1

INTERROGATING THE FLOW BEHAVIOUR IN A NOVEL MAGNETIC

DESICCANT VENTILATION SYSTEM USING COMPUTATIONAL FLUID

DYNAMICS (CFD)

Auwal Dodo*, Valente Hernandez-Perez, Jie Zhu and Saffa Riffat

Faculty of Engineering, University of Nottingham, University Park, Nottingham, NG7 2RD, UK

*e-mail: enxam2@nottingham.ac.uk

ABSTRACT

The air flow behaviour across a magnetic desiccant ventilator

model have been numerically analysed for the purpose of

system design verification. Simulations were performed at

different inlet air flow velocity and magnetic desiccant wheel

rotation speed, and the way this parameters can influence the

air flow around the ventilator geometry was studied.

The simulations were carried out using the software package

Fluent 6.3, which is designed for numerical simulation of fluid

flow, heat and mass transfer. The model consisted of a

magnetic desiccant ventilator. A structured hexahedral mesh

was employed in the computational domain. The condition of

single phase flow was simulated, taking into consideration

turbulence effects using the k-ε model.

The simulation results showed that the air flow pattern across

the ventilator is not affected by an increasing inlet air velocity

or wheel rotation speed. The results show that CFD can be a

useful tool in the study of magnetic desiccant ventilators.

1. INTRODUCTION

The operating conditions and geometry are among some of the

major factors that influence the performance of a magnetic

desiccant ventilator. Comprehensive studies of these parameters

are usually lacking. Although the operating principles of

desiccant dehumidification systems and magnetic refrigeration

systems may be relatively simple, the flow behaviour in both

systems is considered complex and not clearly understood.

Computational fluid dynamics (CFD) numerical approach

could provide reasonable solutions to the aforementioned

problem.

Computational Fluid Dynamics (CFD) is a numerical

modelling technique that solves the Navier-Stokes

equations on a discretised domain of the geometry of

interest with the appropriate flow boundary conditions

supplied. The Navier-Stokes equations are a complex non-

linear set of partial differential equations (PDEs) that

describe the mass and momentum conservation of a fluid.

Additional physics can also be resolved by solving

additional conservation equations, e.g., heat transfer,

multiphase flow and combustion.

The fundamental principles behind the process have been well

established in the field of fluid dynamics analysis and

numerical methods for many years. CFD has the capacity to

simulate flow and energy and deliver simple to complex

solution. CFD offers the means of testing theoretical advances

for conditions unavailable experimentally as described by

Fletcher (1991). According to Fletcher (1991), CFD has many

major advantages in comparison to experimental fluid

dynamics. These advantages are:

The lead time in design and development is significantly

reduced;

CFD can simulate flow conditions not reproducible in

experimental model tests;

CFD provides more detailed and comprehensive

information;

CFD is increasingly more cost-effective than wind tunnel

testing;

CFD produces low energy consumption.

Although CFD modelling cannot be a complete substitute for

real experimental investigations, it however permits the

simulation of different flow conditions and environments

without the rigours and expenses required for real life

experiments, an opportunity which would not have been

possible with physical experiments.

The study presented herein will attempt to interrogate and

determine the effect that different operating parameters of the

ventilator, including air velocity and wheel rotation speed will

have on the fluid flow around the ventilator geometry using

computational fluid dynamics.

2. CFD MODEL

The development and solution of a representative

Computational Fluid Dynamics (CFD) model is a multistage

process. In the present work, the CFD simulations were carried

out using the commercial CFD package Fluent 6.3. Fluent

software uses the Finite Volume discretization technique to

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solve the governing fluid flow equations. The first step in the

Finite Volume Method (FVM) involves the division of the flow

domain into discrete control volumes. The equations governing

the fluid flow are integrated over the control volume and the

resulting integral equations discretized to produce algebraic

equations at the nodal points. These algebraic equations are

then solved by an iterative method. The resultant steady state or

time dependent solutions may be graphically viewed as a series

of two and three dimensional, vector, streamline or contour

plots, etc.

The process of model development and solution described

above is required of all CFD simulations and can be divided

into three main steps. These steps are:

Pre-processing;

Solver extraction;

Post-processing.

2.1 Computational domain

It was important to ensure that the geometry of the CFD model

represented as much as possible, the physical magnetic

desiccant ventilator. Hence, much care was taken in defining

the geometry. The geometry for the case studies modelled is

illustrated in Figure 1.

The geometry consists of three different sections: process

air, regeneration air and the wheel. Two air channels of

dimensions 500 mm width, 240 mm depth and 17 mm

height each, were created to represent the process air

section inlet and outlet air streams. Also, another two

separate air channels were created to represent the inlet

(500 mm width, 240 mm depth and 17 mm height) and

outlet (700 mm width, 240 mm depth and 17 mm height) air

streams of the regeneration section. Both the process and

regeneration air sections have been created using the brick

tool under the geometry panel in Gambit. The wheel section

(magnetic desiccant wheel) was created using the cylinder

tool under the geometry panel in Gambit. Dimensions 17

mm height, 120 mm radius and z-axis orientation were

specified for the wheel.

The magnetic desiccant wheel has been modelled as a

porous medium. The porous zone is selected in this work,

for the fact that the real magnetic desiccant wheel is made

of a honeycomb structure with parallel air channels. The

honeycomb matrix of the wheel is impregnated with silica

gel desiccant material and gadolinium ingots but allowing

sufficient air to flow through the matrix of the wheel.

The rotation of the wheel has been specified in the Motion

Panel of the Magnetic desiccant wheel. The motion type

that was chosen for wheel rotation is Moving Frame and

rotational velocity is specified as desire.

Fig. 1: Computational domain.

2.2 Grid generation

The grid generation process deals with the division of the

domain under consideration into small control volumes

on which the discretised governing equations will be

solved. This process is also known as meshing. The

meshing process is an integral part of the numerical

solution and must satisfy certain criteria to ensure a valid

and accurate solution, Lun et al. (1996).

The geometry and grid generation forms a large part in

terms of person-hour time of the CFD analysis. In

meshing the flow domain, a structured mesh approach

was adopted. This was done in order to achieve the

desired grid density at different parts of the flow domain.

The flow domain was therefore divided into three

different faces with each face meshed separately to

achieve the desired results.

The geometries of the mesh employed is the structured

hexahedral grid, which has been successfully employed

for similar studies. There was need to cluster a large

number of closely spaced grids in the region of flow of

main interest. Figure 2 shows the generated grids for the

ventilation system.

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Fig. 2: Computational mesh generated for the magnetic desiccant ventilator used for CFD simulation.

2.3 Governing equations

To effectively model the ventilator, the motion of an

incompressible single flow has been considered as the flow

scenario. The mass and momentum conservation equations

for the single flow through the domain are represented as:

mSVdivt

(1)

Where ρ is the density, t is the time, V is the velocity vector

of the fluid and Sm is a mass source.

The momentum equation is

divV

x

v

x

v

xpg

Dt

DVij

i

j

j

i

i

(2)

Where g is the gravitational vector, xi is the spatial co-

ordinate, vi is the i component of the velocity vector, is

the ordinary coefficient of viscosity, λ is the coefficient of

bulk viscosity, ij is the Kronecker delta function andDt

D

is the total or substantial derivative.

Solving these sets of equations has been done using a

software package Fluent 6.3.

2.4 Turbulence model

The high operating air velocity across the ducts of desiccant

ventilators contributes to the occurrence of highly turbulent

flow, and as the air flow through the matrix of the wheel, a

developing vortex region is created around the wheel.

Therefore, turbulence must be considered in the numerical

simulation. The accuracy of CFD simulations for turbulent

flows can be affected by turbulence modelling, especially

because of the complex features of the flow. According to

Versteeg and Malalasekera (2007), it has been recognised by

researchers in the field of CFD that the choice of turbulence

models used to represent the effect of turbulence in the time-

averaged mean flow equations represents one of the

principal sources of uncertainty of CFD predictions.

In order to simulate turbulence in the present work, the

standard k-ε model, Launder and Spalding (1974), which

requires that the flow is fully turbulent, was used for several

reasons; the model is computationally efficient, is

implemented in many commercial codes, the geometry is

reasonably not complicated and it has demonstrated

capability to simulate properly industrial processes,

including ventilated systems. The k- models are based on

the Reynolds Averaged Navier Stokes (RANS) equations

and they focus attention on the mean flow and the effect of

turbulence on flow properties. The standard k-ε model is

today the most widely used turbulence model in the

engineering industry, since 1974, DeJesus (1997). The

standard k-ε turbulence model is described by the following

equations, Hernandez-Perez (2008):

i

j

j

i

i

j

t

jk

t

jj

jx

u

x

u

x

u

x

k

xx

ku

(3)

kC

x

u

x

u

x

u

kC

xxxu

i

j

j

i

i

j

t

j

t

jj

j

2

1

(4)

In the above equations, k is the turbulence kinetic energy and

is the turbulence dissipation rate. k , , 1C and 2C

are constants whose values are 1.0, 1.3, 1.44 and 1.92,

respectively. Also, iu is the i component of the fluid

velocity u and jx is the j spatial coordinate.

Furthermore, the fluid viscosity must be corrected for

turbulence in the Navier-Stokes equations employing an

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effective viscosity teff where is the

dynamic viscosity and t is the turbulent viscosity.

2.5 Boundary and initial conditions

The definition of boundary conditions is performed in

Fluent. However, the boundary types to be used in the

simulation have been previously defined in Gambit, once the

mesh was generated. The boundary type specifications

define the physical and operational characteristics of the

model at those topological entities that represent model

boundaries. The boundary types that were used in the CFD

model are summarized in Table 1 below:

TABLE 1: SUMMARY OF BOUNDARY CONDITIONS

Zone Name Boundary Type

Process air inlet Velocity inlet

Process air outlet Pressure outlet

Regeneration air inlet Velocity inlet

Regeneration air outlet Pressure outlet

Magnetic desiccant wheel Porous medium

Wall Wall

Two inlets and two outlets are employed in this model; the

two inlets include the process air inlet and the regeneration

air inlet, whereas the two outlets include the process air

outlet and regeneration air outlet. In addition, a wall

boundary type is applied to the rest of the boundaries. At

both process and regeneration air inlets, a velocity-inlet

boundary type is used in which the inlet air velocity

conditions are specified. Pressure outlet boundary type was

used for the process and regeneration air outlets of the

model.

At time 0t (s), all velocity and pressure components are

set to 0 (m/s) and 0 (kPa), respectively. These chosen initial

conditions ease the convergence process. In addition, an

initial guess for the turbulent kinetic energy and dissipation

rate was applied in all the simulations studied.

2.6 Solution algorithm

In order to numerically solve the system of partial

differential equations in this study, discretization of the

equations has been carried out using the Finite Volume

Method (FVM) with an algebraic segregated solver and co-

located grid arrangement, as implemented in Fluent 6.3.

Values of pressure and velocity are both stored at cell

centres, in this grid arrangement. Full details of the (FVM)

discretization have been given in the work carried out by

Versteeg and Malalasekera (1995) and Zienkiewicz and

Taylor (2000). The continuity and momentum equations are

needed to be linked considering the fact that Fluent uses a

segregated solver. Several of this linkage techniques are

available in Fluent and have been reported in the literature.

The Semi-Implicit Method for Pressure Linked Equations

(SIMPLE) algorithm, of Patankar and Spalding (1972), was

employed due to its good performance in finding a fast and

convergent solution, Abdulkadir (2011).

2.7 Mesh independence study

CFD numerical simulations are computationally expensive.

The size of the computational grid specified by the user is

considered among significant factors influencing the

computation time. In order to identify the minimum mesh

density to ensure that the converged solution obtained from

CFD is independent of the mesh resolution, a mesh

sensitivity analysis has been carried out, in the development

and analysis of the CFD model.

Four 3-Dimensional meshes were investigated in the present

study as illustrated in Figure 3. The sizes of the meshes

investigated are; 144572, 234965, 385440 and 591050 cells.

A CFD calculation was performed to compare the

performance of the above mentioned meshes. The results of

the mesh independence study carried out for the four meshes

are shown in Figure 4. The velocity profile in a vertical

plane passing through the centre of the wheel has been used

as the parameter for comparison. Due to the difficulties

involved with comparing the velocity profiles, the wheel

rotational speed has been kept constant at 0 rpm, while an

inlet air velocity of 1.63 m/s was applied. The results reveal

that the velocity profile has the same shape for all the

different mesh densities utilized, but however, the magnitude

of the velocity is slightly affected by the mesh. We can

clearly see that as the number of cells in the mesh is

increased, the maximum velocity in the profile tends to be

1.2 m/s, as depicted in Figure 4.

It was observed that the changes from mesh c to mesh d are

negligible. Therefore, it can be concluded that mesh c with

385440 cells is sufficient to carry out the simulation, as it

gives a good result and is less computationally expensive

than the mesh d with 591050 cells.

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(a) (b)

(c) (d)

Fig. 3: View of different sizes of computational grid used for mesh independence study (a) 144572 cells (b) 234965 cells (c)

385440 cells (d) 591050 cells.

Fig. 4: Result obtained from the CFD mesh independence studies.

3. RESULTS AND DISCUSSION

The CFD simulations of the flow in the vicinity of the

desiccant wheel model were basically aimed at analysing the

fluid flow in the wheel and determining the directions of

fluid flow in this region. Simulations were carried out to

determine the effect of varying inlet air velocity and

magnetic desiccant wheel rotation speed on the fluid flow

across the ventilator.

The results have given an insight on the fluid behaviour

around the desiccant wheel. The different contour and vector

displays available in the CFD software have been used as a

post-processing tool to analyse the fluid behaviour around

the wheel and then relate this to flow verification exercise.

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3.1 Effect of varying inlet air velocity on fluid flow

Simulations were carried out in order to observe how

different inlet air velocity at the process and regeneration air

sides, affects the fluid flow around the magnetic desiccant

ventilator. In these simulations, a constant wheel rotation

speed of 1.3 rpm was used, the same as that used during the

experimental investigations. Three different inlet air velocity

conditions for process and regeneration sides were

simulated, as shown in Table 2.

TABLE 2: DIFFERENT INLET AIR VELOCITIES USED

FOR SIMULATIONS

Inlet Air Velocity

Setting

Process Inlet Air

Velocity (m/s)

Regeneration Inlet

Air Velocity (m/s)

1 1.63 1.64

2 4.44 4.38

3 7.75 7.34

Figures 5(a) and 5(b) shows the initial results of the

simulations. Initially, a cross sectional view at the horizontal

central plane and the wheel was taken for all the three

different inlet air velocity conditions. However, from the

initial results, the flow behaviour for all conditions was

observed to be very similar. The velocity vectors showing

flow behaviour at the lowest and highest inlet air velocity

conditions have been shown in Figures 5(a) and 5(b),

respectively. In order to have a close and more detailed view

of the flow behaviour in the system, a zoomed cross

sectional view of the horizontal central plane and that of the

wheel were taken separately for all the different simulated

inlet air velocity conditions. These results have been

presented in Figures 6(a) and 6(b) to 8(a) and 8(b). From the

obtained results, it can be observed that, similarly to the case

of the mesh independence study, the flow behaviour is very

similar for all simulation conditions, with the air velocity at

the central part of the wheel being close to zero and the

highest air velocity is obtained far away from the centre of

the wheel. The air velocity within the wheel is relatively less

than that within the air duct, which can be explained by the

larger cross sectional area available for the air in the wheel.

In addition, by comparing the results for all conditions, it can

be observed that the as the inlet air velocity is increased, the

magnitude of the maximum velocity vectors increases

accordingly. On a whole, the simulation results show that

there is proper and sufficient circulation of air in ducts of the

ventilator, as intended. It was also observed that the flow is

expanding as it leaves the magnetic desiccant and entering

into the outlet ducts at both the process and regeneration air

sides of the ventilator.

5(a) 5(b)

Process side inlet air flow velocity: 1.63 m/s Process side inlet air flow velocity: 7.75m/s

Regeneration side inlet air flow velocity: 1.64 m/s Regeneration side inlet air flow velocity: 7.34m/s

Fig. 5: Velocity vectors showing flow behaviour at the (a) lowest inlet air velocity (b) highest inlet air velocity.

6(a) 7(a) 8(a)

6(b) 7(b) 8(b)

Figs. 6, 7 and 8: Velocity vectors showing flow behaviour for inlet air velocity setting 1, 2, 3, respectively. (a) Horizontal central

plane (b) Wheel.

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3.2 Effect of varying wheel rotation speed on fluid flow

Simulations were also carried out in order to determine the

effect of varying the magnetic desiccant wheel rotation

speed on the fluid flow around the magnetic desiccant

ventilator. In these simulations, air velocity of 4.6m/s was

utilised, for the process and regeneration air inlets. A total of

three different wheel rotation speed conditions were

simulated, starting with a stationary wheel. The simulated

wheel rotation speeds include; 0 rpm, 2.6 rpm and 6.4 rpm,

respectively. However, it is worth mentioning that the

magnetic desiccant wheel rotation speeds used for this

simulation study are the same as those used during the

experimental investigations, in order to allow for a more

accurate comparison of simulated and experimental results.

The simulation results for the effect of varying wheel

rotation speed on fluid flow have been presented in Figures

9(a) and 9(b). Similar to section 3.1, a cross sectional view at

the horizontal central plane and the wheel was taken for all

the wheel rotation speeds used in this part of the simulation.

The obtained results were found to be similar for all the

simulated wheel rotation speed conditions, as observed for

the simulations carried out earlier, for the effect of varying

the inlet air velocity on fluid flow. The velocity vectors

showing flow behaviour at 0rpm and at the highest wheel

rotation speed of 6.4rpm have been shown in Figures 9(a)

and 9(b), respectively. In order to have a more close and

detailed view of the flow behaviour in the system, a zoomed

cross sectional view of the horizontal central plane and that

of the wheel were taken separately for all the different

simulated wheel rotation speed conditions. These results

have been presented in Figures 10(a) and 10(b) to 12(a) and

12(b).

From all the results obtained, regarding the effect of varying

wheel rotational speed on the fluid flow behaviour, it can be

concluded that, for the wheel rotation speeds used in this

work (all of which are relatively slow), the effect has been

found to be negligible, as can be observed in the obtained

results. In order for the wheel rotational speed to have a

significant effect on the flow behaviour, the wheel rotational

speed must be significantly higher than those used for these

simulations. However, the simulation results obtained, shows

that there is sufficient and proper circulation of air in the

ducts and through the porous wheel of the ventilator, as

intended.

9(a) 9(b)

Fig. 9: Velocity vectors showing flow behaviour (a) no wheel rotation speed, 0rpm (b) high wheel rotation speed, 6.4rpm.

10(a) 11(a) 12(a)

10(b) 11(b) 12(b)

Figs. 10, 11 and 12: Velocity vectors showing flow behaviour at wheel rotation speeds of 0 rpm, 2.6 rpm and 6.4 rpm, respectively.

(a) Horizontal central plane (b) Wheel.

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4. CONCLUSIONS

CFD simulations of the magnetic desiccant ventilator were

carried out for the purpose of system design verification and

also in order to visualise the flow pattern and further

interrogate the air flow behaviour in the ventilation system.

A design of a magnetic desiccant ventilation system

comprising of a rotary magnetic desiccant wheel as a porous

medium and a series of air ducts at the both the process and

regeneration sections of the ventilator has been developed.

Detailed modelling studies of air flow behaviour across the

ventilation system have been conducted. Simulations were

initially carried out under different inlet air velocity

conditions at a constant wheel rotation speed and later under

different wheel rotation speeds, at constant inlet air flow

velocity. These simulations were aimed at flow

visualisations under the above mentioned operating

conditions in order to fully interrogate the air flow behaviour

across the magnetic desiccant ventilator. Simulation results

obtained shows that the air flow pattern across the ventilator

is not affected by increasing inlet air velocity or wheel

rotation speed. The results also revealed that there is

sufficient air flow through the porous magnetic desiccant

wheel, from the inlets to the outlets of both the process and

regeneration air sides of the ventilator.

In order to achieve more realistic results in the numerical

simulation, further simulations will be required. These will

need to incorporate heat transfer and magnetic field effects

into the CFD model. As a result, the complexity of the CFD

model will increase.

5. NOMENCLATURE

Latin letters

g

Gravitational acceleration [2/ sm ]

k Kinetic energy of turbulence [22 / sm ]

p

Pressure [2/ mN ]

t Time [ s ]

u Velocity [ sm / ]

D Diffusion coefficient [ sm /2]

Greek letters

Volume fraction [-]

Dynamic viscosity [ smkg ./ ]

Material density [3/ mkg ]

Surface tension [ mN / ]

Stress tensor [2/ mN ]

Second (bulk) viscosity [ smkg 3/ ]

Eddy viscosity [ smkg ./ ]

Rate of viscous dissipation [ sm /2]

Subscripts

ji,

Space directions

k Phase index

Other symbols and operators

Gradient operator

t

Partial derivative

DT

D

Total derivative

C , k , , 1C and 2C Empirical constants

Kronecker delta function

x Spatial co-ordinate

div Divergence of a vector field

6. ACKNOWLEDGMENTS

The authors wish to thank Pilkington Energy Efficiency

Trust (PEET) for their financial support through the positive

environmental initiative of Pilkington Group Limited.

7. REFERENCES

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PhD thesis, University of Nottingham. UK

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thesis, University of Toronto, Canada

(3) Fletcher, C.A.J., 1991. Computational techniques for

fluid dynamics. Volume 1, 2nd

edition, Berlin: Springer

(4) Hernandez-Perez, V., 2008. Gas-liquid two-phase flow in

inclined pipes. PhD thesis, University of Nottingham. UK

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(6) Lun I., Calay, R. K., and Holdo, A. E., 1996. Modelling

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299-314

(7) Patankar, S. V., and Spalding, D. B., 1972. A calculation

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(8) Versteeg, H. K., and Malalasekera, W., 1995. An

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(9) Versteeg, H. K., and Malalasekera, W., 2007. An

introduction to Computational Fluid Dynamics: the Finite

Volume Method. 2nd

edition. Pearson Educational Ltd

(10) Zienkiewicz, O.C., and Taylor, R.L., 2000. Finite

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Butterworth - Heinemann