Volume 3 - Year 2016 - Pages 8-16

DOI: 10.11159/jffhmt.2016.002

### Numerical Investigation of Heat Transfer Characteristics for the Annular Flow of Nanofluids using YPlus

Mohamed H. Shedid*, M. A. M. Hassan

Department of Mechanical Engineering, Faculty of Engineering at El- Mattaria,

Helwan University, Cairo, Egypt.

*mshedid@hotmail.com

**Abstract** - *In the present
work, two turbulence models namely, Spalart-Allmaras (S-A) and K-Ɛ models,
are used to study numerically thermal behavior for annular flow of nanofluids.
The nanofluids considered are alumina (Al _{2}O_{3}) and oxide
titanium (TiO_{2}) nanoparticles and water as the base fluid. To
conduct the investigation, a grid is constructed that give y+ for all
velocities below 1. The model is validated with Gnielinski correlation for the
flow of pure water. Validated model was used for different concentration ratios
of Al_{2}O_{3} and TiO_{2} for different Peclet
numbers. The results were compared with many correlations for convection of
nanofluids flow and revealed better agreement with Spalart-Allmaras model
rather than k-Ɛ model. Results of numerical simulations are compared and
showed an enhancement of Nusslet number as Peclet number grows with increasing
concentration ratio.*

** Keywords:** Nanofluid; Heat
Transfer; YPlus; Spalart-Allmara; Annular flow.

© Copyright 2016 Authors - This is an Open Access article published under the Creative Commons Attribution License terms Creative Commons Attribution License terms. Unrestricted use, distribution, and reproduction in any medium are permitted, provided the original work is properly cited.

Date Received: 2014-11-06

Date Accepted: 2015-12-27

Date Published: 2016-03-07

Nomenclature

- C
_{P}: Specific Heat, [J/kg.K] - d
_{h}: Hydraulic diameter, [m] - h: Heat transfer Coefficient, [W/m
^{2}.K] - k: Thermal conductivity, [W/m.K]
- Nu: Nusselt number, [-]
- Q: Heat
flux, [W/m
^{2}] - R: Radius, [m]
- Re: Reynolds number, [-]
- T: Temperature, [K]

Greek Symbols

- φ: Nanoparticle Volumetric Concentration, [%]
- µ: Dynamic Viscosity, [kg/m.s]
- ρ: Density, [kg/m
^{3}]

Subscripts

- bf: Base fluid
- i: Inlet, inner
- nf: Nanofluid
- o: Outlet, outer
- p: Particle
- w: Wall

#### 1. Introduction

Heat transfer by convection is very important for many industrial heating and cooling applications. The heat convection can passively be enhanced by changing flow geometry, boundary conditions or by enhancing fluid thermophysical properties. With presenting the concept of nanofluid (Choi, 1995), it attracted the attention of many researchers who were interested in increasing the heat transfer coefficient of liquids in many industrial applications.

They show that use of nanofluids does enhance heat transfer coefficient for heat transfer applications. Such enhancement mainly depends upon factors such as the shape of particles, the dimensions of particles, the volume fractions of particles in the suspensions, and the thermal properties of particle materials.

Pak et al., 1998; Qiang et al., 2002; Xuan et al., 2003; Yang et al., 2005; Heris et al., 2007; Nguyena et al., 2007; VELAGAPUDI et al., 2008; Duangthongsuk et al., 2010; Nasiri et al., 2011; Murugesan et al., 2012; Darzi et al., 2013; Nield et al., 2014 have investigated the convective heat transfer of nanofluids. They show that use of nanofluids does enhance heat transfer coefficient for heat transfer applications. Such enhancement mainly depends upon factors such as the shape of particles, the dimensions of particles, the volume fractions of particles in the suspensions, and the thermal properties of particle materials.

Pak and Cho (Pak et al., 1998) published a correlation for the turbulent connective heat transfer for dilute dispersed fluids with submicron metallic oxide particles which is given by the following equation:

^{0.8 }Pr

^{0.5}

While Xuan and Li (Xuan et al., 2003) correlate Nusslet number for both laminar and turbulent flow as follows;

^{0.754}Pe

_{p}

^{0.218}) Re

^{0.333}Pr

^{0.4}

*For
laminar flow*

^{0.6886}Pe

_{p}

^{0.001}) Re

^{0.9238}Pr

^{0.4}

*For turbulent flow*

(Qiang et al., 2002) highlighted that the conventional convective heat transfer correlation of the pure fluid is not applicable to the nanofluid and recommended the correlation for Nusslet number for convective heat transfer with nanofluid flows in Equations (2) and (3).

(VELAGAPUDI et
al., 2008)
recommend a new correlations (4) and (5) for water-Al_{2}O_{3}
and water-CuO when the flow is turbulent;

^{0.8 }Pr

^{0.4}

*For water-Al*

_{2}O_{3}^{0.8 }Pr

^{0.4}

*For water-CuO*

(Bianco et al., 2009) concluded that single phase and two phase methods revealed approximated results, especially in the case of using temperature dependent properties. Demir (Demir et al., 2011) mentioned that although the nanofluid is actually a two-phase fluid in nature, the results show that the nanofluid behaves more like a pure fluid than a liquid-solid mixture. Kumar (Kumar, 2011) after validation his numerical study with experimental works, he emphasized single phase approach does not predict heat transfer coefficient as accurately as in the turbulent regime.

(Maїga et
al., 2005) studied
forced convection flow of water-Al_{2}O_{3} and ethylene
glycol-Al_{2}O_{3} nanofluids inside a uniformly heated tube at
the wall and a system of parallel, coaxial for both laminar and turbulent flow.
Numerical results showed better heat transfer enhancement. Finally, he
recommends with a correlation (Maїga et
al., 2006)
to estimate Nu, Equation (6).

^{0.71 }Pr

^{0.35}

(Namburu et
al., 2009)
found in his numerical analysis for different nanofluids (CuO, Al_{2}O_{3}
and SiO_{2}) in ethylene glycol and water mixture flowing through a
circular tube under constant heat flux condition that Gnielinski correlation
can be used in determining the Nusselt number with volume concentration up to
6%.

(Sharifi et al., 2012) examined the heat transfer coefficient for laminar flow inside tube subjected to constant heat flux with a concentration ratio from 1 to 10% by experimental and numerical methods. Good agreement was achieved between both methods and increase of heat transfer coefficient of about 60%.

Most numerical researches in convection heat transfer for nanofluids flow were in a cylindrical shape, (Rostamani et al., 2010) in rectangular duct, (Manca et al., 2012) in a triangular cross sectioned, (Bhattacharya et al., 2009) in rectangular micro-channel, (Pathipakka et al., 2010) in a circular tube fitted with helical twist inserts, (Vajjha et al., 2010) in flat tubes of the radiator and (Manca et al., 2012) in a ribbed channel and square cross section tubes. While others considered the natural convection for annular enclosures (Cianfrini et al., 2011; Abouali et al., 2012) and horizontal tube (Rashmi et al., 2011).

Turbulent flows are significantly affected by the existence of walls, where the areas that described by viscosity-affected areas have large gradients in the solution variables and accurate presentation of the near-wall region determines successful prediction of wall bounded turbulent flows. (Gerasimov, 2006) proposed in his seminar a strategy of computed wall y+ that dealing with near-wall turbulent flows using Fluent from ANSYS. This technique eliminates the traditional step of what is termed the grid independence test that requires a lot of time and computational effort. This strategy assists in selecting the most suitable near-wall treatment (wall functions or near-wall modeling) and the corresponding turbulence model based on the wall y+ (Salim et al., 2009).

The aim of
this paper is to investigate numerically the application of YPlus strategy in
heat transfer characteristics for Al_{2}O_{3}/water and TiO_{2}/water
flow through an annulus by applying two turbulence models of k-e and
Spalart-Allmaras (S-A), evaluating its performance and assessment of the
results with the proposed correlations.

#### 2. Mathematical Formulation

2. 1. Problem Statement

The geometry
of the annular channel has an outer diameter, inner diameter and length of 30,
10 and 1500 mm, respectively. This geometry is represented by a two-dimensional
rectangular duct of height h (difference between outer and inner diameter) and
length l. The tube has an appropriate length in order to obtain fully developed
profiles (velocity and thermal) at the outlet section (L/D_{h} >
10), (Incropera et
al., 2007).
The outer wall is subjected to a constant wall temperature. The nanofluid
enters the duct with uniform velocity and temperature and is affected only by
the duct conditions. The nanofluid is incompressible and the flow is turbulent.

Also, it is assumed that the liquid and solid are in thermal equilibrium and they flow at same velocity. The resultant mixture may be considered as a conventional single phase fluid. The assumption of single phase for a nanofluid is validated to an extent by (Choi et al., 2001; Bianco et al., 2009; Demir et al., 2011; Kumar, 2011). Model is constructed and discretized using the strategy of computed wall y+ that is recommended when dealing with such flows as proposed by Gerasimov in using Fluent from ANSYS (Gerasimov, 2006). This strategy assists in selecting the most suitable near-wall treatment (wall functions or near-wall modeling) and the corresponding turbulence model based on the wall y+ (Salim et al., 2009).

The CFD commercial code, Fluent, is employed to solve the problem by means of finite volume method. Two turbulence model namely k-e and Spalart-Allmaras (S-A) are implemented. Spalart-Allmaras model is the most popular one-equation model. This model has been shown to give acceptable results for a wide variety of situations and is known for its stability. The numerical simulation results are also compared with correlations found by (Pak et al., 1998, Xuan et al., 2003, Maїga et al., 2006, and VELAGAPUDI et al., 2008). To save computational time, half of the domain is selected as the computational domain since the problem is Axi-symmetric (Fig. 1).

2. 2. Governing equations

The single-phase model, which has been used frequently for nanofluids, is also implemented to compare its predictions with the mixture model. The following equations represent the mathematical formulation of the single-phase model by (Choi et al., 2001; Bianco et al., 2009; Demir et al., 2011; Kumar, 2011):

Conservation of mass:

Momentum equation:

Energy equation:

The
compression work and the viscous dissipation are assumed negligible in the
energy equation; the source/sink terms S_{m} and S_{e}
represent the integrated effects of momentum and energy exchange with base
fluid, as shown in the following, and they are equal to zero in the case of
single-phase model.

The previous equations can be solved with the following boundary conditions;

At outer wall:

At inner wall

2. 3. Thermophysical Properties of Nanofluid

In the absence of experimental data for nanofluid densities, constant value temperature independent values, based on nanoparticle volume fraction, the following parameters were used as reported by (Pak et al., 1998; Maїga et al., 2004; Roy et al., 2004) and others.

Density:

Specific heat:

Effective thermal conductivity:

Viscosity

where, µ_{bf},
Cp_{nf}, Cp_{bf}, Cp_{p}, r_{p}, k_{bf},
k_{p}, and ϕ are basefluid viscosity, nanofluid heat capacity,
base fluid heat capacity, nanoparticles heat capacity, nanoparticles density,
thermal conductivity of base fluid, thermal conductivity of nanoparticles, and
volume fraction of nanoparticles respectively.

The results of (Duangthongsuk et al., 2010) showed that use of the viscosity models to describe the Nusselt number of nanofluids gives different values compared with use of the measured data, by about 2-3%.

Specifications of nanoparticles used in this study are available in Table 1.

Table 1. Nanoparticles specifications.

Particle | Size (nm) | r (kg/m^{3}) |
k (W/m.K) | Cp (J/kg.K) |

Al_{2}O_{3} |
25 | 3970 | 46 | 750 |

TiO_{2} |
10 | 3840 | 11.7 | 710 |

2. 4. Data Analysis

Heat transfer (q") between nanofluid and outer wall is obtained through the following equation:

Where DT_{LM}
is mean logarithmic temperature deference and is defined by:

In order to
calculate Reynolds number (Re), Nusselt number (Nu) and Peclet number (Pe) for
annular tube, hydraulic diameter (d_{h}) of the channel is used.

Where d_{h}
is hydraulic diameter and calculated by:

Where d_{o},
d_{i} is the outer and inner diameter of the annular passage. K_{nf},
r_{nf}, V and µ_{nf}
are thermal conductivity, density, velocity, and viscosity of nanofluid
respectively.

#### 3. Results and Discussions

The results of this investigation obtained for distilled water for both k-Ɛ and S-A turbulence models were compared with famous equation of Gnielinski (Incropera et al., 2007) as shown in Fig. 2. It is clear to note that S-A model and k-Ɛ models exhibited good agreement with Gnielinski equation with a maximum deviation of 6% for a S-A model rather than k-Ɛ model which revealed 34% maximum error decreased with increasing Re to reach 2% in the end of the range (about Re = 56000).

The** study**
were conducted for volumetric concentrations of 0.2, 0.5, 1.0 and 5% of Al_{2}O_{3}
and TiO_{2} nanoparticles. The convection heat transfer coefficient
related to Al_{2}O_{3}/water nanofluid versus Peclet number is
presented through Fig. 3. The Peclet number (Pe) is a comprehensive parameter
to describe such effects, (Xuan et al., 2000).

Based on the results, for definite Peclet number, the convective heat transfer coefficient (h) of nanofluid is higher than that of base fluid. This enhancement considerably is dependent on the concentration of nanoparticles. For example at Peclet number about 21,800, the heat transfer coefficients are 6.4% and 36.1% greater than those of the base fluid when the nanoparticle concentration are 1.0 vol.% and 5.0 vol.% respectively.

The results
related to TiO_{2}/water nanofluid are demonstrated through Fig. 4. The
behavior of this nanofluid is similar to that of Al_{2}O_{3}/water.
For example the enhancements of heat transfer coefficient for TiO_{2}/water
nanofluid at Peclet number about 21,800 are about 5.7% and 31.7% for
nanoparticle concentrations of 1.0 vol. % and 5.0 vol. % respectively.

Figures 5 and
6 demonstrate the Nusselt number for Al_{2}O_{3}/water and TiO_{2}/water
nanofluids for different Peclet numbers. Both nanofluids show higher Nusselt
number than those of the base fluids and enhancement increases with
nanoparticle concentration. For example at Peclet number about 21,800, the
enhancement of Nusselt number for Al_{2}O_{3}/water nanofluid
with nanoparticles concentrations of 0.2, 0.5, 1.0 and 5.0% are 0.48%, 1.1%,
2.2% and 11.1% respectively.

For TiO_{2}/water
nanofluid at Peclet number about 21,800, the increment of Nusselt number for
TiO_{2} nanoparticle concentrations of 0.1, 0.5, 1.0 and 1.5% are
0.43%, 1.0 %, 2.1% and 10.8 % respectively. It is be noted that for both
nanofluids, there is no significant enhancement in Nusselt number for
concentrations less than 0.2%, which noted by (Kumar, 2011). Moreover, the relative
enhancement of heat transfer is remarkably not affected by change of Peclet
number (Pe) as observed in Figs. 7 and 8.

Such improvement of heat transfer becomes more pronounced with the increase of the particle concentration. The reduction of the thermal boundary layer thickness due to the presence of particles and their random motion within the base fluid may have important contributions to such heat transfer improvement as well.

The heat
transfer enhancement of nanofluids has been reported for annular channel. (Nasiri et al., 2011) presented the
experimental results for Al_{2}O_{3}/water nanofluid with
concentrations of 0.5% and 1% at Peclet number about 59,000 under turbulent
regime in annular circular tube and showed the increments of Nusselt number
were 6.2 % and 13.6% respectively. The corresponding results, for the present
study, at the same Peclet number and nanoparticle concentrations are 1.1%,
2.3%.

In order to compare the performance of two employed nanofluids in this research, Nusselt number plotted for concentrations of 1.0 and 5.0% in Fig. 9. Based on the results of this figure, there is no significant difference between the Nusselt numbers of two employed nanofluids.

Validation of
results with correlations found by (Pak et al., 1998; Xuan et al.,
2003; Maїga
et al., 2006; VELAGAPUDI
et al., 2008) are presented for concentrations of 1.0 and 5.0%
for both nanofluids. Validation with the correlations was also done with both
results obtained by k-Ɛ and S-A turbulence models. Figures 10 and 11 are
the validation for Al_{2}O_{3}/water for concentrations of 1.0
% and 5.0%, respectively. It is obvious that correlations of (Pak et al., 1998) and (VELAGAPUDI et al., 2008) are best
fitted with results of S-A model with maximum error of 2%, while (Maїga et al., 2006) has
overestimated results, (Xuan et al., 2003) has diversion that gets
growing with increasing Re. Validation of (Pak et al., 1998) correlation also
emphasized to give better results for comparison by (Bianco et al., 2011) for single phase fluid.

Similar
trends can be found for TiO_{2}/water validation Figs. 12 and 13. No
correlation from (VELAGAPUDI et al., 2008) for TiO_{2}/water
nanofluid.

#### 4. Conclusion

Numerical investigation for heat transfer enhancement for flow inside annular tube was done using two turbulence models, k-Ɛ and S-A. A grid was constructed with condition that y+ is always below 1. The following concluded;

- Spalart-Allmaras turbulence model gives better convergence and more stability throughout validation with pure water flow.
- Heat transfer
coefficient does enhanced by increasing concentration ratio of nanoparticles
for each Al
_{2}O_{3}and TiO_{2}.

Although (Pak et al., 1998) correlation is the eldest correlation, however, it gives best results when dealing nanofluid as single phase fluid.

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