ground-based simulation of airplane upset using an enhanced … · 2012-11-01 · abstract...

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.

ii

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.

iii

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

iv

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

v

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

vi

3.24 Stability Derivatives and Simulation Results Using the Roll Model . . . . 57

4.1 Experiment Example Results: Upset Scenario 1 . . . . . . . . . . . . . . 68






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

vii

List of Tables

3.1 Maximum Control Surface Deflections . . . . . . . . . . . . . . . . . . . . 31

4.1 Summary of Reference Upset Scenarios . . . . . . . . . . . . . . . . . . . 59

viii

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

ix

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

1

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


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.


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.

5

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-


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


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


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


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


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


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


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


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


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:


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.


[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


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

;


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.


Figure 3.3: Real-Time Data Blending Block in Simulink


% 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


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)


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


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


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.


% 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;


% 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


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


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.


−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


−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


−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


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


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.


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.


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


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.


−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


−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


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].


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.


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.


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


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.


−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


−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◦


(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


−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◦


(d) ∆Cn(α, r)

Figure 3.15: Dynamic Data - Yawing Moment


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


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].


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.


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].


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].


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]


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


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


−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


−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


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


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


−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


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.


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.


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


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).


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


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


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.


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.


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


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)



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)



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)



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)



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)



−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


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


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].


−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)


−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


1

2

3

4

5

f x(m

/s2

)


−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)


−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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