Optimization of a Centrifugal Blower Using Cfd Techniques

A

PROJECT REPORT

ON

OPTIMIZATION OF A CENTRIFUGAL

BLOWER USING CFD TECHNIQUES

BY

AKASH CHAUDHARI (B 80380815)

AKSHAY WALIMBE (B 80380909)

VIJAY KOLHE (B 80380839)

SAHIL GULHANE (B 80380826)

B.E. (MECHANICAL)

GUIDE

PROF. S. H. BARHATTE

DEPARTMENT OF MECHANICAL ENGINEERING

MIT COLLEGE OF ENGINEERING, PUNE

UNIVERSITY OF PUNE

2012-2013

MIT COLLEGE OF ENGINEERING, PUNE

DEPARTMENT OF MECHANICAL ENGINEERING

CERTIFICATE

This is to certify that the project report entitled “OPTIMIZATION OF A

CENTRIFUGAL BLOWER USING CFD TECHNIQUES” is being submitted by

Akash Chaudhari (B 80380815)

Akshay Walimbe (B 80380909)

Vijay Kolhe (B 80380839)

Sahil Gulhane (B 80380826)

for the partial fulfillment of award of degree “Bachelor of Engineering in Mechanical

Engineering” for the academic year 2012-2013.

Guide Head of Department

Prof. S. H. Barhatte Dr. B. S. Kothavale

External Examiner Principal

Dr. V. M. Wadhai

ACKNOWLEDGEMENT

One of the joys of completion is to look over the journey past and remember all

the people who have helped and supported us along this long but fulfilling road.

We would like to express our heartfelt gratitude to Dr. V. M. Wadhai, and Prof.

Dr. B. S. Kothavale, who are not only mentors but also a great inspiration. We could

not have asked for better role models, each inspirational, supportive, and patient. We

could not be prouder of our academic roots and hope that we can in turn pass on the

research values and the dreams that they have given to us. We would also like to thank

our project guide, Prof. S. H. Barhatte who provided encouraging and constructive

feedback. It is no easy task, guiding a project work, and we are grateful for his

thoughtful and detailed comments. It was only because of his insightful ideas that we

were able to complete this project.

This project was sponsored by Subros Ltd., and we would like to thank the

organisation for its generous support. At Subros, we would like to thank, Mr. Suvankar

Manna (Sr. Manager, R&D) and Mr. Somnath Sen (A.G.M.), who have both been

extremely enthusiastic in guiding us throughout the duration of this project. Thank you

for helping us develop new ideas and lending us your invaluable time for this project.

To all the staff of Mechanical Department, we are grateful for all the guidance

that was given to us during the course of this project work. Lastly to our friends, thank

you for supporting us all along this long journey.

- Akash Chaudhari

Akshay Walimbe

Vijay Kolhe

Sahil Gulhane

SYNOPSIS

The automobile HVAC systems have been around for ages, but the main

difficulty faced with them is the excessive power drain and the noise emanating at high

speeds. Thus there was a need to optimize an existing design of a centrifugal blower

used in a car HVAC system for higher volume flow rates, higher efficiency and reduced

noise levels.

This project primarily focuses on optimization of an automobile HVAC blower

unit using CFD and optimization techniques. Firstly an existing design of a centrifugal

blower was taken and was analysed experimentally. Then the same experiment was

simulated using CFD techniques and a resulting error was found out between the two

experiments. CFD techniques were then modified to get the least error. Later on four

parameters viz. Inlet and Outlet Blade Angles, Nose Radius and Divergence Angle were

decided for optimization. Optimization was performed using Taguchi method and

multiple CFD analyses were carried out on various geometrical combinations. The use

of these techniques led to the optimum combination of the design variables.

Using the parameters for this optimum combination, a new blower model was

generated and its volume flow rate and efficiency was compared with the original model.

It was found that the new model exhibited a 6.29 % rise in volume flow rate and another

2.67 % increase in efficiency and the noise levels reduced by 0.04 %, for the same

power input of 120 W.

In conclusion, this optimized design will make the HVAC system used in cars

more efficient with better cooling characteristics coupled with quiet performance.

KEYWORDS

Centrifugal Blower, Automobile HVAC, CFD, FLUENT, Car Air Conditioning

i

CONTENTS

1 Introduction

1.1 Automobile HVAC System Evolution and Necessity 1

1.2 Need for Research 3

1.3 Literature Review 4

1.3.1 Definition of Air Blower 4

1.3.2 Types of Air Blowers 4

1.4 Blower characteristics curve 8

1.5 Project overview 10

2 Design Parameter

2.1 Impeller Shape 11

2.2 Blade Geometry 11

2.2.1 Blade type 11

2.2.2 Blade angles 11

2.2.3 Blade thickness 11

2.3 Number of Blades 11

2.4 Volute Base Circle Radius 11

2.5 Volute Tongue 14

2.6 Divergence Angle 14

3 Computational Fluid Dynamics

3.1 Basic Governing Equations 15

3.1.1 Finite Control Volume 15

3.1.2 The Continuity Equation 17

3.1.3 The Momentum Equation 17

3.1.4 Multiple Reference Frame Model 19

3.2 Discretization: Finite Volume Method 20

3.3 Realisable K-ε Model 20

3.4 CAD Generation 22

3.5 Meshing 22

3.5.1 Surface Meshing 22

3.5.2 Volume Meshing 23

3.6 Boundary Conditions 23

3.6.1 Pressure Inlet 23

3.6.2 Pressure Outlet 23

3.6.3 Wall Boundary Condition 25

3.7 Solution Methods 25

3.7.1 Coupled Solver 25

3.7.2 Second Order Upwind Scheme 25

3.8 Noise 26

3.8.1 Noise Sources 26

3.8.2 Noise Study 28

ii

3.9 Post Processing 30

3.9.1 Volume Flow Rate (Q) 30

3.9.2 Torque (T) 30

3.9.3 Total Pressure (P) 30

3.9.4 Efficiency (η) 30

3.9.5 Power Consumed (Pi) 30

3.9.6 Noise 30

4 Validation

4.1 Experimental Data 32

4.2 CFD Results of Original Model (3D) 32

4.3 CFD Results of Original Model (2D) 36

4.4 Correlation 39

4.4.1 CFD (3D) and Experimental Data 39

4.4.2 CFD (2D) and CFD (3D) Results 39

5 Optimization

5.1 Parameter Values and Corresponding Deltas 41

5.2 CAD Models 41

5.3 CFD Results 42

5.4 Slope Analysis 42

5.5 Conclusion 45

5.6 Optimization 45

5.7 Different Approaches 45

5.7.1 Trial and Error Approach 45

5.7.2 Design of Experiments 45

5.7.3 Taguchi Method 46

5.8 Taguchi Method 46

5.8.1 Quality Loss Function 46

5.8.2 Normalization 47

5.8.3 Weighting Method 47

5.8.4 MRSN 47

5.9 Setup 48

5.10 Results 49

5.11 Conclusion 51

6 Results and Conclusion

6.1 Optimised Model 52

6.2 Comparisons 52

6.3 Conclusion 55

7 Future Work 56

References 57

iii

LIST OF FIGURES

Figure No. Description Pg. No.

1.1 Layout of Automobile HVAC System 2

1.2 Propeller Fan Figure 5

1.3 Tube Axial Fan 5

1.4 Vane Axial Fan 5

1.5 Radial Blade Blower 7

1.6 Forward Curved Blade Blower 7

1.7 Backward Curved Blades Blower 7

2.1 Impeller Geometry 12

2.2 Blade Angles 12

2.3 Volute 13

2.4 Divergence Angle 13

3.1 Fundamentals Principles which form the basis of

CFD 16

3.2 Models of Flow (a) Finite Control Volume Approach

(b) Infinitesimal Fluid Element Approach 18

3.3 Infinitesimally small, moving fluid element 18

3.4 (a) Hexahedral Mesh 24

3.4 (b) Tetrahedral Mesh 24

3.4 (c) Hybrid Mesh 24

3.4 (d) Polyhedral Mesh 24

3.5 Monopole 27

3.6 Dipole 27

3.7 Quadrupole 27

4.1 Scroll Casing Surface Mesh generated in Hypermesh 34

4.2 Impeller Surface Mesh generated in Hypermesh 34

4.3 Cross Section of Volume Mesh generated in TGrid

(a) 34

4.4 Cross Section of Volume Mesh generated in TGrid

(b) 34

4.5 2D Geometry 37

4.6 Tetrahedral Mesh for 2D Model 37

4.7 Pressure Contours 38

4.8 Velocity Contours 38

6.1 (a) Acoustic Power Level- Original Model 53

6.1 (b) Acoustic Power Level- Optimized Model 53

iv

LIST OF GRAPHS

Graph No. Description Pg. No.

1.1 Blower Characteristics Curve 9

4.1 Fan Curve Plotted from CFD Results 35

4.2 CFD (3D) results superimposed over experimental

results 40

5.1 Slope Values 44

5.2 (a) Volume Flow Rate: Individual Contribution 44

5.2 (b) Efficiency: Individual Contribution 44

6.1 Comparison of Volume Flow Rates of two models 54

LIST OF TABLES

Table No. Description Pg. No.

5.1 Definition of Geometry 41

5.2 Geometrical Parameters of CAD Models 41

5.3 Results 42

5.4 Slope Calculations 43

5.5 Factors and levels table of experiment 48

5.6 Orthogonal Array 48

5.7 Response Variables 49

5.8 Response Variables Values 49

5.9 Quality loss functions and normalized quality loss

values 50

5.10 Weighing Factor 50

5.11 Total normalized quality values and multiple signal to

noise ratio 51

5.12 Optimal combination 51

6.1 Optimised Parameters 52

6.2 Comparison of Various Parameters with original 52

Optimization of a Centrifugal Blower using CFD Techniques

1 Department of Mechanical Engineering, MITCOE, Pune

1. Introduction

HVAC (Heating Ventilation and Air Conditioning) in the car is designed to maintain

the temperature in the car cabin and also provide fresh, filtered, quality air on a constant

basis. Depending on the outside temperature, the supplied air is either heated or cooled.

1.1 Automobile HVAC System Evolution and Necessity

History

Companies first started offering installation of air conditioning for cars during the mid-

30’s. Most of their customers operated limousines and luxury cars.

The Packard Motor Car Company was the first automobile manufacturer to build air

conditioners into its cars, beginning in late 1939 (at the start of the 1940 model year).

The system took up half of the entire trunk space, was not very efficient, and had no

thermostat or independent shut-off mechanism. The option was discontinued after

1941. Considerable developments have taken place since then and now a modern

HVAC comprises of the following components.

HVAC Components

1. Blower: Blower is the device which creates flow by imposing motion of

impeller to air. It also circulates air over the evaporator coil and in the chamber to

be cooled.

2. Evaporator: The refrigerant from expansion valve comes into evaporator coil

below the temperature required to be maintained in evaporator. In car HVAC the

refrigerant flowing through coils and removes heat from air which is circulated in

car through ducts.

3. Compressor It compresses the refrigerant to high temperature and pressure.

The compressor is considered as the heart of Air conditioning system because it

pumps the refrigerant throughout the circuit like human heart pumps the blood.

4. Condenser: Condenser removes heat gained by refrigerant in evaporator and

heat added by compressor and converts vapour refrigerant into liquid refrigerant. It’s

a heat exchanger in which heat transfer takes place between hot refrigerant and air.

http://en.wikipedia.org/wiki/Packard



Figure 1.1: Layout of Automobile HVAC System



5. Receiver and Drier: The Drier is fitted on the high pressure liquid line of an air

conditioning system between the Condenser and Expansion Device. The Drier has two parts

to it, the receiver and, of course, the drier. The receiver section holds the right amount of

refrigerant required by the system to ensure correct operation and to supply a steady flow of

liquid refrigerant to the Expansion Device. The drier section is responsible for removing

moisture from the air conditioning system by means of a bag of desiccant which absorbs

small quantities of moisture.

6. Expansion Valve: Expansion valve reduces pressure and temperature of

refrigerant coming from condenser temperature as per requirement of the system.

Also it regulates the flow of refrigerant as per load requirement.

1.2 Need for Research

High levels of vehicle comfort are being increasingly demanded by users. This

creates a new challenge for climate control engineers. In the past, it typically took a lot

of man-hours and cost to develop and fully characterize the Heating, Ventilation and

Air Conditioning (HVAC) system for a new model vehicle using conventional physical

testing methods. However, the use of computational fluid dynamics (CFD) simulations

can dramatically reduce time of development of automotive HVAC systems, contribute

to improvement of their performance and provide better understanding of the

underlying processes.

Efficiency of air blower is very critical as it directly affects the performance of

HVAC system and hence it was selected for optimization in the current study. Critical

parameters affecting the blower performance were determined and subsequently

optimised via multiple CFD analysis and simulation



1.3 Literature Review

The current study was primarily focussed on improving the performance of air blowers

and hence a detailed literature survey was done to find out the different types of air

blowers and their applications in HVAC systems.

1.3.1 Definition of Air Blower

Mechanical device that blows a strong current of air by rotating an impeller using

energy is called as air blower. Blowers are usually non positive devices which give

pressure rise by imparting velocity head to air.

1.3.2 Types of Air Blowers

Axial flow

The major types of axial flow fans are:

a) Propeller: Propeller fans usually run at low speeds and moderate

temperatures. They experience a large change in airflow with small changes in static

pressure. They handle large volumes of air at low pressure or free air delivery.

Propeller fans are often used indoors as exhaust fans. Outdoor applications include

air-cooled condensers and cooling towers. Efficiency is low – approximately 50% or

less.

b) Tubeaxial: Tubeaxial fans have a wheel inside a cylindrical housing, with

close clearance between blade and housing to improve airflow efficiency. The wheel

turns faster than propeller fans, enabling operation under high-pressures 250 – 400

mm. The efficiency is up to 65%.

c) Vane axial: Vaneaxial fans are similar to tubeaxials, but with addition of

guide vanes that improve efficiency by directing and straightening the flow. As a

result, they have a higher static pressure with less dependence on the duct static

pressure. Such fans are used generally for pressures up to 500 mm. Vaneaxial are

typically the most energy-efficient fans available and should be used whenever

possible.



Figure 1.2: Propeller Fan

Figure 1.3: Tube Axial Fan

Figure 1.4: Vane Axial Fan



Radial flow

The major types of centrifugal blowers are:

a) Radial blades: Radial blowers are designed for industrial use in small exhaust

systems. These air blowers are capable of handling air that contains bits of dirt, grit, lint

and other foreign particles while still maintaining a high-pressure supply of air for

conveying and cooling. Their use in particle-laden air means that this type of blower is

generally designed to be self-cleaning. Radial air blowers have the lowest efficiency

levels because the blades have no curve or lean and are perpendicular to the wheel's

rotational axis.

b) Forward curved blades: Forward-curved blowers are impulse devices with blades

that are curved in the direction of rotation. The blower accelerates air to a high velocity

while rotating at a low speed. Forward-curved blower wheels spin at relatively low

speeds and produce high volumes of air at low static pressures. This type of blower is

incapable of operating at the speeds necessary to create high static pressures because of

its lightweight construction. Still, forward-curved blowers are the most common type

of air blower because they propel the most air volume in relation to blower size and

speed.

c) Backward curved blades: A backward-inclined blower, operating at roughly twice

the speed of a forward-curved air blower, has flat blades that slant away from the

direction of travel. This type of blower is highly efficient (low horsepower requirement)

and has a rugged construction suitable for high static pressure applications. This type

of air blower is best used in locations where the air is either clean or mildly

contaminated. The blades of a backward-curved blower are a single thickness

throughout and curve away from the direction of travel. These blades are sturdier than

backward-inclined blades and can be used in corrosive and erosive environments.



Figure 1.5: Radial Blade Blower

Figure 1.6: Forward Curved Blade Blower

Figure 1.7: Backward Curver Blades Blower



d) Aerofoil blades: Aerofoil blowers have the most efficient design of all air

blowers. Their blades have an aerofoil shape that is wide at the center and curves down

to narrow edges. Aerofoil blowers are extremely efficient because they require lower

horsepower levels to operate. This type of blower is used in clean air situations.

1.4 Blower characteristics curve

Blower characteristics can be represented in form of fan curve(s). The fan curve is a

performance curve for the particular fan under a specific set of conditions. The fan

curve is a graphical representation of a number of inter-related parameters. Typically a

curve will be developed for a given set of conditions usually including: fan volume flow

rate, system static pressure, fan speed, and brake horsepower required to drive the fan

under the stated conditions. Some fan curves will also include an efficiency curve so

that a system designer will know where on that curve the fan will be operating under

the chosen conditions of the many curves shown in the figure, the curve, volume flow

rate versus static pressure (SP) is especially important.

The intersection of the system curve and the static pressure curve defines the operating

point. When the system resistance point changes, the operating point also changes.

Once the operating point is fixed, the power required can be determined by following a

vertical line that passes through the operating point to an intersection with the power

curve.



Graph 1.1: Blower Characteristics Curve



1.5. Project overview-

When the sponsoring company ‘Subros ltd.’ gave the problem statement an extensive

literature survey about the topic was carried out. It was found out that there was a lot

of scope to modify the existing design of the HVAC blower used in automobiles. The

existing design of the forward curved centrifugal blower was designed by engineers

based on empirical formulae and experimentation. It was decided to optimize the

existing design by using CFD techniques. A study of various parameters was conducted

and various optimizing methods were used to carry out optimization of the centrifugal

blower. The blower given by the company is currently used in the HVAC system of

some of the variants of TATA hatchbacks.

The project involved modelling, meshing, analysis and optimization of the centrifugal

blower. The 3D modelling of the blower was done using commercial modelling

software, CATIA V5R19. Different softwares like HYPERMESH and T-GRID were

used for creating the surface mesh and volume mesh of the 3D model. Meshing for 2D

models was created using ANSYS WORKBENCH Meshing tool. The analysis of the

blower unit was conducted in the CFD package, ANSYS FLUENT. Various optimizing

methods were studied and experiments were performed to find out an efficient

optimizing method. Many 2D analyses were conducted for these optimizing methods.

These optimizing methods provided us with an optimized design for the blower.

The blower was optimized for high efficiency and increased volume flow rate, by

changing various parameters of the blower geometry. The optimization of the blower,

led to an increase in the volume flow rate of the blower with slight increase in

efficiency. This optimization resulted in an increased performance of the blower. The

methods used for optimizing and their results are further discussed in the report, in

detail. The conclusions of the entire project are stated in later sections.



2. Design Parameter

2.1 Impeller Shape

Impeller shape includes various parameters like impeller inlet diameter (d1), impeller

outlet diameter (d2) and impeller width (b).Volume flow rate (𝑣)̇ of blower is given by,

�̇� = 𝑐𝑟 ∗ π ∗ 𝑑1 ∗ 𝑏 = 𝑐𝑟 ∗ π ∗ 𝑑2 ∗ 𝑏

From above equation it is clear that volume flow rate is directly proportional to d1, d2

and b.

2.2 Blade Geometry

2.2.1 Blade type

There are different types of blade that are being used for centrifugal blowers viz. curved

sheet metal blades, aerofoil shaped blades etc.

2.2.2 Blade angles

Velocity of swirl at inlet and outlet depends on blade angle at inlet and outlet

respectively. The work capacity and pressure rise across impeller is influenced by blade

angles.

2.2.3 Blade thickness

Blade thickness (t) can affect the flow rate, as the flow area is dependent on blade

thickness and volume flow rate is directly proportional to flow area.

2.3 Number of Blades

The number of blades in centrifugal fan can vary from 2 to 64 depending on the

application and size. Too few blades are unable to impose their geometry on flow,

whereas too many blades restrict the flow passage and lead to higher losses.

2.4. Volute Base Circle Radius

It is radius of base circle of involute of scroll casing. It is shown in the figure 2.3. The

base circle radius (r3) is 1.05 to 1.10 times the impeller radius. This vaneless space

before volute decreases turbulence of flow entering the volute casing as well as noise

level



Figure. 2.1: Impeller Geometry

Figure. 2.2: Blade Angles



Figure. 2.3: Volute

Figure. 2.4: Divergence Angle



2.5 Volute Tongue (Nose Clearance)

Theoretically, the logarithmic curve of involute begins at the impeller exit, but in

practice it is not possible. If shifted to the base circle as shown in figure at θt=0o, a

sharp-edged lip will be formed. This is known as volute tongue. Its size has significant

effect on the performance of centrifugal blower in terms of pressure distribution around

impeller, discharge at maximum efficiency and noise level.

2.6 Divergence Angle

The angle made by the inner wall of outlet duct with respect to the tangent at the point

of intersection of the involute with the base circle is known as divergence angle.



3. Computational Fluid Dynamics

The physical aspects of any fluid flow are governed by three fundamental principles:

(1) mass is conserved; (2) Newton's second law (force = mass x acceleration); and (3)

energy is conserved. These fundamental physical principles can be expressed in terms

of basic mathematical equations, which in their most general form are either integral

equations or partial differential equations. Computational fluid dynamics is the art of

replacing the integrals or the partial derivatives (as the case may be) in these equations

with discretized algebraic forms, which in turn are solved to obtain numbers for the flow

field values at discrete points in time and/or space. The end product of CFD is indeed a

collection of numbers, in contrast to a closed-form analytical solution. However, in the

long run, the objective of most engineering analyses, closed form or otherwise, is a

quantitative description of the problem, i.e., numbers.

3.1 Basic Governing Equations

3.1.1 Finite Control Volume

Consider a general flow field as represented by the streamlines in Figure No.3.2 (a).

Let us imagine a closed volume drawn within a finite region of the flow. This volume

defines a control volume V, a control surface S is defined as the closed surface which

bounds the volume. The control volume may be fixed in space with the fluid moving

through it, as shown at the left of Figure No.3.2 (a). Alternatively, the control volume

may be moving with the fluid such that the same fluid particles are always inside it, as

shown at the right of Figure No.3.2 (a). In either case, the control volume is a reasonably

large, finite region of the flow. The fundamental physical principles are applied to the

fluid inside the control volume and to the fluid crossing the control surface (if the

control volume is fixed in space). Therefore, instead of looking at the whole flow field

at once, with the control volume model we limit our attention to just the fluid in the

finite region of the volume itself. The fluid-flow equations that we directly obtain by

applying the fundamental physical principles to a finite control volume are in integral

form. These integral forms of the governing equations can be



Figure 3.1: Fundamentals Principles which form the basis of CFD



manipulated to indirectly obtain partial differential equations. The equations so

obtained from the finite control volume fixed in space (left side of Figure No.3.2 (a)),

in either integral or partial differential form, are called the conservation form of the

governing equations. The equations obtained from the finite control volume moving

with the fluid (right side of Figure No.3.2 (a)), in either integral or partial differential

form, are called the non-conservation form of the governing equations.

3.1.2 The Continuity Equation

Physical principle: Mass is conserved. The governing flow equation which results from

the application of this physical principle to any one of the four models of the flow

shown in Figure No.3.2 (a) and (b) is called the continuity equation. Moreover, in this

section we will carry out in detail the application of this physical principle

Using four of the flow models illustrated in Figure No.3.2 (a) and (b); in this way we

hope to dispel any mystery surrounding the derivation of the governing flow equation.

That is, we will derive the continuity equation four different ways, obtaining in a direct

fashion four different forms of the equation. Then, by indirect manipulation of these

four different forms, we will show that they are all really the same equation. In

addition, we will invoke the idea of conservation versus non-conservation forms,

helping to elucidate the meaning of those words.

3.1.3 The Momentum Equation

Physical principle: F = ma (Newton's second law). The resulting equation is called the

momentum equation. Unlike the derivation of the continuity equation in section 3.3, we

will restrain ourselves and choose only one model of the flow. Specifically, we will

utilize the moving fluid element model because this model is particularly convenient

for the derivation of the momentum equation as well as the energy equation. However,

the momentum and energy equations can be derived using

( ) 0 V t



Figure 3.2: Models of Flow (a) Finite Control Volume Approach (b) Infinitesimal

Fluid Element Approach

Figure 3.3: Infinitesimally small, moving fluid element



any of the other three models of the fluid; as in the case of the continuity equation

developed, each different model of the flow leads directly to a different form of the

momentum and energy equations.

3.1.4 Multiple Reference Frame Model

The MRF model is, perhaps, the simpler of the two approaches for multiple zones. It is

a steady-state approximation in which individual cell zones can be assigned different

rotational and/or translational speeds. The flow in each moving cell zone is solved using

the moving reference frame equations. If the zone is stationary ( ), the equations

reduce to their stationary forms. At the interfaces between cell zones, a local reference

frame transformation is performed to enable flow variables in one zone to be used to

calculate fluxes at the boundary of the adjacent zone.

It should be noted that the MRF approach does not account for the relative motion of a

moving zone with respect to adjacent zones (which may be moving or stationary); the

mesh remains fixed for the computation. This is analogous to freezing the motion of

the moving part in a specific position and observing the instantaneous flow field with

the rotor in that position. Hence, the MRF is often referred to as the "frozen rotor

approach."

While the MRF approach is clearly an approximation, it can provide a reasonable model

of the flow for many applications. For example, the MRF model can be used for turbo

machinery applications in which rotor-stator interaction is relatively weak, and the flow

is relatively uncomplicated at the interface between the moving and stationary zones.

In mixing tanks, for example, since the impeller-baffle interactions are relatively weak,

large-scale transient effects are not present and the MRF model can be used.

yx xx zx x

Du p f

Dt x x y z



Another potential use of the MRF model is to compute a flow field that can be used as

an initial condition for a transient sliding mesh calculation. This eliminates the need for

a startup calculation. The multiple reference frame model should not be used, however,

if it is necessary to actually simulate the transients that may occur in strong rotor-stator

interactions, the sliding mesh model alone should be used.

3.2 Discretization: Finite Volume Method

The Finite Volume Method is a method for representing and evaluating partial

differential equations in the form of algebraic equations [13, 14]. Similar to the finite

difference method, values are calculated at discrete places on a meshed geometry.

"Finite volume" refers to the small volume surrounding each node point on a mesh. In

the finite volume method, volume integrals in a partial differential equation that

contain a divergence term are converted to surface integrals, using the divergence

theorem. These terms are then evaluated as fluxes at the surfaces of each finite volume.

Because the flux entering a given volume is identical to that leaving the adjacent

volume, these methods are conservative. Another advantage of the finite volume

method is that it is easily formulated to allow for unstructured meshes. The method is

used in many computational fluid dynamics packages.

3.3 Realisable K-ε Model

The realizable k-ε model is a relatively recent development. An immediate benefit of

the realizable k-ε model is that it more accurately predicts the spreading rate of both

planar and round jets. It is also likely to provide superior performance for flows

involving rotation, boundary layers under strong adverse pressure gradients,

separation, and recirculation. The realizable model provides the best performance of

all the - model versions for several validations of separated flows and flows with

complex secondary flow features. This model has been extensively validated for a

wide range of flows including rotating homogeneous shear flows, free flows including

jets and mixing layers, channel and boundary layer flows, and separated flows. For all

these cases, the performance of the model has been found to be substantially better

than that of the standard - model. Especially noteworthy is the fact that the realizable

- model resolves the round-jet anomaly; i.e., it predicts the spreading rate for



axisymmetric jets as well as that for planar jets. The results are obtained with the

solution of the continuity and N-S equations along with the equations for the selected

turbulence model. For the solution of the turbulence, RNG k- ε model is selected. This

model is applicable to complex shear flows involving rapid strains, moderate swirl,

vortices and locally translational flows (e.g., boundary layer separation, massive

separation, room ventilation etc.). After the boundary conditions are set, the solution

and the turbulence model are specified. The solution is obtained by segregated solver

with absolute velocity formulation, three dimensions in space and steady in time. The

turbulence model is selected as RNG k- ε model and the swirl dominated flow feature

is activated to enhance the accuracy for this application. Standard wall functions are

used for near wall treatment. For the numerical solution of momentum, turbulence

kinetic energy and turbulence dissipation rate equations, first order upwind

discretization scheme is selected. Because the flow across the fan has high rates of

swirl and turbulence, and unstructured mesh is constructed in the solution domain, the

flow is not aligned with the grid, thus second order discretization is preferred for higher

accuracy. Linear option is selected for the pressure interpolation scheme that simply

averages the pressures in adjacent cells to obtain face pressure values. To obtain the

pressure field, SIMPLE algorithm is used under pressure-velocity coupling drop-down

list. This algorithm uses a relationship between pressure and velocity corrections to

enforce mass conservation and obtain pressure field. The details of the algorithm can

be found in literature. Another thing to be controlled for the solution is the under-

relaxation factors. Because of the non-linearity of the equation set being solved by the

segregated solver, it is necessary to control the change of solution variables at each

iteration, which is typically done by under-relaxation. For most flows, the default

under-relaxation factors do not usually require modification. If unstable or divergent

behavior is observed, however, the under- relaxation factors for pressure, momentum,

k and ε from their default values may need to be reduced. During the computations for

the axial fan performance, default values were kept unless a divergent and unstable

trend had been observed.



3.4 CAD Generation

CATIA v5R19 was used for geometry creation. ANSYS Blade Gen served the purpose

of generating blade profile with required variations. Blade Gen is a robust software

provided by ANSYS for blade profile generation of various turbo machines. Parametric

models of impeller blades for blowers, pumps, turbines can be built by entering the

necessary design parameters at a click of a button. These blade profiles were then

imported into CATIA to build a complete 2D/3D models of the blower.

3.5 Meshing

3.5.1 Surface Meshing

Surface meshing is the most important step in CFD workflow. A good surface mesh is

essential for a good volume mesh, which subsequently leads to a better computational

solution. It was important that all the necessary i.e. flow affecting features of the

geometry be resolved satisfactorily. Also the unimportant features viz., surface fillets,

free edges should be repaired/disfeatured to keep the mesh count at a solvable level.

Geometry clean-up involved removing free edges and duplicate surfaces. It also

included creating proper dependence between different surface present in the model.

An unstructured mesh was chosen against structured for a faster mesh creation process.

Unstructured mesh also helped in inserting cells in required critical regions of the flow

for a better flow resolution. This flexibility is not supported by structured mesh.

Triangular mesh was preferred over quadrilateral mesh as it preserved the geometric

features to a much greater extent with the best compromise between mesh quality and

cell count.

Hypermesh aided in the initial surface clean-up followed by surface meshing. Special

care was taken to resolve the blade profiles in the 3D model. Appropriate growth rate

was applied on larger surfaces to keep the cell count low.

ANSYS Workbench meshing was utilized for meshing of the 2D blower models. It was

used over Hypermesh for 2D models as mesh generation process was much faster in

workbench. Also the mesh quality and solution results obtained on both these softwares

showed little variation.



3.5.2 Volume Meshing

Volume meshing involves discretizing the flow volume into finite control volumes to

aid subsequent CFD solution. The cells types used in volume mesh generation are

Tetrahedrals, Hexahedrals, Hybrid and Polyhedra.

Tetrahedral cells were chosen over the rest for the following reasons:

They help in resolving the geometric features.

Polyhedral cell help in considerably reducing the cell count, but the softwares

used in its generation could not be accessed.

Tgrid is a Delaunay based mesher provided by ANSYS. It aided in fast volume mesh

generation from surface mesh generated in Hypermesh.

3.6 Boundary Conditions

3.6.1 Pressure Inlet

Pressure inlet boundary conditions are used to define the fluid pressure at flow inlets,

along with all other scalar properties of the flow. They are suitable for both

incompressible and compressible flow calculations. Pressure inlet boundary conditions

can be used when the inlet pressure is known but the flow rate and/or velocity is not

known. This situation may arise in many practical situations, including buoyancy-

driven flows. Pressure inlet boundary conditions can also be used to define a “free”

boundary in an external or unconfined flow.

Pressure Inlet conditions were defined with turbulent intensity ratio of 5% and viscosity

ratio of 10%.

3.6.2 Pressure Outlet

Pressure outlet boundary conditions require the specification of a static (gauge) pressure

at the outlet boundary. The value of the specified static pressure is used only while the

flow is subsonic. Should the flow become locally supersonic, the specified pressure will



Figure 3.4 (a): Hexahedral Mesh

Figure 3.4(b): Tetrahedral Mesh

Figure 3.4 (c): Hybrid Mesh

Figure 3.4 (d):Polyhedral Mesh



no longer be used; pressure will be extrapolated from the flow in the interior. All other

flow quantities are extrapolated from the interior.

A set of “backflow” conditions is also specified should the flow reverse direction at the

pressure outlet boundary during the solution process. Convergence difficulties will be

minimized with realistic values for the backflow quantities.

3.6.3 Wall Boundary Condition

Wall boundary conditions were used to bound fluid and solid regions. In viscous flows,

the no-slip boundary condition was enforced at walls by default, but a tangential

velocity component in terms of the translational or rotational motion of the wall

boundary, or model a “slip” wall by specifying shear can also be used.

3.7 Solution Methods

3.7.1 Coupled Solver

The coupled approach offers some advantages over the non-coupled or segregated

approach. The coupled scheme obtains a robust and efficient single phase

implementation for steady-state flows, with superior performance compared to the

segregated solution schemes. This pressure-based coupled algorithm offered an

alternative to the density-based and pressure-based segregated algorithm with

SIMPLE-type pressure-velocity coupling.

The pressure-based segregated algorithm solves the momentum equation and pressure

correction equations separately. This semi-implicit solution method results in slow

convergence.

The coupled algorithm solves the momentum and pressure-based continuity equations

together thus results in a faster convergence.

3.7.2 Second Order Upwind Scheme

When second-order accuracy is desired, quantities at cell faces are computed using a

multidimensional linear reconstruction approach. In this approach, higher-order



accuracy is achieved at cell faces through a Taylor series expansion of the cell-centred

solution about the cell centroid. Thus when second-order upwinding is selected, the

face value is computed using the following expression:

Where ∅ and ∇∅ are the cell-centered value and its gradient in the upstream cell, and

∇�⃗� is the displacement vector from the upstream cell centroid to the face centroid. This

formulation requires the determination of the gradient in each cell and results in a more

accurate solution compared to first order upwind scheme.

3.8 Noise

Passenger comfort is crucial aspect of an automobile. Noise generated by various sub-

systems play an important part in this domain. The HVAC blower is one such source

of noise.

The optimized design of the blower obtained would have to be changed if it does not

satisfy the noise limit decided upon. It was decided to accept the optimized model if

the noise generated was below or equal to the noise obtained in the original

configuration.

3.8.1 Noise Sources

Monopole

A monopole is a source which radiates sound equally in all directions. A sphere whose

radius alternately expands and contracts sinusoidal can be termed as an example of

monopole. The monopole source creates a sound wave by alternately introducing and

removing fluid into the surrounding area.

Dipole

A dipole source consists of two monopole sources of equal strength but opposite phase

and separated by a small distance compared with the wavelength of sound. While one

source expands the other source contracts. The result is that the fluid (air) near the two

source



Figure 3.5 Monopole

Figure 3.6: Dipole

Figure 3.7: Quadrupole



sloshes back and forth to produce the sound. A dipole source does not radiate sound in

all directions equally.

Quadrupole

Two opposite dipoles make up a quadrupole source. In a quadrupole arrangement the

two dipoles do not lie along the same line (four monopoles with alternating phase at the

corners of a square). The directivity pattern for a lateral quadrupole looks like a clover-

leaf pattern; sound is radiated well in front of each monopole source, but sound is

cancelled at points equidistant from adjacent opposite monopoles.

The aeroacoustic generation mechanisms can be listed as follows: quadrupolar noise,

related with turbulence shear stresses; dipolar noise, produced by steady and unsteady

forces exerted by the moving surfaces on the flow; and monopolar or thickness noise,

due to the volume displacement of the moving surfaces.

Among the above mentioned mechanisms the steady and unsteady forces exerted on

the impeller blades and interaction of the unsteady flow from the impeller with the

volute tongue are of particular importance. The noise is also generated by flow

separation, vortex shedding and secondary unsteady flows. Impeller blades while

rotating displace the fluid equivalent to the blade volume this generates sound at blade

passing frequency and its higher harmonics.

3.8.2 Noise Study

ANSYS Fluent provides the following models for aeroacoustic studies

Ffowcs Williams-Hawkings (FW-H)

The Ffowcs Williams-Hawkings (FW-H) acoustics simulation is the preferred model

for mid- to far-field noise prediction. This model is able to calculate the mid- to far-

field sound signal radiated from near-field flow data provided by a CFD solution. The

goal is to predict small amplitude acoustic pressure fluctuations at the receivers’

locations.



The FW-H model is used only to predict the propagation of sound in free space. It does

not include effects such as sound reflections, refraction, or material property change.

The FW-H model however requires transient flow field data. Transient analysis with the

computers at our disposal was not possible and would have extended the project

duration well beyond the deadline. A much simpler and less time consuming model was

thus required to compare the designs.

Broadband Noise

In the frequency domain, a broadband noise has a continuous spectrum, where the

acoustic energy is continuously distributed at all frequencies in a given range. These

models can compute the location and strength of the main sources of sound generated

aerodynamically. They use flow field quantities from Reynolds-Averaged Navier-

Stokes (RANS) equations, which can provide turbulence time scale and turbulence

length scale coupled with acoustic correlations or noise source terms in few equations.

Because these broadband noise models use RANS simulations, the required

computational time is very small.

The noise associated with large-scale (or resolved) flow features, such as vortex

shedding, do not fall under this category and therefore are unsuitable to be quantified

by these correlations. However, they can be used to:

Identify the location of flow generated aeroacoustic sources.

Approximate the associated sound power or dB level

They provide a reasonable basis on which to

Compare one component design with another.

Decide mesh refinement locations for transient calculations.

The Broadband noise model was thus chosen to compare the optimized design with

the original one, in terms of noise generated, for its low computational cost and

reasonably accurate results.



3.9 Post Processing

Following parameters are found out using post pressing options in Fluent.

3.9.1 Volume Flow Rate (Q)

Volume Flow Rate is the volume of air being blown out of the blower per second. It is

calculated at the outlet. The unit used is cmh that is cubic meter per hour.

3.9.2 Torque (T)

Torque on the impeller due to resistance offered by the air is calculated. This value has

units Nm. It is further used to calculate the power consumed and the efficiency.

3.9.3 Total Pressure (P)

The Total Pressure (Static + Dynamic Pressure) at the out let is calculated in Pascal’s.

This is further used to calculate the power output and the efficiency of the blower.

3.9.4 Efficiency (𝜼)

Efficiency of the blower is calculated by,

𝜂 =𝑃𝑜𝑤𝑒𝑟 𝑂𝑢𝑡𝑝𝑢𝑡

𝑃𝑜𝑤𝑒𝑟 𝐼𝑛𝑝𝑢𝑡=

𝑃 ∗ 𝑄

2𝜋 ∗ 𝑁 ∗ 𝑇60

3.9.5 Power Consumed (Pi)

The power consumed can be calculated by,

𝑃𝑖 =2𝜋 ∗ 𝑁 ∗ 𝑇

60

3.9.6 Noise

Sound pressure levels at all the nodes were obtained by activating the broadband noise

model for some iterations of steady state analysis. These node values were then



averaged using the following formula to calculate the effective noise generated by the

blower design.

Ae = 10*log10( ∑ 10𝐴𝑖/10)𝑛𝑖=1

Where, Ae is the effective sound pressure level

Ai is the sound pressure level at each node

In the cases studied, this noise mechanism is predominant: a strong source of noise

caused by the interaction between the fluctuating flow leaving the impeller and the

volute tongue is appreciated. This noise source is revealed by very high levels of the

coherent level concentrated in a small region in the vicinity of the volute tongue. The

position and extension of this interaction region and the magnitude of the corresponding

levels strongly depend on the flow rate.

The unsteady forces on the blades are generated by a spatially non-uniform flow field,

pressure fluctuations in the turbulent blade boundary layer, vortex separation from the

blades and secondary flows. The acoustic efficiency of the unsteady forces on the

blades is high, and hence, they represent an important contribution to the radiated fan

noise. This can be seen from the high sound pressure levels at the exit region of the

impeller.



4. Validation

Validation of CFD results with the experimental data forms an important part of

successful CFD analysis. A successful validation certifies that the parameters chosen

for CFD analysis viz., solution methods, mesh size and quality, turbulence models were

appropriate. The following procedure was adopted for validation.

4.1 Experimental Data

The fan curve is the most important characteristic of the blower with respect to HVAC

systems. It is a plot of Volume flow rate versus the static pressure rise across the outlet.

The fan curve was thus chosen for validation. For an ideal fan curve the drop of volume

flow rate for a corresponding increase in the static pressure should be small. The static

pressure at outlet corresponds to the resistance offered by the duct and other

components that follow the blower in car HVAC system.

Experimental data of the fan curve for the blower was obtained from Subros Ltd.

4.2 CFD results of the Original Model (3D)

CAD data of the original blower model was supplied by Subros Ltd. This data was

distorted and incomplete to a great extent and needed a lot of corrections and clean-up.

The data was imported into Hypermesh for initial surface clean-up.

After the CAD clean-up surface meshing was also carried out in Hypermesh. For

surface meshing a mesh size of 0.4 mm was selected for the blade tips of impeller blades

at the leading edge. Rest of the blades were meshed for a target mesh size of 1 mm from

an initial mesh size of 0.4 mm applied to the blade tips. The central conical portion was

meshed with a target mesh size of 4 mm. A growth rate of 1.1 was chosen for a smooth

transition of mesh from fine to coarse and for the mesh quality to be kept good. The

minimum and maximum size of mesh on scroll were 2 mm and 6 mm respectively.

Again a growth rate of 1.1 was used for the scroll. The criteria for surface meshing was

to keep the cell skewness below 0.5.

With the surface mesh done in Hypermesh, it was imported into Tgrid for subsequent

volume meshing. The tetrahedral mesh was generated in Tgrid. In volume mesh the cell



skewness should be 0.95 or lower for the mesh to be acceptable for CFD analysis. We

chose skewness of 0.8 or below for faster convergence of the solution. It was decided

to keep the cell count at 3.5 million or below to make the calculations possible for

machines at our disposal. The cell count obtained after volume meshing was 3.2

million.

The volume mesh thus generated was imported into ANSYS Fluent for further pre-

processing and CFD analysis. Boundary conditions of Pressure inlet and pressure outlet

were chosen for the inlet and outlet respectively. The impeller was assigned a moving

wall boundary condition with rotational velocity of 0 rpm with respect to adjacent MRF

region. The MRF cell zone was given a moving reference frame with a rotational

velocity of 3000 rpm. Realizable k-ε turbulence model was used for turbulence

modelling along with enhanced wall functions. Coupled solver was used with second

order upwind scheme for accurate and fast converging solution. The convergence

criteria chosen was stabilization of the solution parameter viz., the volume flow rate at

the outlet.

To plot the fan curve different back pressures were assigned at the outlet and the volume

flow rate was noted.



Figure 4.1: Scroll Casing Surface Mesh generated in Hypermesh

Figure 4.2: Impeller Surface Mesh generated in Hypermesh

Figure 4.4: Cross Section of Volume Mesh generated in TGrid (b)

Figure 4.3: Cross Section of Volume Mesh generated in TGrid (a)



0

100

200

300

400

500

600

700

800

900

1000

0 30 60 90 120 150 180 210 240 270 300 330 360 390 420 450 480 510 540 570 600 630

Vo

lum

e Fl

ow

Rat

e (c

mh

)

Static Pressure (Pa)

Characteristic Curve (Original)

Graph 4.1: Fan Curve Plotted from CFD Results



4.3 CFD results of the Original Model (2D)

To study the effect of variations of various design parameters on volume flow rate and

efficiency many models were required to be constructed. This would have considerably

increased the optimization period. To make the process faster a 2D section of blower

was used for optimization. To validate the results for 2D and 3D analysis a 2D analysis

of original blower model was carried out.

Mesh size of 0.5 mm was chosen for the MRF region while the scroll region was

meshed for a target mesh size of 1 mm using growth rate of 1.05.

The solution parameters used for 3D analysis were used for 2D models as well. Depth

of 0.075 m was applied under the reference values tab which is equal to the actual width

of the blower.

For the original model volume flow rate of 1114 cmh was obtained for zero back

pressure.



Figure 4.5: 2D Geometry

Figure 4.6: Tetrahedral Mesh for 2D Model



Figure 4.7: Pressure Contours

Figure 4.8: Velocity Contours



4.4 Correlation

4.4.1 CFD (3D) and Experimental Data

The fan curve obtained from CFD results was superimposed on the Experimental fan

curve received from Subros Ltd. The results show an acceptable correlation between

CFD and practical data, with a maximum error of 6% for the complete range of the

curve.

4.4.2 CFD (2D) and CFD (3D) Results

The flow rate for 3D model obtained from CFD results was 907 cmh. The 2D model

showed a flow rate of 1114 cmh. Thus there was an increase in flow rate by making the

transition from 3D to 2D version of the blower. This can be attributed to the increase in

impeller width for 2D model as the depth of 0.075 m was applied to all the regions by

Fluent while the impeller width in the 3D model is 0.062 m. Also the clearance between

the impeller and the scroll at both the ends along the width is neglected which aids in

recirculation and subsequent decrease in volume flow rate.

Even with these shortcomings there is a mere 22.8% difference between the 3D and the

2D models of the same blower. The correlation was thus concluded to be successful.



Graph 4.2: CFD (3D) results superimposed over experimental results



5. Optimization

Four parameters, viz. Leading Edge Camber Angle, Trailing Edge Camber Angle, Nose

Radius and Diffuser Angle were selected to study their effects on Volume Flow Rate

and Efficiency of Centrifugal Blower.

5.1 Parameter Values and Corresponding Deltas

The original values of parameters and their corresponding values of deltas are given

below:

Definition

Main Parameters Original Delta

Beta 1 -21.2 4

Beta 2 74.32 4

Nose Radius 8 2.5

Diffuser Angle 4 4

Table 5.1: Definition of Geometry

Delta is the value by which the parameter is changed for creating different models.

5.2 CAD Models

Various CAD models were created based on the data given below

Parameters

Sr. No. Beta 1 Beta 2 Nose Radius Diffuser Angle

A B C D

0 0 -21.2 0 74.32 0 8 0 4

1 -1 -25.2 -1 70.32 -1 5.5 -1 0

2 1 -17.2 -1 70.32 -1 5.5 -1 0

3 -1 -25.2 1 78.32 -1 5.5 -1 0

4 1 -17.2 1 78.32 -1 5.5 -1 0

5 -1 -25.2 -1 70.32 1 10.5 -1 0

6 1 -17.2 -1 70.32 1 10.5 -1 0

7 -1 -25.2 1 78.32 1 10.5 -1 0

8 1 -17.2 1 78.32 1 10.5 -1 0

9 -1 -25.2 -1 70.32 -1 5.5 1 8

10 1 -17.2 -1 70.32 -1 5.5 1 8

11 -1 -25.2 1 78.32 -1 5.5 1 8

12 1 -17.2 1 78.32 -1 5.5 1 8

13 -1 -25.2 -1 70.32 1 10.5 1 8

14 1 -17.2 -1 70.32 1 10.5 1 8

15 -1 -25.2 1 78.32 1 10.5 1 8

16 1 -17.2 1 78.32 1 10.5 1 8

Table 5.2: Geometrical Parameters of CAD Models



5.3 CFD Results

Following results were obtained based on the CFD Analysis carried out on 17 models.

Result

Sr. No. Volume Flow Rate Torque Pressure Efficiency

V T P E

0 1470.5097 2.94253 1845.3284 0.815395455

1 1306.7777 2.09103 1528.1399 0.844407716

2 1306.2757 2.08955 1526.5261 0.843789158

3 1459.259 3.65172 1872.7397 0.661698201

4 1601.4502 4.39403 2261.1074 0.728650451

5 1307.8172 2.12829 1530.6255 0.831635101

6 1305.5556 2.10548 1532.2373 0.840074706

7 1604.6209 4.50798 2285.0754 0.719181696

8 1606.8366 4.53997 2290.5022 0.716798474

9 1376.8345 2.28736 1576.1071 0.838842778

10 1377.7147 2.29028 1577.8583 0.839240313

11 1677.7487 4.85841 2318.3135 0.707868423

12 1681.1829 4.91125 2327.2102 0.704378612

13 1350.7039 2.23254 1562.7811 0.836000779

14 1351.9092 2.23567 1564.0284 0.836242211

15 1660.9196 4.77937 2329.0481 0.715655546

16 1663.316 4.8187 2336.3867 0.713078312

Table 5.3:Results

Formula used for calculating the efficiency:

𝜂 =

𝑉 ∗ 𝑃3600

2𝜋 ∗ 3000 ∗ 𝑇60

5.4 Slope Analysis

The effect of individual parameters and their combinations was analyzed based on

their +1 and -1 values described in the previous tables.

Formula for finding slope of a given parameter:

𝑆𝑙𝑜𝑝𝑒 =𝑉𝑎𝑙𝑢𝑒+1 − 𝑉𝑎𝑙𝑢𝑒−1

2



In cases where combined effect of 2 or more parameters is to be considered the

multiplication of its +1 and -1 values is taken for e.g.:

(+1 ∗ +1) = +1

and

(+1 ∗ −1) = −1

The percent slope is calculated by the formula:

% 𝑆𝑙𝑜𝑝𝑒 =𝑆𝑙𝑜𝑝𝑒𝑖

∑ 𝑎𝑏𝑠(𝑆𝑙𝑜𝑝𝑒𝑖)15𝑖=1

Control

Variables Volume Flow Rate Efficiency

Sr.

No.

Control

Variables Average Slope Average Slope

+1 -1 S1 %

Slope +1 -1 S2

%

Slope

1 A 1486.78 1468.09 9.34746 0.02967 0.77778 0.76941 0.00419 0.03788

2 B 1619.417 1335.45 141.984 0.45074 0.70841 0.83878 -0.0652 -0.59

3 C 1481.46 1473.41 4.02723 0.01278 0.77608 0.77111 0.00249 0.02251

4 D 1517.541 1437.32 40.1085 0.12733 0.77391 0.77328 0.00032 0.00287

5 AB 1512.592 1468 22.2956 0.07078 0.76705 0.77047 -0.0017 -0.0155

6 AC 1468.53 1486.34 -8.903 -0.0283 0.76988 0.77732 -0.0037 -0.0337

7 AD 1469.075 1485.79 -8.3579 -0.0265 0.76873 0.77846 -0.0049 -0.044

8 BC 1487.912 1466.95 10.4793 0.03327 0.77887 0.76832 0.00528 0.04777

9 BD 1488.699 1466.17 11.2665 0.03577 0.77511 0.77208 0.00151 0.01371

10 CD 1462.576 1492.29 -14.856 -0.0472 0.77244 0.77475 -0.0012 -0.0105

11 ABC 1468.709 1486.16 -8.7237 -0.0277 0.76876 0.77843 -0.0048 -0.0437

12 ABD 1468.469 1486.4 -8.9641 -0.0285 0.76963 0.77756 -0.004 -0.0359

13 ACD 1486.247 1468.62 8.8139 0.02798 0.77741 0.76978 0.00381 0.03453

14 BCD 1469.108 1485.76 -8.3243 -0.0264 0.77111 0.77608 -0.0025 -0.0225

15 ABCD 1485.986 1468.88 8.55331 0.02715 0.77856 0.76863 0.00497 0.04495

Sum 315.005 Sum 0.11048

Table 5.4: Slope Calculations



Graph 5.2 (b): Efficiency: Individual Contribution

Graph 5.1: Slope Values

3%

45%

1%

13%

7%

-3%

-3%

3%

3%

-5%

-3%

-3% 3% -4%3% A

B

C

D

AB

AC

AD

BC

BD

CD

ABC

ABD

ACD

BCD

ABCD

4%

-59%

2%

0%

-2%

-3%

-4%

5%

1%

-1% -4%

-4%4%

-2%5%

A

B

C

D

AB

AC

AD

BC

BD

CD

ABC

ABD

ACD

BCD

ABCD

Graph 5.2 (a): Volume Flow Rate: Individual Contribution

A B C D AB AC AD BC BD CDABC

ABD

ACD

BCD

ABCD

Volume Flow Rate 0.0290.4500.0120.1270.070-0.02 -0.02 0.0330.035-0.04 -0.02 -0.02 0.028-0.02 0.027

Efficiency 0.037-0.59 0.0220.002-0.01 -0.03 -0.04 0.0470.013-0.01 -0.04 -0.03 0.034-0.02 0.045

-0.8000

-0.6000

-0.4000

-0.2000

0.0000

0.2000

0.4000

0.6000



5.5 Conclusion

Following conclusions were drawn:

1. Parameter B is the most affecting parameter, Volume Flow Rate increases

with it and efficiency decreases.

2. In most of the cases efficiency decreases with increase in Volume Flow Rate.

It helped in making assumptions for further analysis using Taguchi Method.

5.6 Optimization

Every experimenter has to plan and conduct experiments to obtain enough and relevant

data so that he can infer the science behind the observed phenomenon. He can do so by,

5.7 Different Approaches

5.7.1 Trial and error approach:

It involves performing a series of experiments on different physical models each of

which gives some understanding. Measurements have to be made after each

experiment, to decide the amount of variation of each parameter to obtain the desired

results.

The method is much time consuming compared to other methods, and also requires

more cost as every model that has to be rested is needed to be manufactured.

5.7.2 Design of experiments :

It is a well-planned set of experiments, in which all parameters of interest are varied

over a specified range, is a much better approach to obtain systematic data.

Mathematically speaking, such a complete set of experiments ought to give desired

results. Usually the number of experiments and resources (materials and time) required

are prohibitively large. Often the experimenter decides to perform a subset of the

complete set of experiments to save on time and money. However, it does not easily

lend itself to understanding of science behind the phenomenon. The analysis is not very

easy (though it may be easy for the mathematician/statistician) and thus effects of

various parameters on the observed data are not readily apparent. In many cases,



particularly those in which some optimization is required, the method does not point to

the best settings of parameters.

5.7.3 Taguchi Method:

Dr. Taguchi of Nippon Telephones and Telegraph Company, Japan has developed a

method based on "Orthogonal Array" experiments which gives much reduced

"variance" for the experiment with "optimum settings" of control parameters. Thus

combining the Design of Experiments with optimization of control parameters to obtain

best results is achieved in the Taguchi Method. "Orthogonal Arrays" (OA) provide a

set of well balanced (minimum) experiments and Dr. Taguchi's Signal-to-Noise ratios

(S/N), which are log functions of desired output, serve as objective functions for

optimization, help in data analysis and prediction of optimum results.

5.8 Taguchi Method:

Taguchi method of robust parameter design is one of statistical quality control

techniques in which the levels of controllable factors or input parameters are selected

to nullify the variation in responses owing to uncontrollable factors.

5.8.1 Quality Loss Function

The experiments are performed as per standard orthogonal array in the Taguchi

method. Quality loss values for each quality characteristic are calculated utilizing the

experimental values of the quality characteristic in all experiments. Based on the nature

of the quality characteristics, the quality loss function can be of several types. In the

present study the higher value of the mass flow rate and the efficiency are desired. Thus,

larger the better principles are considered to maximize the flow rate and efficiency air

blower. The larger the better case can be given by

𝐿𝑖𝑗 =1

𝑛∑

1

𝑦𝑖𝑗𝑘2

𝑛

𝑘−1

Where Lij is the loss function of the ith response variable in the jth experiment, yijk is the

experimental value of the ith response variable in the jth experiment at the kth test and n



is the number of repeated runs for each experiment. In this project, i = 1, 2. j =1, 2, 3…

16. n =1.

5.8.2 Normalization

Because each response variable has a different unit of measurement, it is

necessary to normalize the loss function for each response variable as follows,

𝑁𝑖𝑗 =𝐿𝑖𝑗

𝐿𝑖

Where Nij and Lij are the normalized loss function and loss function for the ith response

variable in jth experiment, respectively and Li is the maximum loss function for the ith

response variable.

5.8.3 Weighting Method

Weighting method was applied to determine the importance of each normalized

loss function. The total normalized quality loss function can be given as,

𝑇𝑗 = ∑ 𝑤𝑖𝑁𝑖𝑗

𝑚

𝑖=1

Where wi is the weighting factor for the ith response variable and m is the number of

response variables. When more than one response variable is optimized for a certain

problem, the relative importance of each response variable can be determined through

the weighting values. For instance, if the weighting values are 0.50 and 0.50 for the two

response variable, the both response variables will have the same importance. For the

combination of 0.40 and 0.60, the latter response variable is more important than the

former one. The sum of the weighting values is equal to 1.

5.8.4 MRSN

The multiple response signal to noise (MRSN) ratio is utilized to represent the quality

index at each design point. The total loss function is changed to a multi response signal

to noise ratio by using the following equation.

𝜂𝑗 = −10log (𝑇𝑗)



According to the MRSN ratio for the assigned weighting factors, the optimal

level of combinations can be confirmed by the conventional Taguchi method.

5.9 Setup

Variable Name Symbol Level

1 2 3 4 5

Trailing Edge Camber Angle(deg) A 72 74 75 76 77

Stagger Angle (deg) B 31 34 37 40 43

Nose Radius (mm) C 3 4 5 6 8

Divergence Angle (deg) D 0 2 4 7 8

Table 5.5 :Factors and levels table of experiment

Table 5.6 Orthogonal Array

Control Parameters

Sr.

No. A B C D

1 1 72 1 31 1 3 1 0

2 1 72 2 34 2 4 2 2

3 1 72 3 37 3 5 3 4

4 1 72 4 40 4 6 4 7

5 1 72 5 43 5 8 5 8

6 2 74 1 31 2 4 3 4

7 2 74 2 34 3 5 4 7

8 2 74 3 37 4 6 5 8

9 2 74 4 40 5 8 1 0

10 2 74 5 43 1 3 2 2

11 3 75 1 31 3 5 5 8

12 3 75 2 34 4 6 1 0

13 3 75 3 37 5 8 2 2

14 3 75 4 40 1 3 3 4

15 3 75 5 43 2 4 4 7

16 4 76 1 31 4 6 2 2

17 4 76 2 34 5 8 3 4

18 4 76 3 37 1 3 4 7

19 4 76 4 40 2 4 5 8

20 4 76 5 43 3 5 1 0

21 5 77 1 31 5 8 4 7

22 5 77 2 34 1 3 5 8

23 5 77 3 37 2 4 1 0

24 5 77 4 40 3 5 2 2

25 5 77 5 43 4 6 3 4



5.10 Results

Variable Name Symbol

Volume Flow Rate (cmh) RV1

Efficiency RV2

Table 5.7 Response Variables

Response Variable

Sr. No. RV1 RV2

1 1397.100 83.810

2 1405.300 83.250

3 1413.700 82.840

4 1426.100 82.280

5 1394.700 81.340

6 1497.200 81.930

7 1514.600 81.510

8 1515.000 81.130

9 1409.200 81.300

10 1505.500 79.980

11 1556.060 80.041

12 1464.211 80.418

13 1463.124 79.865

14 1565.280 79.011

15 1579.802 78.297

16 1527.121 78.913

17 1534.943 77.223

18 1640.337 77.467

19 1637.727 77.037

20 1538.684 77.129

21 1582.424 76.210

22 1694.199 74.707

23 1473.599 70.765

24 1599.775 74.773

25 1607.666 74.223

Table 5.8 Response Variables Values



Quality Loss Values Normalized Quality Loss Values

Sr.

No. RV1 RV2 RV1 RV2

1 1951888.410 7024.116 0.680027 1.000000

2 1974868.090 6930.563 0.688033 0.986681

3 1998547.690 6862.466 0.696283 0.976986

4 2033761.210 6769.998 0.708551 0.963822

5 1945188.090 6616.196 0.677693 0.941926

6 2241607.840 6712.525 0.780964 0.955640

7 2294013.160 6643.880 0.799221 0.945867

8 2295225.000 6582.077 0.799643 0.937068

9 1985844.640 6609.690 0.691857 0.941000

10 2266530.250 6396.800 0.789646 0.910691

11 2421322.724 6406.589 0.843575 0.912085

12 2143912.974 6467.010 0.746927 0.920687

13 2140730.669 6378.498 0.745818 0.908085

14 2450100.852 6242.692 0.853601 0.888751

15 2495774.359 6130.402 0.869514 0.872765

16 2332099.465 6227.288 0.812490 0.886558

17 2356051.241 5963.355 0.820835 0.848983

18 2690705.802 6001.162 0.937427 0.854365

19 2682150.054 5934.733 0.934446 0.844908

20 2367548.760 5948.947 0.824841 0.846932

21 2504064.766 5807.926 0.872402 0.826855

22 2870310.590 5581.193 1.000000 0.794576

23 2171492.834 5007.679 0.756536 0.712927

24 2559278.451 5591.006 0.891638 0.795973

25 2584589.646 5509.067 0.900456 0.784308

Table 5.9 Quality loss fuctions and normalized quality loss values

Response Variable RV1 RV2

Weights 0.05 0.95

Table 5.10 Weighing Factors



Total Normalized Quality Values MRSN

Sr. No.

1 0.984001 -0.070043

2 0.971749 -0.124460

3 0.962951 -0.163957

4 0.951059 -0.217927

5 0.928714 -0.321180

6 0.946906 -0.236931

7 0.938535 -0.275496

8 0.930197 -0.314250

9 0.928542 -0.321983

10 0.904639 -0.435247

11 0.908659 -0.415989

12 0.911999 -0.400058

13 0.899972 -0.457709

14 0.886994 -0.520794

15 0.872602 -0.591836

16 0.882855 -0.541107

17 0.847576 -0.718216

18 0.858519 -0.662503

19 0.849385 -0.708954

20 0.845827 -0.727183

21 0.829132 -0.813761

22 0.804847 -0.942866

23 0.715107 -1.456289

24 0.800756 -0.964997

25 0.790115 -1.023097

Table 5.11 Total normalized quality values and multiple signal to noise ratio

5.11 Conclusion:

OPTIMUM COMBINATION

Control Variable Value Levels

A -0.179514 1

B -0.415566 1

C -0.499288 4

D -0.504704 2

Table 5.12 Optimal combination



6. Results and Conclusion

6.1 Optimised Model

The Taguchi method suggested that the optimum combination of parameters was found

to be:

Parameter Level No. Value

Trailing Edge Camber Angle A 1 72

Stagger Angle B 1 31

Nose Radius C 4 6

Divergence Angle D 2 2

Table.6.1: Optimised Parameters

6.2 Comparisions

Geometry

Parameter Original Optimised Change

Trailing Edge Camber Angle A 74.32 72 - 2.32

Stagger Angle B 31 31 0.00

Nose Radius C 8 6 - 2.00

Divergence Angle D 0 2 - 2.00

No. of Blades E 44 42 -2

Volume Flow Rate

For Power Input of 120 W Original Optimised Change

(CMH) 441.89 469.69 + 27.80

Efficiency


(%) 64.67 67.34 + 2.67

RPM


(RPM) 3000 2815 - 185

Noise


(dB) 127.02 126.97 - 0.05

Table 6.2: Comparison of Various Parameters with original



Figure 6.1(a): Acoustic Power Level- Original Model

Figure 6.1(b): Acoustic Power Level- Optimized Model



Graph 6.1: Comparison of Volume Flow Rates of two models

0

100

200

300

400

500

600

700

800

900

1000

0

40

80

120

160

200

240

280

320

360

400

440

480

520

560

600

Flo

w R

ate

(cm

h)

Static Pressure (Pa)

Characteristic Curve

(Optimized)

Characteristic Curve

(Original)



6.3 Conclusion

The experimental data for original blower configuration was first validated with CFD

simulations. Subsequent modifications were then carried out to the impeller and scroll

casing geometry to improve the efficiency and flow rate for the same input power. Use

of aerofoil for the impeller blade geometry had most significant impact on the efficiency

and flow rate. Also changes in the scroll casing divergence angle were made to achieve

the desired goal. Design of Experiment and Taguchi method considerably reduced the

number of simulations required to complete the objective. These methods also showed

the most significant parameter on efficiency and flow rate.

An increase of 28.7 cmh and an improvement of 2.67% in efficiency, was achieved for

the same input power of 120 W. CFD techniques proved crucial in carrying out this

optimization.



7. Future Work

Parameters considered for this study were limited to four due to time constraints. A

more elaborate optimization work can be carried out by increasing the number of design

parameters. Also variation in blade geometry by using different curves is possible.

Broadband noise source model, which uses steady state simulation data to predict noise,

was used for this study. A more detailed study of noise propagation and noise

generation sources is possible with use of transient simulations. This transient data can

be used to predict noise power level using Ffowcs Williams-Hawkings or exporting this

data to MSC Actran to visualize noise propagation. With better computational power

this study can be carried out in the future.



References

Papers

1. “Modeling and Multi-Objective Optimization of Forward-Curved Blade

Centrifugal Fans Using CFD and Neural Networks”, Abolfazl Khalkhali, Mehdi

Farajpoor, Hamed Safikhani, Transactions of the Canadian Society for

Mechanical Engineering, Vol. 35, No. 1, 2011.

2. “Influence of Impeller Geometry on the Unsteady Flow in a Centrifugal Fan:

Numerical and Experimental Analyses”, M. Younsi, F. Bakir, S. Kouidri, R.

Rey, International Journal of Rotating Machinery, Volume 2007, Article ID

34901, 2007.

3. “Design of cooling fan for noise reduction using CFD”, G.V.R. seshagiri rao,

Dr.V.V.subba rao, International Journal of Scientific & Engineering Research

Volume 2, Issue 9, September-2011.

4. “Experimental and CFD Model for design of an Automotive Centrifugal

Cooling Fan”, O.P.Singh, Rakesh Khilwani, T. Sreenivasulu, M Kannan, 37th

National and 4th International Conference on Fluid Mechanics and Fluid Power,

IIT Madras, December 2010.

5. “Bionic Fan Optimization based on Taguchi Method”, Shuming Chen,

Dengfeng Wang, Shaoming Sun, Engineering Applications of Computational

Fluid Mechanics Vol. 5, No. 3, pp. 302-314, 2011.

6. “Performance Analysis and Optimized Design of Backward-Curved Airfoil

Centrifugal Blowers”, Chen-Kang Huang, Mu-En Hsieh, HVAC&R Research,

Vol 15, No. 3, 2009.

7. “Impeller Design of a Centrifugal Fan with Blade Optimization”, Yu-Tai Lee,

Vineet Ahuja, Ashvin Hosangadi, Michael E. Slipper, Lawrence P. Mulvihill,



Roger Birkbeck, and Roderick M. Coleman, International Journal of Rotating

Machinery, Volume 2011, Article ID 537824, 2011.

8. “Experimental determination of the tonal noise sources in a centrifugal fan”,

Sandra Velarde-Sua´ rez, Rafael Ballesteros-Tajadura, Juan Pablo Hurtado-

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Books and Manuals

1. “Computational Fluid Dynamics, The Basics with Applications”, J. D.

Anderson, McGraw-Hill, Inc.

2. “An Introduction to Computational Fluid Dynamics”, H. K. Versteeg, W.

Malalasekera, Pearson Education Limited, Second Edition.

3. “Turbines, Compressors and Fans”, S M Yahya; TATA McGRAW-HILL;

Fourth Edition

4. “Fluent User’s Guide”, ANSYS Fluent 14.0.

5. “Fluent Theory Guide”, ANSYS Fluent 14.0.

Optimization of a Centrifugal Blower Using Cfd Techniques

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