atw 2018-04v6

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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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