The Effect of Baffles on Heat Transfer

Raheleh Jafari

, Sina Razvarz

, Cristóbal Vargas-Jarillo

and Alexander Gegov

Centre for Artificial Intelligence Research (CAIR), University of Agder, Grimstad 4879, Norway

Departamento de Control Automatico CINVESTAV-IPN, National Polytechnic Institute, Mexico City 07360, Mexico

School of Computing, University of Portsmouth, Buckingham Building, Portsmouth PO13HE, U.K.

Keywords: Baffle, Heat Transfer, Computational Fluid Dynamics.

Abstract: For a long time technicians and engineers have used geometric changes of objects for the purpose of

enhancement of heat transfer. The discovery and use of nanofluids and their unique properties lead to a new

revolution on the heat transfer. This paper presents the simulation of Ansis software applied to the flow tube

with a constant flux, also studies the effect of baffles and the use of nano particles on heat transfer.

1 INTRODUCTION

Throughout the recent years with the aid of fast

computers, engineers have become able of operating

numerical computations to predict and simulate the

experiment and improve the design of models. For so

many decades, researching for improving the heat

transfer rate of a coolant liquid had been one of the

goals of engineering. The modification in properties

of fluids that are used for this purpose is one approach

that engineers used in previous years. They have been

utilized micron and nano-sized particles in order to

change and improve this property.

At Argonne National Laboratory, Choi and co-

workers (Choi, 1995) showed a considerable increase

in thermal conductivity of the liquid by suspending

nano-sized particles in a fluid (nanofluids). In another

work, Lee et al. (Lee et al., 1999) showed 20%

increase in effective thermal conductivity with using

suspension of 4.0 %vol, 35 nm CuO particles in

ethylene glycol. Das et al. (Das et al., 2003) studied

the relation between the temperature and the

increment of thermal conductivity in nanofluids

experimentally. Pak and Cho (1998) founded that the

Nusselt number of the nanofluids increases with

increasing the volume fraction of the suspended

nanoparticles and Reynolds number.

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https://orcid.org/0000-0002-6166-296X

In (Xuan and Li, 2003) the increment of heat

transfer is observed by utilizing water–Cu nanofluids,

in the turbulent regime. Furthermore, in (Xuan and

Roetzel, 2000) the device of heat transfer increment

of the nanofluid is studied. In (Wen and Ding, 2004)

the fluid inclusive aluminium nanoparticle is utilized

in the tube flow in order to check the laminar heat

transfer. They applied 1.6 % nanoparticles by volume

in their fluid. The method of particle migration for

non–uniform repartition of thermal conductivity and

convective heat transfer of nanofluids in Re<800 (that

is laminar flow) is proposed in (Ding and Wen, 2005;

Ding et al. 2006). In (Yang et al., 2005) the authors

worked over a horizontal tube heat exchanger and

demonstrated that by increasing the Reynolds number

the heat transfer coefficient increases, also the fluid

temperature and particle volume fraction are in a

reverse connection. In (Nguyen et al., 2007) the

–water nanofluid is used for the enhancement

of the heat transfer. The experiment results show that

by decreasing the size of nanoparticle superior heat

transfer coefficients can be produced. In (He et al.,

2007) the experimental result for TiO

nanofluids in

one vertical pipe is presented. Experiments in two

different regimes laminar and turbulent are carried

out also the experiments are repeated by a different

particle size of nanomaterials. Also, it is concluded

that the utilization of the nanoparticle concentration

Jafari, R., Razvarz, S., Vargas-Jarillo, C. and Gegov, A.

The Effect of Bafﬂes on Heat Transfer.

DOI: 10.5220/0007832206070612

In Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2019), pages 607-612

ISBN: 978-989-758-380-3

607

has an impact on the increment of the convective heat

transfer coefficient. In (Heris et al., 2006; 2007) the

experimental outcomes of the convective heat

transfer of water and Aluminium oxide nanofluids

inside a circular tube is demonstrated. It is concluded

that by utilizing the nanoparticles the heat transfer

coefficient increases. Some researchers have been

worked on numerical methods and have been

introduced the equation for the convective heat

transfer of nanofluids (Roy et al., 2004; Palm et al.,

2006). In (Buongiorno, 2006) the laminar and

turbulence regime are used for modeling, also the

viscosity effect is studied. Furthermore, it is shown

that by reduction of viscosity the heat transfer

increases. Viscosity, as well as particle volume

fraction, have less amount in the viscous sub-layer,

but conductivity in that region has more amount. In

(Daungthongsuk and Wongwises, 2007; Patel et al.,

2005; Bergles, 1985; Nijemeisland and Dixon, 2001;

Vyver et al., 2003; Sivashanmugam, 2008) the

computational fluid dynamics (CFD) models are

introduced, also the heat transfer is increased by

utilizing CFD simulation and changes in the

parameters of the system such as the geometry of tube

and active fluid through the pipe. Also, the model is

comprised of experimental models. In order to

estimate the average shell-side heat transfer

coefficients as well as pressure drop, many

investigations is carried out (Tinker, 1951; Bell,

1963; Donohue, 1949; Palen and Taborek, 1969).

Recently, artificial neural networks (ANNs) have

become universal and many interesting ANN usages

are reported in engineering area (Jafari and Yu, 2015;

Jafari et al., 2016a; 2016b; 2017a; 2017b, 2018a;

2018b; Razvarz et al., 2017; 2018a; 2018b; 2018c,

2019; Yu et al., 2019). In (Aly, 2015) a neural

network controller technique is suggested for control

of the pump flow rate.

The present study concentrates on the heat

transfer enhancement in the laminar developing

region with changing the geometry of pipe via adding

the baffles in the pipeline also effect of adding of

nanoparticle in the enhancement of heat transfer. The

Ansis program is utilized in order to simulate the

model and results.

The rest of the paper is structured as follows:

Section 2 presents the analysis of data and introduces

the properties of nanofluid. Section 3 describes the

experimental details and method of simulation the

approach of changing the geometry of the model is

stated. Section 4 presents the result of the simulation

and effect of changing of the model in the Nusselt

number as well as the thermal conductivity. Finally,

conclusions are given in Section 5.

2 ANALYSIS OF DATA

Analysis of the heat transfer behaviour of the

nanofluids is carried out by the evaluation of the local

heat transfer coefficient and local Nusselt number

which are defined as



















































(1)

where Nu [dimensionless parameter ] is Nusselt

number, q'' [







] is the heat flux, D [m] is the diameter

of the tube, 







.

 and k 



.

 are thermal

connectivity and thermal conductivity of the fluid

respectively, also 



[k] and 



[k] are the local wall

and fluid temperatures (in kelvins) respectively. The

utilized thermal conductivity value is at the average

bulk temperature. The density and specific heat of the

nanofluid is evaluated using the averaged volume

fraction ratio, which is generally acceptable and is

defined as





pbfnf

φρρφ1ρ 

(2)

where 



is the density of nanofluid, 



is the

density of the basic fluid, 



is the density of

nanoparticle and  is Nanomaterial concentration.

Figure 1: Schematic of the experimental setup.

3 EXPERIMENTAL RESULT

3.1 Experimental Conditions

Experimental conditions that are performed in ideal

terms with input and outlet pressure are equal to

atmospheric pressure. It is tried to prevent all factors,

which effect the system. The length of tube that

selected is 1200 mm, the diameter is graded 45mm,

the inlet temperature is 295°K and without accepting

the thickness of the pipe. The wall temperature of

pipe is 310°K. Characteristics of sensors are used to

obtain the temperature of specified locations in the

interval  of 100, 300, 500, 700, 900 and 1100, see

ICINCO 2019 - 16th International Conference on Informatics in Control, Automation and Robotics

608

Figure 1. The Position and distance of the edges are

based on nanofluids and they are applied to verify

other temperatures and liquids which are effective on

baffles. The input speed is based on the input

Reynolds number.

3.2 The Experiment Software

This test is performed with Ansis software. SimpleC

algorithm is used for solving the energy equation,

momentum equation, and mass equation defined in

the heat transfer model.

3.3 Determination of Mesh Building

The measurement is prepared with trial and error

method. Three different number of meshes are used

systematically and constantly, 46000, 96000, and

165000 with increasing 1.2 step, see Figure 2.

Comparison results between meshes are

demonstrated in Figure 3. It can be seen that the

number of 96000 mesh is more suitable and optimal.

In this figure, the meshes with the number of 96000

and 165000 are more close to experimental data.

Figure 2: Type and valve of baffling and meshing.

Figure 3: Effect of meshing in Re=1588 and repeating 500.

3.4 Baffle on the Surface

In order to increase the heat transfer, we want to

evaluate geometric changes of fluid activity.

Normally, there is a simple and regular fluid flow in

the pipe (see Figure 4). The path of the fluid is smooth

and regular. Fluid flow is steady and laminar in the

pipe. The heat transfer and temperature are expanded

along the radius of the pipe regularly (see Figure 5).

Figure 4: Variation of velocity in pipe.

Figure 5: Variation of temperature in steady and laminar

condition.

We use the baffle in the pipe and path of the flow

in order to make changes in the direction of flow and

its geometric shape. The size of baffles is determined

via the scale, size, and length of 15% pipe's diameter,

and the distance of 200mm from each other in the

spiral and zigzag form. The first and last baffles are

neglected due to the immediate effect on the flow.

The rest of the baffles are in the positions of 400, 600,

800, and 1000 mm. Here we want to measure the

influence of baffles on the amount of heat transfer

coefficient variation. The base working fluid is pure

water in this experiment and Al

nanoparticles are

the most common nanoparticles used with 45 nm in

size. Al

particles true density is 3880 kg/m3,

which can be converted to weight fraction and

volume fraction. The nanoparticles and pure water are

mixed by utilizing an ultrasonic homogenizer with the

concentration of 0 and 3% by weight.

3.5 Change Resulting from Baffle

Creation in the System

By changing the geometric shape of the pipe and

creating baffles, the fluid motion inside the pipe

varies, and the fluid flow transforms into the

turbulence by crashing with the baffles and causes an

increment in the heat transfer. As can be seen in

Figure 6, variation around the baffles creates

The Effect of Bafﬂes on Heat Transfer

609

turbulence in the fluid. The created turbulence is

transformed into the laminar flow after having a small

distance from baffles.

a) Velocity vector

b) Variation of temperature

Figure 6: Effect of baffles in the fluid flow a) Velocity

vector b)Variation of temperature.

4 RESULTS

After analysing the data for different numbers of

Reynolds, the optimal coefficient rate of heat transfer

is obtained from different values. By adding the

baffles and nanofluids into the pipe we see a

considerable increase in the rate of heat transfer

coefficient, see Figure 7. As the chart shows in Figure

7, baffles creation improves significantly the level of

heat flux, which is mostly due to turbulence flows, the

nature and mode of baffles, also creation of vortex

and return estate in the back of baffles.

Figure 7: Rate of heat transfer coefficient in tube in

Re=1588.

5 CONCLUSION

Implementation of the baffle and the geometric

deformation of the pipe cause a dramatic increase in

heat transfer. As the experimental results show, the

creation of baffle increases the rate of heat transfer as

much as 10-20 percent. The use of nanofluids also

enhances the heat transfer. The heat transfer

efficiency can be improved by combining

nanoparticle and baffle.

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