Ground-Based Simulation of Airplane Upset Using an Enhanced … · 2012-11-01 · Abstract...
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Ground-Based Simulation of Airplane Upset Using anEnhanced Flight Model
by
Stacey Fangfei Liu
A thesis submitted in conformity with the requirementsfor the degree of Master of Applied Science
Graduate Department of Aerospace Science and EngineeringUniversity of Toronto
c©Copyright by Stacey F. Liu 2011
Abstract
Ground-Based Simulation of Airplane Upset Using an Enhanced Flight Model
Stacey Fangfei Liu
Master of Applied Science
Graduate Department of Aerospace Science and Engineering
University of Toronto
2011
Loss-of-control resulting from airplane upset is a leading cause of worldwide commercial
aircraft accidents. One of the upset prevention and recovery strategies currently being
considered is to provide pilot upset recovery training using ground-based flight simulators.
However, to simulate the large amplitude and highly dynamic motions seen in upset
conditions, both the flight model and the simulator motion need improvement.
In this thesis, an enhanced flight model is developed to better represent the air-
craft dynamics in upset conditions. In particular, extension is made to the aerodynamic
database of an existing Boeing 747-100 (B-747) model to cover large angle of attack,
sideslip and angular rates. The enhanced B-747 model is then used to conduct a set
of upset recovery experiments in a flight simulator without motion. The experimental
results can be used to identify and potentially correct major motion cueing errors caused
by the conventional motion drive algorithm in upset conditions.
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Acknowledgements
I would like to express my deep and sincere gratitude to my thesis supervisor Professor
Peter Grant for his help, guidance, and encouragement. I would also like to thank my
research assessment committee members, Professor Hugh Liu and Professor Christopher
Damaren for providing valuable advice and taking the time to review my research.
I would like to thank Bruce Haycock for his tremendous support in setting up the
simulator experiments and for testing the experiments many times.
I am also grateful for the help of the pilots who participated in the upset recovery
experiments. Their contributions are invaluable for this thesis and the continuing research
on airplane upsets.
Thanks also go out to students from the Vehicle Simulation Group, Amir Naseri,
Nestor Li, Tim Peterson, and Eska Ko for their feedback on some of the issues related to
this study, and to Ton Hettema for implementing the stick shaker model that was used
in the experiments.
Last but not least, I would like to thank my parents for their constant support during
the course of my studies.
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Contents
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Scope and Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Literature Review 5
2.1 Upset Recovery Training . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Upset Recovery Training Effectiveness . . . . . . . . . . . . . . . . . . . 6
2.3 Acquiring Aerodynamic Data Beyond the Normal Flight Envelope . . . . 9
2.4 MDA and Study of Motion Fidelity . . . . . . . . . . . . . . . . . . . . . 12
3 Flight Model 14
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.2 The Existing UTIAS B-747 Model . . . . . . . . . . . . . . . . . . . . . . 15
3.3 NASA T2 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.4 Data Extension . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.4.1 Basic Static Effect . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.4.2 Control Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.4.3 Dynamic Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.5 Model Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.5.1 Database Validation . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.5.2 Model Behavior Validation . . . . . . . . . . . . . . . . . . . . . . 46
3.5.3 Roll-Off and Directional Divergence at Stall . . . . . . . . . . . . 48
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4 Upset Recovery Experiments 58
4.1 Upset Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.2 Experimental Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
4.4 Example MDA Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
5 Conclusions 79
5.1 Summary of Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.2 Future Research Needs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
A Microburst Model 82
References 85
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List of Figures
3.1 Axes Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.2 Data Blending Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.3 Real-Time Data Blending Block in Simulink . . . . . . . . . . . . . . . . 22
3.4 Basic Lift and Pitching Moment Coefficients . . . . . . . . . . . . . . . . 27
3.5 Basic Drag Coefficient . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.6 Sideslip Effects on Basic Lift and Pitching Moment Coefficients . . . . . 28
3.7 Cl and Cn vs. β . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.8 Boeing 747-100 Control Surfaces (figure adapted from ref.[23]) . . . . . . 30
3.9 Stabilizer and Elevator Effects on Cm and CD . . . . . . . . . . . . . . . 35
3.10 Aileron Effect on Cl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.11 Rudder Effect on Cn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.12 Spoiler Effects on CD and Cl . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.13 Dynamic Data - Longitudinal . . . . . . . . . . . . . . . . . . . . . . . . 42
3.14 Dynamic Data - Rolling Moment . . . . . . . . . . . . . . . . . . . . . . 43
3.15 Dynamic Data - Yawing Moment . . . . . . . . . . . . . . . . . . . . . . 44
3.16 Stall Maneuver Used for Coefficient Comparison . . . . . . . . . . . . . . 51
3.17 Coefficient Comparison - Longitudinal . . . . . . . . . . . . . . . . . . . 52
3.18 Coefficient Comparison - Lateral . . . . . . . . . . . . . . . . . . . . . . . 53
3.19 Comparing to Boeing Simulation: Large Roll Upset . . . . . . . . . . . . 54
3.20 Comparing to Accident Data: Stall . . . . . . . . . . . . . . . . . . . . . 55
3.21 Comparing to EUR Stall Simulation and Flight Test . . . . . . . . . . . 56
3.22 Roll and β at Stall . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
3.23 Comparing Roll-Off Behavior . . . . . . . . . . . . . . . . . . . . . . . . 56
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3.24 Stability Derivatives and Simulation Results Using the Roll Model . . . . 57
4.1 Experiment Example Results: Upset Scenario 1 . . . . . . . . . . . . . . 68
4.2 Experiment Example Results: Upset Scenario 2 . . . . . . . . . . . . . . 69
4.3 Experiment Example Results: Upset Scenario 3 . . . . . . . . . . . . . . 70
4.4 Experiment Example Results: Upset Scenario 4 . . . . . . . . . . . . . . 71
4.5 Experiment Example Results: Upset Scenario 5 . . . . . . . . . . . . . . 72
4.6 Experiment Example Results: Upset Scenario 6 . . . . . . . . . . . . . . 73
4.7 QLC Envelopes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
4.8 Example MDA Outputs for Upset Scenario 1 . . . . . . . . . . . . . . . . 77
4.9 Example MDA Outputs for Upset Scenario 3 . . . . . . . . . . . . . . . . 78
A.1 Wind Experienced by Aircraft On Approach . . . . . . . . . . . . . . . . 84
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List of Tables
3.1 Maximum Control Surface Deflections . . . . . . . . . . . . . . . . . . . . 31
4.1 Summary of Reference Upset Scenarios . . . . . . . . . . . . . . . . . . . 59
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Nomenclature
α angle of attack, degrees
β sideslip angle, degrees
φ Euler roll angle, degrees
θ Euler pitch angle, degrees
ψ Euler yaw angle, degrees
p roll rate, deg/s
q pitch rate, deg/s
r yaw rate, deg/s
u airspeed along the body x-axis, m/s
v airspeed along the body y-axis, m/s
w airspeed along the body z-axis, m/s
X force along the body x-axis, N
Y force along the body y-axis, N
Z force along the body z-axis, N
L rolling moment about the body x-axis, N ·mM pitching moment about the body y-axis, N ·mN yawing moment about the body z-axis, N ·mnx longitudinal acceleration, G
ny lateral acceleration, G
nz normal load factor, G
ωss steady-state rate (wind-axis roll rate)
V true airspeed, knots or m/s
Veq equivalent airspeed, knots or m/s
Wi wind speed, m/s: i = x,y,z
b wing span, m
c mean aerodynamic chord, m
g acceleration of gravity, m/s2
Ci non-dimensional aerodynamic coefficient: i = X,Y,Z,L,D,l,m,n
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δa aileron deflection, degrees: positive right aileron up and left aileron down
δe elevator deflection, degrees: positive trailing edge down
δf flap deflection, degrees
δr rudder defection, degrees: positive trailing edge left
δs stabilizer deflection, degrees: positive trailing edge down
δspo speed brake handle position (0-1) or spoiler panel deflection (deg)
∆ incremental value
Ii moment of inertia, kg ·m2: i = xx, yy, zz, xz
AAIB UK Air Accidents Investigation Branch
ADI attitude director indicator
CFIT controlled flight into terrain
C.G. center of gravity
EUR enhanced upset recovery (NASA’s full-scale enhanced flight model)
FAA U.S. Federal Aviation Administration
FDR flight data recorder
FRS flight research simulator
IFS in-flight simulator
ILS instrument landing system
JTSB Japan Transport Safety Board
LaRC Langley Research Center
LOC loss-of-control
MDA motion drive algorithm
NASA U.S. National Aeronautics and Space Administration
NTSB U.S. National Transportation Safety Board
URT upset recovery training
UTIAS University of Toronto Institute for Aerospace Studies
Subscripts
X force component along the body x-axis
Y force component along the body y-axis
Z force component along the body z-axis
x
L lift component
D drag component
l rolling moment component
m pitching moment component
n yawing moment component
b component expressed in body axes
s component expressed in stability axes
osc oscillatory component of total angular rate
ss steady-state component of total angular rate
Other Notations
ˆ non-dimensional value
˙ time derivative
xi
Chapter 1
Introduction
1.1 Motivation
A recent report by The Boeing Company [1] showed that during the period of 2000-
2009, loss-of-control (LOC) was the leading cause of worldwide fatal commercial aircraft
accidents. Reference [2] defines LOC as an abnormal flight condition that is characterized
by the following behaviors:
1. Aircraft motion not predictably altered by pilot control inputs
2. Nonlinear effects such as kinematic/inertial coupling, disproportionately large re-
sponses to small state variable changes, or oscillatory/divergent behavior
3. High angular rates and displacements
4. Difficulty or inability to maintain heading, altitude, and wings-level flight
A quantitative definition of LOC is also given in Reference [2], where five envelopes were
developed for identifying LOC conditions. The key aircraft state variables in identifying
LOC are the angle of attack, sideslip, Euler angles (pitch and roll), structural load factor,
airspeed, and the behavior of the aircraft with respect to the control commands [2].
The focus of this study, airplane upset, is commonly described as a situation where
the aircraft is unintentionally brought outside of its normal flight envelope. Airplane
upset can often develop into a LOC condition. The types of airplane upset range from
large attitude excursions to the more serious situations involving stall. Numerous factors
can lead to airplane upset: pilot error, environmental disturbance such as windshear and
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Chapter 1. Introduction 2
wake turbulence, flight system failure, or a combination of these [3]. A 2008 study by the
U.S. Federal Aviation Administration (FAA) [4] reviewed LOC accidents resulting from
airplane upset that occurred worldwide between 1993 and 2007. This study identified
the leading causes of airplane upsets, which were: aerodynamic stall (36%), flight control
system malfunction (21%), pilot spatial disorientation (11%), contaminated airfoils (11%,
excluding stall), atmospheric disturbance (8%), and other/undetermined causes (13%).
A total of 74 accidents were found during the period studied including 3241 fatalities [4].
It is relatively recently that LOC has surpassed the previous leading cause of world-
wide commercial aircraft accidents: Controlled Flight Into Terrain (CFIT). While the
number of CFIT accidents has been largely reduced through the introduction of the En-
hanced Ground Proximity Warning System, the number of LOC accidents has stayed rel-
atively constant. In response to this situation, the aviation industry is currently putting
significant effort to develop an effective upset prevention and recovery strategy.
Several upset prevention and recovery strategies are currently being considered: 1)
development of advanced flight control technology, 2) advanced warning and advisory
technology, and 3) pilot training programs [5]. Advanced flight technologies, such as
the flight envelope protection system, can be effective in preventing accidents in some
scenarios. However, as long as pilots remain the chief commander in flight, they also
need to be trained to effectively recognize and respond to an usual situation. Conducting
flight tests and training using an actual aircraft in upset conditions is impractical due
to the high risk and cost. Using an in-flight simulator (IFS), which is an actual aircraft
that can be programmed to represent the behavior of other aircraft, is another option
as it can provide real motion and visual cues. However, the excessive cost and limited
accessibility can be prohibitive. A more practical option is to use ground-based flight
simulators, which are safe, inexpensive to run, easily accessible, and have played crucial
role in pilot training for years.
Researchers however, have been concerned with two critical shortcomings of using
current ground-based flight simulators for upset recovery training. One of the shortcom-
ings is that most flight model aerodynamic databases only cover the aircraft’s normal
flight envelope. Using the flight model outside of its aerodynamic database requires ex-
trapolation, which would most likely result in inaccurate aircraft response and could in
turn lead to negative training. The second shortcoming of the current ground-based
flight simulators is the fidelity of the motion produced in upset conditions. Even with
an enhanced aerodynamic database covering a larger flight envelope, it is unknown if the
Chapter 1. Introduction 3
hexapod motion system used in most simulators will be sufficient to provide motion cues
that can lead to positive transfer of training. The ongoing research at the University of
Toronto Institute for Aerospace Studies (UTIAS) Vehicle Simulation Group intends to
address both of these issues by the following means:
1. An enhanced flight model with an extended aerodynamic database will be developed
so that aircraft dynamics in upset conditions are better represented. Particularly,
the extended aerodynamic database should cover stall, post-stall and large angular
rates flight conditions.
2. Before any upset simulation can be run with motion, the motion drive algorithm
(MDA) and corresponding tuning method (software that compute the best motion
cues within the hardware limits) must be improved to account for the large ampli-
tude, highly dynamic motions seen in upset conditions. To identify potential areas
of improvement, a representative set of upset recovery maneuvers will be flown by
pilots in the UTIAS Flight Research Simulator (FRS) without motion.
3. The aircraft state time histories of the upset recovery maneuvers will be used to ex-
amine and evaluate potential methods for improving the MDA and tuning method.
4. Lastly, the effectiveness of the simulator motion improvement on transfer of training
will be studied.
This thesis examined the first two tasks. For the first task, the aerodynamic database
of an existing Boeing 747-100 (B-747) flight model was extended to cover high angle of
attack, large sideslip and large angular rates. The data used for extension were from a
series of wind tunnel tests conducted at NASA Langley Research Center using subscale
models of a generic commercial transport aircraft. The new B-747 model with the ex-
tended database will be referred to as the enhanced B-747 model. For the second task,
a set of upset recovery experiments was conducted using the enhanced B-747 model in
the UTIAS FRS with the help of pilots and without motion. Six upset recovery maneu-
vers were studied including three stall maneuvers, two unusual attitude upsets, and one
windshear encounter. The aircraft state time histories recorded from the upset recovery
experiments were used to examine motion cueing issues that could be experienced with
the current MDA. This report will summarize the development of the enhanced B-747
model, describe the set of upset recovery maneuvers chosen, and discuss results from the
simulator experiments.
Chapter 1. Introduction 4
1.2 Scope and Organization
The rest of this document is organized as follows:
• Chapter 2: This chapter will review the past research on airplane upset. The
review will primarily focus on research related to upset recovery training and the
flight model/motion fidelity issues.
• Chapter 3: This chapter will describe the methodology used to extend the aerody-
namic database of the B-747 model.
• Chapter 4: This chapter will provide a description of the upset recovery maneuvers
studied, followed by discussion of the experimental results.
• Chapter 5: The final chapter will summarize the main conclusions drawn from this
study and suggest future work.
Chapter 2
Literature Review
2.1 Upset Recovery Training
Current upset recovery training provided by most airlines consists of classroom lectures
and training in ground-based flight simulators [6]. In the simulator training, many airlines
use Level D flight simulators with motion [5]. Typically, the maneuvers practiced in the
training are unusual attitude upsets and the flight conditions attained are restricted to
be within the limits of the aerodynamic database 1 [6]. Additionally, some airlines use
aerobatic training to let pilots familiarize with the extreme flight conditions.
In response to concerns over the large number of LOC accidents resulting from air-
plane upsets, a team of government and industry representatives created a training guide
called Airplane Upset Recovery Training Aid (URT Aid) [3]. The aim of the URT Aid is
“to increase the pilot’s ability to recognize and avoid situations that can lead to airplane
upsets and improve the pilot’s ability to recover control of an airplane that has exceeded
the normal flight regime”(p.1.2, [3]). The URT Aid describes the causes and types of
upset, explains the aerodynamics and flight dynamics, and recommends recovery proce-
dures for a representative set of upset conditions. Airplane upset is defined in the URT
Aid as one or more of the following situations:
• Pitch attitude greater than 25◦ nose-up
• Pitch attitude greater than 10◦ nose-down
1The limits of the aerodynamic database are usually defined with respect to the aerodynamic envelope(i.e. aerodynamic data are available to certain angle of attack and sideslip angle). An aircraft can be inan unusual attitude condition (large pitch or bank) but the angle of attack and sideslip angle can stillstay within the flight model’s aerodynamic envelope.
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Chapter 2. Literature Review 6
• Bank angle greater than 45◦
• Within the above parameters but flying at airspeed inappropriate for the condition
This widely used quantitative definition is a guideline to recognize an upset. An example
upset recovery training program is suggested in the URT Aid, but it also recommends the
simulator to stay within the limits of the aerodynamic database and simulator motion
capability. This means that the more critical upsets such as stall and highly dynamic
maneuvers are not included. In fact, full stall recovery training is typically not provided
in ground-based flight simulators because the limited fidelity of the flight model and mo-
tion cues produced at stall and post-stall flight conditions may lead to a negative training
effect. Many commercial airplane pilots today only have pre-stall recovery training (usu-
ally on a small single engine aircraft during their private pilot’s license training) as less
pilots come from military backgrounds.
As previously noted in Chapter 1 however, a large number of LOC accidents are
associated with unsuccessful recovery from stall. A recent example is the 2009 accident
in Buffalo, New York (Flight 3407). The crew kept applying nose-up input even though
the aircraft was already stalled, which resulted in further increase in the angle of attack.
Had the pilots been trained for a fully developed stall condition, this accident might have
been avoided. The question of whether full stall recovery training in a ground-based
flight simulator is necessary (and feasible) is a long-debated issue [6]. The purpose of
the on-going research at UTIAS is to determine if improved flight model and simulator
motion can contribute to meaningful upset recovery training for the critical upsets such
as stall.
2.2 Upset Recovery Training Effectiveness
Burki-Cohen and Sparko refer to the current simulator upset recovery training as “more
successful in helping pilots to recognize and prevent upset conditions than to actually
recover from a situation that is beyond the normal [flight] envelope” (p.6, [5]). A number
of studies examined if the current upset recovery training can actually contribute to
successful recovery from upset.
Gawron’s Airplane Upset Training Evaluation Report [6] is a well-known study that
examined the effectiveness of current upset recovery training strategies. A Learjet in-
flight simulator (IFS) was used in this study, with the wheel, column and pedals pro-
Chapter 2. Literature Review 7
grammed to replicate the force and displacement characteristics of those installed in the
simulated aircraft [6]. The IFS had three degrees of freedom simulation capability in roll,
pitch, and yaw [7]. A computer installed in the IFS took the pilot control inputs and
augmented the Learjet control surface positions to make the Learjet follow the simulated
aircraft’s response [7]. As in ground simulation, a flight model was used to calculate the
simulated aircraft’s response. The flight model used was a simplified model of a generic
medium size transport aircraft [6], but the development of this model is not documented
in Gawron’s report.
Gawron studied the effect of various types of upset recovery training (URT) as well as
the effect of different upset scenarios on pilot recovery performance [6]. The study tested
eight upset scenarios that were all based on past accidents. The test scenarios were flown
in the Learjet IFS, where evaluation pilots were asked to recover from each scenario. The
evaluation pilots were categorized into five groups according to the types of training they
received: 1) no URT, 2) only aerobatic training, 3) only ground training by their airlines
(classroom and simulator training), 4) both aerobatic and ground training, and 5) IFS
training. No significant differences in recovery performance were found among the five
groups, but the statistical power of the study was reduced due to the large variability in
the evaluation pilot background and performance [6]. Nevertheless, it provided valuable
information such as the common errors pilots tend to make during recovery and pointed
out the shortcomings of current upset recovery training.
Most importantly, the study showed that while many pilots with airplane upset re-
covery training had the knowledge to recover, not all had proficiency [6]. For example,
many pilots failed to disconnect the autopilot before applying recovery inputs and did not
use bank angle to aid recovery during a nose-high upset, even though both are recovery
procedures often included in training. This suggests that repeated training is required
to gain both knowledge and proficiency. Another important issue noted in the study was
that many pilots used pre-stall recovery technique for fully developed stall during an icing
induced stall scenario [6]. Pre-stall and a fully developed stall require opposite initial
longitudinal input [6]. Pre-stall recovery requires adding more power and pulling nose-up
to prevent altitude loss, but when the aircraft is already stalled, the pilot must first and
foremost apply nose-down input to reduce the angle of attack. The mistake however, is
largely due to pilots not being trained for recovery from an actual stall. On the contrary,
most pilots were able to easily recover from the windshear scenario because windshear
training is provided by most airlines, and the scenario and recovery technique are well
Chapter 2. Literature Review 8
understood [6]. In conclusion, Gawron suggests that upset recovery training needs to be
improved, by “increasing the complexity of events to which the pilots are exposed to, and
by integrating the training into qualification and recurrent training throughout pilots’
careers” (p.xxxvi, [6]).
Gawron also emphasizes the need to develop quantitative measure of pilot recovery
performance [6]. Pilot performances were compared using parameters such as time to an-
nounce the problem, time to recover, time to make first correct elevator/aileron/rudder
input, and loss of altitude [6]. However, the study found that using the timing and
sequence parameters to measure pilot performance was inadequate, since no significant
differences were seen in these parameters between pilots who recovered and those who
did not [6, 7]. A pilot may take more time to diagnose the situation before applying
recovery inputs [7]. Furthermore, a different set of performance measurement parame-
ters/evaluation methods may be used in other studies, which could make it difficult to
directly compare results from two studies.
An unpublished study by FedEx/Calspan also used an IFS to evaluate two groups
of pilots with and without ground-based flight simulator URT. The upset recovery ma-
neuvers tested were restricted to be within the limits of the flight model aerodynamic
database. The results showed that simulator training can enhance pilots’ recovery skills
for maneuvers that are familiar to the pilots (such as nose-high/nose-low upsets), while
the lack of motion cues in the simulator could cause pilots to misdiagnose an unfamiliar
upset event in the actual aircraft. However, similar to Gawron’s study, variability in the
evaluation pilots reduced the statistical power of the study and no concrete conclusion
can be drawn [5]. It is also unknown whether the simulator motion was tuned for the
upset recovery maneuvers tested.
Furthermore, two recent studies by the Environmental Tectonics Corporation exam-
ined the transfer of URT using an unconventional ground-based flight simulator [8]. In
the two studies, experiments were conducted using a centrifuge-based flight simulator,
which is capable of generating sustained G forces. In a joint study with Embry-Riddle
Aeronautical University and the FAA, the researchers evaluated transfer of URT provided
in two different flight simulation devices: one in a centrifuge-based flight simulator, and
another using a desktop Microsoft Flight Simulator [8]. Student pilots from the university
participated in the study and were divided into three groups: the control group (with no
training), the Microsoft Flight Simulator trained group, and the centrifuge-based simu-
lator trained group [8]. The evaluation flights were conducted using four sets of upset
Chapter 2. Literature Review 9
recovery maneuvers in an aerobatic aircraft [8]. The results showed that the trained
groups significantly outperformed the untrained group, but little difference was seen be-
tween the Microsoft Flight Simulator trained group and the centrifuge-based simulator
trained group [8]. However, the researchers commented that the dynamics and the large
variability in recovery paths made it difficult to evaluate the recovery performance [8]. In
another joint study with NASA, pilot recovery performance prior to and after URT were
evaluated using seven upset scenarios to measure improvements from training [8]. Airline
transport pilots participated in the study and went through an URT program provided
in the centrifuge-based flight simulator [8]. Based on a five level recovery performance
score, the results indicated that recovery performances of the pilots were improved for
all seven scenarios after training [8].
These past studies showed that although current URT works in some upset scenarios,
it needs to be improved so that pilots can develop the skills to recover from a wider range
of upsets. The first step towards improvement is to develop an enhanced flight model
that covers a much larger flight envelope. The next section will review past research that
examined methods to improve the flight model in upset conditions.
2.3 Acquiring Aerodynamic Data Beyond the Nor-
mal Flight Envelope
The aerodynamic database of a typical flight model consists of look-up tables of non-
dimensional aerodynamic coefficients. For transport configurations, the flight test and
wind-tunnel data used to construct the aerodynamic database are typically acquired at
conditions within the normal flight envelope [9]. If the aircraft states go beyond that
described by the database, the default behavior of the database is to either extrapolate
or hold the last value in the data constant. This will make the predicted aircraft response
most likely incorrect which could in turn lead to a negative training effect. Therefore, the
flight model aerodynamic database must be extended to cover a larger flight envelope in
order to provide meaningful training for the most critical upsets such as stall. Due to the
high risk and cost associated with full-scale flight tests in upset conditions, alternative
methods must be considered for data collection. One possibility is to construct the
aerodynamic database from accident data, but there is very little data available and it
is difficult to access. Using Computational Fluid Dynamics to compute the aerodynamic
Chapter 2. Literature Review 10
data is another option, but it requires flight or wind-tunnel data to validate the calculated
results. Additionally, it is expensive time and cost-wise, as 3D models need to be built
and stall modeling requires solving the full Navier-Stokes equations. A more practical
option is to use wind-tunnel testing to collect data beyond the normal flight envelope.
In fact, stall and post-stall modeling of military aircraft using wind-tunnel data is a
mature field of research and the experiment methods and apparatus are well established.
Numerous past NASA high angle of attack wind-tunnel studies can be found for military
aircraft, for example see Reference [10].
The studies on airplane upset by NASA Langley Research Center (LaRC) [9, 11, 12,
13] were the main resource used to develop the enhanced B-747 model. As part of NASA’s
Aviation Safety Program and in collaboration with The Boeing Company, a series of
wind-tunnel tests were conducted on a commercial transport aircraft configuration similar
to the tests conducted for military aircraft stall/post-stall modeling. The studies used
3.5% and 5.5% subscale aircraft models to conduct static, forced oscillation, and rotary
balance wind-tunnel tests [12]. The subscale aircraft models were representative of a
medium size twin-jet commercial transport aircraft that has a close resemblance to the
Boeing 757. Data were collected up to angle of attack of 85◦ and sideslip angle of ±45◦
[9]. Static wind-tunnel tests obtained changes in the aerodynamic forces and moments
due to changes in angle of attack, sideslip, and control surface deflections [12]. Two types
of dynamic wind-tunnel tests were required to obtain dynamic data because post-stall
motion is not represented well by either forced oscillation tests or rotary balance tests
alone [14]. To capture the damping effects due to pitch, roll and yaw rates, the subscale
“model was oscillated in a sinusoidal motion over a range of frequencies and amplitudes
that corresponded to typical full-scale short-period and Dutch roll motions” (p.3,[12])
in the forced oscillation tests. The rotary balance tests are important for predicting
steady spin dynamics [13], and the subscale aircraft model was rotated at a steady rate
(ωss) about the free-stream velocity vector [14]. Past research has shown that the data
obtained from these two types of dynamic tests can be combined to better represent the
stall and post-stall dynamic motions. NASA LaRC used a blending method developed
by Kalviste [15] to combine the forced oscillation and rotary balance data. The blending
method referred to as Hybrid Kalviste was shown to give the best prediction of spin
dynamics when compared to the free-spin wind-tunnel test results [11, 14]. The Hybrid
Kalviste method will be discussed in more detail in Chapter 3.
One concern with using subscale model data for full-scale flight model is the difference
Chapter 2. Literature Review 11
in Reynolds number which affects the lift and drag measurements [13]. To examine
Reynolds number effects, NASA compared their wind-tunnel test data to those obtained
at higher Reynolds number and to some flight test data. The comparison showed that
Reynolds number effect is most significant near stall (10 to 20 degrees of angle of attack
region), but diminishes at higher angle of attack due to the flow separation [12, 13]. This
indicates that scaling corrections on the low Reynolds number wind-tunnel data may not
be necessary at post-stall angle of attack [13].
The wind-tunnel data collected were incorporated into NASA’s baseline twin-jet
transport aircraft flight model to create an Enhanced Upset Recovery (EUR) model
[9]. Simulations using the EUR model were run in a desktop mode and in a real-time,
pilot-in-the-loop mode [9] to examine the improvement from model enhancement. The
real-time, pilot-in-the-loop simulation was conducted in NASA’s Integration Flight Deck
simulator without motion [9]. The maneuvers tested were pilot-induced stalls where the
pilot followed the pitch time histories from flight tests of stall [9]. The simulation time
histories were then compared to the flight test data for model validation. The comparison
showed promising results, with the EUR simulation time histories more closely matching
the flight test data than those of the baseline model (Figure 15 to 20 in Reference [9]).
The same research group is using a remotely piloted subscale flying test aircraft to
further validate the modeling methods and flight dynamics characteristics for airplane
upset [13]. The unaddressed areas in the NASA LaRC research include: engine surge or
other high α/β effects on engine models, enhancements to aero-elastic models at high
angles of attack, and the options of adding a buffet model and motion cueing [9].
The NASA LaRC research was a suitable framework for extending the B-747 aero-
dynamic database. Firstly, a notable remark made by the researchers was that the data
trends from the wind-tunnel tests may be applicable for other types of transport aircraft,
since the expected variations in the aerodynamic characteristics due to configuration
differences are small as a result of the large degree of flow separation [12]. Thus, by
incorporating the key data trends through data extension, the enhanced B-747 model
will be representative of a generic commercial transport aircraft in upset conditions.
Secondly, the NASA wind-tunnel data, which were processed into non-dimensional aero-
dynamic coefficients, could be blended with the existing UTIAS B-747 database. This
made the process easier as there was no need to largely modify the existing simulation
technique. The aerodynamic data used for extension are from a public-domain flight
model provided by LaRC. This is not the full-scale EUR model but a flight model of
Chapter 2. Literature Review 12
their subscale flying test aircraft, and will be referred to as the NASA T2 model. Never-
theless, the aerodynamic database of the NASA T2 model is constructed using the LaRC
wind-tunnel data.
2.4 MDA and Study of Motion Fidelity
In addition to the flight model, simulator motion also needs to be improved to support the
large amplitude, highly dynamic motions seen in upset conditions. Most ground-based
flight simulators use a hexapod motion system which provides six degrees of freedom
motion by moving the six actuators simultaneously. The motion drive algorithm (MDA)
is the software that provides commands to the hexapod motion system. The purpose of
the MDA is to provide the best motion cues while restricting the actuator travel to be
within the simulator’s physical limits [16]. This is done by limiting, scaling and high/low-
pass filtering the flight model specific force and angular rate outputs [16]. Pitch and roll
angles are also used to simulate sustained X and Y specific forces respectively, which is
called tilt-coordination [16].
High-pass filters are used to filter out the low frequency aircraft motion signals that
tend to over-extend the motion system actuators [16]. In tilt-coordination, the specific
forces are low-pass filtered as tilting is used to simulate the low frequency specific forces.
The filter parameters and the scale factors can be tuned to provide the best motion
possible. Since the current MDA is designed and tuned for relatively mild maneuvers, it
is expected that modifications will be required to the MDA and the tuning method to
effectively simulate upset motions.
A preliminary study by Chung [17] examined the motion fidelity in upset conditions.
This study focused on identifying specific cueing issues that can be caused by the MDA
of a conventional hexapod motion system when simulating airplane upsets. The study
examined a roll and a pitch upset and recovery maneuvers using two sets of MDA param-
eters: one with typical MDA filter settings for a civil transport aircraft, and the other
based on Medium Fidelity criteria for rotorcraft [17].
In Chung’s study, one of the important issues was identified by examining the power
spectrum density (PSD) of angular rates, accelerations and specific forces. Plots of
the PSD showed that most of the power lies in the low frequency region [17], which is
undesirable as low frequency motions tend to over-extend the motion system actuators
[16]. This also indicates that much of the motion will be filtered out by the high-pass
Chapter 2. Literature Review 13
filters, and therefore simulator motion is less representative of the aircraft motion.
The angular commands to the simulator consist of the motion command from high-
pass filtering the angular rates and the motion command from tilt-coordination. In tilt-
coordination, the pitch and roll angles are used to represent the sustained X and Y specific
forces, but the rates and accelerations must be limited to be below the human sensing
threshold so that pilot only senses the gravity vector and not the rotational motion [16,
17]. However, it was observed in Chung’s study that the MDA pitch angular acceleration
output was dominated by tilt command and did not represent the aircraft motion well
[17]. This suggests that care must be taken when adjusting the MDA parameters for
tilt-coordination to simulate large and frequent reversal of the specific forces [17].
These are examples of the issues identified with a typical MDA used in current ground-
based flight simulators. For improving the motion fidelity, Chung suggests looking at
“nonlinear scaling based on a particular maneuver dependent parameter and offsetting
the simulator to a preset position to increase available travel” (p.8,[17]).
In summary, these past research have provided the basis for this work. The studies
on transfer of URT showed that current simulator URT programs work for the relatively
simple upset scenarios, but will need improvement to train pilots for the more critical
upsets such as stall. The studies from NASA LaRC provided a method to extend the
flight model aerodynamic database, and the enhanced B-747 model was developed based
on the NASA LaRC wind-tunnel data, as will be discussed in the next chapter. Using
the enhanced B-747 model, six upset recovery maneuvers were studied in the UTIAS
FRS without motion. The URT Aid and Gawron’s study provided critical information
(particularly the recovery procedures for different upset scenarios) for designing the upset
recovery experiments. Similar to Chung’s study, the aircraft state time histories recorded
from the upset recovery maneuvers can be used to identify and potentially correct motion
cueing issues that could be experienced by the current MDA. A preliminary analysis of
the MDA results will be presented in Chapter 4.
Chapter 3
Flight Model
3.1 Introduction
This chapter describes the methodology used to extend the aerodynamic database of the
B-747 model. In the discussion, three axis systems will appear: body axes, stability axes,
and inertial axes. First, the body axes is a right-handed coordinate system fixed to the
aircraft, with the origin located at the aircraft’s center of gravity (C.G.) [18]. The x-axis
lies along the fuselage reference line, the y-axis points along the starboard wing, and the
z-axis is positive downward [18]. The stability axes is obtained by rotating the body axes
about the y-axis through the angle of attack [18]. The two axes are shown in Figure
3.1. The inertial axes is a reference system used to describe the aircraft flight path and
orientation, with the origin arbitrarily fixed to a flat, non-rotating earth and the z-axis
pointing towards the center of the earth [18].
Figure 3.1: Axes Definition
14
Chapter 3. Flight Model 15
In this chapter, any term (non-dimensional coefficients, aircraft states, axis) with the
subscript ‘b’ denotes that it is expressed in the body axes. Similarly, all terms with
the subscript ‘s’ are expressed in the stability axes. For simplicity, angle of attack and
sideslip angle will be referred to as α and β. Roll, pitch and yaw rates will be referred to
as p, q, and r respectively. The symbol δ will be used for control surface deflection where
δa = aileron, δe = elevator, δf = flap, δr = rudder, δs = stabilizer, and δspo = spoilers.
A notation such as CL(α, β, δf ) describes that the coefficient CL is a function of angle
of attack, sideslip, and flap deflection. Also, in example plots of the extended data, a
notation such as CL(α, β = 5◦, δf = 0◦) will appear. This means the data is plotted for
a range of α at β = 5◦ and δf = 0◦. Finally, when referring to multiple coefficients, a
simplified notation will be used. For example, Cl/n/Y is equivalent to Cl, Cn, CY .
3.2 The Existing UTIAS B-747 Model
The existing UTIAS B-747 model employs the full six degrees of freedom nonlinear flight
equations governing the motion of a rigid body, as given below [19]. All quantities are
expressed in the body axes: u, v, and w are airspeeds along the xb, yb, and zb-axis; X, Y ,
and Z are the forces along the xb, yb, and zb-axis; L, M , and N are the rolling, pitching,
and yawing moments; φ, θ, and ψ are the Euler roll, pitch, and yaw angles; Ixx, Iyy, and
Izz are the moment of inertia about the xb, yb, and zb-axis, Ixz is the product of inertia;
and finally g is the acceleration of gravity.
u =X
m− g sinθ − qw + rv (3.1)
v =Y
m+ g cosθ sinφ− ru+ pw (3.2)
w =Z
m+ g cosθ cosφ− pv + qu (3.3)
p = C1pq + C2qr + C3L+ C4N (3.4)
q = C5pr + C6(p2 − r2) + C7M (3.5)
r = C8qr + C9pq + C10L+ C11N (3.6)
where
Chapter 3. Flight Model 16
C0 = (IxxIzz − I2xz)−1 C1 = Ixz(Ixx − Iyy + Izz)C0
C2 = ((Iyy − Izz)Izz − I2xz)C0 C3 = IzzC0
C4 = IxzC0 C5 = (Izz − Ixx)/Iyy
C6 = −Ixz/Iyy C7 = 1/Iyy
C8 = Ixz(Iyy − Izz − Ixx)C0 C9 = ((Ixx − Iyy)Ixx + I2xz)C0
C10 = IxzC0 C11 = IxxC0
The UTIAS B-747 model is implemented in MATLAB Simulink. Its aerodynamic
database contains the modeling data provided in a NASA/Boeing report [20]. Most of
the aerodynamic data is found by using Simulink’s look-up table blocks but some data
are also simplified into equation form. The B-747 model’s aerodynamic envelope covers
α = [-5◦,25◦] and β = [-15◦,15◦]. All data are given in the stability axes. The non-
dimensional aerodynamic coefficients, CL, CD, Cm, Cl, Cn, and CY are the final outputs
from the B-747 aerodynamic database. Each coefficient is the sum of the basic static
effects, control effects and dynamic effects, as shown in the equations below [20]. The
first term in each equation describes the basic static effect, the terms with δ describe the
control effects, and the terms with either ps, qs, rs, ˆα or ˆβ describe the dynamic effects.
Each term in the equation is either implemented in look-up table or in equation form.
The computed aerodynamic coefficients are transformed to the body axes before being
used in the equations of motion.
CL = CL,Basic + ∆CL,Aeroelastic +dCL
d ˆαˆα +
dCLdqs
qs +dCLdnZ
nZ (3.7)
+ ∆CL,δs + ∆CL,δe + ∆CL,δspo + ∆CL,δa
+ ∆CL,Landing Gear + ∆CL,Ground Effect
where qs = qsc2V
, ˆα = αc2V
, nz = normal load factor
CD = K[CD,Basic + ∆CD,δs ] + [1 −K][CD]M + ∆CD,Sideslip (3.8)
+ ∆CD,δspo + ∆CD,δr + ∆CD,Landing Gear + ∆CD,Ground Effect
where K = 0 for flaps-up and K = 1 for flaps-extended
Cm = Cm,Basic + ∆Cm,Aeroelastic +dCm
d ˆαˆα +
dCmdqs
qs +dCmdnZ
nZ (3.9)
+ CL(C.G.x − 0.25) + ∆Cm,Sideslip + ∆Cm,δs + ∆Cm,δe + ∆Cm,δspo
+ ∆Cm,δa + ∆Cm,δr + ∆Cm,Landing Gear + ∆Cm,Ground Effect
Chapter 3. Flight Model 17
Cl = Clββ + Clpps + Clrrs + ∆Cl,δspo + ∆Cl,δa + ∆Cl,δr (3.10)
Cn = Cnββ +dCn
d ˆβ
ˆβ + Cnpps + Cnrrs (3.11)
+ ∆Cn,δspo + ∆Cn,δa + ∆Cn,δr
CY = CYββ + CY pps + CY rrs + ∆CY,δspo + ∆CY,δr (3.12)
where ps = psb2V
, rs = rsb2V
, ˆβ = βb2V
3.3 NASA T2 Model
The NASA T2 model, mentioned in Chapter 2, was used to extend the B-747 model
because its aerodynamic database is constructed using the LaRC wind-tunnel data. The
NASA T2 model is also implemented in MATLAB Simulink. Its aerodynamic database
has a similar structure to the B-747 model and all data are implemented in look-up
tables. The data however, are given in the body axes.
Longitudinal Terms [14]:
Ci = Ci,Basic(α, β) + ∆Ci,δ(α, β, δ) + ∆Ci,qosc(α, qosc) + ∆Ci,ωss(α, β, ωss) (3.13)
Lateral Terms [14]:
Cj = Cj,Basic(α, β) + ∆Cj,δ(α, β, δ) + ∆Cj,posc(α, posc) + ∆Cj,rosc(α, rosc) (3.14)
+ ∆Cj,ωss(α, β, ωss)
where i = X,Z,m; j = Y, l, n; posc = poscb2V
, qosc = qoscc2V
, rosc = roscb2V
, ωss = ωssb2V
.
The first terms in both equations describe the basic static effects. The second terms
in both equations describe the control effects. The rest of the terms with p, q, r and ω
describe the dynamic effects. The meanings of the subscripts osc and ss will be described
in Section 3.4.3.
The NASA T2 data were transformed to the stability axes to blend with the B-747
data. The transformation from the body axes to the stability axes is given below:
Chapter 3. Flight Model 18
CL,s = CX,b sinα− CZ,b cosα (3.15)
CD,s = −CX,b cosα− CZ,b sinα (3.16)
Cm,s = Cm,b (3.17)
Cl,s = Cl,b cosα + Cn,b sinα (3.18)
Cn,s = −Cl,b sinα + Cn,b cosα (3.19)
CY,s = CY,b (3.20)
3.4 Data Extension
The static and control effects data were extended to α = 85◦ and β = ±45◦ which are
the limits of the NASA T2 model’s static database. Similarly, the dynamic effects data
were extended to the limits of the NASA T2 model’s dynamic database 2. The exact
extension method used for each coefficient was different and depended on how the NASA
data compared to the B-747 data. The general idea however, was to keep the original B-
747 data unchanged at small α, β and angular rates and use the NASA T2 data at larger
values where the B-747 model does not have data. The following subsections describe
each data extension in detail.
3.4.1 Basic Static Effect
The basic static effect refers to the following terms in Equations 3.7 to 3.12:
CL,Basic, CD,Basic, ∆CD,Sideslip, Cm,Basic, ∆Cm,Sideslip, Clββ, Cnββ, CYββ
These describe the changes in the aerodynamic forces and moments due to changes in α
and β.
Longitudinal Data
The α extension for CL,Basic and Cm,Basic were done by Lewis Menzies [21], a former
exchange student at UTIAS. The NASA data Menzies used in his work were not from the
NASA T2 model, but were the data provided in NASA’s published literature (References
2The limits of the dynamic database are different for each term and will be given in Section 3.4.3.
Chapter 3. Flight Model 19
[9] and [12]) because the NASA T2 model was not available at the time. Menzies fitted
the NASA data to the B-747 data at small α before using them for data extension. For
example, Menzies scaled the NASA CL,Basic(α) curve using the slope CLα at small α
and then shifted horizontally and vertically based on αstall and CL,max values to fit with
the B-747 CL,Basic(α) curve at small α [21]. Similarly, Menzies scaled the NASA Cm(α)
curve using the slope Cmα at small α and shifted vertically to have a best match with
the B-747 Cm,Basic(α) curve at small α [21]. Additionally, Menzies used a percentage
blending method to blend the B-747 data with the NASA data in the data transition
section to smoothly shift from using B-747 data to NASA data. This is illustrated in
Figure 3.2. For example, if the data transition section is set to α = [15◦, 25◦], then at its
midpoint, α = 20◦, the blended data equals to 0.5× original B-747 data + 0.5× NASA
data.
15 16 17 18 19 20 21 22 23 24 25−0.04
−0.035
−0.03
−0.025
−0.02
−0.015
−0.01
α (deg)
Non
−di
men
sion
al A
erod
ynam
ic C
oeffi
cien
t
Original B−747 DataNASA T2 DataBlended Data
Figure 3.2: Data Blending Method
Figures 3.4(a) to 3.4(d) show the extended CL,Basic(α) and Cm,Basic(α) for zero sideslip
case. Figures 3.4(a) and 3.4(b) show the extended data at different flap settings. The
decrease in CL is seen at αstall as expected. Cm continues to decrease as α increases,
which indicates that the aircraft retains static pitch stability. Figures 3.4(c) and 3.4(d)
compare the extended B-747 data to the original B-747 data. Figure 3.4(c) shows that
Chapter 3. Flight Model 20
with the original B-747 data, the decrease in lift due to stall is not captured. The Cm
plot in Figure 3.4(d) shows that with the original data, the static pitch stability could
be under-predicted (hold last value constant) or over-predicted (extrapolation). Slightly
nonlinear behavior (small pitch up) can be seen near α = 20◦ in the Cm vs. α plots. A
previous NASA high angle of attack study [10] suggests that this nonlinearity at high
lift coefficient arises due to the decreased horizontal tail contribution to pitch stability
as it becomes immersed in the wing wake. A high mounted tail causes more significant
nonlinearity (more pitch up) than a low mounted tail because low mounted tails exit the
wing wake as α increases while high mounted tails enter the wing wake as α increases
[10]. The B-747 aircraft’s horizontal tail is low mounted so the nonlinearity seen near
stall is mild. The pitch up seen at a higher angle, near α = 40◦, may be due to the flow
separation occurring on the horizontal tail itself.
The rest of the basic static data were extended in this thesis and the percentage data
blending method devised by Menzies was adopted. In most cases, α extension was first
performed using the NASA T2 data at β = 0◦. The α extension process is described in
the pseudo code below. The original B-747 data were used for α < 15◦ and the NASA
T2 data were used for α > 25◦. Data blending was performed in the data transition
section, which was set to α = [15◦, 25◦]. When NASA T2 data and B-747 data showed
very similar trends at small α, NASA T2 data was scaled to fit the B-747 data at small
α. This will be referred to as the fitted NASA T2 data. When the NASA T2 data and
B-747 data showed different trends, raw NASA T2 data was used.
Aerodynamic Data Blending and Extension: Pseudo Code #1
% Data Required
CB−747(α) % B-747 Data
CNASA−T2(α, β) % Corresponding NASA Data
α = -5:1:85;
% α Extension
for α = -5 to 85
if α < 15
CExtended1 (α) = CB−747(α);
else if α ≤ 25
% Compute blending factor
FF = (25−α)10
;
Chapter 3. Flight Model 21
CExtended1 (α) = FF × CB−747(α) + (1 − FF ) × CNASA−T2(α, β = 0◦);
else
CExtended1 (α) = CNASA−T2(α, β = 0◦);
end;
end;
The α extension for CD,Basic uses the algorithm described in Pseudo Code #1 but is
performed real-time during the simulation instead of generating an extended data set off-
line. This is because the B-747 model uses different sets of data for flaps-up configuration
([CD]M in Equation 3.8) and flaps-extended configuration (CD,Basic in Equation 3.8). In
addition, [CD]M is not directly a function of α but a function of Mach number and an
intermediate value of lift coefficient called CL∗ (equals to the first 5 terms in Equation
3.7). Thus, real-time data blending and extension, which switches between B-747 data,
blended data, and NASA T2 data, depending on α at each time step, was considered
appropriate for this case. Figure 3.3 shows the data-blending block implemented in
Simulink. The general trend of the CD(α) curve from the NASA T2 data is plotted in
Figure 3.5(a), which shows that the value of CD increases with increasing α. With the
original [CD]M table however, the predicted CD would decrease for increasing α because
CL∗ decreases due to stall. This under-prediction of CD is illustrated in Figure 3.5(b)
where the corresponding α is also shown.
Additionally, β effects were added to CL,Basic, Cm,Basic, and CD,Basic as increments to
the β = 0◦ data. For example,
Cm,Basic(α, β, δf ) = Cm,Basic(α, β = 0◦, δf ) + ∆Cm,Basic(α, β, δf )
The β extensions to Cm and CD are discussed first. The B-747 β effects data ∆CD,Basic(β, δf )
(denoted ∆CD,sideslip in Equation 3.8) and ∆Cm,Basic(β, δf ) (denoted ∆Cm,sideslip in Equa-
tion 3.9) at each flap setting are available up to β = ±15◦. First, the percentage blending
method was used to extend the B-747 β effects to large β. The first half of Pseudo Code
#2 below describes the β extension process. An example β extension result is shown for
∆Cm,Basic(α, β, δf = 0◦) in Figure 3.6(a), where different lines are for different values of
α. The B-747 β effects extended to large β were then used for α < 15◦, and blended with
the raw NASA β effects in the data transition section α = [15◦, 25◦]. Above α = 25◦, the
raw NASA β effects were used. This is illustrated in the second half of Pseudo Code #2.
Chapter 3. Flight Model 22
Figure 3.3: Real-Time Data Blending Block in Simulink
Aerodynamic Data Blending and Extension: Pseudo Code #2
% Data Required
CExtended1 (α) % Data extended using Pseudo Code #1
∆CB−747(β) % B-747 β effect
∆CNASA−T2(α, β) % NASA β effect
α = -5:1:85;
β = -45:1:45;
% β Extension for α ≤ 25◦
for α = -5 to 25
for β = -45 to 45
if |β| < 5
∆Cβ−Effect(α, β) = ∆CB−747(β)
else if |β| ≤ 15
% Compute blending factor
FF = (15−|β|)10
;
∆Cβ−Effect(α, β) = FF × ∆CB−747(β) + (1 − FF ) × ∆CNASA−T2(α, β)
else
∆Cβ−Effect(α, β) = ∆CNASA−T2(α, β)
Chapter 3. Flight Model 23
end;
end;
end;
% Blend in α again
for α = -5 to 85
for β = -45 to 45
if α < 15
CExtended(α, β) = CExtended1 (α) + ∆Cβ−Effect(α, β);
else if α ≤ 25
% Compute blending factor
FF = (25−α)10
;
CExtended(α, β) = CExtended1 (α) + FF × ∆Cβ−Effect(α, β)
+(1 − FF ) × ∆CNASA−T2(α, β);
else
CExtended(α, β) = CExtended1 (α) + ∆CNASA−T2(α, β);
end;
end;
end;
The B-747 CL,Basic data is a function of α and flap deflection (δf ) but not β as its
effect is negligible at small α and β. The NASA T2 data however, shows that the β effect
becomes more significant at higher α. The method used to incorporate the β effect to
CL,Basic is different from that described in Pseudo Code #2. For CL,Basic, the β effect
is added real-time during the simulation as follows. First, the output from the extended
CL,Basic(α, δf ) look-up table is taken as CL,Basic(α, β = 0◦, δf ). This value, along with
β are the inputs to a second look-up table that takes the β effect into account. The
data used in this second look-up table is shown in Figure 3.6(b), with different lines for
different input values of CL,Basic(α, β = 0◦, δf ). The data was obtained by scaling the
NASA T2 CL vs β curves using CL,max at β = 0◦ for each flap setting. The β effect
is divided into two tables, one for pre-stall and another for post-stall, as the behavior
is different in these two regimes. During the simulation, α is checked against αstall to
Chapter 3. Flight Model 24
determine which table to use. The output from the second look-up table therefore is the
final output CL,Basic(α, β, δf ).
Lateral Data
The lateral data Clββ, Cnββ, and CYββ were extended differently from the longitudinal
data. The original B-747 model uses the stability derivatives Clβ , Cnβ , CYβ , which are
linear with respect to β at small values of β and are functions of α. The NASA data
however, shows that nonlinearity with respect to β arises as α and β increase. Hence
the NASA T2 model does not use the stability derivatives and instead, Cl,Basic, Cn,Basic,
and CY,Basic are computed from 2D look-up tables with α and β as inputs. This way the
nonlinearity with respect to β can be captured.
For the data extension, B-747 data were first extended to large β to account for the
nonlinearity seen at large values of β. The NASA T2 Cl/n/Y vs. β curves have linear
sections at small β and α. Slopes were computed from this linear section at different
values of α. Then the NASA T2 data was scaled by matching the calculated slopes to
the B-747 Cl/n/Yβ values at the same α. The scaled NASA T2 data was then equivalent
to the B-747 data extended to large β. This was used for α < 15◦ and then blended with
raw NASA T2 data for α = [15◦, 25◦]. Above α = 25◦, the raw NASA T2 data was used.
The pseudo code below illustrates this extension process.
Aerodynamic Data Blending and Extension: Pseudo Code #3
% Data Required
CB−747iβ
(α) where i = l, n, Y
CNASA−T2(α, β)
α = -5:1:85;
β = -45:1:45;
% β Extension for α ≤ 25◦
for α = -5 to 25
% Compute Ciβ for NASA T2 data
% Data in β2 and β1 are linear
ScaleNASA−T2 =CNASA−T2(α,β2)−CNASA−T2(α,β1)
β2−β1 ;
ScaleB−747 = CB−747iβ
(α)
CExtended1 (α, all β) = CNASA−T2(α, all β) ∗ ScaleB−747
ScaleNASA−T2;
end;
Chapter 3. Flight Model 25
% α Extension
for α = -5 to 85
for β = -45 to 45
if α < 15
CExtended(α, β) = CExtended1 (α, β)
else if α ≤ 25
% Compute blending factor
FF = (25−α)10
;
CExtended(α, β) = FF × CExtended1 (α, β) + (1 − FF ) × CNASA−T2(α, β);
else
CExtended(α, β) = CNASA−T2(α, β);
end;
end;
end;
Figures 3.7(a) and 3.7(b) compare the original B-747 data, raw NASA T2 data and
the extended B-747 data (fitted NASA T2 data) at small α for Cl and Cn. Nonlinearity
with respect to β can be observed for both Cl and Cn at large values of β. Note that
the B-747 Cl,Basic data has an additional term(Clβ )β
(Clβ )β=0multiplied to Clβ , which causes
the original B-747 data to become nonlinear as β increases. Figures 3.7(c) and 3.7(d)
show the Cl/n vs. β curves at higher α. The local derivatives of these plots are the
local stability derivatives Clβ and Cnβ , which must be negative and positive respectively
for lateral and directional static stability. Figures 3.7(c) and 3.7(d) show that static
instabilities occur at higher α. The lateral and directional instabilities will be discussed
next.
In Figure 3.7(c), lateral instability can be observed at α = 25◦. At small α, wing
dihedral is one of the main contributors to lateral static stability Clβ . When an aircraft
with dihedral is in sideslip, the wing heading into the wind will be at higher α as the
velocity normal to the wing will have an increase from v. For example, if Γ is the dihedral
angle, then the velocity normal to the wing is Vnormal = w cosΓ + v sinΓ [22]. On the
contrary, the other wing will experience a decrease in α as Vnormal = w cosΓ − v sinΓ
[22]. Higher α means more lift on the wing heading into the wind. Thus, with positive
Chapter 3. Flight Model 26
sideslip, a negative rolling moment is produced, and with negative sideslip, a positive
rolling moment is produced. This dihedral effect helps the aircraft to fly with wings-
level. For example, when an aircraft is rolled to the right, it experiences positive sideslip,
but the dihedral effect will produce a restoring negative rolling moment that brings the
wings back to level. In addition to dihedral, wing sweep is another major contributor to
Clβ . For a swept wing, the wing heading into the wind has more lift as it experiences a
larger velocity component normal to the quarter chord line of the wing which determines
the amount of lift generated [22]. This too, creates a restoring rolling moment that tends
to bring the wings back to level. However, as α increases, the wing heading into the
wind will stall first because it is at higher α, resulting in loss of the dihedral effect. This
causes Clβ to become positive (unstable) near stall. The stability effect from sweep also
diminishes at stall due to the separated flow. Figure 3.7(c) however, shows that stability
is restored at higher α, where both wings are stalled.
In Figure 3.7(d), directional instability is observed at high α. The vertical tail pro-
duces side force when the aircraft is in sideslip. This side force provides restoring yawing
moment and therefore directional stability to the aircraft [22], but Figure 3.7(d) shows
that the aircraft starts to lose directional stability as α increases. Previous high angle of
attack study on military aircraft [10] suggests some possible causes of loss of directional
static stability at high α. As α approaches αstall, an adverse sidewash field is induced
by the wing-fuselage combination [10]. Then as α increases further, the vertical tail be-
comes immersed in the adverse sidewash field and the dynamic pressure at the vertical
tail also starts to decrease due to shielding by the aft fuselage and/or the wake of the
stalled wing [10]. As a result, the vertical tail starts to lose its effect. In addition to the
vertical tail, wing dihedral also contributes to directional stability. When the aircraft
is in sideslip, the wing heading into the wind experiences higher α and thus more drag,
producing a restoring yawing moment. The loss of dihedral effect due to stall therefore
also contributes to the reduction in directional stability.
Chapter 3. Flight Model 27
−20 0 20 40 60 80 100−0.5
0
0.5
1
1.5
2
2.5
α (deg)
CL(α
,δf)
CL(α, β = 0◦, δf )
δf = 0◦
δf = 1◦
δf = 5◦
δf = 10◦
δf = 20◦
δf = 25◦
δf = 30◦
(a) CL,Basic
−20 0 20 40 60 80 100
−1
−0.8
−0.6
−0.4
−0.2
0
0.2
α (deg)
Cm
(α,δ
f)
Cm(α, β = 0◦, δf )
δf = 0◦
δf = 1◦
δf = 5◦
δf = 10◦
δf = 20◦
δf = 25◦
δf = 30◦
(b) Cm,Basic
−20 0 20 40 60 80 100−0.5
0
0.5
1
1.5
2
2.5CL(α, β = 0◦, δf = 0◦)
α (deg)
CL(α
)
Original B−747 Data − Hold Last ValueOriginal B−747 Data − ExtrapolateExtended B−747 Data
(c) Compare CL,Basic
−20 0 20 40 60 80 100−3.5
−3
−2.5
−2
−1.5
−1
−0.5
0
0.5
α (deg)
Cm
(α)
Cm(α, β = 0◦, δf = 0◦)
Original B−747 Data − Hold Last ValueOriginal B−747 Data − ExtrapolateExtended B−747 Data
(d) Compare Cm,Basic
Figure 3.4: Basic Lift and Pitching Moment Coefficients
Chapter 3. Flight Model 28
−20 0 20 40 60 80 1000
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
α (deg)
CD
(α)
CD(α) Curve: General Trend
(a) CD,Basic vs. α Trend
4
6
8
10
12
14
16
18
α(d
eg)
Underprediction of CD at Higher α
10 15 20 25 30 35 400.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
time (s)
CD
,Basic
Original B−747 DataExtended B−747 Data
(b) Compare CD,Basic
Figure 3.5: Basic Drag Coefficient
−50 0 50−0.7
−0.6
−0.5
−0.4
−0.3
−0.2
−0.1
0
β (deg)
ΔC
m(α
,β)
ΔCm(α = [−5◦, 20◦], β, δf = 0◦)
Original B-747 Data (Extrapolated for |β| > 15◦)
Extended B-747 Data
α = −5° to 8°
α = 9° to 20°
(a) β Effect on Cm,Basic
0
0.5
1
CL(α
,β)
β effect:pre-stall
−50 0 50
0.2
0.4
0.6
0.8
1
1.2
1.4
β (deg)
CL(α
,β)
β effect:post-stall
α
CL,max
CL,max α
(b) β Effect on CL,Basic
Figure 3.6: Sideslip Effects on Basic Lift and Pitching Moment Coefficients
Chapter 3. Flight Model 29
−50 0 50−0.1
−0.08
−0.06
−0.04
−0.02
0
0.02
0.04
0.06
0.08
0.1
β (deg)
Cl(
β)
Cl(α = 10◦, β, δf = 0◦)
Original B−747 DataRaw NASA DataExtended B−747 Data
(a) Cl vs. β at Small α
−50 0 50−0.08
−0.06
−0.04
−0.02
0
0.02
0.04
0.06
0.08
β (deg)
Cn(β
)
Cn(α = 0◦, β, δf = 0◦)
Original B−747 DataRaw NASA DataExtended B−747 Data
(b) Cn vs. β at Small α
−50 0 50−0.2
−0.15
−0.1
−0.05
0
0.05
0.1
0.15Cl(α, β, δf = 0◦)
β (deg)
Cl(
α,β
)
α = 25◦
α = 40◦
α = 50◦
α = 60◦
α = 70◦
Negative Slope= Stable
(c) Cl vs. β at Higher α
−50 0 50−0.08
−0.06
−0.04
−0.02
0
0.02
0.04
0.06
0.08Cn(α, β, δf = 0◦)
β (deg)
Cn(α
,β)
α = 25◦
α = 40◦
α = 50◦
α = 60◦
α = 70◦
Positive Slope= Stable
(d) Cn vs. β at Higher α
Figure 3.7: Cl and Cn vs. β
Chapter 3. Flight Model 30
3.4.2 Control Effects
The control surfaces of the Boeing 747-100 aircraft are illustrated in Figure 3.8 below.
One of the important static effects at high α is the reduction in the control surface
effectiveness that occurs as angle of attack increases. The reduced control effectiveness
must be incorporated in the extended aerodynamic database especially for training since
it can affect recovery procedures.
Figure 3.8: Boeing 747-100 Control Surfaces (figure adapted from ref.[23])
The NASA T2 model has the same set of control surfaces available as the B-747
aircraft, since the configuration of the T2 model is based on an aircraft with close re-
semblance to the B-757. The maximum control surface deflections are in similar range
as the the B-747 aircraft, as shown in Table 3.1. Thus the NASA T2 data were matched
directly to the B-747 data: for example, B-747 data at δr = 10◦ followed the NASA T2
data at δr = 10◦. This makes the assumption that two different aircraft at a given control
Chapter 3. Flight Model 31
Control B-747 Model NASA T2 Model
δe [−23◦, 17◦] [−30◦, 20◦]
δs [−12◦, 3◦] [−12◦, 4◦]
δa,inboard [−20◦, 20◦] N/A
δa,outboard [−25◦, 15◦] [−20◦, 20◦]
δr [−25◦, 25◦] [−30◦, 30◦]
δspo,inboard [0◦, 20◦] [0◦, 15◦]
δspo,outboard [0◦, 45◦] [0◦, 45◦]
δf [0◦, 30◦] [0◦, 30◦]
Table 3.1: Maximum Control Surface Deflections
deflection follow the same control effect trend 3.
Elevator and Stabilizer
First, the elevator (δe) and stabilizer (δs) effects on longitudinal coefficients are discussed.
The NASA T2 longitudinal control effects are functions of α, β, and δe/s. Menzies per-
formed the α extension for ∆CL,δe/s and ∆Cm,δe/s as part of his project [21]. The extended
∆Cm,δe and ∆Cm,δs vs. α plots are shown in Figure 3.9(a). These plots show that the
elevator and stabilizer control effects on pitching moment start to decrease as α increases.
Although not shown here, the ∆CL,δe/s data follow the same trend. The primary cause
of the decrease in longitudinal control effectiveness is attributed to the immersion of the
3 It should be noted that the enhanced B-747 model used in the upset recovery experiments containedseveral errors in its control effects data. First, the lateral control effects data were extended using adifferent method from that described above. The percentage of maximum deflection (for example 50%of δa,max) was used to match the T2 data to the corresponding B-747 data. Also, δmax of the T2 modelwere taken as the δ limits of the T2 model’s control effect database, but this was later found to beincorrect because the T2 database contains data beyond δmax for ailerons, rudders and spoilers. Finally,the aileron effect data for the longitudinal coefficients were reversed in sign and the β effects of theaileron effect data for lateral coefficients were also reversed in sign. These errors make small differencesat small α where the B-747 data are used and sideslip effect is typically small, but they have affectedthe experimental results for the three stall scenarios tested. While the reduction in aileron effectivenessin roll control at high α was captured, the reversed β effects would change the handling quality of theaircraft at high α when large aileron inputs were applied. In particular, with the correct aileron data,applying aileron inputs at stall might have helped the aircraft pitch down faster during stall recoveryand could have resulted in slightly larger sideslip. While these errors affected the experimental results,they are not likely to have changed the aircraft behavior drastically since the basic static and dynamiceffects typically determine the stability of the aircraft. The errors are now corrected for the currentmodel.
Chapter 3. Flight Model 32
horizontal tail in the wing wake [13]. Further decrease in the post-stall region is due to
the flow separation occurring on the horizontal tail itself [13].
In this thesis, α and β data extensions were performed for ∆CD,δe/δs . Also, β effects
were added to the ∆CL,δe/s and ∆Cm,δe/s data that were extended to high α by Menzies.
Since the B-747 ∆CL,δe/s , ∆Cm,δe/s , and ∆CD,δe/s are not functions of β, β effects of the
B-747 model were simply taken as zero. The algorithm described in Pseudo Code #2 was
used for β extension. The α extension for ∆CD,δe/s was performed using the algorithm
in Pseudo Code #1. The B-747 model does not have ∆CD,δe data so it was also taken as
zero. Figure 3.9(b) shows the extended ∆CD,δe/δs . The elevator and stabilizer effects on
drag increase at higher α, which could be due to the separated flow from the horizontal
tail.
Ailerons
The original B-747 model employed simplified computations for aileron effects, where data
were implemented in equation forms and not all the data available in the NASA/Boeing
report [20] were used. In this thesis, in addition to data extension, these simplified
computations were replaced with the complete wheel-aileron conversion algorithm and
aileron effect look-up tables using the modeling data from the NASA/Boeing report [20].
The total aileron effect for the B-747 model is the sum of the effects from the inboard
and outboard ailerons. The B-747 inboard aileron deflections are direct functions of the
wheel input. The outboard ailerons also depend on the flap setting and can only be
deployed when flaps are extended. The NASA T2 model only has data for the outboard
ailerons, but it was taken as the total aileron effect. Since the B-747 model does not have
aileron effect data for CD and CY , they were taken as zero at small α.
The data extension method used was that described in Pseudo Codes #1 and #2.
Figure 3.10(a) shows the extended B-747 data for Cl at β = 0◦, where the decrease in
aileron roll control can be observed with increasing α. The decrease in aileron control
effectiveness is due to wing stall. Figure 3.10(b) shows the ∆Cl,δa vs. α curves at different
values of β for a left roll aileron input. Up to α = 20◦, the effect of sideslip is small.
Once α > 60◦, a large increase in control effect is seen at negative β and control reversal
is seen at positive β. For a right roll aileron input, a large increase in control effect is
seen at large positive β and control reversal is seen at large negative β. The cause of this
effect is currently unknown.
Chapter 3. Flight Model 33
Rudder
As previously mentioned, the reduction in the vertical tail effectiveness with increasing α
is attributed to two factors: 1) airflow sidewash effects and 2) a reduction in the dynamic
pressure at the vertical tail caused by the stalled wing wake and shielding by the aft
fuselage [10]. Rudder effect data extension was done in the same way as the ailerons.
Figure 3.11(a) shows the extended data for Cn at β = 0◦ where smooth transition from
B-747 data to NASA data can be seen. The rudder effect decreases up to α = 70◦, but
an abrupt control reversal is seen for α > 75◦. Figure 3.11(b) shows ∆Cn,δr vs. α curves
at different values of β. The β effects are small up to α = 50◦ except at β = 20◦ where
reduction in rudder effect is seen at small α, but again the cause is unknown.
Spoilers
In addition to ailerons, the simplified spoiler effect computations employed in the original
B-747 model were replaced with the complete spoiler deflection algorithm and spoiler
effect look-up tables using the modeling data from the NASA/Boeing report [20]. The
B-747 model’s spoilers are controlled by a speed brake handle and the wheel. There are
three modes for the B-747 spoiler deployment.
1. Speed Brake Mode: speed brake handle input, no wheel input
2. Spoiler-Wheel (Wheel Only) Mode: wheel input, no speed brake handle input
3. Combined Wheel and Speed Brake Mode: speed brake handle input and wheel
input
There are twelve spoiler panels in total, six on each wing. Five out of the six spoilers
on each wing (except the inboard spoiler) are used for lateral control and all spoilers are
used as speed brakes [23]. The B-747 spoiler panels each deflects independently according
to the speed brake handle and wheel inputs. Due to the complexity of the B-747 spoiler
effect calculations, the algorithms described in Pseudo Code #1 and #2 are performed
real-time during the simulation.
Figure 3.12 shows example data for ∆CD,δspo and ∆Cl,δspo . Note that in these figures
δspo indicates the speed brake handle input with a maximum value of 1 rather than
denoting spoiler panel deflection. Figure 3.12(a) illustrates the differences in output
from different modes of spoiler operation for the drag coefficient. Using the speed brake
Chapter 3. Flight Model 34
handle creates more drag at small α than wheel only mode, since spoilers on both wings
are deployed. For wheel only mode, only spoilers on one wing are deployed. Figure 3.12(b)
illustrates the differences in rolling moment for different modes of spoiler operation. Speed
brake mode does not make any contribution to Cl/n/Y because the effects generated on the
two wings cancel each other. At small α, the combined wheel and speed brake mode has
bigger effect than wheel only mode, most likely because the combined mode has bigger
spoiler deflections. As the aircraft stalls, flow separation starts to occur throughout the
wing, thus the spoiler effect on rolling moment starts to decrease.
Flaps
NASA LaRC collected wind-tunnel data at various flap configurations, but the flap effects
data were not included in the NASA T2 model. Instead, the NASA T2 model uses a low
fidelity estimation for flap effects. Flap effects on CL,Basic(α) and Cm,Basic(α) were avail-
able in one of NASA’s publications (Reference [9]), and were included by Menzies in his
work [21]. These were shown in Figures 3.4(a) and 3.4(b) in the previous section. For all
the other data, flap effects at high α follow the NASA T2 data at flaps-up configuration.
Chapter 3. Flight Model 35
−0.2
0
0.2
0.4
0.6Δ
Cm
,δs(α
,δs)
ΔCm,δs (α, β = 0◦, δs, δf = 0◦)
−20 0 20 40 60 80 100−0.4
−0.2
0
0.2
0.4
0.6
α (deg)
ΔC
m,δ
e(α
,δe)
ΔCm,δe (α, β = 0◦, δe, δf = 0◦)
δe = −23◦
δe = −12◦
δe = 0◦
δe = 8◦
δe = 17◦
δs = −12◦
δs = −6◦
δs = 0◦
δs = 3◦
(a) ∆Cm,δs and ∆Cm,δe
−0.3
−0.2
−0.1
0
ΔC
D,δ
s(α
,δs)
ΔCD,δs (α, β = 0◦, δs, δf = 10◦)
δs = −12◦
δs = −6◦
δs = 0◦
δs = 3◦
−20 0 20 40 60 80 100−0.4
−0.3
−0.2
−0.1
0
0.1
α (deg)Δ
CD
,δe(α
,δe)
ΔCD,δe (α, β = 0◦, δe)
δe = −23◦
δe = −12◦
δe = 0◦
δe = 8◦
δe = 17◦
(b) ∆CD,δs and ∆CD,δe
Figure 3.9: Stabilizer and Elevator Effects on Cm and CD
−0.025
−0.02
−0.01
0
ΔC
l,δ a
(α)
ΔCl,δa(α, β = 0◦, δa = −20◦, δf = 10◦)
Original B-747 Data
Fitted NASA Data
Extended B-747 Data
−20 0 20 40 60 80 1000
0.01
0.02
0.025
α (deg)
ΔC
l,δ a
(α)
ΔCl,δa(α, β = 0◦, δa = +20◦, δf = 10◦)
(a) ∆Cl,δa
−20 0 20 40 60 80 100−0.15
−0.1
−0.05
0
0.05
0.1
α (deg)
ΔC
l,δ a
(α,β
)
ΔCl,δa(α, β, δa = −20◦, δf = 10◦)
β = −20◦
β = −10◦
β = 0◦
β = 10◦
β = 20◦
(b) ∆Cl,δa
Figure 3.10: Aileron Effect on Cl
Chapter 3. Flight Model 36
−0.15
−0.1
−0.05
0
0.05Δ
Cn
,δr(α
)
ΔCn,δr (α, β = 0◦, δr = −25◦)
Original B−747 DataFitted NASA DataExtended B−747 Data
−20 0 20 40 60 80 100−0.05
0
0.05
0.1
0.15
α (deg)
ΔC
n,δ
r(α
)
ΔCn,δr (α, β = 0◦, δr = +25◦)
(a) ∆Cn,δr
−20 0 20 40 60 80 100−0.12
−0.1
−0.08
−0.06
−0.04
−0.02
0
0.02
0.04
0.06
0.08
α (deg)Δ
Cn
,δr(α
,β)
ΔCn,δr (α, β, δr = −25◦)
β = −20◦
β = −10◦
β = 0◦
β = 10◦
β = 20◦
(b) ∆Cn,δr
Figure 3.11: Rudder Effect on Cn
0 20 40 60 80−0.05
−0.04
−0.03
−0.02
−0.01
0
0.01
0.02
0.03
α (deg)
ΔC
D,δ
sp
o(α
)
ΔCD,δspo (α, β = 0◦, δspo, δw)
Speed Brake (δspo = 0.4)
Combined(δspo = 0.4, δw = 50◦)
Wheel Only(δw = 50◦)
(a) ∆CD,δspo
0 20 40 60 80−2
0
2
4
6
8
10
12x 10
−3
α (deg)
ΔC
l,δ s
po(α
)
ΔCl,δspo (α, β = 0◦, δspo, δw)
Speed Brake (δspo = 0.4)
Combined(δspo = 0.4, δw = 50◦)
Wheel Only(δw = 50◦)
(b) ∆Cl,δspo
Figure 3.12: Spoiler Effects on CD and Cl
Chapter 3. Flight Model 37
3.4.3 Dynamic Effects
There is a significant difference between the B-747 database and the NASA T2 database
with regards to the dynamic data computation. The original B-747 dynamic data are
calculated using the damping derivatives (e.g. Cmq, Clp, Cnr), which are linear with
respect to the non-dimensional angular rates p, q, r and are functions of α. The damping
derivatives are calculated analytically or obtained experimentally from forced oscillation
tests, and describe the effects of the angular rates. The equivalent data in the NASA
T2 model is therefore the forced oscillation data. On the other hand, rotary balance
tests are used to study spin modes because the rotary balance motion is very similar to
a non-oscillatory steady spin [14]. Since there is no B-747 data comparable to the rotary
balance data, raw NASA rotary balance data were used.
NASA LaRC conducted two types of dynamic wind-tunnel tests to construct the dy-
namic database for the following reasons. Past research found that the angular rates
attained in highly dynamic maneuvers can exceed the limits of the forced oscillation
database and drive the simulation unstable [11], whereas using the rotary balance test
data alone would not be able to properly capture the dynamics of non-spinning ma-
neuvers [15]. Hence using either forced oscillation data or rotary balance data alone is
inadequate. Additionally, the motions seen at out-of-control conditions typically “in-
volve a combination of large amplitude, uncoordinated (i.e. the angular rate vector is
not closely aligned with the velocity vector), and coupled motions which are difficult to
replicate with existing wind tunnel motion rigs” (p.3 [11]). This led researchers to inves-
tigate methods to blend the forced oscillation and rotary balance data to better capture
the dynamic effects at extreme flight conditions. A collaborative work from Georgia In-
stitute of Technology and NASA LaRC [11, 14] examined several methods for combining
data from the two dynamic tests. A method called Hybrid Kalviste was used for the T2
model’s dynamic database since the simulation results using this method gave the best
match to NASA’s free-spin wind-tunnel test data.
The basic idea behind the data blending method is to vectorially resolve the total
angular rates pb, qb, and rb into the oscillatory components posc, qosc, rosc and a steady-
state component ωss using the equations in next page [11, 14]. In the Hybrid Kalviste
method, the four components are determined based on the position of the total angular
rate vector relative to the velocity vector, and there are three cases to consider [11, 14].
Chapter 3. Flight Model 38
posc = pb − ωss cosα cosβ (3.21)
qosc = qb − ωss sinβ (3.22)
rosc = rb − ωss sinα cosβ (3.23)
Case 1 is when the projection of the total angular rate vector (sum of the body axes
angular rate vectors) on the x − z plane is closer to the body yaw axis (zb) than the
roll axis (xb) [11]. In this case, posc is set to zero, and qosc, rosc, ωss are calculated using
Equations 3.21 to 3.23. This case is representative of a general dynamic maneuver [15].
Case 2 is when the projection of total angular rate in the x − z plane is closer to the
body roll axis (xb) [11]. In this case, rosc is set to zero and posc, qosc, ωss are calculated
using Equations 3.21 to 3.23. This case is representative of a spin condition [15]. Case
3 is when p and r have opposite signs (uncoordinated) [11]. In this case the wind-axis
component ωss is set to zero, so posc = pb, qosc = qb, and rosc = rb. At small α, Hybrid
Kalviste method mainly chooses Case 1 or 3. Case 2 starts to dominate when p � r or
p > r and α is large. The computed oscillatory components posc, qosc, rosc along with α
then become the inputs to the forced oscillation data look-up tables, and the steady-state
component ωss along with α and β 4 are the inputs to the rotary balance data look-up
tables. The sum of the outputs from the forced oscillation look-up tables and the rotary
balance look-up tables describes the total dynamic effect.
In addition to using data collected from two different wind-tunnel tests, one of the
key characteristics seen in the NASA dynamic data is the nonlinearity with respect to the
angular rates that arises at high α and/or large values of angular rates. The NASA forced
oscillation data are modeled as functions of non-dimensional angular rates in addition
to α in the T2 model so that the nonlinearity is captured. The local derivatives of the
∆Ci,dynamic vs. angular rates curves provide indications of stability. For example, the
local derivative of ∆Cl,dynamic vs. p is Clp, the roll damping derivative, which must be
negative for stability. Another important trend seen in the NASA dynamic data at high
α is the dynamic instability that arises at stall and post-stall regions. Incorporating
nonlinearity and instability is the key task in the dynamic data extension.
Finally, the NASA T2 data is based on angular rates in the body axes (pb, qb, rb) while
the B-747 dynamic data is based on angular rates in the stability axes (ps, qs, rs). They
are related as follows:
4Only the rotary balance data depend on β.
Chapter 3. Flight Model 39
ps = pb cosα + rb sinα (3.24)
qs = qb (3.25)
rs = −pb sinα + rb cosα (3.26)
Due to these differences between the NASA T2 and B-747 dynamic databases, real-time
data blending and extension was considered most appropriate. The algorithm in Pseudo
Code #1 is performed real-time during the simulation. That is, the original dynamic
database for the B-747 is used for α < 15◦, the T2 model’s combined forced oscillation
and rotary balance database using Hybrid Kalviste method is used for α > 25◦, and
in the data transition section α = [15◦, 25◦], the outputs from the B-747 database and
the T2 database are blended using the percentage blending method described in Pseudo
Code #1. The limits of the NASA T2 dynamic database are summarized below.
Roll Forced Oscillation: α = [−10◦, 90◦], p = ±0.107
Pitch Forced Oscillation: α = [−30◦, 50◦], q = ±0.0075
Yaw Forced Oscillation: α = [−30◦, 60◦], r = ±0.112
Rotary Balance: α = [0◦, 90◦], β = ±45◦, ωss = ±0.5
In addition, the small α B-747 data were extended to large angular rates to incorporate
the nonlinearity with respect to the angular rates observed in the NASA forced oscillation
data at small α and large values of angular rates. The method is described in the following
sections.
Longitudinal Data
The B-747 CLq and Cmq are not functions of α but are functions of Mach number and
altitude (i.e. speed and density). Thus, they were taken as constants at given values of
Mach number and altitude. The B-747 model does not have CDq so it was taken as zero.
Since qs = qb, the NASA forced oscillation data were directly compared to the B-747
data. The NASA T2 CL and Cm forced oscillation data are mostly linear with pitch rate
q at small α, but slight nonlinearity is seen at large values of q. To incorporate this,
the same method as the Cl/n/Y,Basic extension in β was used to extend the B-747 data
to large q (see Pseudo Code #3, replace β with q). That is, NASA T2 data were scaled
using the slopes CLq and Cmq.
Chapter 3. Flight Model 40
Figures 3.13(a) and 3.13(b) compare the original B-747 dynamic data, NASA T2
forced oscillation data and the extended B-747 data for CL and Cm. It can be seen that
the NASA T2 data are significantly larger in magnitude compared to the B-747 data.
Thus, instead of using raw NASA T2 data for α > 25◦, NASA T2 data were scaled to
have reasonable continuity with the small α B-747 data. Figures 3.13(c) and 3.13(d) show
the extended data plotted against q. Slight nonlinearity with q is seen for CL at large
values of q but not for Cm. Since Cmq stays negative throughout, longitudinal instability
does not occur.
Lateral Data
The following is an example of matching the NASA T2 roll forced oscillation data to
the corresponding B-747 data. Note that ∆CB−747l/n/Y,s(α, ps) are each equal to Clp(α) ×
ps,Cnp(α) × ps and CY p(α) × ps.
∆CB−747l,s (α, ps) = ∆CNASA−T2
l,b (α, posc) cosα + ∆CNASA−T2n,b (α, posc) sinα (3.27)
= ∆CNASA−T2l,s (α, posc)
∆CB−747n,s (α, ps) = −∆CNASA−T2
l,b (α, posc) sinα + ∆CNASA−T2n,b (α, posc) cosα(3.28)
= ∆CNASA−T2n,s (α, posc)
∆CB−747Y,s (α, ps) = ∆CNASA−T2
Y,b (α, posc) (3.29)
The nonlinearities with respect to p and r were incorporated into the B-747 data at
small α using the same method as the longitudinal data: i.e. the derivatives Clp, Cnp,
CY p, Clr, Cnr and CY r were used to scale NASA T2 data at small α. The assumption
made was that ps ≈ pb ≈ posc at small α so that the B-747 data at ps = 0.01 for example
could follow the nonlinear trend of the NASA data at posc = 0.01.
Figures 3.14(a), 3.14(b), 3.15(a) and 3.15(b) compare the B-747 data to the NASA
T2 roll forced oscillation data and yaw forced oscillation data. Note that the NASA data
are at posc and rosc while the B-747 data are at ps and rs. Unlike the longitudinal data,
the NASA T2 lateral data exhibit significantly different trends from the B-747 lateral
data despite having similar magnitudes. This is especially notable for ∆Cn(α, p) where
the NASA T2 data appears to be chaotic.
Figures 3.14(c), 3.14(d), 3.15(c) and 3.15(d) are the extended B-747 data and raw
NASA T2 data plotted against p and r. The raw NASA ∆Cl vs. p plot shows that
Chapter 3. Flight Model 41
instability occurs at high α. Since the main contributor to Clp is the wing, the aircraft
starts to lose the damping effect when the wing stalls. The dynamic instability as well as
the static instabilities in Clβ and Cnβ may lead to roll-off and directional divergence at
stall and post-stall flight conditions. This will be discussed further in the next section.
One additional point that should be noted is that in the enhanced B-747 model aero-
dynamic database, pre-lookup tables were used for tables with more than 2 dimensions.
Pre-look up tables find the input index and fraction within the breakpoint data set before
interpolating the n-dimensional table, which can significantly accelerate the interpolation
process. In fact, the enhanced B-747 model did not run in real-time on the UTIAS Con-
current iHawk when normal look-up tables were used for 3D, 4D, and 5D databases.
Chapter 3. Flight Model 42
−0.4
−0.2
0
0.2
Raw NASA Forced Osc. DataΔ
CL(α
,q)
−0.05
0
0.05
Original B-747 Dynamic Data (CLq q)
ΔC
L(α
,q)
−30 −20 −10 0 10 20 30 40 50
−0.1
−0.05
0
0.05
Extended B-747 Forced Osc. Data
α (deg)
ΔC
L(α
,q)
q = -0.0075
q= -0.0032
q = -0.0013
q = 0
q = 0.0013
q = 0.0032
q = 0.0075
(a) ∆CL(α, q)
−0.4
−0.2
0
0.2
0.4
Raw NASA Forced Osc. Data
ΔC
m(α
,q)
−0.2
−0.1
0
0.1
0.2Original B-747 Dynamic Data (Cmq q)
ΔC
m(α
,q)
−30 −20 −10 0 10 20 30 40 50
−0.2
−0.1
0
0.1
0.2
Extended B-747 Forced Osc. Data
α (deg)
ΔC
m(α
,q)
q = -0.0075
q = -0.0032
q = -0.0013
q = 0
q = 0.0013
q= 0.0032
q = 0.0075
(b) ∆Cm(α, q)
−8 −6 −4 −2 0 2 4 6 8
x 10−3
−0.08
−0.06
−0.04
−0.02
0
0.02
0.04
Extended B-747 Forced Oscillation Data
q
ΔC
L(α
,q)
α = −10◦
α = 0◦
α = 10◦
α = 30◦
α = 50◦
(c) ∆CL(α, q)
−0.01 −0.005 0 0.005 0.01−0.25
−0.2
−0.15
−0.1
−0.05
0
0.05
0.1
0.15
0.2Extended B-747 Forced Oscillation Data
q
ΔC
m(α
,q)
α = −10◦
α = 0◦
α = 10◦
α = 30◦
α = 50◦
(d) ∆Cm(α, q)
Figure 3.13: Dynamic Data - Longitudinal
Chapter 3. Flight Model 43
−0.04
−0.02
0
0.02
0.04
ΔC
l(α,p
)Raw NASA Roll Forced Osc. Data
0 20 40 60 80−0.04
−0.02
0
0.02
0.04
α (deg)
ΔC
l(α,p
)
Original B-747 Roll Dynamic Data (Clpp)
p = -0.107
p = -0.038
p = -0.009
p = 0
p = 0.009
p = 0.038
p = 0.107
(a) ∆Cl(α, p)
−0.06
−0.04
−0.02
0
0.02
0.04
0.06
ΔC
l(α,r
)
Raw NASA Yaw Forced Osc. Data
−10 0 10 20 30 40 50 60−0.05
0
0.05
α (deg)
ΔC
l(α,r
)
Original B-747 Roll Dynamic Data (Clr r)
r = -0.112
r = -0.038
r = -0.009
r = 0
r = 0.009
r = 0.038
r = 0.112
(b) ∆Cl(α, r)
−0.04
−0.02
0
0.02
0.04
ΔC
l(α,p
)
Extended B-747 Roll Dynamic Data Due to Roll Rate
−0.1 −0.05 0 0.05 0.1−0.02
−0.01
0
0.01
0.02
0.03
p
ΔC
l(α,p
)
Raw NASA Roll Forced Oscillation Data
α = −5◦
α = 0◦
α = 5◦
α = 10◦
α = 15◦
α = 20◦
α = 30◦
α = 40◦
α = 50◦
α = 60◦
Negative Slope= Stable
(c) ∆Cl(α, p)
−0.04
−0.02
0
0.02
0.04
ΔC
l(α,r
)
Extended B-747 Roll Dynamic Data Due to Yaw Rate
−0.1 −0.05 0 0.05 0.1−0.1
−0.05
0
0.05
0.1
r
ΔC
l(α,r
)
Raw NASA Yaw Forced Oscillation Data
α = −5◦
α = 0◦
α = 5◦
α = 10◦
α = 15◦
α = 20◦
α = 30◦
α = 40◦
α = 50◦
α = 60◦
(d) ∆Cl(α, r)
Figure 3.14: Dynamic Data - Rolling Moment
Chapter 3. Flight Model 44
−0.015
−0.01
0
0.01
0.015
ΔC
n(α
,p)
Raw NASA Roll Forced Osc. Data
0 20 40 60 80−0.02
−0.01
0
0.01
0.02
α (deg)
ΔC
n(α
,p)
Original B-747 Yaw Dynamic Data (Cnpp)
p = -0.107
p = -0.038
p = -0.009
p = 0
p= 0.009
p = 0.038
p = 0.107
(a) ∆Cn(α, p)
−0.04
−0.02
0
0.02
0.04
ΔC
n(α
,r)
Raw NASA Yaw Forced Osc. Data
−10 0 10 20 30 40 50 60−0.03
−0.02
−0.01
0
0.01
0.02
0.03
α (deg)
ΔC
n(α
,r)
Original B-747 Yaw Dynamic Data (Cnr r)
r = -0.112
r = -0.038
r = -0.009
r = 0
r = 0.009
r = 0.038
r = 0.112
(b) ∆Cn(α, r)
−0.015
−0.01
0
0.01
0.015
ΔC
n(α
,p)
Extended B-747 Yaw Dynamic Data Due to Roll Rate
−0.1 −0.05 0 0.05 0.1−0.015
−0.01
0
0.01
p
ΔC
n(α
,p)
Raw NASA Roll Forced Oscillation Data
α = −5◦
α = 0◦
α = 5◦
α = 10◦
α = 15◦
α = 20◦
α = 30◦
α = 40◦
α = 50◦
α = 60◦
(c) ∆Cn(α, p)
−0.015
−0.01
0
0.01
0.015
ΔC
n(α
,r)
Extended B-747 Yaw Dynamic Data Due to Yaw Rate
−0.1 −0.05 0 0.05 0.1−0.04
−0.02
0
0.02
0.04
r
ΔC
n(α
,r)
Raw NASA Yaw Forced Oscillation Data
α = 20◦
α = 30◦
α = 40◦
α = 50◦
α = 60◦
α = −5◦
α = 0◦
α = 5◦
α = 10◦
α = 15◦
Negative Slope= Stable
(d) ∆Cn(α, r)
Figure 3.15: Dynamic Data - Yawing Moment
Chapter 3. Flight Model 45
3.5 Model Validation
3.5.1 Database Validation
The first step of model validation is to check the enhanced aerodynamic database using
coefficient comparison. This can be done by directly comparing the aerodynamic database
outputs from the original B-747 model, the enhanced B-747 model, and the NASA T2
model. The following trends should be expected for the enhanced B-747 data:
• α < 15◦: the enhanced B-747 data should follow the original B-747 data
• α = [15◦, 25◦]: this is the blended region where the enhanced B-747 data starts to
deviate from the original B-747 data to follow the NASA T2 data
• α > 25◦: the enhanced B-747 data should follow the NASA T2 data
Similarly, as β becomes significant, the enhanced B-747 data should also start to slowly
deviate from the original B-747 data to follow the NASA T2 data. To directly compare the
aerodynamic databases of the three models, they must all take the same inputs. Thus
the aircraft state variables from a stall maneuver were run through the aerodynamic
databases of the original B-747 model and the NASA T2 model. The outputs were then
compared to the coefficients computed from the enhanced B-747 model aerodynamic
database. The results are shown in Figure 3.17 and Figure 3.18 and the aircraft state
variables are shown in Figure 3.16. Note that nx, ny, and nz in Figure 3.16 refer to the
longitudinal acceleration, lateral acceleration (normalized by the acceleration of gravity,
g) and the normal load factor respectively. Some differences are expected between the
original and enhanced B-747 models. As previously mentioned, the original B-747 model
employed simplifications where some data were approximated using linear and quadratic
equations. Many of these simplifications were replaced with the complete data when
creating the enhanced B-747 model. Furthermore, since some NASA T2 data were scaled,
there will also be differences between the NASA T2 data and enhanced B-747 data.
However, these cases should be obvious because they are simply different in scale and
the behavior in data remains the same. Finally, some differences are expected between
the NASA T2 data and the enhanced B-747 data for CL/m,Basic and ∆CL/m,δe/δs , as
the α extensions for these data were performed using the data in References [9] and
[12] by Menzies. Taking these differences into account, the enhanced B-747 model was
Chapter 3. Flight Model 46
determined to be behaving correctly. Several maneuvers were used to further validate
the enhanced model using coefficient comparisons.
3.5.2 Model Behavior Validation
Conventionally, a flight model is validated by comparing the aircraft state time histories
to flight test data using the same set of control inputs and initial conditions. This is
difficult for airplane upsets since flight test data at extreme flight conditions is scarce.
Alternatively, simulation time histories can be compared to accident flight recorder data
for model validation, but precise matching of the simulation time history and the accident
data could be difficult. Since the aircraft tends to be unstable in upset conditions, errors
could easily build up in long duration maneuvers such as stall and cause deviation in re-
sponses [13]. For instance, one of the observations made in the NASA LaRC research was
the lack of repeatability for stall and departure simulations, because the aircraft behavior
is very sensitive to the rate of control input and the wind incidence path [13]. Moreover,
there is limited accident data available for the B-747-100 aircraft and environmental fac-
tors (real world vs. simulation) are difficult to account for. Therefore, accident data from
other commercial transport aircraft are also used for model validation, but only similar
trends can be expected in this case.
Nevertheless, an accident report was found for a large roll upset that involved a B-
747-200 aircraft and this was used to validate the enhanced B-747 model. This accident
was one of the upset scenarios used in the simulator experiments and will be discussed
in further detail in the next chapter. The accident report [24], available from the UK
Air Accidents Investigation Branch (AAIB), contained enough information to simulate
the scenario and a close match should be expected with the enhanced B-747 model since
the accident aircraft was also a B-747. For accident investigation purposes, Boeing con-
ducted simulation analysis to determine the roll profile of the accident as the Flight Data
Recorder (FDR) roll data was inaccurate. Since the Boeing simulation had more air-
craft state time histories available, the enhanced B-747 model was compared to Boeing’s
simulation for validation. The column, wheel and pedal inputs used in the Boeing simu-
lation were run through the enhanced B-747 model and the simulation results are shown
in Figure 3.19 5. The wheel input needed to be increased by 25% in order to bring the
5The Boeing simulation data were extracted from Figures 1 and 2 in Appendix I of the accidentreport available from the UK AAIB [24].
Chapter 3. Flight Model 47
maximum roll angles into agreement. It is uncertain why this was the case, but there was
no additional scaling required for the column and pedal inputs. Considering the possible
differences in the simulation setup and configuration differences between the B-747-200
and the B-747-100, the match in Figure 3.19 seems to be reasonable.
Validating the model at stall conditions is difficult due to the chaotic behavior of the
aircraft under these conditions. However, a trend comparison was still required to exam-
ine if the enhanced B-747 model could capture some of the important stall characteristics.
Two examples are discussed subsequently. The first example is a trend comparison to a
past accident caused by a stall. The accident report was available from the Japan Trans-
port Safety Board (JTSB) and the accident involved an Airbus A300 aircraft [25]. The
stall was caused by a combination of full nose-up stabilizer trim and maximum throttle
input. This accident was also one of the upset scenarios used in the simulator experi-
ments and will be discussed in further detail in the next chapter. The elevator, throttle,
and stabilizer inputs were extracted from the FDR data and modified to obtain a similar
pitch profile as the accident using the enhanced B-747 model. Figure 3.20 compares the
simulation results to the FDR data 6. Note that the FDR’s angle of attack data may
be unreliable near the end (the flat region). Also, the accident aircraft exhibited aggres-
sive roll-off, but the cause of the roll-off was unknown. Thus, in the simulation, a small
amount of turbulence was applied instead to introduce a lateral disturbance at stall 7.
Looking at the results, the longitudinal accelerations (nx) and vertical load factors (nz)
show very similar trends. The pitch time history has a close match to the FDR data
and the increase in α from 60 seconds to 65 seconds looks similar. The altitude time
history also closely follows the FDR data while differences are seen in the airspeed time
history, but the airspeed measurement may be unreliable at high α. The main difference
observed between the accident data and the B-747 model was that the B-747 model did
not enter roll-off or directional divergence from the small amount of disturbance applied.
This aspect will be further discussed in the last part of this section.
The second example is a comparison to the NASA EUR model stall simulations
mentioned in Chapter 2. The stall was induced by applying maximum elevator input,
so the elevator input history from the EUR stall simulation was used in the enhanced
6The FDR data were extracted from Appendix 6 of the accident report available from JTSB [25].7The turbulence model can be scaled to give different intensities as well as the onset disturbance
direction. The term “scale = 0.5” seen in the subsequent plots indicates that the turbulence intensitywas decreased to half of the default intensity.
Chapter 3. Flight Model 48
and original B-747 models to illustrate the improvements 8. NASA’s simulation results,
available from Reference [9], are plotted on the same figure for comparison 9, and the
results are shown in Figure 3.21. The increase in α and corresponding decrease in airspeed
that characterize stall are seen for the enhanced B-747 model while α stayed at 30◦
for the original B-747 model. The pitch time history of the enhanced B-747 model
also matches closely to the EUR simulation and the reference flight test data. The
EUR model stall simulations in Reference [9] however, exhibits aggressive roll-off and
directional divergence at stall (not shown here; see Fig 15-20 in Reference [9]). Using
the wheel and rudder inputs from the EUR stall stimulation did not produce the same
lateral and directional responses. To examine if roll-off or directional divergence occurs
for this stall maneuver, a small amount of turbulence was again applied to introduce a
lateral disturbance at stall. While this triggered oscillatory roll and sideslip responses as
shown in Figure 3.22, it did not lead to an aggressive roll-off or directional divergence.
Aggressive roll-off was only seen to occur when α was maintained at the post-stall value
for a long period of time, for example during a slow-entry stall or a secondary stall. This
difference in behavior between the enhanced B-747 model and the NASA EUR model
(and also the accident data) will be discussed next.
3.5.3 Roll-Off and Directional Divergence at Stall
There are several factors that could be contributing to the difference in lateral and
directional responses at stall. First, the B-747 is a much larger aircraft than the medium
size transport aircraft used for the NASA EUR model. Larger aircraft responds slower
and may have less tendency to roll compared to smaller aircraft. To test if this is the
case, the aircraft configuration parameters, such as weight, wing geometry and moment of
inertia were changed to those of the medium size transport configuration used for the EUR
model. An example comparison is shown in Figure 3.23. This change in configuration
resulted in slightly bigger roll when a small amount of turbulence was applied at stall,
but it did not lead to aggressive roll-off or directional divergence.
Secondly, the difference in behavior may have resulted from the differences between
the NASA T2 data and the NASA EUR data. The EUR model is a more complete model
that includes more nonlinear effects at high angles of attack and the data are Reynolds
8The elevator input from the EUR stall simulation was scaled for the B-747 models because themaximum nose-up elevator deflection of the EUR model (−30◦) is bigger than the B-747 model(−23◦).
9The NASA simulation results and flight test data were estimated from Figure 15 of Reference [9].
Chapter 3. Flight Model 49
number corrected. Reference [13] noted that in addition to lift and drag, Reynolds
number may affect the pitching moment and roll damping characteristics, but this was
not discussed in further detail in the literature. Moreover, the EUR model includes the
flap effects described in Reference [9] as well as aerodynamic asymmetry data obtained
from wind-tunnel and flight tests which were not available in the NASA T2 model. The
aerodynamic asymmetry data are the non-zero values of Cl, Cn, and CY at zero sideslip,
which are likely caused by the asymmetric wing stall or asymmetric flow fields from the
aircraft forebody [13]. Inclusion of these data could be important for stall and departure
simulations [13].
In addition, differences between the NASA T2 and EUR data were noted in the sta-
bility derivatives. The main stability derivatives which govern the lateral and directional
characteristics at stall are Clβ , Cnβ , and Clp. The Clβ , Cnβ , and Clp vs. α plots for the
EUR model, computed using the data at small β and p, are available in Reference [9].
The same method was then used to compute the Clβ , Cnβ , and Clp values for the NASA
T2 model for comparison. Since the y-axis scales for the EUR model Clβ/nβ/lp vs. α plots
were not available due to proprietary reasons, they were scaled to match the NASA T2
data at α = 0◦. The comparison is shown in Figures 3.24(a) and 3.24(b) 10. The NASA
T2 model and EUR model have good match at small α but start to deviate as α increases.
The most notable difference is observed for Clp in Figure 3.24(b), which shows that the
EUR model becomes more aggressively unstable than the NASA T2 model. Additionally,
when compared to the B-747 model at small α, the NASA T2 and EUR models seem to
be generally less stable. For example, Clp for the NASA models decrease to zero near
α = 12◦ while the B-747 model Clp slowly becomes less negative as α increases and does
not reach zero. Thus the B-747 model could produce a larger damping effect once α
returns to a small value after recovery from a stall.
To examine the effects of these differences in data, a modified enhanced B-747 model
was created. The modified model will be referred to as the roll model. The roll model em-
ploys modified Cl,Basic, Cn,Basic and ∆Cl(α, p) databases. These were obtained by scaling
the NASA T2 Cl,Basic, Cn,Basic and ∆Cl(α, p) data to approximately match the Clβ/nβ/lp
vs. α profiles of the NASA EUR model. For example, Cl,Basic(α = 30◦, β) was scaled
byClβ (α=30◦)EUR
Clβ (α=30◦)T2. Additionally, the aerodynamic asymmetry data for Cl, available from
Reference [13], was added in the roll model. Since the y-axis scale for the aerodynamic
10The NASA EUR data were estimated from Figures 8, 9, 11 of Reference [9].
Chapter 3. Flight Model 50
asymmetric data was not available due to proprietary reasons, a scale was assumed for
this data using the scale of the aerodynamic asymmetry data from Reference [26] 11. A
parameter called AeroAsym was created for setting the asymmetry to zero and changing
the sign.
Figures 3.24(c) and 3.24(d) show the lateral/directional responses at stall using the
roll model. The stall was induced using the elevator input from the EUR stall simulation.
First, Figure 3.24(c) shows the case where no lateral disturbance (including control inputs
and turbulence) was used. The sign of the aerodynamic asymmetry was changed by
changing the value of AeroAsym to give the onset disturbance in the opposite direction.
Much larger responses in β and roll are observed for the roll model. Similarly, Figure
3.24(d) shows the case where small amount of turbulence was introduced at the time of
stall but without the addition of the aerodynamic asymmetry for Cl. This again shows
much bigger β and roll responses.
In summary, this analysis has shown that the main cause of the difference in lat-
eral/directional responses at stall between the enhanced B-747 model and the NASA
EUR model is likely due to the differences in the aerodynamic data at high angles of
attack. Therefore, potential methods to estimate the missing effects and scale the stabil-
ity derivatives based on aircraft configuration may be examined in future studies if more
data do not become available.
11The example aerodynamic asymmetry data in Ref.[26] is also for a commercial transport aircraftconfiguration, but is slightly different from the data shown in Ref.[13]. The aerodynamic asymmetrydata used in the roll model was assumed to have similar scale as the data in Ref. [26]
Chapter 3. Flight Model 51
8000
9000
10000
11000
Alt
itude
(ft)
140
160
180
200
220
V(k
not
s)
10 15 20 25 30 35 40 4510
20
30
40
50
time (s)
α(d
eg)
10 15 20 25 30 35 40 45−10
0
10
20
time (s)
β(d
eg)
−20
−10
0
10
p(d
eg/s
)
−4
−2
0
2
4
q(d
eg/s
)−2
−1
0
1
r(d
eg/s
)
10 20 30 40−40
−20
0
20
40
time (s)
φ(d
eg)
10 20 30 40−10
0
10
20
30
time (s)
θ(d
eg)
10 20 30 4080
85
90
95
100
time (s)
ψ(d
eg)
(a) Aircraft States (1)
−0.1
0
0.1
0.2
0.3
nx(G
)
−0.4
−0.2
0
0.2
0.4
ny(G
)
0.8
1
1.2
1.4n
z(G
)
−30
−20
−10
0
δ e(d
eg)
−3
−2
−1
0
δ s(d
eg)
−20
−10
0
10
20
δ a(d
eg)
−4
−2
0
2
4
δ r(d
eg)
−1
−0.5
0
0.5
1
δ f(d
eg)
−1
0
1
2
Thro
ttle
(R)
(0-1
)
10 20 30 40−1
0
1
2
time (s)
Thro
ttle
(L)
(0-1
)
10 20 30 400
2
4
6
time (s)
δ spo(L
)(d
eg)
10 20 30 400
2
4
6
time (s)
δ spo(R
)(d
eg)
(b) Aircraft States (2)
Figure 3.16: Stall Maneuver Used for Coefficient Comparison
Chapter 3. Flight Model 52
0.8
1
1.2
1.4
1.6C
L,B
asic
−0.1
0
0.1
ΔC
L,D
yn
am
ic−0.1
0
ΔC
L,δ
e
−0.02
−0.01
0
ΔC
L,δ
s
−0.02
0
0.02
0.04
ΔC
L,δ
a
−0.01
0
ΔC
L,δ
r
10 20 30 40−0.02
−0.01
0
0.01
time (s)
ΔC
L,δ
sp
o
10 20 30 40
0.8
1
1.2
1.4
time (s)
CL
Original B−747Enhanced B−747NASA T2
(a) CL
0
0.5
1
CD
,Basic
−0.06
0
0.02
ΔC
D,D
yn
am
ic
−0.08
−0.06
0
ΔC
D,δ
e
−0.01
−0.005
0
ΔC
D,δ
s
−0.02
−0.01
0
0.01
ΔC
D,δ
a
−0.01−0.008
00.002
ΔC
D,δ
r
10 20 30 40
−0.005
00.001
time (s)
ΔC
D,δ
sp
o
10 20 30 400
0.5
1
time (s)
CD
(b) CD
−1
−0.8
−0.6
−0.4
−0.2
Cm
,Basic
−0.05
0
0.05
0.1
0.15
ΔC
m,D
yn
am
ic
0
0.2
0.4
ΔC
m,δ
e
0.02
0.04
0.06
ΔC
m,δ
s
−0.08
−0.06
0
0.02
ΔC
m,δ
a
−0.02
−0.01
0
0.01
ΔC
m,δ
r
10 20 30 40−0.01
0
time (s)
ΔC
m,δ
sp
o
10 20 30 40−0.6
−0.4
−0.2
0
0.2
time (s)
Cm
(c) Cm
Figure 3.17: Coefficient Comparison - Longitudinal
Chapter 3. Flight Model 53
−0.03
−0.02
0
0.02
0.03C
l,B
asic
−0.005
0
0.005
ΔC
l,δ
a
−0.001
0
0.001
0.0015
ΔC
l,δ
r
−0.002
−0.001
0
ΔC
l,δ
sp
o
10 20 30 40−0.015
−0.01
0
0.015
time (s)
ΔC
l,D
yn
am
ic
10 20 30 40
−0.03
−0.02
−0.01
0
0.01
0.02
time (s)
Cl
(a) Cl
−0.01
0
0.01
0.02
0.03
Cn
,Basic
−0.004
0
0.003
ΔC
n,δ
a
−0.006
0
0.008
ΔC
n,δ
r
−0.0006
0
ΔC
n,δ
sp
o
10 20 30 40−0.02
0
0.005
time (s)
ΔC
n,D
yn
am
ic
10 20 30 40−0.04
−0.03
−0.02
−0.01
0
0.01
0.02
time (s)
Cn
(b) Cn
−0.3
−0.2
−0.1
0
0.1
CY
,Basic
−0.01
0
0.01
0.015
ΔC
Y,δ
a
−0.015
−0.01
0
0.01
0.015
ΔC
Y,δ
r
−0.0015
0
0.002
ΔC
Y,δ
sp
o
10 20 30 40
−0.04
0
0.02
time (s)
ΔC
Y,D
yn
am
ic
10 20 30 40
−0.3
−0.2
−0.1
0
0.1
0.2
time (s)
CY
(c) CY
Figure 3.18: Coefficient Comparison - Lateral
Chapter 3. Flight Model 54
−5
0
5
10
15α
(deg
)
Enhanced B−747 ModelBoeing Simulation
−10
−5
0
5
β(d
eg)
0
1000
2000
3000
Altitude
(ft)
10 15 20 25 30 35 40 45150
200
250
300
time (s)
VE
(knots
) Note: Boeing simulation data is calibrated airspeed
(a)
0.05
0.1
0.15
0.2
0.25
0.3
time (s)
nx
(G)
−0.1
−0.05
0
0.05
0.1
0.15
time (s)
ny
(G)
10 15 20 25 30 35 40 450.8
1
1.2
1.4
1.6
1.8
time (s)
nz
(G)
(b)
−100
−50
0
50
φ(d
eg)
−40
−20
0
20
θ(d
eg)
100
150
200
250
time (s)
hea
din
g(d
eg)
(c)
−3
−2
−1
0
1
time (s)
Col
um
nIn
put
(deg
)
−1
−0.5
0
0.5
1
1.5
time (s)
δ s(d
eg)
−60
−40
−20
0
20
Whee
lIn
put
(deg
)
−2
−1.5
−1
−0.5
0
0.5
Ped
alIn
put
(Inch
es)
10 20 30 409
9.5
10
10.5
11
11.5
time (s)
δ f(d
eg)
10 20 30 40−0.5
0
0.5
1
1.5
2
time (s)
Thro
ttle
(0-1
)
(d)
Figure 3.19: Comparing to Boeing Simulation: Large Roll Upset
Chapter 3. Flight Model 55
0
20
40
60
time (s)
α(d
eg)
Enhanced B−747 ModelFDR Data
−10
−5
0
5
time (s)
β(d
eg)
0
1000
2000
time (s)
Altitude
(ft)
10 20 30 40 50 60 700
100
200
time (s)
VE
(knots
)
Note: FDR Data is computed airspeed
(a)
−0.2
0
0.2
0.4
0.6
nx
(G)
−0.1
−0.05
0
0.05
0.1
0.15
time (s)
ny
(G)
10 20 30 40 50 60 700
0.5
1
1.5
time (s)
nz
(G)
(b)
−40
−20
0
20
40
60
φ(d
eg)
−40
−20
0
20
40
60
θ(d
eg)
10 20 30 40 50 60 70300
320
340
360
380
time (s)
hea
din
g(d
eg)
(c)
−30
−20
−10
0
10
20
δ e(d
eg)
−14
−12
−10
−8
−6
−4
−2
δ s(d
eg)
−20
−10
0
10
20
δ a(d
eg)
−20
−10
0
10
20
30
δ r(d
eg)
20 40 605
10
15
20
25
30
time (s)
δ f(d
eg)
20 40 600.2
0.4
0.6
0.8
1
time (s)
Thro
ttle
(0-1
)
(d)
Figure 3.20: Comparing to Accident Data: Stall
Chapter 3. Flight Model 56
0
20
40α
(deg
)
−30
−20
−10
0
δ e(d
eg)
−40
−20
0
20
θ(d
eg)
10 20 30 40 50 60 7050
100
150
200
250
time (s)
VE
(knots
)
Enhanced B−747 ModelNASA EUR ModelNASA Flight Test
(a)
0
20
40
α(d
eg)
−30
−20
−10
0
δ e(d
eg)
−40
−20
0
20
θ(d
eg)
10 20 30 40 50 60 7050
100
150
200
250
time (s)V
E(k
nots
)
Original B−747 ModelNASA EUR ModelNASA Flight Test
(b)
Figure 3.21: Comparing to EUR Stall Simulation and Flight Test
10
20
30
40
50
α(d
eg)
−6−4−2
02
β(d
eg)
−2
0
2
p(d
eg/s)
10 20 30 40 50 60 70
−5
0
5
10
time (s)
φ(d
eg)
Without TurbulenceWith Turbulence(Scale = 0.5)
Figure 3.22: Roll and β at Stall
0
20
40
60
α(d
eg)
−10
−5
0
5
β(d
eg)
−10
0
10
p(d
eg/s)
10 20 30 40 50 60 70−10
0
10
20
time (s)
φ(d
eg)
B−747Mid−Size Aircraft
Figure 3.23: Comparing Roll-Off Behavior
Chapter 3. Flight Model 57
−5
−4
−3
−2
−1
0
1x 10
−3C
l β
−20 0 20 40 60 80 100−0.015
−0.01
−0.005
0
0.005
0.01
α (deg)
Cn
β
B−747 ModelNASA T2 ModelNASA EUR Model
Unstable
Stable
Unstable
Stable
(a)
−20 0 20 40 60 80 100−0.5
0
0.5
1
1.5
2
α (deg)
Clp
B−747 ModelNASA T2 ModelNASA EUR Model
Unstable
Stable
(b)
10
20
30
40
50
α(d
eg)
−20
0
20
β(d
eg)
−20
−10
0
10
p(d
eg/s)
10 20 30 40 50 60 70−100
−50
0
50
time (s)
φ(d
eg)
Enhanced B−747 ModelRoll Model (AeroAsym = 1)Roll Model (AeroAsym = −1)
(c)
10
20
30
40
50
α(d
eg)
−20
−10
0
β(d
eg)
−10
0
10
20
p(d
eg/s)
10 20 30 40 50 60 70
−20
0
20
40
time (s)
φ(d
eg)
Default Enhanced B−747 ModelRoll Model
(d)
Figure 3.24: Stability Derivatives and Simulation Results Using the Roll Model
Chapter 4
Upset Recovery Experiments
4.1 Upset Scenarios
A representative set of upset scenarios were selected to collect aircraft state time histories
during piloted recoveries from upsets. For the scenarios to be realistic, they were based
on past incidents and accidents. The FAA’s list of upset accidents between 1993 and 2007
[4] was used to survey past accidents. Accident reports were obtained from government
accident investigation branches such as the U.S. National Transportation Safety Board
(NTSB). Out of the 31 accident reports found, only a few had extensive Flight Data
Recorder (FDR) data that could be used to simulate the accident. In the end, five
accident scenarios were selected. Additionally, a pilot-induced stall maneuver based on
the NASA EUR model stall simulation was selected as the sixth scenario. For this
scenario, pilots were asked to follow the pitch time history of the NASA stall flight test.
Table 4.1 summarizes the six scenarios chosen. The following sections will describe each
scenario in detail. The recovery procedures described subsequently are based on those
recommended in the URT Aid [3] or by Gawron [6], and were tested in the simulator
before the experiments were conducted. Direct use of the FDR control input histories
in the B-747 model did not lead to the exact same aircraft responses because in five of
the scenarios the aircraft type was different and in all of the six scenarios environmental
factors (such as turbulence) could not be taken into account. Thus, the FDR control
input histories were modified accordingly such that the B-747 aircraft was brought into
similar upset conditions that led to the accidents/incident.
Scenario 1 was based on a 1994 accident involving an Airbus A300 aircraft that
occurred in Nagoya, Japan. The accident report was available from JTSB [25]. The
58
Chapter 4. Upset Recovery Experiments 59
No. Registration No. Location Aircraft Date Type of Upset
1 B1816 Nagoya, Japan A300B4-622R 04/26/1994 Stall
2 G-THOF Hampshire, UK B737-3Q8 09/23/2007 Stall
3 HL-7451 London, UK B747-2B5F 12/22/1999 Large Roll Upset
4 N513AU Pittsburgh, U.S. B737-300 09/08/1994 Rudder Hardover
5 N954VJ Charlotte, U.S. DC-9-31 07/02/1994 Microburst
6 Flight Test N/A N/A N/A Stall
Table 4.1: Summary of Reference Upset Scenarios
accident occurred during an ILS approach when the first officer accidentally triggered
the go-around mode. The autopilot was then engaged, which commanded a pitch-up
due to the go-around mode. The crew applied nose-down elevator input to stay on the
glideslope, but the autopilot moved the horizontal stabilizer to its full nose-up position.
The crew kept the nose-down elevator input to counter the nose-up pitching moment from
the stabilizer, but the angle of attack slowly increased. As a result, the alpha-floor system
of the Airbus A300 moved the thrust levers to the full thrust position. This combination
of full thrust and full nose-up stabilizer trim created a large nose-up pitching moment
that subsequently led to stall. This accident was used for validation in the previous
chapter and was also one of the upset scenarios tested in Gawron’s study [6]. To recover,
up to full nose-down elevator input should be applied and at the same time the nose-up
stabilizer trim should be reduced to prevent the angle of attack from increasing further.
Briefly reducing the thrust for aircraft with under-wing mounted engines may also help
arrest the increase in angle of attack [3]. After the angle of attack is sufficiently reduced,
thrust should be increased again to gain altitude. Using the enhanced B-747 model, the
full thrust and nose-up stabilizer trim combination created a deep stall scenario.
Scenario 2 was based on a 2007 incident involving a Boeing 737 aircraft that occurred
in Hampshire, UK. The incident report was available from the UK AAIB [27]. The
incident occurred during an ILS approach. The auto-throttle accidentally disengaged
and the thrust levers remained at the idle thrust position, which led to a rapid decrease
in airspeed. After realizing the situation, the crew initiated a go-around and increased
thrust levers to the full thrust position. The increase in thrust combined with nose-up
stabilizer trim caused the aircraft to pitch up and resulted in stall. While the crew
attempted recovery by applying full nose-down elevator input, the large nose-up pitching
Chapter 4. Upset Recovery Experiments 60
moment from the thrust and the stabilizer overwhelmed the elevator input, and the
angle of attack gradually increased to nearly 40◦ before it started to decrease. The crew
eventually reduced the thrust and the nose-up stabilizer trim and the aircraft recovered
from the stall. The recovery procedure is the same as scenario 1. When using the
enhanced B-747 model, a larger nose-up stabilizer trim was required to overwhelm the
elevator input in the same manner as the accident. The cause of this accident was similar
to scenario 1, but the differences in the initial setup and control input time histories led
to a milder stall when simulated using the enhanced B-747 model. However, a more
aggressive secondary stall could occur if a proper recovery from the first stall was not
made.
Scenario 3 was based on a 1999 accident involving a Boeing 747 aircraft that occurred
near London Stansted Airport, UK. The accident report was available from the UK
AAIB [24]. This was the accident used for model validation in the previous chapter. The
accident was a large roll upset caused by Attitude Director Indicator (ADI) malfunction
and erroneous pilot input. The ADI of the aircraft was indicating zero roll throughout the
flight while the pitch attitude was correctly indicated. The pilot maintained a left wheel
control input and rolled the aircraft to 90◦ even though there were warnings from the
ADI comparator. In addition, no control input was made to correct the pitch attitude.
To recover, up to full opposite aileron input should be applied to bring the aircraft back
to wings-level [3]. When the aircraft starts to approach wings-level, nose-up elevator
input should be applied to correct the nose-down pitch attitude [3]. In the simulator
experiment, the ADI was set to show zero roll throughout the flight as in the accident.
Scenario 4 was based on a 1994 accident involving a Boeing 737 aircraft that occurred
near Pittsburgh, U.S. The accident report was available from the U.S. NTSB [28]. The
accident aircraft experienced a rudder hardover due to a mechanical jam, which caused
it to yaw and roll uncontrollably to the left. This accident was also one of the scenarios
used in Gawron’s study and was rated as the most difficult scenario to recover from
[6]. To recover, full opposite aileron input should be applied to roll the aircraft back
to wings-level. When the aircraft is flying at a speed below the cross-over speed, where
full aileron input cannot counter opposite full rudder input [28], asymmetric thrust input
should also be applied to help counter the rudder effect [6]. As the aircraft approaches
wings-level, nose-up elevator input should be applied to correct the pitch attitude. In the
simulator experiment, the rudder deflection followed the accident history and remained
at the jammed position, so the pedal inputs were inactive.
Chapter 4. Upset Recovery Experiments 61
Scenario 5 was based on a 1994 accident involving a DC-9 aircraft that occurred
in Charlotte, U.S. The accident report was available from the U.S. NTSB [29]. The
accident aircraft encountered a microburst (powerful and concentrated downdraft [3])
during a landing approach. The crew initiated a go-around during the landing approach
in response to the severe weather condition; however, they failed to establish the required
go-around pitch attitude (15◦) and the necessary thrust to escape the high sink rate from
the microburst. To recover, nose-up elevator input and maximum thrust should be
applied to prevent the aircraft from descending further [6]. This was one of the scenarios
used in Gawron’s study and was rated as the easiest scenario to recover from [6]. A
microburst model was implemented to simulate this scenario. The microburst intensity
was adjusted to provide similar downwind and head/tailwind profiles as the accident. A
description of the microburst model is given in Appendix A.
In scenario 6, pilots were asked to track the pitch time history of the NASA stall flight
test. The target pitch angle was shown using a Flight Director on the instrument panel.
Pilots were asked to initiate recovery when the Flight Director disappeared. The Flight
Director was set to disappear at the maximum pitch angle reached in NASA’s flight test.
For recovery, applying nose-down elevator input alone is sufficient to reduce the angle of
attack. To be consistent with the reference flight test, aircraft C.G. was set to an aft
position (30% Mean Aerodynamic Chord).
4.2 Experimental Methodology
In the experiments, the aircraft was brought into the upset conditions using pre-programmed
control inputs with the exception of scenario 6 where pilot induced the upset. Pilots’ task
was to recognize the upset and recover from it. The experiments were conducted without
motion since the current MDA and tuning method must be improved before running any
upset motions, otherwise significant actuator limiting will occur, leading to large false
motion cues. Pilots were only informed that the upset scenarios consisted of three stalls,
two unusual attitude upsets and a microburst encounter. A detailed description for each
scenario was not given except for the instruction for Scenario 6. Nevertheless, generic
recovery techniques for stall, large roll upsets and microburst encounters (that are sug-
gested in the URT Aid [3] and Gawron’s study [6]) were provided in the pilot briefing
document. This required pilots to assess the upset from aircraft states and determine
the appropriate recovery procedure to use.
Chapter 4. Upset Recovery Experiments 62
Prior to the experiments, pilots were asked to perform three upset recovery exercises
to become familiar with the UTIAS FRS and to practice recovery techniques that were
useful in the experiments. The exercises consisted of two nose-high upsets and one large
roll upset. In the experiments, if recovery was not successful, pilots were asked to repeat
until successful recovery. After all six scenarios were tested and successfully recovered,
they were repeated for a second trial. The data recorded from the first trial therefore
would represent the motions attained when pilots first started the training. The data
recorded from the second trial would represent the motions attained when pilots became
familiar with the maneuvers. A stick shaker model was used for stall warning in the
experiment and a g-meter was also added to the instrument panel to help pilots moderate
the normal load factor since there were no acceleration cues from motion. No control
augmentation was used during the experiments.
4.3 Experimental Results
Four pilots participated in the experiments, but one pilot was only able to complete the
first trial due to a schedule conflict. Thus, seven sets of data were collected for each
scenario. In addition, two of the pilots tested the three stall maneuvers using the roll
model described in Section 3.5.3. The results were used to examine if the modifications
made to Clβ ,Cnβ ,Clp and the addition of aerodynamic asymmetry data for Cl would result
in large lateral/directional responses during stall and recovery from stall.
This section will examine the recorded aircraft state time histories and summarize
the comments given by the pilots. Two or three example results will be shown for each
scenario.
Example results for scenario 1 are shown in Figure 4.1. Example results 1 and 2
employed the default enhanced B-747 model and example result 3 employed the roll
model. A small amount of turbulence was applied to introduce some lateral disturbance
during stall. Example results 1 and 2 in Figure 4.1 used the same turbulence intensity
but with opposite onset disturbance direction. Turbulence was not used for example
result 3 with the roll model, so that the effect of the aerodynamic asymmetry data for
Cl could be examined. This scenario was the most difficult to recover from; no one was
able to recover from this scenario on the first attempt. During the successful recoveries,
most pilots reduced both the nose-up stabilizer trim and thrust in addition to applying
nose-down elevator input. Since the B-747 aircraft has under-wing mounted engines, very
Chapter 4. Upset Recovery Experiments 63
briefly reducing the thrust helped arrest the rapid increase in angle of attack. Maximum
thrust was then applied to gain altitude. One pilot however, was able to recover without
reducing thrust (see example results 2 and 3). In this case the pilot immediately applied
full nose-down elevator input and reduced the nose-up stabilizer trim after recovery was
signalled. The nose-down elevator input was also held longer than recoveries with brief
thrust reduction, thus the timing to pull-up after pushing the column to reduce the angle
of attack also seemed to be important in the recovery. Due to the large nose-up pitching
moment from the thrust and the stabilizer, the maximum pitch angle reached a little over
50◦ as in the accident, and the average peak angle of attack (αpeak) was 65◦ 12. Large
change was also seen in the normal load factor, nz. On average, nz decreased to around
0.27 G because pilots were pushing on the column to decrease α. It then often increased
to nearly 1.8 G as the pilot pulled up for climb. Additionally, approximately ±10◦
of sideslip was developed during stall due to the turbulence for the default enhanced
model and due to the roll asymmetry for the roll model. The aircraft did not enter
lateral/directional divergence but all four pilots applied some amount of aileron and
rudder inputs to correct the rolling. The lateral/directional responses developed using
the roll model were in similar magnitudes 13 compared to the flights using the default
enhanced B-747 model and turbulence, but the roll model resulted in larger sideslip and
roll rate. One of the two pilots that tested the roll model commented that the roll model
felt slightly more laterally unstable for scenario 1.
Example results for scenario 2 are shown in Figure 4.2. Similar to scenario 1, example
results 1 and 2 employed the default enhanced B-747 model and example result 3 em-
ployed the roll model. Pilots were able to recover from this scenario with less difficulty
than scenario 1 because the increase in angle of attack was much less aggressive. The
αpeak typically stayed below 30◦ so this maneuver was a much milder stall (the αstall is
12The NASA pitch forced oscillation data are only available up to α = 50◦, and the large pitch ratedeveloped during a stall could exceed the pitch forced oscillation database, where extrapolation is used.Since the pitch forced oscillation data are relatively linear with respect to the pitch rate as well as withα, near α = 50◦, the errors caused by extrapolation are expected to be small. When the pitch forcedoscillation database was set to hold the last value in the data constant, there was no significant change inthe aircraft responses. Similarly, the NASA yaw forced oscillation data are only available up to α = 60◦,so the yaw forced oscillation database uses extrapolation beyond α = 60◦. As the yaw rate effect ismuch more nonlinear compared to the pitch rate effect, errors from extrapolation could be significant;however, α exceeded 60◦ very briefly in scenario 1, for only about 3 to 4 seconds.
13This is based on an onset disturbance from the aerodynamic asymmetry in roll since data were notcollected with turbulence applied to the roll model. As shown in Figure 3.24(d) in Section 3.5.3 however,the roll model would likely have resulted in larger lateral/directional responses than the default modelif turbulence was applied (but it also depends on how pilots respond to the rolling).
Chapter 4. Upset Recovery Experiments 64
around 19◦). Again, a small amount of turbulence was applied to introduce some lat-
eral disturbance during the stall for the default enhanced model, but turbulence was not
used for the roll model to test the effect of the aerodynamic asymmetry data. Example
result 1 in Figure 4.2 was a case where the pilot recovered on the first attempt but the
stabilizer was not reduced until much later, because the pilot was not immediately aware
it. Example result 2 was from a second trial where the pilot had already flown the sce-
nario once. In this case the pilot reduced the nose-up stabilizer trim immediately after
recovery was signalled. Divergent lateral-directional responses did not develop for this
scenario for both the default enhanced B-747 model and the roll model as the angle of
attack did not become very large. In this scenario, the small roll motion developed was
quickly countered by the pilots, but led to small amplitude oscillations in several flights.
The average minimum and maximum normal load factor reached were 0.68 G and 1.37
G respectively which were relatively mild.
A point that should be noted is that flap deflections for scenarios 1 and 2 followed
the flap histories of the accidents that were available in the accident reports, while flap
deflections remained at the initial setup for the other scenarios (i.e. they were not under
pilot control). In scenarios 1 and 2, flap deflection was reduced during the flight by the
pilots in the reference stall accident and incident. While reducing flap deflection can
cause the aircraft to pitch up, the pitching moment was negligible compared to the large
nose-up pitching moment caused by the maximum thrust and the nose-up stabilizer trim.
Example results for scenario 3 are shown in Figure 4.3. The large roll upset occurred
during climb, thus unsuccessful recovery would lead to loss of altitude and potentially
impact with the ground. All pilots were able to recover from this scenario on the first
attempt. Full opposite aileron input alone was sufficient to roll the aircraft back to
wings-level, but the normal load factor could become large depending on how aggressive
the pilot pulled up to correct the large nose-down pitch. In four of the seven flights, the
maximum normal load factor reached around 2.3 G which was beyond the 2.0 G limit
recommended for transport aircraft at flaps-extended configurations [3]. Example result 1
in Figure 4.3 shows the case where the normal load factor exceeded 2.0 G. In comparison,
example result 2 shows a case where the maximum normal load factor reached was within
the 2.0 G limit. The angular rates can also become large depending on how the pilot
reacted to the upset. The maximum roll rate experienced varied widely from -9.4 to 26
deg/s, and the maximum pitch rate ranged from 4.4 to 9.3 deg/s.
Example results for scenario 4 are shown in Figure 4.4. This was the second most
Chapter 4. Upset Recovery Experiments 65
difficult scenario to recover from, but once pilots discovered (or were told) to use asym-
metric thrust, recovery was easily performed. The example results in Figure 4.4 show
two cases with large differences in the responses: example result 2 was when asymmetric
thrust input was applied immediately after recovery was signalled, and example result 1
was when asymmetric thrust input was applied 5 seconds after recovery was signalled.
In example result 1, the pilot also pushed on the column first to unload the aircraft in
order to increase roll authority. Similar to scenario 3, large normal load factor was at-
tained when the pilot pulled up aggressively. The maximum normal load factor reached
in the experiments ranged widely from 1.2 G to 2.3 G, where a large value resulted when
asymmetric thrust input was not applied immediately. The maximum lateral accelera-
tion reached also varied widely from -0.14 G to -0.31 G. Among the successful recoveries,
the maximum roll reached was -66◦, and the minimum was -39◦, which varied widely due
to the differences in pilot recovery inputs. The maximum sideslip experienced was more
consistent, in the range of 9◦ to 11◦. The maximum roll rate experienced varied from
−8.3 deg/s to 15 deg/s, and the maximum pitch rate varied from -3.5 deg/s to 10 deg/s.
The maximum yaw rate was typically in the range of -6 deg/s, but in one case reached
-14 deg/s where asymmetric thrust input was not applied immediately.
Example results for scenario 5 are shown in Figure 4.5. Similar to Gawron’s study,
this scenario was the easiest to recover from because pilots are typically familiar with
windshear recovery. All pilots recovered without difficulty on the first attempt and little
difference was seen in the pilot recovery inputs. Two example results are shown in
Figure 4.5. The angle of attack exceeded the stall angle for a brief moment during the
recovery in most flights, which was due to the pilots pulling up to arrest the rapid descent.
Nevertheless, the angle of attack was quickly reduced before the aircraft started to lose
significant lift. Angular rates and translational accelerations did not become very large
in this scenario.
Example results for scenario 6 are shown in Figure 4.6. Example results 1 and 2
employed the default enhanced B-747 model and example result 3 employed the roll
model. The aircraft state time histories varied the most for this maneuver because the
upset was pilot-induced. It was observed that pilots had the tendency to pull up too
early after pushing to reduce the angle of attack during the recovery, and in two cases
this resulted in secondary stall. Roll-off or directional divergence did not occur in most
cases (see example result 1), but relatively large lateral/directional responses developed
when the aircraft entered secondary stall (see example result 2). Also, using the roll
Chapter 4. Upset Recovery Experiments 66
model, relatively large roll and very large sideslip angles developed without the aircraft
entering a secondary stall (see example result 3). One pilot commented that the roll
model felt much more laterally unstable for this maneuver. In both example results 2
and 3, the pilot responded to the onset roll disturbance with large aileron and rudder
inputs which then induced oscillatory responses. An important comment made by the
pilots was that it was difficult to get the sense of angle of attack because there were
limited cues to help them “feel” the stall. Similar comments were made by pilots in the
NASA study, which was that adding aerodynamic buffet and motion could be important
for stall [13]. Another comment made by the pilots was that the stick shaker felt weak.
This is because the control loading system itself was used to provide the stick shaker
forces. The forces it produced were smaller than an in-flight stick shaker system, and
was particularly true when the pilot applied a large displacement on the control stick as
the hardware further limited the size of the oscillatory shaker forces. An external shaking
mechanism should be installed in future studies to provide a more realistic stick shaker
warning.
An useful tool to further examine the upset maneuvers is the five Quantitative Loss-
Of-Control Criteria (QLC) envelopes developed in Reference [2]. Since not all upsets
result in LOC, plotting the five QLC envelopes will help determine the cause and severity
of the event. The basic guideline for using the QLC envelopes is that an event is classified
as “out of control” when the aircraft states exceed three or more of the five QLC envelopes
[2]. The five QLC envelopes are: adverse aerodynamic (AA) envelope, unusual attitude
(UA) envelope, structural integrity (SI) envelope, and the dynamic pitch/roll control
(DPC/DRC) envelopes. The AA envelope is an indication of stall and large sideslip
conditions [2]. The UA envelope is defined using the general definition of upset (φ > ±45◦,
θ > 25◦ nose-up or 10◦ nose-down) [2]. The SI envelope is used to indicate accelerated
stall, overspeed, and structural overload [2]. The DPC and DRC envelopes are used to
indicate if the pitch or roll motion is consistent with the control command [2].
Example QLC envelopes are plotted for upset scenarios 1 to 4 in Figure 4.7 14. It can
14Note that α, β and airspeed are normalized in the AA and SI envelopes. α is normalized by theangle at which stall warning activation occurs, and β by the sideslip for non-crabbed approach in themaximum demonstrated crosswind for takeoff or landing [2]. In the example QLC envelopes, stick shakerα was used to normalize α. Also, according to Ref.[30], sideslip associated with maximum crosswindtakeoff or landing is typically in the order of ±10◦ so this value was used to normalize β. Airspeed wasnormalized such that reaching the maximum operating speed at the flap setting corresponds to 1 andreaching the stall speed at the flap setting corresponds to 0.
Chapter 4. Upset Recovery Experiments 67
be seen that more than three envelopes were exceeded for all four scenarios. Also, part
of the data lie in the second or forth quadrants in the DPC and DRC control envelopes
for all four scenarios, which indicates that control inputs were made to oppose the air-
craft motion. The QLC envelopes for scenario 1 show similar trends as the example QLC
envelopes plotted for dynamic stall flight test and accident data in Reference [2]. One no-
table difference is that the flight test and accident data in Reference [2] have larger lateral
excursions (i.e. larger β, φ and DRC envelope excursions). This difference comes from
the fact that the current model does not usually develop aggressive lateral/directional
responses at stall, as discussed in Chapter 3. Since scenario 2 is a much milder stall
compared to scenario 1, the QLC envelopes have closer resemblance to the normal stall
QLC envelope shown in Reference [2]. The envelope excursions are much smaller for a
normal stall. Scenario 3 is similar to the roll upset accident data in Reference [2] but is
much less aggressive as it is a recovered flight. Nonetheless, four of the five envelopes
are still exceeded. In Scenario 4, part of the data lies in the third quadrant of the DPC
envelope because the pilot first pushed on the column to unload the aircraft in order to
increase roll authority.
Finally, several suggestions can be made for future upset recovery experiments involv-
ing pilots. First, a more extensive warning system should be installed in the simulator to
help pilots better monitor the aircraft states. For example, as pilots exceeded the speed
limits on several occasions, various aural and visual speed warnings should be added. In
addition, the g-meter that was installed to help pilots moderate the normal load factor
could be improved as the current g-meter on the instrument panel can be difficult for
pilots to check while trying to recover. Secondly, pilots could have been given more
extensive familiarization flights prior to the experiments, since they may need time and
practice to become accustomed to the flight and control characteristics if they do not
have previous flying experience in the B-747. This could also help pilots better monitor
the normal load factor and speed limits. Thirdly, while the aircraft was considered to
be recovered in the experiments based on the pilot and the experimenter’s judgement,
a more objective method may be used, such as setting target speed and attitude for
recovery and evaluate if the target aircraft states are achieved. In summary, there are a
number of improvements that can be made in future upset recovery experiments, espe-
cially for statistical analysis. Nevertheless, the data collected from the six upset recovery
experiments can be used to identify and potentially correct major motion cueing issues
that could be experienced with the current MDA.
Chapter 4. Upset Recovery Experiments 68
20
40
60α
(deg
)
−20
0
20
40
60
θ(d
eg)
−10
−5
0
5
q(d
eg/s
)
500
1000
1500
2000
Alt
itude
(ft)
10 20 30 40 50 60 70 80 90 10050
100
150
200
time (s)
VE
(knot
s)Example 1Example 2Example 3
(a)
−10
0
10
β(d
eg)
−10
0
10
φ(d
eg)
−5
0
5
p(d
eg/s)
80
90
100
ψ(d
eg)
10 20 30 40 50 60 70 80 90 100−4
−2
0
2
time (s)
r(d
eg/s
)
(b)
0.1
0.2
0.3
0.4
0.5
nx
(G)
−0.2
−0.1
0
0.1
0.2
ny
(G)
10 20 30 40 50 60 70 80 90 1000
0.5
1
1.5
2
time (s)
nz
(G)
(c)
−20−10
010
δ e(d
eg)
−20
0
20
δ a(d
eg)
−10
0
10
20
δ r(d
eg)
−12−10−8−6−4−2
δ s(d
eg)
5
10
15
20
δ f(d
eg)
10 20 30 40 50 60 70 80 90 1000
0.5
1
time (s)
Thro
ttle
(0-1
)
Recovery at 62 sec
(d)
Figure 4.1: Experiment Example Results: Upset Scenario 1
Chapter 4. Upset Recovery Experiments 69
0
10
20
30α
(deg
)
−20
0
20
40
θ(d
eg)
−5
0
5
q(d
eg/s
)
0
1000
2000
Alt
itude
(ft)
10 20 30 40 50 60 70 80 900
100
200
300
time (s)
VE
(knot
s)
Example 1Example 2Example 3
(a)
−4−2
02468
β(d
eg)
−5
0
5
φ(d
eg)
−5
0
5
p(d
eg/s
)
85
90
95
100
105
ψ(d
eg)
10 20 30 40 50 60 70 80 90
−2
0
2
time (s)
r(d
eg/s
)
(b)
0
0.1
0.2
0.3
0.4
0.5
nx
(G)
−0.1
−0.05
0
0.05
0.1
ny
(G)
10 20 30 40 50 60 70 80 900.5
1
1.5
time (s)
nz
(G)
(c)
−10
0
10
20
δ e(d
eg)
−20
0
20
δ a(d
eg)
−20
0
20
δ r(d
eg)
−15
−10
−5
0
δ s(d
eg)
10
20
30
δ f(d
eg)
10 20 30 40 50 60 70 80 900
0.5
1
time (s)
Thro
ttle
(0-1
)
Recovery at 35 sec
(d)
Figure 4.2: Experiment Example Results: Upset Scenario 2
Chapter 4. Upset Recovery Experiments 70
2468
10α
(deg
)
−20
0
20
θ(d
eg)
−202468
q(d
eg/s
)
1000
2000
3000
4000
Alt
itude
(ft)
10 20 30 40 50 60 70 80 90150
200
250
time (s)
VE
(knots
)Example 1Example 2
(a)
−6−4−2
02
β(d
eg)
−80−60−40−20
020
φ(d
eg)
−505
1015
p(d
eg/s)
0
50
100
ψ(d
eg)
10 20 30 40 50 60 70 80 90−8
−6
−4
−2
02
time (s)
r(d
eg/s
)
(b)
0
0.1
0.2
0.3
nx
(G)
−0.05
0
0.05
0.1
ny
(G)
10 20 30 40 50 60 70 80 900.5
1
1.5
2
2.5
time (s)
nz
(G)
(c)
−10
0
10
δ e(d
eg)
−20
0
20
δ a(d
eg)
−10
−5
0
5
δ r(d
eg)
0
0.5
1
δ s(d
eg)
0
10
20
δ f(d
eg)
10 20 30 40 50 60 70 80 900
0.5
1
time (s)
Thro
ttle
(0-1
)
Recovery at 35 sec
(d)
Figure 4.3: Experiment Example Results: Upset Scenario 3
Chapter 4. Upset Recovery Experiments 71
510152025
α(d
eg)
−20
−10
0
10
θ(d
eg)
−10
0
10
q(d
eg/s
)
4500
5000
5500
6000
Alt
itude
(ft)
10 20 30 40 50 60 70
180
200
220
240
time (s)
VE
(knots
)Example 1Example 2
(a)
0
5
10
β(d
eg)
−60
−40
−20
0
20
φ(d
eg)
−10
0
10
p(d
eg/s
)
0
50
100
ψ(d
eg)
10 20 30 40 50 60 70−8
−6
−4
−2
0
time (s)
r(d
eg/s
)
(b)
−0.4
−0.2
0
0.2
0.4
nx
(G)
−0.2
−0.1
0
0.1
0.2
ny
(G)
10 20 30 40 50 60 70−0.5
0
0.5
1
1.5
2
time (s)
nz
(G)
(c)
−10
0
10
δ e(d
eg)
−20
0
20
δ a(d
eg)
−20
0
20
δ r(d
eg)
−1
0
1
δ f(d
eg)
−4
−2
0
δ s(d
eg)
0
0.5
1
Thro
ttle
(L)
(0-1
)
10 20 30 40 50 60 700
0.5
1
time (s)
Thro
ttle
(R)
(0-1
)
Recovery at 20 sec
(d)
Figure 4.4: Experiment Example Results: Upset Scenario 4
Chapter 4. Upset Recovery Experiments 72
0
10
20
30
α(d
eg)
0
10
20
30
θ(d
eg)
−5
0
5
10
q(d
eg/s)
0
500
1000
1500
Alt
itude
(ft)
10 20 30 40 50 60 70100
150
200
time (s)
VE
(knot
s)Example 1Example 2
(a)
0
1
2
β(d
eg)
−4
−2
0
2
4
φ(d
eg)
−2
−1
0
1
p(d
eg/s)
90
92
94
ψ(d
eg)
10 20 30 40 50 60 70−1
−0.5
0
0.5
time (s)
r(d
eg/s
)
(b)
0
0.2
0.4
0.6
0.8
nx
(G)
−0.03
−0.02
−0.01
0
0.01
ny
(G)
10 20 30 40 50 60 70
0.8
1
1.2
1.4
1.6
time (s)
nz
(G)
(c)
−20
−10
0
10
δ e(d
eg)
−20246
δ a(d
eg)
0
2
4
6
δ r(d
eg)
−4
−2
0
δ s(d
eg)
18
19
20
21
δ f(d
eg)
10 20 30 40 50 60 700
0.5
1
time (s)
Thro
ttle
(0-1
)
Recovery at 42 sec
(d)
Figure 4.5: Experiment Example Results: Upset Scenario 5
Chapter 4. Upset Recovery Experiments 73
0
20
40
60α
(deg
)
−40
−20
0
20
θ(d
eg)
−15
−10
−5
0
5
q(d
eg/s)
4000
6000
8000
10000
12000
Alt
itude
(ft)
10 20 30 40 50 60 70 80 90100
200
300
time (s)
VE
(knot
s)
Example 1Example 2Example 3
(a)
−20
0
20
β(d
eg)
−20
0
20
40
φ(d
eg)
−10
0
10
p(d
eg/s
)
80
100
120
ψ(d
eg)
10 20 30 40 50 60 70 80 90
−5
0
5
time (s)
r(d
eg/s
)
(b)
−0.2
0
0.2
0.4
0.6
nx
(G)
−0.4
−0.2
0
0.2
0.4
ny
(G)
10 20 30 40 50 60 70 80 900
0.5
1
1.5
2
2.5
time (s)
nz
(G)
(c)
−40
−20
0
20
δ e(d
eg)
−20
0
20
δ a(d
eg)
−20
0
20
40
δ r(d
eg)
−1
−0.5
0
δ s(d
eg)
−1
0
1
δ f(d
eg)
10 20 30 40 50 60 70 80 900
0.5
1
time (s)
Thro
ttle
(0-1
)
(d)
Figure 4.6: Experiment Example Results: Upset Scenario 6
Chapter 4. Upset Recovery Experiments 74
−2 −1 0 1 2−1
0
1
2
3
4
Normalized β
Nor
mal
ized
α
Adverse Aerodynamic Envelope
−100 −50 0 50 100−40
−20
0
20
40
60
φ (deg)
θ (d
eg)
Unusual Attitude Envelope
−1 0 1 2
−1
0
1
2
3
Normalized Airspeed
Nor
mal
Load
Fac
tor
(G)
Structure Integrity Envelope
−100 −50 0 50 100−20
0
20
40
60
Percent Pitch Control (%)
Dyn
amic
P
itch
Atti
tude
(de
g)
Dynamic Pitch Control Envelope
−100 −50 0 50 100−50
0
50
Percent Lateral Control (%)
Dyn
amic
Rol
l Atti
tude
(de
g)
Dynamic Roll Control Envelope
Scenario 1(Example 1 in Figure 4.1)Scenario 2(Example 1 in Figure 4.2)
(a) QLC Envelopes for Scenarios 1 and 2
−2 −1 0 1 2−1
−0.5
0
0.5
1
1.5
2
Normalized β
Nor
mal
ized
α
Adverse Aerodynamic Envelope
−100 −50 0 50 100−50
0
50
φ (deg)
θ (d
eg)
Unusual Attitude Envelope
−1 0 1 2−2
−1
0
1
2
3
4
Normalized Airspeed
Nor
mal
Load
Fac
tor
(G)
Structure Integrity Envelope
−100 −50 0 50 100−30
−20
−10
0
10
20
30
Percent Pitch Control (%)
Dyn
amic
P
itch
Atti
tude
(de
g)
Dynamic Pitch Control Envelope
−100 −50 0 50 100−80
−60
−40
−20
0
20
40
60
Percent Lateral Control (%)
Dyn
amic
Rol
l Atti
tude
(de
g)
Dynamic Roll Control Envelope
Scenario 3(Example 1 in Figure 4.3)Scenario 4(Example 1 in Figure 4.4)
(b) QLC Envelopes for Scenarios 3 and 4
Figure 4.7: QLC Envelopes
Chapter 4. Upset Recovery Experiments 75
4.4 Example MDA Results
Example MDA results will be examined in this section using two sets of data collected
from the upset recovery experiments. To examine specific motion cueing issues, the
recorded specific forces and angular rates were run through the UTIAS classical MDA
(which uses a set of MDA parameters called CW2) that is used with the original B-747
model in the simulator [16, 31]. The classical MDA has the structure described in Section
2.4. Since the outputs from scaling, limiting and filtering can still command motions that
exceed the actuator extension limits, the UTIAS classical MDA also employs software
limiting, which further limits the MDA outputs to ensure that the motion system’s
physical limits are not reached [31].
Some of the major problems that were noted from examining the MDA outputs were
the following:
• Motion system actuator software limiting was often activate
• Significant motion cue errors, such as false cues and phase errors were observed
• Motion jerkiness (noisy output), which refers to unexpected high frequency motion,
was often seen
False cues refer to the cases where the MDA output is in the opposite direction from the
motion of the aircraft or contain unexpected motion cues [32]. Phase errors are produced
as a result of using the high and low-pass washout filters. The high-pass filters can cause
phase lead near the break frequency and the low-pass filters can cause phase lag [32].
Phase errors can have significant effect when motion cues are used for controlling the
aircraft [32]. The MDA outputs for scenarios 1 and 3 will be discussed next as example.
Figures 4.8(a) and 4.8(b) compare the MDA outputs to the aircraft motion for scenario
1, where fx, fy, and fz denote the X, Y, and Z specific forces. Note that the MDA outputs
are scaled using a factor of 0.5, but the results shown in Figures 4.8(a) and 4.8(b) are
divided by the scale factor so that direct comparison of the shape can be made to the
aircraft motion. It should also be noted that the fz motion is severely limited due to the
limited capability of the hexapod motion system to produce sustained vertical specific
force. The spikes in the MDA outputs (seen at 80 and 83 seconds) indicate that actuator
software limiting is active. Looking at the results, false cues can be observed in several
places. For example, the q output from 65 to 70 seconds and r output from 69 to 75
Chapter 4. Upset Recovery Experiments 76
seconds do not follow the aircraft motion well. Moreover, p and q outputs appear to
be noisier compared to the aircraft motion. Phase lead is also seen for p, q, and r
outputs. Figure 4.8(c) compares the magnitude of the high-pass filter command to the
tilt-coordination command for pitch and roll motions, where it can be seen that tilt-
coordination command dominates for pitch and is large for roll as well. This may be a
problem as less motion is available to simulate the large angular rates and accelerations
that are often seen in upset conditions. Finally, Figure 4.8(d) shows the motion system
actuator extensions. The UTIAS FRS actuators’ displacement limit is 0.457 meter in
each direction, thus the flat regions seen for the fourth and fifth actuators between 80
and 83 seconds indicate that the actuator software limiting is activate.
Figures 4.9(a) and 4.9(b) compare the MDA outputs to the aircraft motion for scenario
3. Notable error is seen in the fy output from 35 to 45 seconds, which is in the opposite
direction from the aircraft motion. The q and r outputs near 40 seconds also deviate from
the aircraft motion. Moreover, the p and q outputs are noisier compared to the aircraft
motion. Figure 4.9(c) shows that the tilt-coordination command is again dominant for
pitch, while the high-pass filter command is more dominant for roll motion. The actuator
software limiting is activate for the fourth and fifth actuators between 40 to 45 seconds
as shown in Figure 4.9(d).
Motion cueing errors observed for upset scenario 2 were minor false cues in q, but
the actuator extensions stayed within the limits for all flights. For scenario 4, actuator
software limiting was often activate and false cues in p were observed. The classical MDA
produced reasonable outputs for scenario 5 as it was a mild maneuver, with only minor
false cues seen in q. For scenario 6, phase lag error was often seen for fx and jerkiness
was seen in the angular rates for most flights.
Several modifications to the current MDA can be considered in future study to ad-
dress the motion cueing issues. One of them is adding body frame high-pass filters in
addition to the current inertial frame high-pass filters. This could help reduce the cross-
coupling among the motion components that can occur when pitch, roll and yaw angles
become large [16]. Secondly, the current classical MDA uses first order high-pass filters
for the rotational channels, but second or third order filters may be required for the most
demanding low frequency maneuvers [16]. Finally, the experimental results can also be
examined using other types of MDA, such as the adaptive MDA which uses time varying
filter or scaling parameters [32].
Chapter 4. Upset Recovery Experiments 77
−2
0
2
4
6f x
(m/s2
)
Aircraft MotionClassical MDA
−3
−2
−1
0
1
2
f y(m
/s2
)
30 40 50 60 70 80 90−20
−15
−10
−5
0
time (s)
f z(m
/s2
)
Note: MDA outputs are divided by the scale factor (0.5)
(a)
−10
−5
0
5
10
p(d
eg/s)
Aircraft MotionClassical MDA
−15
−10
−5
0
5
10
q(d
eg/s)
30 40 50 60 70 80 90−2
0
2
4
time (s)
r(d
eg/s)
(b)
−5
0
5
φ(d
eg)
MDA TotalHigh−Pass FilterTilt−Coordination
−5
0
5
10
15
θ(d
eg)
−2
0
2
4
dφ dt
(deg
/s)
30 40 50 60 70 80 90−5
0
5
dθ
dt
(deg
/s)
time (s)
(c)
−0.4
−0.3
−0.2
−0.1
0
L1
(m)
−0.4
−0.3
−0.2
−0.1
0
L2
(m)
−0.2
−0.1
0
0.1
0.2
0.3
L3
(m)
−0.4
−0.2
0
0.2
0.4
0.6
L4
(m)
40 60 80−0.4
−0.2
0
0.2
0.4
0.6
time (s)
L5
(m)
40 60 80−0.2
0
0.2
0.4
0.6
time (s)
L6
(m)
(d)
Figure 4.8: Example MDA Outputs for Upset Scenario 1
Chapter 4. Upset Recovery Experiments 78
1
2
3
4
5
f x(m
/s2
)
Aircraft MotionClassical MDA
−2
−1
0
1
2
f y(m
/s2
)
10 20 30 40 50 60−25
−20
−15
−10
−5
time (s)
f z(m
/s2
)
Note: MDA outputs are divided by the scale factor (0.5)
(a)
−10
0
10
20
p(d
eg/s)
Aircraft MotionClassical MDA
−10
−5
0
5
10
q(d
eg/s)
10 20 30 40 50 60−10
−5
0
5
10
time (s)
r(d
eg/s)
(b)
−5
0
5
10
φ(d
eg)
−10
0
10
20
θ(d
eg)
−5
0
5
dφ dt
(deg
/s)
10 20 30 40 50 60−5
0
5
dθ
dt
(deg
/s)
time (s)
MDA TotalHigh−Pass FilterTilt−Coordination
(c)
−0.2
−0.1
0
0.1
0.2
L1
(m)
−0.2
−0.1
0
0.1
0.2
L2
(m)
−0.1
0
0.1
0.2
0.3
L3
(m)
−0.2
0
0.2
0.4
0.6
L4
(m)
10 20 30 40 50 60−0.2
0
0.2
0.4
0.6
time (s)
L5
(m)
10 20 30 40 50 60−0.1
0
0.1
0.2
0.3
0.4
time (s)
L6
(m)
(d)
Figure 4.9: Example MDA Outputs for Upset Scenario 3
Chapter 5
Conclusions
5.1 Summary of Work
Typical flight model and motion system for a ground-based flight simulator are only de-
signed to work within the aircraft’s normal flight envelope. An aircraft in upset condition
however, can experience states that are far beyond the normal flight envelope. Therefore,
to provide meaningful upset recovery training in ground-based flight simulators, both the
flight model and simulator motion need to be improved.
In this thesis, an enhanced flight model was developed to better represent the air-
craft dynamics during upsets and recoveries from upsets. In particular, the aerodynamic
database of an existing Boeing 747 (B-747) model was extended to high angle of attack,
large sideslip, and large angular rates using a set of wind-tunnel data from NASA. Im-
portant aerodynamic characteristics, such as the loss of lift, increase in drag, reduced
control effectiveness seen at high angle of attack, and the nonlinearities and instabilities
seen at high angle of attack or large sideslip/angular rates, were incorporated into the
enhanced B-747 model.
For model validation, the elevator input from NASA’s enhanced flight model stall
simulation was run through the enhanced B-747 model and the original B-747 model for
comparison. The simulation results showed that the enhanced B-747 model produced air-
craft states that matched more closely to NASA’s enhanced flight model stall simulation
and stall flight test data. Additionally, slightly modified control inputs from two past
accidents were run through the enhanced B-747 model for trend comparison, and the
simulation results showed similar trends to the accident data. A notable difference ob-
served was that the current enhanced B-747 model does not typically develop aggressive
79
Chapter 5. Conclusions 80
lateral/directional responses at stall. Analysis showed that this could be due to the lack
of more extensive data in the current model. In particular, including the aerodynamic
asymmetry data and more nonlinear lateral stability data at high angle of attack made
the model more laterally and directionally unstable.
Using the enhanced B-747 model, a set of upset recovery experiments were conducted
in the UTIAS FRS with the help of pilots and without motion. The upset recovery sce-
narios tested were aggressive and mild stalls, unusual attitude upsets, and a windshear
encounter. In the experiments, pilots were asked to recover from each of these upset sce-
narios. Four pilots participated in the experiments and seven sets of data were obtained
for each scenario. Additionally, two of the pilots also flew the stall scenarios using a more
laterally unstable model. Several results from the experiments were shown in this thesis
as examples, and large angular rates, displacements, and translational accelerations were
observed in many of the experimental results.
Example motion cueing issues were shown in this thesis using the UTIAS classical
MDA and two sets of data from the experiments. The cueing errors caused by the current
MDA and its baseline tuning included false cues, motion jerkiness, and phase errors. Also,
since most of the upset scenarios resulted in large amplitude aircraft states, the motion
actuator software limiting was often activate.
The enhanced B-747 model and the data recorded from the experiments can be used
in future studies to further address the motion cueing issues and to evaluate potential
methods for improving the MDA.
5.2 Future Research Needs
There are several aspects of the current research that are worth pursuing in future studies.
First, the current enhanced B-747 model assumes that the trends of the NASA data are
directly applicable to the B-747 model even though the NASA data are for a smaller
commercial transport aircraft. This assumption is valid in the generic sense. That is,
the loss of lift after stall, the reduced control effectiveness at high angle of attack, the
static and dynamic instabilities that occur at stall and post-stall are characteristics that
can be expected for all types of commercial transport aircraft. However, the severity
of the static and dynamic instabilities for example, could be different depending on the
aircraft configuration. Scaling the NASA data based on configuration differences, such
as the difference in dihedral or sweep angles, is one area that should be explored. While
Chapter 5. Conclusions 81
methods are available for estimating the effects of configuration on the flight dynamics
during nominal maneuvering, little data or theory is currently available for estimating
the effects during high angle of attack or sideslip conditions. Alternatively, surveying
past high angle of attack studies on military aircraft may be useful, as a number of past
NASA studies have examined the effects of configuration differences at high angle of
attack.
In addition, stall hysterisis, which refers to the asymmetric aircraft response seen
during pitching up to stall versus pitching down from stall, may be an important effect
for training [33]. Some stall hysterisis data for military aircraft is available [33] and may
be useful to estimate the effects for commercial aircraft. Finally, inclusion of icing and
wake vortex models will be extremely useful as both are common causes of upset.
Appendix A
Microburst Model
Windshear encounter is a common cause of airplane upset. A microburst is a concentrated
and powerful downdraft [3] that can cause the aircraft to deviate from its glideslope or
flight path. A triple-ring vortex model that simulates a microburst was developed by
Robinson [19].
Robinson’s triple-ring vortex model was implemented in MATLAB Simulink for this
thesis to simulate a windshear encounter scenario. Details of Robinson’s triple-ring vortex
model can be found in Reference [19]. In short, the microburst is represented by three
concentric ring vortices where Stokes’ stream function is used to calculate a velocity
profile around each ring. A velocity profile consists of radial velocity Vr and vertical
velocity Vz at different points around the ring. The velocity profile for each ring can be
added to describe the total velocity of a triple-ring vortex model. The intensity of the
microburst is determined by the size of the ring vortices and and the vortex core velocity.
Using a fixed size and intensity, a pre-calculated velocity profile can be stored in look-
up tables for use in the simulation. The microburst can then be placed at any inertial
(x, y, z) coordinates and its distance to the aircraft center of gravity (C.G.) determines
the wind experienced by the aircraft. The output from the microburst model is the wind
speed Wx, Wy, and Wz in the body axes. Equations 3.1 to 3.3 are obtained by assuming
the atmosphere is at rest. When there is wind, they become [19]:
82
Appendix A. Microburst Model 83
u =X
m− g sinθ − Wx − [q(w +Wz) − r(v +Wy)] (A.1)
v =Y
m+ g cosθ sinφ− Wy − [r(u+Wx) − p(w +Wz)] (A.2)
w =Z
m+ g cosθ cosφ− Wz − [p(v +Wy) − q(u+Wx)] (A.3)
To make the microburst realistic, Robinson chose the microburst size and intensity
to be representative of two specifications from Juneau Airport Wind System (JAWS)
wind profiles. The specification used in this thesis was based on a microburst with a 4
kilometer diameter. The velocity profile was calculated using a vortex core velocity of
10 m/s but a scale can be used to scale up the intensity. Figure A.1 shows the wind
experienced along a 3◦ glideslope using the 4 kilometer microburst and a scale factor of
2.75. The simulation setup was based on an example from Reference [19]. XE in the
plot denotes the inertial x-axis which is placed along the runway center line with the
touchdown point at XE = 0 m [19]. In this example, the microburst was placed 900
m ahead of the aircraft (at the beginning of the simulation), 600 m above the ground,
and was tilted by 10◦ with respect to the ground. Its center was set to align with the
runway centerline. The wind speeds are with respect to the inertial frame. This example
wind profile is used to illustrate that a microburst encounter on approach is characterized
by the rapid change from headwind to tailwind (Wx) and a large downwind (Wz). The
downwind is particularly large near the center of the microburst.
Appendix A. Microburst Model 84
−4000 −3500 −3000 −2500 −2000 −1500 −1000 −500 0−10
−5
0
5
10
15
Wx(m
/s)
Example Microburst Wind Profile
−4000 −3500 −3000 −2500 −2000 −1500 −1000 −500 0−5
−4
−3
−2
−1
0
1
Wy(m
/s)
−4000 −3500 −3000 −2500 −2000 −1500 −1000 −500 0−2
0
2
4
6
8
XE(m)
Wz(m
/s)
Figure A.1: Wind Experienced by Aircraft On Approach
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