optimization of a centrifugal blower using cfd techniques
DESCRIPTION
A Poject report on optimization of centrifugal blowersTRANSCRIPT
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
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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
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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
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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.
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Figure 1.1: Layout of Automobile HVAC System
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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
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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.
Optimization of a Centrifugal Blower using CFD Techniques
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Figure 1.2: Propeller Fan
Figure 1.3: Tube Axial Fan
Figure 1.4: Vane Axial Fan
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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.
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Figure 1.5: Radial Blade Blower
Figure 1.6: Forward Curved Blade Blower
Figure 1.7: Backward Curver Blades Blower
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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.
Optimization of a Centrifugal Blower using CFD Techniques
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Graph 1.1: Blower Characteristics Curve
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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.
Optimization of a Centrifugal Blower using CFD Techniques
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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
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Figure. 2.1: Impeller Geometry
Figure. 2.2: Blade Angles
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Figure. 2.3: Volute
Figure. 2.4: Divergence Angle
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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.
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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
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Figure 3.1: Fundamentals Principles which form the basis of CFD
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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
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Figure 3.2: Models of Flow (a) Finite Control Volume Approach (b) Infinitesimal
Fluid Element Approach
Figure 3.3: Infinitesimally small, moving fluid element
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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
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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
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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.
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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.
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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
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Figure 3.4 (a): Hexahedral Mesh
Figure 3.4(b): Tetrahedral Mesh
Figure 3.4 (c): Hybrid Mesh
Figure 3.4 (d):Polyhedral Mesh
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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
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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
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Figure 3.5 Monopole
Figure 3.6: Dipole
Figure 3.7: Quadrupole
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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.
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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.
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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
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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.
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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
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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.
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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)
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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
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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.
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Figure 4.5: 2D Geometry
Figure 4.6: Tetrahedral Mesh for 2D Model
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Figure 4.7: Pressure Contours
Figure 4.8: Velocity Contours
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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.
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Graph 4.2: CFD (3D) results superimposed over experimental results
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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
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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
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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
Optimization of a Centrifugal Blower using CFD Techniques
44 Department of Mechanical Engineering, MITCOE, Pune
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
Optimization of a Centrifugal Blower using CFD Techniques
45 Department of Mechanical Engineering, MITCOE, Pune
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,
Optimization of a Centrifugal Blower using CFD Techniques
46 Department of Mechanical Engineering, MITCOE, Pune
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
Optimization of a Centrifugal Blower using CFD Techniques
47 Department of Mechanical Engineering, MITCOE, Pune
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)
Optimization of a Centrifugal Blower using CFD Techniques
48 Department of Mechanical Engineering, MITCOE, Pune
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
Optimization of a Centrifugal Blower using CFD Techniques
49 Department of Mechanical Engineering, MITCOE, Pune
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
Optimization of a Centrifugal Blower using CFD Techniques
50 Department of Mechanical Engineering, MITCOE, Pune
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
Optimization of a Centrifugal Blower using CFD Techniques
51 Department of Mechanical Engineering, MITCOE, Pune
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
Optimization of a Centrifugal Blower using CFD Techniques
52 Department of Mechanical Engineering, MITCOE, Pune
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
For Power Input of 120 W Original Optimised Change
(%) 64.67 67.34 + 2.67
RPM
For Power Input of 120 W Original Optimised Change
(RPM) 3000 2815 - 185
Noise
For Power Input of 120 W Original Optimised Change
(dB) 127.02 126.97 - 0.05
Table 6.2: Comparison of Various Parameters with original
Optimization of a Centrifugal Blower using CFD Techniques
53 Department of Mechanical Engineering, MITCOE, Pune
Figure 6.1(a): Acoustic Power Level- Original Model
Figure 6.1(b): Acoustic Power Level- Optimized Model
Optimization of a Centrifugal Blower using CFD Techniques
54 Department of Mechanical Engineering, MITCOE, Pune
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)
Optimization of a Centrifugal Blower using CFD Techniques
55 Department of Mechanical Engineering, MITCOE, Pune
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.
Optimization of a Centrifugal Blower using CFD Techniques
56 Department of Mechanical Engineering, MITCOE, Pune
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.
Optimization of a Centrifugal Blower using CFD Techniques
57 Department of Mechanical Engineering, MITCOE, Pune
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
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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.
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Fourth Edition
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5. “Fluent Theory Guide”, ANSYS Fluent 14.0.