numerical
simulation is the validation of
results against well-known experimental
data, the model under consideration
in the present study has
been validated against correlation of
Presser for square array and pure
water as presented by Equation (17)
through Equation (19). Results are
plotted in Figure 5 and Figure 6
which demonstrates that there is
an excellent agreement between
numerical data and theoretical
prediction for the specified range of
inlet Re.
4.3 Validation of turbulence
model for nanofluid
Despite in the present study it is
assumed that nanofluid would behave
as a single-phase homogeneous fluid
and hence, all of the general conservation
equations of mass, momentum
and energy can directly be applied in
case of nanofluid, however, a successful
comparison of numerical Nu obtained
realizable k-ε model has been
carried out against both empirical
correlation and experimental data of
Pak & Cho [1] for turbulent flow
inside a round pipe of inside diameter
10.66 mm using alumina nanofluid
(φ=2.78%) as coolant for inlet Re
spanning from 5.03×10 4 to 1.48×10 4 .
The results are plotted in Figure 7
which clearly delineates that this
model can perform quite satisfactorily
with nanofluids.
5 Numerical results
and discussion
5.1 Temperature
Temperature profile along the centerline
of subchannel (P/D =1.25) for
different coolants at inlet Re = 6×10 5
are illustrated in Figure 8 from which
it is clear that there is a steady increase
in the coolant temperature due to absorption
of heat while flowing through
the subchannel and bulk temperature
of nanofluid is decreased with the increasing
particle volume concentration.
Numerically obtained fluid average
temperature (in case
of pure water at P/D =1.25 and
inlet Re = 6×10 5 ) at different axial
locations within the subchannel is
compared against the theoretical
predictions from energy balance
according to equation (20) [28] and
results are tabulated in Table 4.
(20)
The analogy shows that maximum
deviation between numerically obtained
axial temperature and theoretical
prediction is less than 0.6%.
5.2 Velocity
Development of axial velocity along
the centerline of subchannel (P/D
| | Fig. 6.
Validation of numerical model against correlation for P/D =1.35.
=1.25) for different coolants at inlet
Re = 6×10 5 is presented in Figure 9
which clearly states that fullydeveloped
velocity profile occurs
approximately after z=0.3 m and if
the current models are implemented
to evaluate physical properties of
nanofluid, development of velocity
| | Fig. 7.
Validation of turbulence model against Pak & Cho’s correlation.
| | Fig. 8.
Temperature along centerline of subchannel at Re = 6×10 5 .
RESEARCH AND INNOVATION 253
Axial Position
(m)
Average Bulk Fluid Temperature T m (K) %
of Deviation
Start-CCM+ Energy Balance
0 569 569 0.000
0.15 569.2431 570.6885 0.2532
0.30 570.1277 572.3771 0.3929
0.45 571.2205 574.0656 0.4956
0.60 572.4116 575.7542 0.5805
| | Tab. 4.
Comparison of numerically obtained axial temperature against theoretical predictions for pure water
(P/D =1.25 and inlet Re = 6×10 5 ).
| | Fig. 9.
Velocity along centerline of subchannel at Re = 6×10 5 .
Research and Innovation
Nanofluid Applied Thermo-hydro dynamic Performance Analysis of Square Array Subchannel Under PWR Condition ı Jubair Ahmed Shamim and Kune Yull Suh
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