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ESTIMATION AND ANALYSIS OF PERFORMANCE, STABILITY AND CONTROL FOR UNMANNED AERIAL VEHICLE (UAV) TAMINGSARI
USING SEMIEMPIRICAL METHOD
by
AIZAM SHAHRONI BIN MOHD ARSHAD
Thesis submitted in fulfillment of the requirements for the degree of
Master of Science
September 2010
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provided by Repository@USM
ii
ACKNOWLEDGEMENT
I would like to express my deepest gratitude to my supervisor, Dipl. Ing. Endri
Rachman, for his guidance and insight. He provided me with the motivation to get
through this research and made it a worthwhile experience. His commitment to the
success of this research truly inspired me. My sincerely thanks to Perak State
Government for the sponsorship given to do my research in School of Aerospace
Engineering, Universiti Sains Malaysia. I must also acknowledge other people whose
work was critical to the success of this research. First to Mr. Mohd Amir Wahab for his
significant help to ensure the aircraft properly prepared. His meticulous understanding of
the aircraft airframe saved countless hours of work. Also very special thanks to Mr.
Azizul and Mr. Ikhsan for their help in my research work. The research could not have
been done without these men. Special thanks to my family for their continued love and
support even as my graduate work seemed to drag on and on. This thesis is dedicated
special to my late father, Mohd Arshad with him is always in my heart. Finally, I want to
express my gratitude to my lovely wife, Mrs. Siti Maryam Mohd Nor, I love her very
much. As this project drew to a close and the hours spent away from her steadily
increased, her expressions of love and support kept my confidence up, my morale high
and my courage alive. She is truly a remarkable person.
iii
TABLE OF CONTENTS
Acknowledgement ….…………………………………………………….......................ii
Table of Contents …..…………………………………………………………………...iii
List of Tables .…………………………………………………………………………...ix
List of Figures..………………………………………………………………………….xi
List of Abbreviation …………………………………………………………………...xvi
List of Symbols ……………………………………………………………………….xvii
Abstrak ………………………………………………………………………………....xx
Abstract ………………………………………………………………………………..xxi
CHAPTER 1 – INTRODUCTION
1.1 Background……………………………………………………………………….1
1.2 Problem Statement………………………………………………………………..2
1.3 Research Objectives……………………………………………………………...3
1.4 Methodology…………………………………………………………………….. 3
1.5 Thesis Outline…………………………………………………………………….4
CHAPTER 2 – LITERATURE REVIEW
2.1 Introduction............................................................................................................6
2.2 Techniques and Approaches…………………………………………….……….7
2.2.1 Calculation Techniques…………………………………………………..7
2.2.2 Wind Tunnel Measurement……………………………………………..10
2.2.3 Flight Test Measurement………………………………………………..10
iv
2.2.3 Advanced Aircraft Analysis (AAA)……………………...……………..12
2.3 Methodology Selection………………………………………………………….12
2.4 Summary of Reviews…………………………………………………………...14
2.5 Thesis Contributions…………………………………………………………….17
CHAPTER 3 – UNMANNED AERIAL VEHICLE (UAV) TAMINGSARI
3.1 Introduction……………………………………………………………………..18
3.2 UAV Configuration……………………………………………………………..20
3.2.1 Three View Drawing of UAV Tamingsari……………………………...20
3.2.2 Wing Geometry…………………………………………………………23
3.2.3 Fuselage Geometry……………………………………………………...24
3.2.4 Horizontal Tail Geometry……………………………………………….24
3.2.5 Vertical Tail Geometry………………………………………………….25
3.2.6 Other Geometry…………………………………………………………26
CHAPTER 4 – METHODOLOGY
4.1 Introduction……………………………………………………………………..28
4.1.1 Aircraft Configuration…………………………………………….…….28
4.1.2 Flight Condition…….…………………………………………….……..29
4.2 Drag Polar Estimation Methods………………………………………………...29
4.2.1 Wing Zero-Lift Drag Coefficient……………………………………….30
4.2.2 Fuselage Zero-Lift Drag Coefficient……………………………………32
4.2.3 Horizontal Tail and Vertical Tail Zero-Lift Drag Coefficient…………..34
4.2.4 Tailboom Zero-Lift Drag Coefficient ………………………………..…35
v
4.2.5 Flap Profile Drag Increment………………………………………….…35
4.2.6 Landing Gear Drag…………………………………………………..….36
4.2.7 Estimation of Span Efficiency Factor ………………………………..…38
4.3 Lift Estimation Methods……………………………………………………..….40
4.3.1 Airfoil Analysis Characteristics………………………………………...41
4.3.2 Wing Lift and Maximum Lift: Flaps Up……………………………..…44
4.3.3 Wing Lift and Maximum Lift: Flaps Down…………………………….48
4.3.4 Airplane Lift and Maximum Lift: Flaps Up…………………………….53
4.3.5 Airplane Lift and Maximum Lift: Flaps Down…………………………60
4.4 Moment Estimation Methods…………………………………………………...64
4.4.1 Airplane Zero-Lift Pitching Moment Coefficient………………………64
4.4.2 Airplane Pitching Moment Variation with Lift Coefficient…………….66
4.5 Stability Derivatives…………………………………………………………….70
4.5.1 Steady State Coefficients………………………………………………..70
4.5.2 Speed Derivatives……………………………………………………….71
4.5.3 Angle of Attack Derivatives…………………………………………….72
4.5.4 Angle of Attack Rate Derivative………………………………………..73
4.5.5 Pitch Rate Derivatives…………………………………………………..74
4.5.6 Sideslip Derivatives……………………………………………………..77
4.5.7 Sideslip Rate Derivatives……………………………………………….93
4.5.8 Roll Rate Derivatives…………………………………………………...97
4.5.9 Yaw Rate Derivatives………………………………………………….103
4.6 Control Derivatives……………………………………………………………109
4.6.1 Stabilizer Control Derivatives…………………………………………109
vi
4.6.2 Elevator Control Derivatives…………………………………………..110
4.6.3 Aileron Control Derivatives…………………………………………...113
4.6.4 Rudder Control Derivatives……………………………………………117
4.7 Flight Performance…………………………………………………………….118
4.7.1 Equations of Motion…………………………………………………...118
4.7.2 Steady Level Flight……………………………………………………119
4.7.3 Glide…………………………………………………………………...121
4.7.4 Takeoff………………………………………………………………...123
4.7.5 Landing………………………………………………………………...126
4.7.6 Range and Endurance………………………………………………….129
4.7.7 Climb…………………………………………………………………..130
CHAPTER 5 – RESULTS AND DISCUSSION
5.1 Introduction……………………………………………………………………132
5.2 Estimation of Drag Polar…………………………………………………........133
5.2.1 Drag Polar for Cruise…………………………………………………..133
5.2.2 Estimation of Drag Polar for Takeoff and Landing……………………139
5.3 Estimation of Lift Characteristics……………………………………………...142
5.3.1 Airfoil Characteristics…………………………………………............142
5.3.2 Wing Lift and Maximum Lift: Flaps Up………………………………145
5.3.3 Wing Lift and Maximum Lift: Flaps Down…………………………...147
5.3.4 Airplane Lift and Maximum Lift: Flaps Up…………………………...149
5.3.5 Airplane Lift and Maximum Lift: Flaps Down………………………..154
5.4 Estimation of Moment…………………………………………………............156
vii
5.4.1 Airplane Zero-Lift Pitching Moment Coefficient……………………..156
5.4.2 Airplane Pitching Moment Variation with Lift Coefficient…………...159
5.5 Estimation of Stability Derivatives……………………………………………163
5.5.1 Steady State Coefficients………………………………………………163
5.5.2 Speed Derivatives……………………………………………………...163
5.5.3 Angle of Attack Derivatives…………………………………………...164
5.5.4 Angle of Attack Rate Derivative………………………………………165
5.5.5 Pitch Rate Derivatives…………………………………………………166
5.5.6 Sideslip Derivatives……………………………………………………169
5.5.7 Sideslip Rate Derivatives……………………………………………...174
5.5.8 Roll Rate Derivatives………………………………………………….177
5.5.9 Yaw Rate Derivatives………………………………………………….182
5.6 Estimation of Control Derivatives……………………………………………..185
5.6.1 Stabilizer Control Derivatives…………………………………………185
5.6.2 Elevator Control Derivatives…………………………………………..186
5.6.3 Aileron Control Derivatives…………………………………………...188
5.6.4 Rudder Control Derivatives……………………………………………190
5.7 Flight Performance Analysis…………………………………………………..191
5.7.1 Glide and Level Flight…………………………………………………191
5.7.2 Takeoff………………………………………………………………...193
5.7.3 Landing………………………………………………………………...195
5.7.4 Range and Endurance………………………………………………….197
5.8 Validation……………………………………………………………………...198
viii
CHAPTER 6 – CONCLUSIONS AND FUTURE WORKS
6.1 Conclusion……………………………………………………………………..202
6.2 Future Works…………………………………………………………………..203
REFERENCES.............................................................................................................204
APPENDICES
ix
LIST OF TABLES
Page
Table 2.1 Calculation Techniques for Configuration Aerodynamic Analyses 7
Table 3.1 Description Parameters of Wing, Flap and Aileron 23
Table 3.2 Description Parameters of Horizontal Tail and Elevator 24
Table 3.3 Description Parameters of Vertical Tail and Rudder 26
Table 5.1 Airfoil lift increment due to flaps deflection, lc∆ 144
Table 5.2 Airfoil maximum lift increment due to flaps, maxlc∆ 145
Table 5.3 Wing lift increment due to flaps deflection, wLC∆ 148
Table 5.4 Wing maximum lift increment due to flaps deflection, maxwLC∆ 149
Table 5.5 The increase in tail downwash angle due to different wing flap deflection
154
Table 5.6 The Airplane Lift Increment due to Various Flap Deflection 154
Table 5.7 The Airplane Maximum Lift Increment due to Various Flap Deflection
155
Table 5.8 Tamingsari Fuselage Section Data 157
Table 5.9 Evaluation of the Fuselage Contribution to Airplane Aerodynamic Location
160
Table 5.10 Evaluation of the Tailboom Contribution to Airplane Aerodynamic Location
161
Table 5.11 Longitudinal Stability and Control Derivatives 199
Table 5.12 Lateral-Directional Stability Derivatives 199
Table 5.13 Longitudinal Control Derivatives 200
Table 5.14 Lateral-Directional Control Derivatives 200
x
Table 5.15 Stability Analysis 201
xi
LIST OF FIGURES
Page
Figure 3.1 Three Dimensional View of UAV Tamingsari 20
Figure 3.2 Top View of UAV Tamingsari 21
Figure 3.3 Front View of UAV Tamingsari 22
Figure 3.4 Side View of UAV Tamingsari 23
Figure 3.5 Definition of Wing, Flap and Aileron Parameters 24
Figure 3.6 Definition of Fuselage Parameters 24
Figure 3.7 Definition of Horizontal Tail and Elevator Parameters 24
Figure 3.8 Definition of Vertical Tail and Rudder Parameters 25
Figure 3.9 Definition of CG and AC Coordinates (Top) 26
Figure 3.10 Definition of CG and AC Coordinates (Side) 27
Figure 3.11 Definition of Quarter Chord Z-Coordinates 27
Figure 4.1 Friction Coefficient as a Function of Mach Number and Reynolds Number
31
Figure 4.2 Lifting Surface Correction Factor 32
Figure 4.3 Airfoil Thickness Location Parameter 32
Figure 4.4 Definition of Fuselage Parameters 34
Figure 4.5 Profile Drag Increment: Plain Flaps 36
Figure 4.6 Drag Increment: Non-retractable Gears Attacher to Wing or Fuselage, Type 2
37
Figure 4.7 Definition of Reference Area of the Gear 38
Figure 4.8 Nosegear Drag Increments 38
Figure 4.9 Span efficiency factor for unswept wings 39
xii
Figure 4.10 Span efficiency factor for fuselages 39
Figure 4.11 Flap correction factor, fK to span efficiency factor 40
Figure 4.12 Definition of Airfoil Lift Curve Parameters 42
Figure 4.13 Definition of Airfoil Lift Curve Parameters at Flaps Down Configuration
43
Figure 4.14 Plain Flap Geometry 43
Figure 4.15 Relationship Between Wing Incidence, Wing Angle of Attack and Airplane Angle of Attack
45
Figure 4.16 Effect of Linear Twist on Wing Angle of Attack for Zero Lift 46
Figure 4.17 Mach Number Correction for Zero-Lift Angle of Attack of Cambered Airfoils
46
Figure 4.18 Definition of α∆ 48
Figure 4.19 Fundamental Wing Lift Versus Angle of Attack Curve with the Flaps Down
49
Figure 4.20 Procedure for Estimating bK 50
Figure 4.21 Effect of Taper Ratio and Flap Span on bK 50
Figure 4.22 Effect of Aspect Ratio and Flap-Chord Ratio on the Three Dimensional Flap Effectiveness
51
Figure 4.23 Definition of Flapped Wing Area 52
Figure 4.24 Effect of Sweep on Planform Correction Factor 52
Figure 4.25 Airplane Lift Coefficient Versus Angle of Attack 53
Figure 4.26 Relative Arrangement of Canard, Wing and Horizontal Tail 56
Figure 4.27 Geometric Parameters Required for Computing Overall Airplane Lift
56
Figure 4.28 Definition of Canard and Horizontal Tail Areas which are Submerged Propeller Slipstream
57
Figure 4.29 Geometric Parameters for Horizontal Tail Location 58
xiii
Figure 4.30 Factors for Computing Downwash 59
Figure 4.31 Airplane Lift Curve with the Flaps Down Configuration 61
Figure 4.32 Incremental Downwash Angle at the Horizontal Tail due to Flaps 63
Figure 4.33 Effect of Fuselage Slenderness on the Apparent Mass Factor 65
Figure 4.34 Fuselage Segmentation and Fuselage Camber 66
Figure 4.35 Definition of Aerodynamic Center Location Parameters and Moment Reference Center
67
Figure 4.36 Effect of Fuselage Segment Location on Upwash Gradient 69
Figure 4.37 Layout Computing Fuselage Contribution to Airplane Aerodynamic Location
69
Figure 4.38 Airplane Drag Polar Curve 70
Figure 4.39 Determination of the Variation of Airplane Drag Coefficient with Mach Number
71
Figure 4.40 Figure 4.40 Definition of Geometric Parameters 76
Figure 4.41 Correction Constant for Wing Contribution to Pitch Damping 77
Figure 4.42 Definition of Wing Geometric Dihedral 79
Figure 4.43 Wing-Fuselage Interference Factor 79
Figure 4.44 Definition of Wing-Fuselage Parameters 80
Figure 4.45 Determination of 0S 80
Figure 4.46 Fuselage Station Where Flow Become Viscous 81
Figure 4.47 Definition of Vertical Tail Geometry 81
Figure 4.48 Twin Vertical Tail Parameters 82
Figure 4.49 Wing-Fuselage-Horizontal Tail Interference on Side Force due to Sideslip of Twin Vertical Tails
82
Figure 4.50
Effective Value of the Side Force due to Sideslip Derivative for Twin Vertical Tails
83
xiv
Figure 4.51 Effective Value of Vertical Tail Aspect Ratio 83
Figure 4.52 Wing Sweep Contribution to Rolling Moment due to Sideslip 86
Figure 4.53 Compressibility Correction to Wing Sweep 87
Figure 4.54 Fuselage Correction Factor 87
Figure 4.55 Wing Aspect Ratio Contribution to Rolling Moment due to Sideslip
88
Figure 4.56 Wing Geometric Dihedral Contribution to Rolling Moment due to Sideslip
88
Figure 4.57 Compressibility Correction to Wing Dihedral 89
Figure 4.58 Contribution of Wing Twist to Rolling Moment due to Sideslip 89
Figure 4.59 Geometry for Locating Vertical Tail 89
Figure 4.60 Factor Accounting for Wing-Fuselage Interference with Directional Stability
91
Figure 4.61 Effect of Fuselage Reynolds Number on Wing-Fuselage Directional Stability
92
Figure 4.62 Sidewash Contribution due to Angle of Attack 94
Figure 4.63 Sidewash Contribution due to Wing Dihedral 94
Figure 4.64 Sidewash Contribution due to Wing Twist 95
Figure 4.65 Sidewash Contribution due to the Fuselage 95
Figure 4.66 Definition of pl and pz 96
Figure 4.67 Roll Damping Parameter 99
Figure 4.68 Drag due to Lift Roll Damping Parameter 100
Figure 4.69 Effect of Wing Twist on pnC 102
Figure 4.70 Effect of Symmetrical Flap Deflection 103
Figure 4.71 Wing Rolling Moment due to Yaw Rate Derivative 105
xv
Figure 4.72 Effect of Wing twist on rl
C 105
Figure 4.73 Effect of Symmetrical Flap Deflection on rl
C 106
Figure 4.74 Wing Yaw Damping Derivative: Lifting Effect 107
Figure 4.75 Wing Yaw Damping Derivative: Drag Effect 108
Figure 4.76 Correction Factor for Plain Flap Lift 111
Figure 4.77 Lift Effectiveness of Plain Flap 112
Figure 4.78 Correction Factor for Nonlinear Lift Behavior of Plain Flaps 112
Figure 4.79 Effect of Aspect Ratio and Flap-Chord Ratio on the Three-Dimensional Flap Effectiveness
113
Figure 4.80 Aileron Rolling Moment Parameter 116
Figure 4.81 Correlation Constant for Yawing Moment due to Aileron Deflection
116
Figure 4.82 Definition of Forces in an Accelerated Flight 118
Figure 4.83 Gliding Flight Configuration 121
Figure 4.84 Definition of Take-off Parameters 123
Figure 4.85 Definition of Landing Parameters 126
Figure 5.1 Wortmann FX 63-137 142
Figure 5.2 NACA 0012 Airfoil 143
Figure 5.3 Layout Fuselage Section Contribution to Airplane Aerodynamic Location
160
xvi
LIST OF ABBREVIATIONS
AAA Advanced Aircraft Analysis
ac Aerodynamic center
CDI Course Deviation Indicator
cg Center of Gravity
GPS Global Positioning System
HP Horse Power
mac Mean Aerodynamic Center
RC Remote Controlled
UAV Unmanned Aerial Vehicle
USM Universiti Sains Malaysia
xvii
LIST OF SYMBOLS
a Speed of sound
A Wing aspect ratio
hA Horizontal tail aspect ratio
b Wing span
hb Horizontal tail span
vb Vertical tail span
ab Aileron span
eb Elevator span
eb Rudder span
β Compressible sweep correction factor
rc Root Chord
tc Tip Chord
ec Elevator chord
fc Flap chord
ac Aileron chord
c Mean geometric chord
( )0=MatLC
α Mach zero lift curve slope
( )0=MatL h
Cα
Mach zero horizontal tail lift curve slope
wLCα
Wing lift curve slope
xviii
fd Fuselage diameter
bd Equivalent base diameter
i Incidence angle
K Ratio of airfoil lift curve slope to Π2
hK Ratio of horizontal tail airfoil lift curve slope to Π2
NL Nose length
fL Fuselage length
M Mach number
S Wing area
hS Horizontal tail area
vS Vertical tail area
wetS Wetted area
hV Horizontal tail volume coefficient
cgX Distance between center gravity of aircraft and leading edge of wing
α Angle of attack
hh Ratio of horizontal tail to wing dynamic pressure
2cΛ Semi-chord sweep angle
4cΛ Quarter-chord sweep angle
λ Taper ratio
t c Thickness ratio
fiY Inboard flap span
xix
foY Outboard flap span
aiY Inboard aileron span
aoY Outboard aileron span
eiY Inboard elevator span
eoY Outboard elevator span
riz Inboard rudder span
roz Outboard rudder span
xx
ANGGARAN DAN ANALISIS PRESTASI, KESTABILAN DAN KAWALAN
UNTUK PESAWAT TANPA PEMANDU TAMINGSARI MENGGUNAKAN
KAEDAH SEPARUH EMPIRIK
ABSTRAK
Tesis ini menerangkan tentang anggaran dan analisis prestasi penerbangan,
kestabilan dan kawalan khasnya untuk pesawat tanpa pemandu berdasarkan kaedah
separuh empirik. Pesawat tanpa pemandu adalah tidak mempunyai juruterbang di dalam
kokpit, tetapi ianya terbang secara autonomi ataupun dikawal menggunakan kawalan
jarak jauh. Ianya banyak digunakan di banyak negara untuk tujuan seperti pengawasan,
penyelidikan atau peperangan berdasarkan keupayaannya melakukan penerbangan
secara autonomi. Pesawat tanpa pemandu Tamingsari dipilih sebagai kes penyelidikan
ini yang mana pesawat ini merupakan milik Pusat Pengajian Kejuruteraan Aeroangkasa,
Universiti Sains Malaysia. Hasil yang diperolehi daripada kaedah separuh empirik telah
dibandingkan dengan perisian Advance Aircraft Analysis (AAA) sebagai alat untuk
menentukan kesasihan. Pesawat tanpa pemandu Tamingsari telah menunjukkan
keputusan yang baik untuk sebahagian besar parameter yang dianalisis. Ini
membuktikan bahawa kaedah separuh empirik memberikan ramalan baik untuk
menganalisis parameter aerodinamik, prestasi penerbangan, kestabilan dan kawalan
untuk pesawat tanpa pemandu Tamingsari yang bergantung kepada geometri pesawat
dalam menilai rekabentuk pesawat tanpa pemandu.
xxi
ESTIMATION AND ANALYSIS OF PERFORMANCE, STABILITY AND
CONTROL FOR UNMANNED AERIAL VEHICLE (UAV) TAMINGSARI
USING SEMI EMPIRICAL METHOD
ABSTRACT
This thesis gives deep insight on the estimation and analysis of flight
performance, stability and control derivatives particularly for unmanned aerial vehicle
(UAV), based on semiempirical methods. UAV is airplane that have no human onboard,
but rather are flown autonomously or piloted remotely. It is commonly used in many
countries to develop tasks such as surveillance, research or combat due to its autonomy
of flight. UAV Tamingsari was selected to be a research case, which belong to the
School of Aerospace Engineering, Universiti Sains Malaysia. The results obtained from
semiempirical methods have been validated by Advance Aircraft Analysis (AAA)
computational software as a tool to check the validity. The validation results of UAV
Tamingsari have shown good agreement for most of the parameters analyzed. In this
manner, the results showed that the semiempirical methods provide good prediction of
aerodynamics, flight performance, stability and control derivatives for UAV Tamingsari
due to its approximate geometry inputs, which used to evaluate the UAV configuration.
1
CHAPTER 1
INTRODUCTION
1.1 Background
Unmanned aerial vehicles (UAVs) are airplanes that have no humans on board,
but rather are flown autonomously or piloted remotely (Anderson, 2005). UAVs are
being used for various potential applications such as environmental monitoring and
protection, traffic monitoring, meteorological surveillance, agriculture, aerial target
system, airborne surveillance for military land operations, and reconnaissance missions.
In recent years, there has been a significant interest in the research of UAVs as
evidenced by the conferences and journal contributions dedicated on quite number of
different disciplines ranging from aerodynamics, flight performance, stability and
control. The air vehicle design approaches for UAVs follows the principles outlined for
manned aircraft (Davies, 2002).
The School of Aerospace Engineering, Universiti Sains Malaysia (USM) has
been conducting research and development on UAVs since 2002. Research work to date
has produced promising results towards the development of fully autonomous
capabilities for unmanned aircraft, and has brought the core autonomous flight control
system to an advanced stage of development. An aircraft firstly being developed is the
UAV named Tamingsari at USM. Being developed primarily to provide a flight research
platform in support of the various research activities, Tamingsari is also used to enhance
skills in airframe design and fabrication, flight performance, stability, control,
instrumentation, flight control systems, and operational aspects of UAVs. It forms the
2
basis of a technology demonstrator for many aspects of a generic UAV system
development and became an integral part of the research.
Current plans are based on the premise that UAVs have the potential to replace
manned aircraft in a variety of missions. The advantage of UAVs over manned aircraft is
their cost which is only a small fraction of the cost of a manned airplane. Thus they have
become an integral part of modern warfare since many missions such as electronic
deception, visual identification, laser designation of targets and bomb damage
assessment deep in the enemy territory can be performed without endangering any lives
or risking any expensive aircraft. They can be launched from practically any type of
platform making them ideal for use in the Navy. They are also usually very hard to be
detected with radar or infrared systems due to their small size, composite materials and
low noise and speed. UAVs are also not limited by the pilot “g” tolerance or fatigue.
The Control System Lab led by Dipl. Ing. Endri Rachman has identify long-term
research opportunities for supporting the development of technologies for UAVs at
USM. The objectives of the research and development of UAVs were to identify
technological developments that would improve the performance and reliability of next
generation UAVs at lower cost and to recommend areas of fundamental research in
structures, design and autonomous system.
1.2 Problem Statement
UAVs are finally and rapidly coming into their own as major tactical and
strategic systems on the modern battlefield and academic applications. UAVs share
inherent characteristics with manned aircraft but the only main difference is the handling
capability. Therefore, this research is to develop the estimation framework for
3
aerodynamics, performance, stability and control specific for unmanned aircraft using
semiempirical methods then to compare and prove the analytical results with established
software which is Advanced Aircraft Analysis (AAA).
This research will provides the School of Aerospace Engineering, Universiti
Sains Malaysia a foundation or platform for future development of UAV capability.
Prior to this research, there is no well documentation existed at USM which focus on
analysis of aerodynamics, flight performance, stability and control derivatives of UAV
especially using semiempirical methods.
1.3 Research Objectives
The main objective of this research is to analyse to important parameter of
aircraft flight mechanics as it applies to UAVs as well as to provide a proper procedure
to estimate the flight performance, stability and control derivatives that applicable in
UAV design and analysis process. Basically, the objective can be broken down into two
sub-objectives which will be the thoroughly covered in this thesis.
To develop the procedure of estimation and analysis of performance, stability
and control derivatives of UAV Tamingsari using semiempirical approach.
To compare the analytical value with AAA.
1.4 Methodology
Methodology differed for each objective. The estimation and analysis of
performance, stability and control of UAV extensively relied upon the UAV
configuration. Physical measurements of the UAV were performed in the Flight
4
Mechanics and Control Laboratory. The method used to analyse the aerodynamics,
performance, stability and control coefficients will be considered to achive the objective.
One objective of this study was to create a procedure for UAV to quickly and
with relative ease predict the aerodynamics, performance, stability and control of new
designs, preferably, while still in the design stage and also after manufacture. To
accomplish this goal, predictive methods presented by Roskam (1990) were used to
analyse of the UAV of interest. Although the methods presented in these textbooks are
intended for large airplanes, it was found that with a few modifications, they can be
applied quite well to small UAVs in the subsonic regime.
The performance, stability and control estimation and analysis of UAV presented
in this research associated mostly in analytical approach and also a graphical approach.
These two approaches complement each other. The graphical approach is the process of
communicating visually using graph to present a clear picture of how various
characteristics vary over a range of, parameters. In contrast, the analytical approach, is
mainly dealing with equations. For these reasons, combination of both approaches seems
to be a good approval.
1.5 Thesis Outline
This thesis is arranged in accordance to the objectives and approach as
mentioned above. Chapter 2 provides a literature review of related subject. Here, the
development on previous research and past knowledge will be explained. The topic
discussed include calculation techniques and proposed research methodology.
Chapter 3 gives description of UAV Tamingsari configuration with figures of
wing, horizontal tail, vertical tail and fuselage is showed with drawing and dimensions.
5
Chapter 4 discusses the methodology to calculate of aerodynamics, performance and
stability and control derivatives. The method will be disccused in more detailed.
Chapter 5 provides the result of the parameter based on methodology in Chapter
4. This includes a reasonable validation to support the results. The thesis closed with
conclusion and contribution of the overall research in Chapter 6. There are also
recommendations for the improvement for further research.
6
CHAPTER 2
LITERATURE REVIEW
2.1 Introduction
Today’s UAVs are the result of an evolution in the analysis that compose the
vehicle and the processes used in its design and development. The analysis includes
aerodynamics, flight performance, stability and control. In each of these areas as they
apply to the small UAVs are discussed in the subsequent chapters. This thesis describes
a research in the estimation and analysis of unmanned aerial vehicle (UAV) specific into
flight performance, stability and control. Specifically, topics include the flight
performance prediction, stability and control estimation. The results of study case UAV
is discussed and validating with an established tool which is Advanced Aircraft Analysis
(AAA) by DARcorporation.
All UAV must be analysed to meet the standard requirements in order to be
certified for flight. It is essential for researcher to evaluate the flight performance,
stability and control authority of UAV concepts early in the conceptual design phase or
existing aircraft. Normally the researcher should consider numerous possible methods
depends on the availability data before the flight performance, stability and control can
be estimated. In this report a methodology and procedure for estimate and analysis of
existing UAV configurations in that particular flight conditions is established. First, the
flight conditions has to be considered have to be select. Next, to examine the UAV
configurations and the process of estimating stability and control derivatives and flight
performance.
7
Then the results from semiempirical methods of aerodynamics, performance,
stability and control derivatives based on geometric data to be compare with AAA. As
an educational document, both the report and the software will be continually refined.
The intent is not be encyclopedic, or to replace the semiempirical approach. Rather, the
intent is to develop well documentation procedure of the analysis of performance,
stability and control of UAV. This should provide a much better starting point for more
detailed work in modeling and simulation, design of control laws and autonomous
systems.
2.2 Techniques and Approaches
A number of methods are used to evaluate the aerodynamic, performance,
stability and control derivatives. From aerodynamic data, the flight performance,
stability and control can be solved. The degree of confidence associated with the
estimation is dependent on the quality of the aerodynamic source material and the
method of evaluation used.
2.2.1 Calculation Techniques
Three classes of aerodynamic calculation techniques are: (a) analytical, (b)
semiempirical and empirical, and (c) numerical. As a consequence, it is possible to
categorise existing calculation methods into the following distinct classes, see Table 2.1
Table 2.1 Calculation Techniques for Configuration Aerodynamic Analyses
Analytical Semiempirical/Empirical Numerical Lifting Line Theory RAE Standard Method Vortex Lattice Method Swept-Wing Theory Hoerner Panel Method
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Swept Lifting Line Theory DATCOM Finite Difference Method (CFD)
Low Aspect Ratio Wing Theory
ESDU Finite Element Method (CFD)
Loading Function Method Schemenski Finite Volume Method (CFD)
Modified Lifting Line Method
Missile DATCOM Spectral Method (CFD)
2.2.1(a) Analytical
Numerous analytical theories have been developed since the beginning of
manned flight, see Table 2.1. Those method range from the most simple to the most
complex with the aim theoretically analyse two and three dimensional lifting systems.
However, the solutions to the configuration aerodynamics problem are predominantly
limited to imcompressible, inviscid fluids. Classical analytical theories are still used to
estimate of the forces and moments involved. Those theories are primarily employed , to
identify the influence of gross geometric parameters used in the analytical formulations.
Overall, the strength of analytical theory is the ability to gain an insight on the physical
role of key parameters. Although analytical methods are based on physical reasoning,
the modeling is restricted in practice to specific geometries and operational applications.
2.2.1(b) Empirical and Semiempirical
The difficult balance between model complexity, calculation speed, and
calculation accuracy has been classically satisfied with the use of empirical (database)
methods and semiempirical (engineering) aerodynamic methods. Empirical knowledge
is based on practical experience and observation rather than theories and the
semiempirical methods means that the parameters used in the correlations were
9
reasonable parameters based on the physics of the situation. In the development of
semiempirical methods, basic aerodynamic theory is used to make a first order estimate
of the lift and drag and to define reasonable aerodynamic parameters to be used in the
correlations. Then empirical corrections are made to the theory to produce good
agreement with wind tunnel and flight test data. Clearly, empirical and semiempirical
aerodynamic estimation methods like Datcom and ESDU (Engineering Sciences Data)
data sheet are restricted to conventional configurations only. This methods do not
function outside their underlying database especially for unconventional configuration.
2.2.1(c) Numerical
Numerical estimation methods comprise linear methods like the vortex lattice
and panel method families, and CFD (computational Fluid Dynamics) techniques, see
Table 2.1. CFD methods require the construction of a grid to fill the flow field volume
of interest, resulting in a large number of mesh points. This consequently leads to a very
large system of equations, posing particular demands on computational resources
Linear numerical estimation methods are most suitable for conceptual analysis
work. Vortex lattice methods (VLM) study the mean geometric surface and panel
methods study the geometric surface. Both methods circumvent the modeling task of the
geometric volume, thereby avoiding much of the CFD typical pre-processing
complexity. However, linear estimation techniques do not estimate effects like boundary
layer and wake roll up. They do provide reasonable estimates of the inviscid
aerodynamics, including drag, for a large class of airplane geometries in subsonic.
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2.2.2 Wind Tunnel Measurement
The classical wind tunnel test is one in which a reduced scale model of the
aircraft is attached to a balance and the six components of force and moment are
measured for various combinations of wind velocity, incidence angle, sideslip angle and
control surface angle. The essential feature of such tests is that the conditions are static
when the measurements are made. Provided the experiments are carefully designed and
executed wind tunnel tests can give good estimates of the force-velocity abd moment-
velocity derivatives in particular. Scale effects can give rise accuracy problems,
especially when difficult full scale flight conditions are simulated and although some
derivatives can be estimated with good accuracy it may be very difficult to devise
experiments to measure other derivatives adequately. However, despite the limitations of
the experimental methods, measurements are made for real aerodynamic flow conditions
and in principle it is possible to obtain derivative estimates of greater fidelity than is
likely by analytical, semiempirical/empirical and numerical.
2.2.3 Flight Test Measurement
The estimation of aerodynamic derivatives from flight test measurements is an
established and well developed experimental process. However, derivative estimates are
usually obtained indirectly since it is not possible to measure the aerodynamic
components of force and moment acting on the airframe directly. Also, since the aircraft
has six degrees of freedom it is not always possible to perturb the single motion variable
of interest without perturbing some, or all, of the others as well. However, as in wind
tunnel testing, some derivatives are easily estimated from flight test experiment with a
good degree of confidence, whereas others can be difficult to estimate.
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Today parameter identification techniques are commonly used in which
measurements are made following the deliberate excitation of multi-variable dynamic
conditions. Complex multi-variable response analysis then follows from which it is
possible to derive a complete estimate of the mathematical model of the aircraft
corresponding with the flight condition at which the measurements were made.
Parameter identification is an analytical process in which full use is made of state space
computational tools in order to estimate the aircraft state description that best matches
the input-output response measured in flight. It is essentially a multi-variable curve
fitting procedure and the computational output is the coefficients in the aircraft state
equation from which estimates of the aerodynamic stability and control derivatives may
be obtained.
The disadvantages of parameter identification methods includes the requirement
for substantial computational and the essential need for recorded flight data of the very
highest quality. Despite these constraints, the process is now used routinely by many of
the leading flight test organisations. Given adequate resources the advantages of
parameter identification methods are significant. All of the aerodynamic stability and
control derivatives can be estimated in one pass and the dynamic conditions to which
they relate do not necessarily have to be linear. For example, it is now routinely possible
to identify aircraft models in extreme manoeuvring conditions such as the stall, the spin
at very angles of attack when the aerodynamics are substantially non-linear. It is
interesting to note that the method can also be used for estimating aerodynamic
derivatives from dynamic wind tunnel experiments.
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2.2.4 Advanced Aircraft Analysis (AAA)
Advanced Aircraft Analysis (AAA) is the industry standard aircraft design,
stability, and control analysis software. AAA is installed in over 45 countries and is used
by major aeronautical engineering universities, aircraft manufacturers, and military
organizations worldwide.
Advanced Aircraft Analysis provides a powerful framework to support the
iterative and non-unique process of aircraft preliminary design. The software consists of
ten application modules, a detailed help system, environment setup and project handling
tools. Appendix A shows a few snapshots of the program. Advanced Aircraft Analysis
uses both British and S.I. units.
Advanced Aircraft Analysis can be used for small (civil), UAV, military and
transport airplanes. The program is designed to assist in the design learning process
while reserving for the user the individual creative judgment which is essential to the
process of airplane design.
Advanced Aircraft Analysis applies to most fixed wing configurations (civil and
military aircraft) and allows design engineers to rapidly evolve an airplane configuration
from weight sizing through detailed performance calculations and cost estimations. All
applicable performance and flying quality regulations are available in the AAA program.
2.3 Methodology Selection
To estimate and analysis the performance, stability and control of UAV
Tamingsari, we need to select the suitable approach. The estimation can be determined
either by analytical, semiempirical, computational fluid dynamics (CFD), or
experimental methods. Several empirical and semiempirical methods have been
13
developed over the years for evaluating the aerodynamics, performance, stability and
control derivatives of aircraft configurations of practical interest. Semiempirical
methods from Roskam (1990) is one of the most widely used sources for estimating
aerodynamics, performance, stability and control derivatives for conventional aircraft. It
is a comprehensive compilation of all the available engineering methods for obtaining
the flight mechanics characteristics of aircraft.
On the other hand, the semiempirical or engineering methods provide quick and
cost effective estimates of the aerodynamics, flight performance, static and dynamic
stability derivatives , which can be readily used for assessing the vehicle flyablity. Also,
the predictions based on semiempirical methods can be used in the design of flight
control and guidance systems. The only disadvantage is that such predictions are less
accurate compared to the CFD, wind tunnel and flight test data. However, this type of
information is very useful and quickly evaluate configuration for their suitability in
design stages and also developing the modeling and simulation.
In this thesis, the semiempirical methods proposed by Roskam (1990) is going
to be implemented suitable for evaluating the flight mechanics characteristics. Panel
method from Drela (2000) is used to determine airfoil characteristics and aerodynamics.
Drela (2000) stated that the XFOIL software is helpful to understand the basic
aerodynamic concepts underlying the flight performance, stability and control. The
results from semiempirical method from Roskam (1990) will be compared with fast
software calculation of Advanced Aircraft Analysis (AAA).
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2.4 Summary of Reviews
There are currently is a great deal of interest in the UAV, both in the military and
civilian sectors, owing to their potential to perform dangerous, repetitive tasks in remote
or hazardous environments. Most of the UAV require a good flying characteristics of
performance, stability and control before the UAV can be assumed able to fly and for
the development of flight control laws, simulation and autonomous systems. Since there
are many literature have shown that the estimation of aerodynamics, performance,
stability and control using semiempirical methods are seemed to be useful and
important. Furthermore, a literature review reveals there are no specific documents for
UAV especially for the estimation of performance, stability and control. A lot of
documents applied to only piloted aircraft and the others just informed that the method
that the researcher used in his worked without any documents and procedure involved.
Literature becomes less available when it comes to UAVs and this problems inspire me
to contribute something in the development of flight mechanics area at Malaysia. Few
relevant articles discussing the worked done in this area development of UAVs were
found.
This part reviewed the existing literature review in the flight performance
analysis as well as stability and control. There is a lot of existing literature reviewed by
the ealier reviewers. Conference paper from Rachman (2007) discussed the step by step
procedure in the design and development of the control laws of UAV Tamingsari. The
process starts with calculating data of aerodynamics, stability and control needed for
nonlinear simulation. Also stated that in the process determination of aerodynamics,
performance, stability and control derivatives have been estimated using the USAF
Digital Datcom and the semiempirical formulas from Roskam book. The aerodynamic
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data, the stability and control derivatives are used as the parameter for the equations of
motion describing the motion of the UAV Tamingsari in the air. The design and
development of the control laws/algorithms for UAV Tamingsari is not only just
designing and simulating the linear control laws, but there are some issues such as
getting the UAV data. This issues is important have to be done and solved in order to
achieve autonomous.
Rachman et al. (2009) studied the aerodynamics, performance, stability and
control for UAV Tamingsari using semiempirical method. Semiempirical is used to
make a first order estimate of the lift and drag and to define reasonable aerodynamic
parameters to be used in the estimation of performance, stability and control.
Sweeten et al. (2009) focuses on the flight performance analysis using Roskam
approach and AAA. The comparison were made with approach from flight testing
methods. Arroyo (2007) wrote a Masters thesis which used computational analysis to
study the aerodynamic of an UAV, in order to calibrate the equations of stability and
control needed for the programming of their autopilot. Fluent and semiempirical
methods are used to calculate the aerodynamic derivatives. This thesis used the
measurements of the geometries that can be used in the modeling into a software of
design steps to the simulation of the aircrafts. Jodeh (2006), in his masters thesis work,
stated the development of autonomous UAV in the platform of modeling, simulating and
flight testing. In order to achieve this, in first component of his thesis that the analysis of
stability and control based on physical characteristics, which used semiempirical
methods.
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Conference paper by Tinapp et al. (2008), stated that in order to optimize flight
control system and the performance of aircraft, a dynamical model of the aircraft is
needed that can be obtained by calculating the stability and control derivatives. This
paper described the results of aerodynamic modeling of a UAV, using free available
calculations tool for aerodynamic analysis and flight mechanics such as XFOIL, AVL
(Anthena Vortex Lattice) and Roskam.
Jung et al. (2007), this article describes a complete simulation environment
which has been developed at Georgia Tech for testing and validating UAV control
systems. Here the aerodynamic, stability and control derivatives are obtained from
DATCOM and Roskam. Keshmiri et. al (2008), describes a modeling and simulation of
unmanned aerial vehicle using parameter and system identification. The numerical
values of the aerodynamic derivatives are computed using semiempirical methods
software to investigate the geometric parameters of the airplane. For comparison a series
of flight tests are conducted. The comparison results are used in the development of a
new autopilot system for small UAVs.
Bergen et. al, (2008), This article describes a correlation of handbook method
results with wind tunnel data for a subsonic UAV. The handbook methods are used to
size the aircraft and to predict its aerodynamic characteristics and flight performances.
For a new twin-boom pusher UAV a systematic comparison of theoretical predictions
and wind tunnel test data is performed. It is shown that although performance and
stability parameters are generally well predicted important deviations can occur if the
specific airplane configuration is not taken into account in a sufficiently accurate
manner.
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Garcia and Becker (2007) described about the hardware-in-the-loop simulation.
This paper stated that the use of a vortex lattice aerodynamic code and manual methods
from Roskam for the estimation of stability derivatives and qualitative flight testing for
the determination of the final values. Leong et al. (2008), wrote a conference paper
about the development of a pilot training platform for UAVs using a six degree of
freedom nonlinear model with flight test validation. In this paper describes that the
semiempirical methods is utilized to compute the aerodynamics, performance, stability
and control derivatives, which are used to compose the state space models and after that
validate with flight test.
From all the reviews shown that the semiempirical methods played an important
role to determine and estimate the aerodyanmics, performance, stability and control
derivatives in early stage of design and development and also can be used in the
modeling and simulation.
2.5 Thesis Contributions
In the section 2.4 the reviewers stated that the important of semiempirical
methods for estimating aerodynamics, performance, stability and control in the
development of modeling and simulation, design of flight control laws, autopilot and
autonomous systems. There are no papers or theses that desribed the procedure in the
implementation of semiempirical methods to the UAV. So, to fulfill the need of the case
of UAV aerodynamics, performance, stability and control, the development of
estimation framework that specific to the UAV need to work out and used UAV
Tamingsari as an example in the analysis and estimation.
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CHAPTER 3
UNMANNED AERIAL VEHICLE (UAV) TAMINGSARI
3.1 Intoduction Tamingsari is a low altitude, short range Unmanned Aerial Vehicle (UAV)
which performs aerial surveillance and reconnaissance missions for civil use such as
remote sensing, air photography, sampling of plantation topography, and monitoring of
traffic and flooding area. After detailed design and manufacturing, the configuration of
Tamingsari has following features:
Dimension: a length of 2.5 m, a wing span of 3 m and the maximum fuselage
diameter of 0.3 m.
Airframe configuration: circular fuselage, straight and non-tapered wing, twin
tail booms with elevator on the top of vertical tails.
Engine mounting: pusher (aft)
Landing gear: triple, fixed landing gear.
Material: fiberglass (body, empennage), fiberglass covered balsa-wood (wing)
The propulsion system uses two stroke piston propeller engine of Model Motoren
that is equipped with course deviation indicator (CDI) and can produce gross power of
5.2 HP. The Tamingsari equipped with an onboard computer Micropilot that is
connected to a flight control unit for autonomous flight. The flight Control System
consists of the radio remote control (Transmitter and Receiver), servo motor, push-pull
rod control linkage system. The Tamingsari can be operated manually using remote
controlled (RC) or autonomous and monitored on ground with portable personal
19
computer. Beside that the onboard computer is connected to a sensor system for
measuring the attitude, rate, airspeed and altitude of the Tamingsari and an navigation
system (GPS) for position determination. Wiring system has been put into Tamingsari
and consists of two unit of battery and voltage regulator to operate the UAV system. A
battery of twelve volt is used for autopilot, navigation GPS, wireless modem, servo
motor, RC-control system, sensor system and payload camera and other battery of 6 volt
for engine CDI.
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3.2 UAV Configuration
3.2.1 Three View Drawing of UAV Tamingsari
Figure 3.1 Three Dimensional View of UAV Tamingsari
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Figure 3.2 Top View of UAV Tamingsari
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Figure 3.3 Front View of UAV Tamingsari
Figure 3.4 Side View of UAV Tamingsari
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3.2.2 Wing Geometry
Figure 3.5 Definition of Wing, Flap and Aileron Parameters (unit in meter)
Table 3.1 Description Parameters of Wing, Flap and Aileron
Parameter Value Planform Shape Rectangular wing Wing Span, b 3 m Airfoil Wortmann FX 63-137 Thickness ratio, t c 0.137 Wing Planform Area, S 1.17 m2 Root Chord, rc 0.39 m Tip Chord, tc 0.39 m Mean Aerodynamic Chord, c 0.39 m Leading Edge Sweep 0 deg Half chord sweep, 2cΛ 0 deg Quarter chord sweep, 4cΛ 0 deg Dihedral Angle 0 deg Twist Angle 0 deg Incidence Angle 2− deg Taper Ratio 1 Aspect Ratio 7.6923
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3.2.3 Fuselage Geometry
Figure 3.6 Definition of Fuselage Parameters (unit in meter)
3.2.4 Horizontal Tail Geometry
Figure 3.7 Definition of Horizontal Tail and Elevator Parameters (unit in meter)
Table 3.2 Description Parameters of Horizontal Tail and Elevator
Parameter Value Planform Shape Rectangular Span 0.9 m Airfoil NACA 0012 Area 0.207 m2 Root Chord 0.23 m Tip Chord 0.23 m Mean Aerodynamic Chord 0.23 m
