August 2012 

NASA/TM–2012-217601 

Algorithm for Simulating Atmospheric 

Turbulence and Aeroelastic Effects on 

Simulator Motion Systems 

Anthony V. Ercole and Frank M. Cardullo 

State University of New York, Binghamton, New York 

Lon C. Kelly 

Unisys Corporation, Hampton, Virginia 

Jacob A. Houck 

NASA Langley Research Center, Hampton, Virginia 
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August 2012 

NASA/TM–2012-217601 

Algorithm for Simulating Atmospheric 

Turbulence and Aeroelastic Effects on 

Simulator Motion Systems 

Anthony V. Ercole and Frank M. Cardullo 

State University of New York, Binghamton, New York 

Lon C. Kelly 

Unisys Corporation, Hampton, Virginia 

Jacob A. Houck 

NASA Langley Research Center, Hampton, Virginia 
Available from: 

NASA Center for AeroSpace Information 

7115 Standard Drive 

Hanover, MD 21076-1320 

443-757-5802 

Acknowledgments 

We would like to thank Kirill Zaychik for his exceptional insight throughout the course 

of this research. Our appreciation is extended to Harry Verstynen of NASA Langley 

Research Center for piloting all experiments and for his exceptional contribution to the 

subjective analysis and tuning of the algorithms. Our gratitude also extends to all other 

members of NASA Langley Research Center who assisted in the on-site testing and 

tuning of these algorithms. 

The use of trademarks or names of manufacturers in this report is for accurate re porting and does not constitute an 

official endorsement, either expressed or implied, of such products or manufacturers by the National Aeronautics 

and Space Administration.
iLi 

Abstract 

Atmospheric turbulence produces high frequency accelerations in aircraft, typically 

greater than the response to pilot input. Motion system equipped flight simulators must present 

cues representative of the aircraft response to turbulence in order to maintain the integrity of the 

simulation. Currently, turbulence motion cueing produced by flight simulator motion systems 

has been less than satisfactory because the turbulence profiles have been attenuated by the 

motion cueing algorithms. There have been advances in motion cueing algorithm development 

at the Man-Machine Systems Laboratory, at SUNY Binghamton. In particular, the system used 

to generate turbulence cues has been studied. An investigation of this implementation indicated 

that the turbulence simulation produced inadequate cues and caused the simulator motion 

platform to diverge during long runs. 

This report presents a new turbulence motion cueing algorithm, referred to as the 

augmented turbulence channel. Like the previous turbulence algorithms, the output of the 

channel only augments the vertical degree of freedom of motion. This algorithm employs a 

parallel aircraft model and an optional high bandwidth cueing filter. 

Simulation of aeroelastic effects is also an area where frequency content must be 

preserved by the cueing algorithm. The current aeroelastic implementation uses a similar 

secondary channel that supplements the primary motion cue with aeroelastic oscillations. This 

particular model computes the pitch and yaw axes fuselage bending of a slender, high-speed, 

transport aircraft. Integration of the augmented turbulence channel and aeroelastic model was 

completed to allow high frequency motion from both sources to augment the cueing algorithm. 

Offline testing was performed to examine the frequency content of the augmented motion cues. 

Upon completion of this testing, the algorithm was implemented on two NASA Langley 

Research Center flight simulators: the Visual Motion Simulator and the Cockpit Motion Facility, 

and subjectively tuned to specifications detailed in the Federal Aviation Administration 

Airman’s Guide to Turbulence Reporting. 

Two studies were conducted to evaluate the effect of the turbulence channel and 

aeroelastic model on pilot control input. Quantitative results of the experiments indicate that 

while compensating for atmospheric turbulence, the pilot is better correlated with the aircraft 
iY

response, when the augmented channel is in place. During the severe turbulence runs, with the 

augmented channel, results indicate that the pilot is unable to compensate for the turbulence 

intensity. Subjective results of the studies show that during most runs, the augmented turbulence 

channel increased the necessary compensation to adequately perform the task. This 

demonstrates that the added motion induces more work from the pilot. It is recommended that a 

multi-pilot study be conducted to verify the turbulence algorithm and better support the 

subjective and quantitative results presented in this report. 

 
v 

Table of Contents

Abstract ........................................................................................................................ iii

List of Tables ................................................................................................................... viii

List of Figures .................................................................................................................... ix

Nomenclature ..................................................................................................................... xi

Symbols ....................................................................................................................... xii

1. Introduction .................................................................................................................... 1

1.1 Introduction ................................................................................................................. 1

1.2 High frequency motion cueing .................................................................................... 2

1.3 Objectives and Scope of Research .............................................................................. 3

1.4 Atmospheric Turbulence ............................................................................................. 5

1.5 Turbulence Models ...................................................................................................... 6

1.6 Aeroelasticity ............................................................................................................... 7

2. Literature Review of Current Motion Cueing Algorithms ............................................ 8

2.1 Introduction ................................................................................................................. 8

2.2 Classical Motion Cueing Algorithm ............................................................................ 9

2.3 NASA Coordinated Adaptive Washout Algorithm ................................................... 13

2.4 Modified NASA Adaptive Motion Cueing Algorithm .............................................. 14

2.5 SUNY Linear Optimal Motion Cueing Algorithm .................................................... 16

2.6 SUNY Nonlinear Motion Cueing Algorithm ............................................................ 18

3. Higher Frequency Motion Cueing Algorithms ............................................................ 21

3.1 Introduction ............................................................................................................... 21

3.2 Simulation of Atmospheric Turbulence .................................................................... 22

3.3 Motion Cueing Due to Aeroelastic Effects ............................................................... 24

4. Design of Atmospheric Turbulence Motion Cueing Algorithm and Integration with 

Aeroelastic Model ......................................................................................... 32

4.1 Introduction ............................................................................................................... 32

4.2 Augmented Turbulence Channel Design .................................................................. 33

4.3 Reduce Order Aircraft Model Development ............................................................. 36

4.4 Parameter Identification Methods ............................................................................. 37

4.5 Large Civil Transport: Reduce Order Aircraft Model Development ........................ 39

4.6 GHSCT: Reduced Order Aircraft Model Development ............................................ 43

4.7 Turbulence Cueing Filter ........................................................................................... 48

4.8 Integration with Aeroelastic Model ........................................................................... 51

4.9 Testing: Offline Simulation of Algorithms ............................................................... 52
vi 

4.9 Testing: Offline Simulation of Algorithms ............................................................... 52

4.9.1 Large Civil Transport: Offline Testing ................................................................... 53

4.9.2 Generic High-Speed Civil Transport: Offline Testing ............................................ 60

5. Design of Experiments ................................................................................................. 69

5.1 Introduction ............................................................................................................... 69

5.2 Background Information ........................................................................................... 69

5.3 Tuning the Augmented Turbulence Channel (ATC) ................................................. 72

5.4 Design of Experiments .............................................................................................. 75

6. Results of Real-Time Experiments .............................................................................. 83

6.1 Introduction ............................................................................................................... 83

6.2 Large Civil Transport Study – Subjective Analysis .................................................. 83

6.2.1 Large Civil Transport Experiment 1: Subjective Analysis ..................................... 84

6.2.2 Large Civil Transport Experiment 2: Subjective Analysis ..................................... 86

6.2.3 Large Civil Transport Experiment 3: Subjective Analysis ..................................... 90

6.3 Large Civil Transport Study – Quantitative Results ................................................. 93

6.3.1 Large Civil Transport: Experiment 1 – Quantitative Results ................................. 93

6.3.2 Large Civil Transport: Experiment 2 – Quantitative Results ............................... 106

6.3.3 Large Civil Transport: Experiment 3 – Quantitative Results ............................... 115

6.4 Generic High-Speed Civil Transport Study – Subjective Analysis ........................ 116

6.4.1 GHSCT Experiment 1: Subjective Analysis ......................................................... 116

6.4.2 GHSCT Experiment 2: Subjective Analysis ......................................................... 121

6.4.3 GHSCT Experiment 3: Subjective Analysis ......................................................... 125

6.5 Generic High-Speed Civil Transport Study – Quantitative Results ........................ 130

6.5.1 GHSCT: Experiments 1,2, and 3: Quantitative Results ........................................ 130

7. Conclusions and Future Recommendations ............................................................... 132

7.1 Summary and Conclusions ...................................................................................... 132

7.2 Future Recommendations ........................................................................................ 140

 
vii 

Appendices ..................................................................................................................... 143

Appendix A: Turbulence Spectrums Defined: ........................................................................ 143

Appendix B: Fortran code for ATC; DASE model parameters .............................................. 145

Appendix C: FAA Airman’s Guide to Reporting Turbulence Intensity (TBL 7-9-1) ............ 148

Appendix D: Gain Tuning Test Plans ..................................................................................... 149

Appendix E: Cooper-Harper Rating Scale .............................................................................. 152

Appendix F: Experiment Conditions and Run Order ............................................................. 153

References ..................................................................................................................... 157
viii 

List of Tables 

Table 5.4.1 – Run order and conditions for GHSCT experiments .................................... 81 

Table 6.2.1 – Large Civil Transport Experiment 1, Turbulence Intensity and CHR .........84

Table 6.2.2 – Large Civil Transport Experiment 1, FAA Turbulence Reporting Criteria 86

Table 6.2.3 – Large Civil Transport Experiment 2, Turbulence Intensity and CHR ........ 88

Table 6.2.4 – Large Civil Transport Experiment 2, FAA Turbulence Reporting Criteria 89

Table 6.2.5 – Large Civil Transport Experiment 3, Turbulence Intensity and CHR ........ 90

Table 6.2.6 – Large Civil Transport Experiment 3, FAA Turbulence Reporting Criteria 92 

Table 6.3.1 – Mean Square of Pitch Stick Input, Large Civil Transport Experiment 1 . 100

Table 6.3.2 – Mean Square of Pitch Stick Input, Large Civil Transport Experiment 2 . 109

Table 6.3.3 – Mean Square of Pitch Stick Input, Large Civil Transport Experiment 3 . 116 

Table 6.4.1 – GHSCT Experiment 1, Turbulence Intensity and CHR ........................... 118

Table 6.4.2 – GHSCT Experiment 1, FAA Turbulence Reporting Criteria ................... 120

Table 6.4.3 – GHSCT Experiment 1, Turbulence Intensity and CHR ........................... 122

Table 6.4.4 – GHSCT Experiment 2, FAA Turbulence Reporting Criteria ................... 124

Table 6.4.5 – GHSCT Experiment 3, Turbulence Intensity and CHR ........................... 126

Table 6.4.6 – GHSCT Experiment 3, FAA Turbulence Reporting Criteria ................... 128
ix 

List of Figures 

Figure 1.1 – NASA CMF with IFD on motion base (Courtesy of NASA LaRC) .............. 1

Figure 2.2.1 – Classical Algorithm Block Diagram ......................................................... 10 

Figure 2.3.1 – NASA Coordinated Adaptive Washout Algorithm Block Diagram (NASA 

Adaptive Algorithm) ................................................................................. 13

Figure 2.4.1 – UTIAS Modified NASA Adaptive Motion Cueing Algorithm Block Diagram

................................................................................................................... 14

Figure 2.4.2 – SUNY Binghamton Modified NASA Adaptive Motion Cueing Algorithm Block 

Diagram..................................................................................................... 16 

Figure 2.5.1 – Linear Optimal Cueing Algorithm Optimization Structure ....................... 17

Figure 2.5.2 – Linear Optimal Cueing Algorithm Block Diagram ................................... 18 

Figure 2.6.1 – Nonlinear Optimal Cueing Algorithm Optimization Structure ................. 18

Figure 2.6.2 – Nonlinear Optimal Cueing Algorithm Block Diagram ............................. 19 

Figure 3.1.1 – Reid/Robinson Algorithm with Turbulence Channel (Reid, 1990)........... 22 

Figure 3.2.1 – Original SUNY Implementation of Turbulence Channel (Telban, 2005) . 23 

Figure 3.3.1 – Artist rendition of Generic High Speed Civil Transport (GHSCT) .......... 25

Figure 3.3.2 – DASE Model, Flexible Aircraft Modes (Davis, 2009).............................. 27

Figure 3.3.3 – DASE Model Block Diagram (Davis, 2009) ............................................. 30 

Figure 4.2.1 – Original SUNY Implementation of Turbulence Channel (Telban, 2005) . 33

Figure 4.2.2 – Implementation 1 of Augmented Turbulence Channel ............................. 34

Figure 4.2.3 – Implementation 2 of Augmented Turbulence Channel ............................. 34 

Figure 4.5.1 – Input and Output Data for Large Civil Transport ROAC Development ... 39

Figure 4.5.2 – Bode Diagram of Large Civil Transport ROAC ....................................... 41

Figure 4.5.3 – PSD of Large Civil Transport ROACs and Aircraft Acceleration (NZ) ... 42

Figure 4.5.4 – Time Response of Large Civil Transport ROACs and Aircraft Acceleration (NZ)

................................................................................................................... 43 

Figure 4.6.1 – PSD of Large Civil Transport ROAC and GHSCT Aircraft Acceleration (NZ)

................................................................................................................... 44

Figure 4.6.2 – Input and Output Data for GHSCT ROAC Development ......................... 45

Figure 4.6.3 – Bode Diagram of GHSCT ROACs............................................................ 46

Figure 4.6.4 – PSD of GHSCT ROACs and Aircraft Acceleration (NZ)..........................47

Figure 4.6.5 – Time Response of GHSCT ROACs and Aircraft Acceleration (NZ) ....... 48 

Figure 4.7.1 – Bode Diagram of Turbulence Cueing Filter .............................................. 49

Figure 4.7.2 – Step Response of Turbulence Cueing Filter .............................................. 50 

Figure 4.8.1 – Integration of DASE Model and Augmented Turbulence Channel .......... 51 

Figure 4.9.1 – Large Civil Transport Model GUI (courtesy of NASA LaRC) ................. 54

Figure 4.9.2 – Large Civil Transport Implementation 1 of Augmented Turbulence Channel

................................................................................................................... 55

Figure 4.9.3 – Large Civil Transport Implementation 2 of Augmented Turbulence Channel

................................................................................................................... 55

Figure 4.9.4 – Large Civil Transport Testing, Time Response, ACT Off, Vertical Channel Only

................................................................................................................... 56

Figure 4.9.5 – Large Civil Transport Testing, Time Response, Implementation 1 of ATC, 

Vertical Channel Only .............................................................................. 57
x 

Figure 4.9.6 – Large Civil Transport Testing, Time Response, Implementation 2 of ATC, 

Vertical Channel Only .............................................................................. 58

Figure 4.9.7 – Large Civil Transport Testing, Time Response, Comparison of Implementations 1 

and 2 .......................................................................................................... 59

Figure 4.9.8 – GHSCT Implementation 1 of Augmented Turbulence Channel and DASE 

Aeroelastic Model ..................................................................................... 60

Figure 4.9.9 – GHSCT Implementation 2 of Augmented Turbulence Channel and DASE 

Aeroelastic Model ..................................................................................... 61

Figure 4.9.10 – GHSCT Testing, Time Response, ATC Off, Vertical Channel Only ..... 63

Figure 4.9.11 – GHSCT Testing, Time Response, Implementation 1 of ATC, Vertical Channel 

Only........................................................................................................... 64

Figure 4.9.12 – GHSCT Testing, Time Response, Implementation 2 of ATC, Vertical Channel 

Only........................................................................................................... 65

Figure 4.9.13 – GHSCT Testing, Time Response, ATC Off, DASE Model On .............. 66

Figure 4.9.14 – GHSCT Testing, Time Response, ATC On, DASE Model On............... 67 

Figure 5.2.1 – Visual Motion Simulator (Courtesy of NASA LaRC) .............................. 70

Figure 5.2.2 – Visual Motion Simulator Flight Deck (Courtesy of NASA LaRC) .......... 70

Figure 5.2.3 – Artist Rendition of Cockpit Motion Facility (Courtesy of NASA LaRC) 71

Figure 5.2.4 – Generic Flight Deck on CMF motion system (Courtesy of NASA LaRC)72 

Figure 5.4.1 – Large Civil Transport Experiment Configuration Block Diagrams .......... 78

Figure 5.4.2 – GHSCT Experiment Configuration Block Diagrams ................................ 79 

Figure 6.3.1 – Large Civil Transport Experiment 1, Normalized Mean Square of Vertical 

Velocity ..................................................................................................... 95

Figure 6.3.2 – Large Civil Transport Experiment 1, Light Turbulence, PSD of Pitch Stick Input 

and Normalized PSD of Pitch Stick Input ................................................ 97

Figure 6.3.3 – Large Civil Transport Experiment 1, Moderate Turbulence, PSD of Pitch Stick 

Input and Normalized PSD of Pitch Stick Input ....................................... 98

Figure 6.3.4 – Large Civil Transport Experiment 1, Severe Turbulence, PSD of Pitch Stick Input 

and Normalized PSD of Pitch Stick Input ................................................ 99

Figure 6.3.5 – Coherence Function of Vertical Velocity and Pitch Stick Input; PSD of Pitch Stick 

Input, Large Civil Transport Experiment 1, Level 2 Turbulence ........... 102

Figure 6.3.6 – Coherence Function of Vertical Velocity and Pitch Stick Input; PSD of Pitch Stick 

Input, Large Civil Transport Experiment 1, Level 4 Turbulence ........... 104

Figure 6.3.7 – Coherence Function of Vertical Velocity and Pitch Stick Input; PSD of Pitch Stick 

Input, Large Civil Transport Experiment 1, Level 6 Turbulence ........... 105

Figure 6.3.8 – Large Civil Transport Experiment 2, Normalized Mean Square of Vertical 

Velocity ................................................................................................... 107

Figure 6.3.9 – Coherence Function of Vertical Velocity and Pitch Stick Input; PSD of Pitch Stick 

Input, Large Civil Transport Experiment 2, Turbulence Off .................. 110

Figure 6.3.10 – Coherence Function of Vertical Velocity and Pitch Stick Input; PSD of Pitch 

Stick Input, Large Civil Transport Experiment 2, Turbulence Level 2 .. 111

Figure 6.3.11 – Coherence Function of Vertical Velocity and Pitch Stick Input; PSD of Pitch 

Stick Input, Large Civil Transport Experiment 2, Turbulence Level 4 .. 113

Figure 6.3.12 – Coherence Function of Vertical Velocity and Pitch Stick Input; PSD of Pitch 

Stick Input, Large Civil Transport Experiment 2, Turbulence Level 6 .. 114
xi 

Nomenclature 

ARMAX Auto-Regressive Moving Average with External Input 

ARX Auto-Regressive with External Input 

ASL Above Sea Level 

ATC Augmented Turbulence Channel 

BJ Box-Jenkins 

CHR Cooper Harper Rating 

CMF Cockpit Motion Facility 

DASE Dynamic Aeroservoelastic 

DME Distance Measuring Equipment 

DOF Degree-of-Freedom 

GFD Generic Flight Deck 

GHSCT Generic High-Speed Civil Transport 

GUI Graphic User Interface 

IFD Integration Flight Deck 

LaRC Langley Research Center 

LCT Large Civil Transport 

M Mach Number 

NLAC Nonlinear Aircraft Model 

OE Output Error 

Ref-H Reference-H High Speed Civil Transport Configuration 

ROAC Reduced Order Aircraft Model 

VMS Visual Motion Simulator 
xii 

Symbols 

Symbol Definition Units 

A area in2

a acceleration of a point m/s2

E modulus of elasticity psi 

g acceleration due to gravity m/s2

G turbulence gust vector m/s or ft/s 

I cross section area moment of inertia in4

ki roots of the general solution of the wave equation rad/in 

L length in 

R radius in 

S simulator centroid displacement m 

Sm speed of sound in fuselage material in/s 

t thickness in 

Z vertical channel displacement m 

Z vertical channel acceleration cue m/s2

X A



 aircraft state (   x,y,z) (m/s2

), (p,q,r) (rad/s) 

X M



 motion system state (x,y,z) (m), (φ,θ,φ) (rad) 

X M



 acceleration cue (   x,y,z) (m/s2

), (p,q,r) (rad/s) 

β Euler angles β = [φ θ φ]

T

 rad 

κi roots of frequency equation rad 

ρ density lbf/in3

ω angular velocity ω = [p q r] rad/s 

ωi frequency rad/s 

Δ( ) augmentation cue m 

Subscripts Definition 

( )A relates to aircraft 

( )AE relates to aeroelastic bending 

( )G relates to turbulence wind gust 

( )M relates to simulator motion cue 

( )S relates to simulator 

( )SR relates to simulator rotation 

( )T relates to turbulence response 
xiii

Superscripts Definition 

( )A aircraft reference frame 

( )I inertial reference frame 

( )S simulator reference frame 
1 

1. Introduction 

1.1 Introduction 

One of the most important objectives of any vehicle simulation is providing an 

environment in which the pilot or driver will sense the motion of the vehicle. The operator’s 

perception of the vehicle motion affects his/her response and control strategy. In addition to out 

the window visual simulation and vehicle sensor displays, many simulators incorporate six 

degree-of-freedom motion platforms. These motion systems are intended to stimulate the body’s 

motion sensing apparatus and provide a more immersive experience. Shown below is the NASA 

Cockpit Motion Facility, with one of the interchangeable flight decks on the motion system 

(Figure 1.1). 

Figure 1.1 – NASA CMF with IFD on motion base (Courtesy of NASA LaRC) 
2 

Motion cueing algorithms are designed to handle a wide range of dynamic inputs to 

produce the most realistic motion at the pilot station. These include the aircraft response to pilot 

control inputs, atmospheric disturbances, and various other external inputs. This presents the 

designer with multiple issues, as the algorithm needs to provide the pilot with realistic cues while 

keeping the simulator within its geometric limitations and the constraints of the hardware. 

Because of this, within the algorithm, there are various filters and mathematical operations that 

can mute certain characteristics of the aircraft motion. One main area of focus is in the 

simulation of high frequency cues. 

1.2 High frequency motion cueing 

Conventional motion cueing algorithms do not produce satisfactory results when 

generating cues in high frequency scenarios, i.e. simulating atmospheric turbulence or aeroelastic 

effects. This is due to the presence of motion cueing washout filters designed to washout the cue 

arising from pilot control, along with various operations designed to scale and limit the outputs. 

Iteration rate smoothing filters also present a problem in this regard. At the Man-Machine 

Systems Laboratory, SUNY Binghamton, Cardullo and Ellor developed an approach whereby 

the turbulence state bypassed the cueing algorithm and the iteration rate filter, directly porting 

the turbulence model output to the motion system. 

Other advancements were pioneered by Lloyd Reid and Paul Robinson. In their 

implementation, a second channel was used in parallel with the primary cueing channel. This 

second channel employed a reduced order model of the aircraft response to turbulence along with 

a high-pass filter. The filtered output was then integrated twice and summed with the output of 
3 

the primary cueing algorithm, augmenting the motion in just the vertical channel. This output is 

then sent to the cueing hardware which drives the motion base. See chapter 3 for a more in depth 

explanation of the Reid Robinson algorithm. 

A version of the Reid Robinson approach was later implemented by Telban (a) at the 

Man-Machine Systems Laboratory to the nonlinear cueing algorithm, improving the turbulence 

simulation. In this implementation, the overall turbulence simulation was improved, however, 

further investigation led to the revision of this algorithm. Chapter 4 goes into greater detail on 

the changes implemented to the algorithm to produce the new algorithm that is used in the offline 

and on-site testing. 

Aircraft with long and slender fuselages have a tendency to bend, deflecting the pilot 

station. This aeroelastic bending can be excited by various pilot inputs or atmospheric loads. 

When providing cues for the flexural vibrations of an aircraft, it is necessary to prevent 

attenuation or distortion by the cueing algorithm. There have been advancements in this regard, 

using both complex finite element models and more simplified linear models. Both approaches 

will be discussed and assessed in Chapter 3; also detail will be provided on the integration with 

the augmented turbulence channel in Chapter 4. 

1.3 Objectives and Scope of Research 

Providing pilots with appropriate turbulence simulation is quite desirable. Whether it is 

training exercises or analysis of the added workload of flying through moderate or severe 

turbulence, the motion cues must be realistic or the legitimacy of these studies can be 
4 

compromised. The state of the art with regard to augmented motion cueing, specifically for 

atmospheric turbulence, produces acceptable results augmenting the amplitude of the simulator 

excursions, but does not do much to address the frequency content. This report looks at the 

shortcomings of the current algorithms and presents new augmented motion cueing algorithms. 

There is also investigation as to whether it is necessary to use complex or finite element 

models when producing aeroelastic motion cues. Calculations of aeroelastic bending typically 

utilize very complex structural models. Recent advances in the simulation of flexible aircraft 

have implemented much simpler models to provide the pilot with aeroelastic motion cues in realtime. 

It is also desirable to provide the pilot with aeroelastic motion cues while flying through 

atmospheric turbulence as the gusts cause significant loads on the aircraft, contributing to the 

bending. The current aeroelastic cueing algorithm augments the primary aircraft motion cue in a 

similar fashion to the augmented turbulence motion cueing channel. Integrating the two 

algorithms will provide pilots with realistic cues for a wide range of scenarios. 

In the final chapter of this report, conclusions are drawn from the real-time experiments 

conducted at the NASA Langley Research Center. Even though these are not standard multipilot evaluations, verdicts can be drawn from the analysis of the data and subjective pilot 

evaluation. The future work section also presents possible multi-pilot experiments to be 

conducted in the verification process of the augmented turbulence channel. There is also room 

for investigation into the change in pilot workload when maintaining level flight or performing 

any number of tasks while flying through atmospheric turbulence. 
5 

Main objectives of this research: 

 Investigate the issues with the current turbulence cueing algorithm 

 Develop a new turbulence motion cueing algorithm 

 Verify the turbulence cueing algorithm offline 

 Implement the turbulence cueing algorithm on six degree of freedom flight 

simulators and tune the algorithm 

 Integrate the turbulence cueing algorithm with existing aeroelastic cueing 

algorithm and verify through real-time simulation 

 Develop and run real-time piloted experiments; set up for future multi-pilot 

experiments 

1.4 Atmospheric Turbulence 

The phenomenon of atmospheric turbulence is characterized by small-scale, irregular air 

motions caused by winds that vary in speed and direction. The mixing of warm and cold air in 

the atmosphere by wind causes clear-air-turbulence. Jet streams can cause severe turbulence due 

to high wind shear. This is also a problem at lower altitudes and near mountains as the 

conditions usually allow for wind shear. 

Thunderstorms commonly cause aircraft to buffet upward, downward, and laterally; they 

are the most common source of severe turbulence. These situations can be extremely hazardous 
6 

and subject the aircraft to the possibility of lightning strikes which can lead to structural damage 

and disruption of electrical systems. Because of the many hazards, pilots are urged not to fly 

through thunder storms. 

1.5 Turbulence Models 

There are multiple models within the industry that are used to simulate atmospheric 

turbulence. Two of the more common models are the von Karman and Dryden turbulence 

spectra. Both of these turbulence spectrums are defined in the Terrestrial Environment Criteria 

Handbook20. Simulating turbulence using the von Karman spectrum is not a simple process as it 

is quite computationally burdensome. Because of this, most simulations implement the Dryden 

turbulence model. To add rotational turbulent gust inputs to the simulation, the MIL STD 

disturbance model can also be implemented. Details on both the von Karman and Dryden 

turbulence models are provided in MIL-STD-1797A8. 

The von Karman model is an analytical representation of turbulence spectra and is 

usually generated using a sum-of-sinusoids method (Houbolt, 1964). There are variations 

designed to reduce computation time, but each is developed off of this original model. One of 

the main advantages to these spectra is the physical accuracy of the gusts. It may be desirable to 

use the von Karman model to perform a more accurate structural analysis, but for the purposes of 

simulating turbulence in real-time, the Dryden spectrum has proven more than adequate. 

The Dryden turbulence model was developed using in-flight data, acquired by flying 

through atmospheric turbulence in an aircraft equipped with various sensors. The data acquired 
7 

by the sensors was used to develop the Dryden spectrum. This spectrum was later used to 

develop the more commonly used turbulence model known as the simplified Dryden disturbance 

model8. Instead of using a prerecorded set of data, band limited white noise is passed through 

appropriate linear filters to produce the spectrum. This eliminates any repetitious nature of using 

a prerecorded dataset. T. R. Beal (1993) goes into great detail on the development of the 

necessary filters to convert the white noise input into turbulence wind gusts within the Dryden 

Spectrum. 

The Military Standard, MIL-F-8785B, includes definitions for rotational turbulence 

inputs. Since atmospheric turbulence is not a uniform field, uneven gust gradients will cause the 

aircraft to rotate about its axes. This model defines gust in the form of angular velocity. 

Typically, these terms are applied to the aircraft equations of motion, through the aerodynamic 

terms. Each of the Large Civil Transport real-time experiments was conducted using both 

translational and rotational turbulence models. Discussion is also provided to support the use of 

the rotational turbulence model to provide the pilot with realistic turbulence cues. Definitions 

for each turbulence model spectra are detailed in Appendix A. 

1.6 Aeroelasticity 

Aeroelasticity is a phenomenon associated with structural deformations in the aircraft, 

which lead to changes in the aerodynamic forces. In 1947, Arthur Collar defined aeroelasticity 

as; “the study of the mutual interaction that takes place within the triangle of the inertial, elastic, 

and aerodynamic forces acting on structural members exposed to an airstream, and the influence 

of this study on design.” Aeroservoelasticity represents a combination of several theories, each 
8 

related to aircraft dynamics. According to Dinu (2006), the main focus of aeroservoelasticity 

includes the interaction between the flexible structure, aerodynamic forces, and the control laws 

acting on it.

Flexing of the aircraft structure can cause very undesirable results; including substantial 

reduction of control effectiveness as well as aircraft stability. Both of these effects are 

compounded when dealing with supersonic flight as certain maneuvers result in higher 

aerodynamic loads on the control surfaces. Aeroelasticity can also cause the pilot station to 

accelerate periodically, sometimes with high enough amplitude to cause involuntary pilot input. 

This involuntary input known as bio-dynamic coupling (BDC) also leads to an increase in 

difficulty controlling the aircraft. Some conceptual aircraft designs take advantage of wing 

flexibility to aid in maneuverability. These concepts only drive the need for more complex and 

accurate aeroelastic models.

2. Literature Review of Current Motion Cueing Algorithms 

2.1 Introduction 

Research regarding the development and verification of motion cueing algorithms has 

been conducted since vehicle simulator motion platforms were driven by them. It has been 

shown in the past that proper motion cueing and a robust motion system allow for a more 

realistic overall simulation. Not all simulation tasks justify the use of a motion system, but with 

the added fidelity, a more immersive experience can be provided to the pilot and his/her 

performance in the simulator improves. 
9 

Motion systems employed by flight simulators cannot duplicate aircraft motion; 

therefore, it is necessary to employ a set of algorithms to maximize the performance of the 

motion base. The basic mathematical operations done in these algorithms include limiting and 

scaling the motion commands, and washing out the motion commands with filters. The 

complexity of the cueing algorithms can vary quite a bit. With advances in computational 

power, the number of mathematical operations that can be completed during each time step 

grows continuously. 

A literature review was conducted in order to become familiarized with the various 

motion cueing algorithms used at the NASA Langley Research Center and their bases. The first 

algorithm discussed is one of the least complex cueing algorithms; known as the Classical 

Algorithm. The Coordinated Adaptive Washout Algorithm (also known as the NASA Adaptive 

Algorithm), along with two modified versions, are also discussed. Finally, two other algorithms 

developed at SUNY Binghamton, the Linear Optimal and Nonlinear Optimal cueing algorithms 

are presented. 

The major thrust of this research is to enhance cues arising from specific atmospheric 

conditions or aircraft dynamics. The reader will find a lengthy discussion of this specialized 

topic in Chapter 3. 

2.2 Classical Motion Cueing Algorithm 

The classical algorithm was first published by Conrad (1970); a block diagram of this 

algorithm is shown in Figure 2.2.1. It typically uses linear scaling and limiting on the 
10 

translational acceleration and angular velocity inputs ( A

Aa and A ωA ). Like the other cueing 

algorithms discussed here, there are two separate channels for the translational and rotational 

degrees of freedom, each with separate washout filters (Reid, 1985) 

Figure 2.2.1 – Classical Algorithm Block Diagram 

High-pass filters 1 and 2 are designed to wash out motion cues. In order to provide the 

reader with a better understanding of this washout process, time domain and frequency analyses 

are provided. Equation 2.2.1 represents a typical second order high-pass filter. Figure 2.2.2 

shows the frequency response of the high-pass filter and Figure 2.2.3 shows the step response. 

Referring to the Bode Diagram, this filter passes all frequencies over 10 rad/s, anything below 

that frequency is attenuated. The step response indicates that the input signal will be washed out 

after about 0.5 seconds. 

A

A a

A ωA S β

S β



S β A g I g



I S
11 

High-Pass Filter =

2

2 6.283 9

s

s s  

Eq. 2.2.1 

Figure 2.2.2 – Bode Diagram: Second Order High-Pass Filter 

10

-1

10010

1

10

2 -60

-40

-20

0

Bode Diagram: High-Pass Filter

Magnitude (dB)

10

-1

10010

1

10

2 0

45

90

135

180

Phase (deg)

Frequency (rad/sec)
12 

Figure 2.2.3 – Step Response: Second Order High-Pass Filter 

Referring back to the block diagram (Figure 2.2.1); there is a crossover path to provide 

the pilot with the sensation of sustained acceleration. Because of the limitations of a typical 

motion base, translational acceleration cannot be sustained for very long. This is overcome by 

using a method known as tilt coordination, which takes advantage of the inability of the otoliths 

to distinguish between acceleration and tilt and consequently to rotate the motion platform and 

alter the direction of the steady-state specific force. In most cases, the algorithm will provide an 

“onset” cue with actual translational acceleration, and then tilt the platform as the translational 

motion is washed out. As long as this transition is done under the pilot’s angular velocity 

indifference threshold, the tilting will be unnoticeable. 

0 0.5 1 1.5 2 2.5 3

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Step Response: High-Pass Filter

Time (sec)

Amplitude
13 

2.3 NASA Coordinated Adaptive Washout Algorithm 

This algorithm, also referred to as the NASA Adaptive Algorithm, was developed at 

NASA Langley Research Center by Parrish (1973). The architecture is similar to that of the 

classical algorithm; there are separate channels for the translational and rotational motion cues, 

and a crossover path for steady-state translational acceleration cues (Figure 2.3.1). The adaptive 

feature of this algorithm is applied using variable gain parameters which are based on the current 

state of the simulator. They are designed to minimize a cost function, and adapt constantly 

throughout the simulation. 

Figure 2.3.1 – NASA Coordinated Adaptive Washout Algorithm Block Diagram (NASA 

Adaptive Algorithm) 

A set of two adaptive parameters are used in this algorithm; one for the translational 

channel and one for the rotational channel. These parameters directly impact the severity of the 

washout filters, with the intent of maximizing the available geometry of the motion base. 

Scaling

A

A a

Scaling

A ωA

Coordinate

Transformation

Coordinate

Transformation Limiting AdaptiveParameter 2 

Adaptive

Parameter 1

Angular Rate

Limit Gain 1

Gain 2

Gain 3

 

Limiting _

+

A g

+

S g

+ +_

+

+

++

S β

S β

I S

S β
14 

2.4 Modified NASA Adaptive Motion Cueing Algorithm 

The NASA Adaptive Algorithm went through modifications conducted at both the 

University of Toronto (Reid, 1985) and SUNY Binghamton (Telban, 2000). The modified 

algorithms included a third adaptive parameter, along with the original two (Figure 2.4.1). 

Modifications were also made to the cost function; terms were added to the function to improve 

the effect of the adaptive parameters on the washout filters. Detailed descriptions of the changes 

are shown in Reid (1985). 

Figure 2.4.1 – UTIAS Modified NASA Adaptive Motion Cueing Algorithm Block Diagram 

The new adaptive parameter (adaptive parameter 2) is a part of the crossover path that 

calculates the necessary rotation for the sustained translational acceleration cue. As with the 

original NASA Adaptive Algorithm, each adaptive term is updated constantly throughout the 

simulation. 

Scaling

A

A a

Scaling

A ωA

Coordinate

Transformation

Coordinate

Transformation Limiting AdaptiveParameter 3 

Adaptive

Parameter 1

Angular Rate

Limit

Adaptive

Parameter 2

Gain 2

Gain 3

 

Limiting _

+

A g

+

I g

+ +_

+

+

++

S β

S β

I S

S β
15 

Further modifications were made at SUNY Binghamton in order to rectify an instability 

in the algorithm. Reid et al. (a) discussed the unstable characteristics, and suggested limiting the 

adaptive parameters as a possible solution. This would also impede the ability of these 

parameters to adapt as they were intended, furthermore, the algorithm could still remain unstable 

under certain conditions. 

The instability in the rotational channel was apparent when the simulator attempted to 

reach the neutral position. The algorithm is designed to force the simulator to continue moving 

and remain out of the neutral position. But, the adaptive washout algorithm, like all washout 

algorithms, attempts to bring the simulator to the neutral position under steady state conditions. 

According to Telban (2000), once the simulator reached the neutral position, it would begin to 

oscillate. 

The changes to the algorithm include using gSR, which is g in the imaginary reference 

frame FrSR (Figure 2.4.2). When the simulator responds to an angular input, the simulator frame 

is defined as FrSR. When dealing with a purely rotational case, FrSR = FrS and gSR = gS. Under 

pure rotational input, in the original algorithm, the algorithm employed an active translational 

and tilt channel. This also led to stability issues; in the revised algorithm, a null translational 

channel is used when dealing with pure rotational input. This version of the Modified NASA 

Adaptive Algorithm is stable under both translational and rotational inputs, and any combination 

of the two. 
16 

Figure 2.4.2 – SUNY Binghamton Modified NASA Adaptive Motion Cueing Algorithm Block 

Diagram 

2.5 SUNY Linear Optimal Motion Cueing Algorithm 

The Linear Optimal Cueing Algorithm, developed at the SUNY Man-Machine Systems 

Lab, is designed to optimize parameters of the different washout filters. The optimization takes 

place offline in MATLAB. This is done using a mathematical model of the human vestibular 

system, shown in Telban, et al. (b). The washout filter design is based on minimizing sensation 

error. These filters, W(s), relate the simulator motion states to the aircraft states, as do the 

classical washout filters. The difference is the criterion against which the filter is designed. The 

operations used to calculate the components of W(s) are shown in figure 2.5.1. 

A

A a

A ωA



  SR g I g S β

S β

I S

S β

 SR β
17 

Figure 2.5.1 – Linear Optimal Cueing Algorithm Optimization Structure 

The structure of the algorithm itself is quite similar to the Classical Algorithm. There are 

two separate channels for the translational and rotational cues, and just like the other algorithms 

discussed, a crossover path is used to provide the tilt coordination cue to the rotational channel 

(Figure 2.5.2). One main difference in this algorithm is the use of nonlinear scaling and limiting. 

W22, W11, and W12 are each 6th or 7th order filters. They are computed by adjusting weighting 

parameters in the cost function and using the Riccati equation to evaluate the terms. 
18 

Figure 2.5.2 – Linear Optimal Cueing Algorithm Block Diagram 

2.6 SUNY Nonlinear Optimal Motion Cueing Algorithm 

The nonlinear optimal motion cueing algorithm incorporates models of the human 

vestibular system, similar to the linear optimal algorithm. This algorithm takes sensor modeling 

a step further by implementing semicircular canals and otolith models, as well as a visualvestibular perception model and the optimization, all in real-time (Telban, et al (b)). Figure 2.6.1 

shows the structure of the algorithm that calculates perceptual error. 

Figure 2.6.1 – Nonlinear Optimal Cueing Algorithm Optimization Structure 





A

A a

A ωA

A β

I S

S β

A β
19 

The top level block diagram of the algorithm is quite similar to the linear optimal 

algorithm (Figure 2.6.2). Since this algorithm solves the Riccati equation in real-time, it 

optimizes the filters before each iteration. This allows the algorithm to take advantage of the full 

geometry of the simulator. The filters have nonlinear characteristics; smaller motion cues are 

sustained for longer periods of time, and larger excursions are washed out quicker to make more 

motion available to the pilot. 

Figure 2.6.2 – Nonlinear Optimal Cueing Algorithm Block Diagram 

This implementation solves two separate Riccati equations; one for the translational 

motion cue, and one for the rotational motion cue. The development of a real-time solution to 

the Riccati equation was quite challenging. Each iteration used the previous solution as initial 

conditions when computing the current solution. This algorithm used the offline computed 

solutions used in the Linear Optimal Algorithm for the first iteration. In order to continue 

 



A β

A β

A

A a

A ωA

I S

S β
20 

updating the filters in real time, a structured neural network is used to solve the Riccati equation. 

The final version of this algorithm ran at 60 Hz; all real-time requirements were met. 

 
21 

3. Higher Frequency Motion Cueing Algorithms 

3.1 Introduction 

The objective of this area of research is to ensure high frequency motion cues are 

preserved by the cueing algorithm. Typical motion cueing algorithms apply limiting and scaling 

along with a set of washout filters to calculate the motion cue. These washout filters are low 

bandwidth and not able to preserve turbulence cues; furthermore, it is not necessary to wash out 

turbulence motion as it produces smaller excursions than most pilot inputs. 

In these cases, the aircraft state includes an appropriate response to turbulence, however 

the motion cue does not. Rather than modifying the primary motion cueing algorithm to improve 

the simulation, an augmentation channel can calculate the lacking high frequency cues and 

combine them with the primary cue. The various motion cueing algorithms discussed in Chapter 

2 are affected by this problem and can benefit from the addition of an augmentation channel that 

preserves high frequency cues. Augmenting motion cueing algorithms to improve atmospheric 

turbulence simulation has been attempted previously. One approach was developed by Cardullo 

and Ellor4; the high frequency effects bypassed the primary motion cueing algorithm, and were 

directly ported to the motion system. This augmentation was implemented on the USAF 

Undergraduate Pilot Training Simulators for the T37 and T38 aircraft. 

Another method to improve turbulence simulation was developed by Lloyd Reid and Paul 

Robinson (1990). In their approach, there is a secondary channel that uses a reduced order 

aircraft model to calculate the aircraft response to atmospheric turbulence. This output is then 

scaled and filtered before being integrated twice. The integrated output is summed with the 

output of the primary flight channel in degree-of-freedom space (Figure 3.1.1). 
22 

Figure 3.1.1 – Reid/Robinson Algorithm with Turbulence Channel (Reid, 1990) 

An algorithm similar to the Reid/Robinson algorithm was implemented on the Nonlinear 

Motion Cueing Algorithm discussed in Chapter 2. The implementation produced more 

distinguishable turbulence cues, however when run for long periods of time, the output of the 

vertical channel would begin to diverge. The following sections in this chapter discuss the 

implementation. Chapter 4 describes a modified version, which was tested offline and 

implemented in real-time. 

3.2 Simulation of Atmospheric Turbulence 

The first algorithm discussed is the implementation on the Nonlinear Motion Cueing 

Algorithm, developed by Telban et al. (b), at SUNY Binghamton (Figure 3.2.1). This algorithm, 

like the Reid/Robinson approach, comprises a secondary flight channel to calculate the aircraft 

response to atmospheric turbulence. A transfer function (Hw) represented the aircraft dynamics 

in the vertical channel, specifically the response to turbulence. In this case, this was modeled as 

a high-pass filter. The turbulence input, G, is a gust vector with the units of velocity. The output 

of Hw is AT X ; the acceleration of the aircraft due to turbulence. This is then augmented by a 




23 

gain term and summed with the scaled and limited acceleration output of the primary flight 

channel. 

Figure 3.2.1 – Original SUNY Implementation of Turbulence Channel (Telban, 2005) 

Unlike the secondary flight channel, the primary flight channel uses a nonlinear aircraft 

model. This nonlinear model calculates the dynamic aircraft response to all external inputs and 

pilot control. This includes the response to turbulence. This design is based on the ability to 

amplify simulator motion system response to turbulence by calculating it in the secondary 

channel and then summing it with the primary output. Both terms being summed are in the units 

of acceleration. 

This summed output is then put through a cueing filter, to washout the cue, and integrated 

twice to generate the displacement of the simulator, in degree-of-freedom space. One issue with 

performing this summation before the cueing filter is applied is that the cueing filter was 

designed to generate cues which are optimized to the pilot. As discussed in Chapter 2, this 

algorithm continuously optimizes the filters, in a nonlinear fashion, with the intent of reducing 

Nonlinear

A/C Model

ROAC Gain

CS

Transformation

Nonlinear

Limiting and

Scaling

Pilot Input

Turbulence (G)

+

+

X A







Nonlinear

Motion

Cueing X AT Algorithm





X M

 X M




24 

the pilot’s perceptual error. This filter is designed for cues of a much lower frequency range than 

atmospheric turbulence. 

Another area of concern is the two consecutive integrations. The aircraft response to 

atmospheric turbulence is not necessarily a zero mean process; therefore, double integrating that 

term can cause the amplitude of the displacement to grow with time. It is also noted that 

integrations naturally smooth high frequency inputs as they effectively average the magnitude. 

The integrations deteriorate the high frequency content of the cue before it is sent to the motion 

base and also cause the divergent tendency of the algorithm. 

Each of these issues was taken into account when designing the turbulence channel. 

Discussion on the new algorithm, along with the results of offline testing, is presented in Chapter 

4. The verification of the real-time implementation of the turbulence channel is conducted at 

length in Chapter 5. 

3.3 Motion Cueing Due to Aeroelastic Effects 

Aeroelastic motion cues tend to be in a higher frequency range than cues from pilot 

control input. Preserving the aeroelastic cue presents similar hurdles to simulating atmospheric 

turbulence. For these reasons, it is desirable to augment the primary motion cue to provide the 

pilot with the aeroelastic motion cues. This research was done with an aircraft model known as 

the Generic High-Speed Civil Transport (GHSCT). Raney (2002) describes the aircraft (Figure 

3.3.1) as having a slender fuselage and delta wing design based on the Reference H Supersonic 

Transport (Ref H). It is supplemented with an aeroelastic bending model to simulate the 

flexibility of the fuselage. 
25 

Figure 3.3.1 – Artist rendition of Generic High Speed Civil Transport (GHSCT) 

The specific model described in Raney (2002) comprises improved-fidelity models for 

aerodynamics, inertia, engines, landing gear, and actuation systems. The model includes quasielastic flexible aerodynamics and the ability to calculate actuator hinge moments. It also has 

engine inlet models that predict and illustrate the supersonic inlet unstart phenomena, described 

in Jackson (1999). The aircraft model is based on wind tunnel and computational fluid dynamics 

studies. These studies included experiments at low sub-sonic speeds, as well as runs at up to 

Mach 2.4. High fidelity finite-element models were based on multi-material structural designs 

(Raney, 2002). These finite-element structural models were refined to evaluate the overall 

strength and rigidity of the aircraft, and to formulate flutter dynamic predictions. 

This iteration of the model also included aeroservoelastic structural modes. The purpose 

of this extension of the model is to reproduce the effects of structural flexibility and gauge their 

impact on the pilot’s flying abilities. As mentioned before, the aeroelastic bending provides the 
26 

pilot station with additional high frequency motion, and in turn, can change the dynamics of the 

pilot’s control inputs. A specific portion of this study focused on the increase in high frequency 

motion from both turbulence and aeroelasticity, and the overall effect on the pilot control inputs. 

The GHSCT was developed with the intent of implementing an aeroelasticity model that 

could be easily modified and used in real-time simulations. Data from the complex finite 

element model of the aircraft was used to verify the flexural frequencies of the Generic HighSpeed Civil Transport Aircraft (Davis, 2009). As with the complex aeroelastic model; the 

flexibility of this new model could be excited by both atmospheric turbulence and pilot control 

input. 

Oscillatory excitation of the pilot station not only provides the pilot with aeroelastic cues, 

it can also induce an involuntary response to the vibration. These control inputs may also be 

oscillatory; and can cause divergent vibrations in the fuselage. This phenomenon is known as 

bio-dynamic coupling. The primary objective of the GHSCT study is to determine the specific 

frequency range and structural characteristics that induce bio-dynamic coupling. 

This particular Dynamic Aeroservoelastic Model, or DASE Model, comprises six flexible 

aircraft modes, described in Davis (2009) (Figure 3.3.2). Three of which are symmetric, and 

three anti-symmetric. The symmetric modes represent the bending in the pitch axis; the antisymmetric modes represent the bending in the yaw axis. Each of these aeroelastic modes can be 

excited by pilot control input, atmospheric turbulence, or other external inputs. With the DASE 

model activated, the pilot is provided with both lateral and vertical cues, each representative of 

fuselage bending. 
27 

Figure 3.3.2 – DASE Model, Flexible Aircraft Modes (Davis, 2009) 

Because the DASE Model is linear, this allows for easy modification of parameters 

between runs. This meets the requirements of the study while still maintaining necessary fidelity 

in the cues. The first DASE model was designed to mimic the bending modes of the complex 

aircraft. This simplified model was developed using the complex aircraft dimensions and mass 

properties. The modes were identified by conducting beam analysis and defining the aircraft 

fuselage as a thin hollow tube. The simplified model was compared with the complex model. 

Once they were verified, it became possible to scale the linear model to represent different sized 

supersonic jets. Details on the development of the simplified model, derived in Davis (2009), are 

shown below. 

The first two equations (Eq. 3.3.1 and Eq. 3.3.2) represent the cross sectional area of the 

hollow tube and the cross sectional stiffness inertia; where A is the area (in2

), R is the radius (in), 

and t is the thickness (in) of the tube. 
28 

A = 2πRt

Eq. 3.3.1 

2 1

2

I = R

A

Eq. 3.3.2 

The lateral vibration of a beam with free ends is determined by the wave equation (Eq. 

3.3.3); ki represents the roots of this equation. Dividing by L obtains κi; the roots of the 

frequency equation (rad). κ1 , κ2, and κ3 are the first three fundamental bending modes; 

representing the mode shapes shown in Figure 3.3.2. The frequencies of free vibration (ωi) of a 

simple prismatic beam are shown in Eq. 3.3.4. They are proportional to the square root of the 

bending stiffness (K), and the distributed mass (M). This equation can be simplified to Eq. 3.3.5, 

where Sm represents a material property of the fuselage. Sm is defined in Eq. 3.3.6, where E 

represents the Young’s modulus of the material and ρ is the density. 

i i k L = κ = 0, 4.73, 7.853, 10.996, ...

Eq. 3.3.3 

  2

i i

K

ω = k

M

Eq. 3.3.4 

2

i i

im m 2

k κ R

ω = S R = S

2 2 L

           

Eq. 3.3.5 
29 

m

Eg S = 

ρ

Eq. 3.3.6 

The block diagram of the linear DASE model is shown in Figure 3.3.3. This figure 

illustrates the different parameters used to calculate the fuselage bending of the aircraft. Inputs 

to the model are derived from the aircraft state, which is provided by the generic aircraft model, 

or GHSCT. Since this version of the DASE model is designed to provide the pilot with bending 

in both the pitch and yaw degrees-of-freedom (DOF), the rotational acceleration in those DOF 

are used as inputs. The pitch bending modes are combined with the static sag of the aircraft. 

This represents the neutral vertical position of the pilot station when the aircraft is in flight. 
30 

Figure 3.3.3 – DASE Model Block Diagram (Davis, 2009) 

One component of the bending modes model that is not included is the flexibility of the 

wings, and the impact these forces have on the dynamics of the aircraft. Since this aircraft is 

designed for supersonic flight, the wings are configured with a delta design and are much more 

rigid than a conventional wing design. If the aircraft had a conventional or swept wing design, 

such as a Large Civil Transport, there would be much greater presence of flexibility in the roll 

axis and would merit the design of a model for these aeroelastic effects. 

Chapter 4 presents the implementation of the DASE model along with its integration with 

the turbulence channel. Results of offline testing are provided there as well. Real-time 
31 

experiments are discussed in Chapter 5, the results of the experiments are presented and analyzed 

in Chapter 6. See Appendix B for details with regard to the different parameters in the DASE 

models. 

 
32 

4. Design of Atmospheric Turbulence Motion Cueing Algorithm and 

Integration with Aeroelastic Model 

4.1 Introduction 

The Augmented Turbulence Channel, or ATC, is executed in parallel with the primary 

motion cueing algorithm. An advantage to this setup is that it enables the ATC to be 

implemented with most cueing algorithms without much trouble. By design, the only portion of 

the primary motion cueing algorithm being augmented is the degree-of-freedom (DOF) output 

that drives the motion base. The visual displays and instruments are not augmented by the 

channel. The Augmented Turbulence Channel went through many revisions before its real-time 

implementation (discussed in Chapter 5). This chapter details the algorithm design and provides 

analysis of the offline testing of the algorithm. 

Integrating this ATC with the Dynamic Aeroservoelastic Model was also a convenient 

operation from a programming standpoint. Since the DASE Model output also augments the 

DOF output of the primary motion cueing algorithm, the two cues could be combined before this 

operation takes place. Verification of the integration of the two augmented cueing algorithms 

(ATC and DASE) was conducted offline in MATLAB and Simulink. The version of the motion 

cueing algorithm (NASA Adaptive) was implemented in Fortran. To test the implementation, an 

offline version of this algorithm was executed in batch mode. The results of this analysis are 

presented in Chapter 4; results from the real-time implementation are detailed in Chapter 5. 
33 

4.2 Augmented Turbulence Channel Design 

Two implementations of the Augmented Turbulence Channel were tested offline. Each 

design is based off the original turbulence channel implemented on the Nonlinear Cueing 

Algorithm (Figure 4.2.1). The nonlinear cueing algorithm summed the output of the turbulence 

channel with the primary motion cue before sending them through the nonlinear cueing filter. 

This filter is designed to wash out motion that arises from pilot control input and would filter out 

cues representing the aircraft response to turbulence. 

Figure 4.2.1 – Original SUNY Implementation of Turbulence Channel (Telban, 2005) 

The original implementation was only applied to the heave channel. It is noted in Reid 

1990, that the heave channel appears to be the most attenuated by the motion cueing algorithm. 

The implementation in the Nonlinear Algorithm produced somewhat more defined turbulence 

cues, but had severe issues with divergence. During long runs, up to 5 minutes, the motion base 

would begin to diverge. Possible explanations for this are in the configuration of the different 

components of the algorithm. By sending the turbulence cue through the same nonlinear 

washout filter as the primary motion cue, they are treated in the same fashion. In most cases, the 

Nonlinear

A/C Model

ROAC Gain

CS

Transformation

Nonlinear

Limiting and

Scaling

Pilot Input

Turbulence (G)

+

+

X A







Nonlinear

Motion

Cueing X AT Algorithm





X M

 X M




34 

turbulence cue is going to drive the base at a higher frequency than the pilot. Consequently, this 

nonlinear filter is not capable of preserving the cue that is produced by the turbulence channel. 

Another contributing factor to the divergence is the two integrations of the turbulence 

channel output. This operation will not only amplify the divergence of the signal, but also 

smooth the turbulence cue. In the end, the final cue had more amplitude, but the frequency 

content was deteriorated. The two new implementations of the turbulence channel used in the 

offline testing are shown in Figures 4.2.2 and 4.2.3 (Ercole, (2009)). 

Figure 4.2.2 – Implementation 1 of Augmented Turbulence Channel 

Figure 4.2.3 – Implementation 2 of Augmented Turbulence Channel 

Nonlinear

A/C Model

Nonlinear

A/C Model



Gain

Motion Cueing Algorithm

Pilot Input

Turbulence (G)

ZM

Z

MT Z

ZM

+

+

X A/C





AT Z



ZM

Z

MT Z

ZM X A/C





AT Z
35 

The Nonlinear Aircraft Model (NLAC) in the top channel calculates the aircraft state 

( X A



 ). This is then sent through a coordinate transformation, limiting and scaling, and the filters 

of the cueing algorithm. The output of the cueing algorithm comprises the entire state of the 

simulator, however, this figure only shows the vertical channel output (ZM

 ). ZM is then 

integrated twice to obtain the simulator vertical displacement provided by the primary motion 

cueing channel (ZM ). 

The second aircraft model is designed to calculate the aircraft response to turbulence 

alone ( AT Z ). In implementation 1 of this algorithm, the output of the aircraft model is scaled by 

a gain to become the turbulence augmentation cue ( M T Z ). This output is technically a scaled 

acceleration; however, it is treated as a position cue. 

When testing had first started on this ATC, the output of the primary motion cueing 

algorithm ( ZM

 ) was summed with M T Z . The combined signal was then integrated to form the 

vertical displacement of the simulator (ZM ). It became apparent very quickly that the two 

consecutive integrations were causing a great deal of divergence in the output. Further testing 

performed the integrations before summing the signals (similar to the Reid/Robinson approach). 

The turbulence channel output was always divergent. 

Numerical integration is a process that will smooth a signal, something that is not 

desirable when preserving motion due to turbulence. The aircraft response to turbulence is not a 

zero-mean process. This is part of the reason why the signal diverges after being integrated 

twice. For these reasons, the output is just scaled. 
36 

In implementation 2, AT Z is scaled and sent through a specially designed cueing filter. 

The cueing filter conditions the turbulence motion cue before it is combined with the primary 

flight cue. The filter is designed to wash out the long period motion that is generated by the 

aircraft model in the turbulence channel. The high frequency motion of the primary flight cue is 

the portion that is most attenuated by the cueing algorithm. The purpose of the turbulence 

channel is to recover what is lost by the primary motion cueing algorithm. Since the long period 

motion of the aircraft is preserved by the cueing algorithm, there is no need to add more long 

period motion with the turbulence channel. With the turbulence cueing filter in place, the long 

period motion will be washed out of the turbulence channel output. 

In Figures 4.2.2 and 4.2.3, the ATC uses a nonlinear aircraft model. This is done to 

ensure all the offline testing only examines the structure of the algorithm, and is not impacted by 

an inaccurate aircraft model. Details on the development of a Reduced Order Aircraft Model, for 

use in the real-time experiments, are presented in sections 4.5 and 4.6. Since the ATC was 

implemented along with two different aircraft models, two reduced order models were 

developed. Discussion on each component of the channel is conducted at length in the following 

sections of this chapter. 

4.3 Reduced Order Aircraft Model Development 

For real-time implementation, it was necessary to use a Reduced Order Aircraft Model, 

or ROAC, in the turbulence channel. The turbulence channel utilizes the aircraft response to 

turbulence alone. It was not possible to access this specific output from the nonlinear model 

because it is treated as a black box system. Only the aircraft state information is available as an 
37 

output; the aircraft response to turbulence alone had to be computed from a separate aircraft 

model. 

In order to run the simulation in real-time with a high sampling rate (at least 50 Hz), only 

one Nonlinear Aircraft Model (NLAC) could be executed per iteration. Theoretically, if the 

computational power was high enough, two NLAC models could run in parallel. The aircraft 

model in the ATC represents the vertical aircraft response to turbulence wind gusts in the vertical 

direction. Since the ATC is implemented on two very different aircraft, it was necessary to 

design two ROAC models. 

Multiple models were developed for each aircraft, the best were chosen based on an 

assessment of their accuracy. The models were evaluated in both the frequency and time 

domains. Since atmospheric turbulence is defined as a spectrum, the model cannot be verified 

using time response alone. Furthermore, the frequency response analysis of the ROAC output 

holds much more value than time domain analysis because the frequency content of the 

turbulence is what must be preserved in the cue. 

4.4 Parameter Identification Methods 

There are many methods available for parameter identification. Some which are built 

into the MATLAB – Simulink environment including: Auto-Regressive with External Input 

(ARX), Auto-Regressive Moving Average with External Input (ARMAX), Box-Jenkins (BJ), 

and Output-Error (OE), among others. There are desirable qualities for each of these methods, 

however the ARX tool was chosen because of its versatility. 
38 

Like most parameter identification methods, ARX allows the user to choose the order of 

the polynomial. One of the shortcomings when working with ARX is the need to use large 

datasets. When using small samples of a time response, ARX tends to produce unstable models. 

The only way to avoid this is by using longer runs or decreasing the time step to add more data 

points. 

Another method known as Automatic Parameter Identification, or APID, was developed 

here at SUNY Binghamton by Dr. Kirill Zaychik. For developing the ROAC, this parameter 

identification method is superior to ARX as it determines the most accurate model based on the 

Power Spectrum Density (PSD) of the output. Before outputting a model in polynomial form, 

multiple polynomials are evaluated. Each model is examined in the frequency domain until the 

error between the input and output signals is reduced as much as possible. The accuracy of the 

model is also dependent on whether the user has selected the most applicable polynomial order. 

The technique was originally developed by Zaychik (2009) to identify subject specific 

parameters that represent a human operator. It uses a bit climbing genetic algorithm. 

Verification of the ROACs developed in this chapter proves it can be applied to various singleinput, single-output, scenarios. The only noted disadvantage to this algorithm is the time it takes 

to run. The models are identified using an algorithm coded in MATLAB, which can take 10 

times longer than running ARX. 

Both APID and ARX were executed to identify parameters of both aircraft models. The 

implemented models were both developed by APID because the results of the frequency 

response in each APID developed model showed better performance. The following sections 

(4.5 and 4.6) detail the ROAC development for each aircraft. 
39 

4.5 Large Civil Transport: Reduced Order Aircraft Model Development 

For research purposes, we have been provided with an aircraft model representing a 

Large Civil Transport. This full nonlinear model operates in a black box form. There is no 

access to the parameters affecting the aircraft dynamics; however, we do have full control over 

initial conditions, pilot control input, and atmospheric conditions, along with other settings. In 

order to develop a ROAC, the Large Civil Transport was trimmed at 30,000 ft ASL at M 0.8, and 

flown through severe turbulence (level 5), with altitude hold on. Since no pilot control input is 

applied to the aircraft, the response in the vertical channel is primarily due to atmospheric 

turbulence. With this input/output data, parameter identification software can be used to identify 

a linear model. See Figure 4.5.1 for the input/output data used to develop the model. 

Figure 4.5.1 – Input and Output Data for Large Civil Transport ROAC Development 

0 2 4 6 8 10 12 14 16 18 20

-5

0

5

10

Time Response: Wgust and NZ

Time (seconds)

0 2 4 6 8 10 12 14 16 18 20

0.9

0.95

1

1.05

1.1

Time (seconds)

Wgust (m/s)

NZ (g)
40 

The first model was developed using ARX. The data was from running the NLAC at 60 

Hz for about 5 minutes. Smaller datasets caused issues with developing unstable models; using 5 

minute datasets eliminated this issue. The most accurate ROAC from ARX (Eq 4.5.1) is shown 

below. It is a 4th order transfer function. Verification was done by comparing the PSD of each 

model, and varying the polynomial order up to 6th. 

AT Z ()

G

s 

 3 2

4 32 5 4

.2422 34 845.5 2829

126.7 9762 3.855*10 8.964*10

ss s

s ss s

 

  

Eq. 4.5.1 

The model developed using APID was 2nd over 4th order transfer function (Eq. 4.5.2). 

Multiple polynomial orders were tested here, also up to 6, to determine the best format. The 

frequency response of both of these models is quite different. Figure 4.5.2 shows increased gain 

across most frequencies in the APID developed model. The APID developed model also has 

much less phase shift throughout all frequencies. 

AT Z ()

G

s 

 2

432

7 2.94 0.63

28.84 423.6 336.9 12.8

s s

ssss

 

  

 Eq. 4.5.2 
41 

Figure 4.5.2 – Bode Diagram of Large Civil Transport ROAC 

Plots comparing the PSD of each model are shown below (Figure 4.5.3). Since each 

aircraft model was developed with data representing the aircraft vertical response to turbulence 

(NZ), the output PSD of the ROACs is compared to the PSD of NZ. The units of Power in the 

following PSD plots are 

2 2 (Aircraft Vertical Acceleration (m/s ))

Hz . The ARX developed model 

shows considerably higher power in very low frequencies, under 0.15 Hz. After this frequency, 

there is quite a bit of underestimation of the power. Comparing the APID developed model with 

NZ, the power is more aligned with that of the actual response of the full aircraft model in 

10-1100101102 -55

-50

-45

-40

-35

-30

Bode Diagram: LCT ROAC

Magnitude (dB)

10-1100101102 -100

0

100

200

300

400

Phase (deg)

Frequency (rad/sec)

ARX

APID
42 

frequencies over 0.1 Hz. Even though the APID ROAC shows less power in the low 

frequencies, the model is sufficient to produce appropriate turbulence cues. 

Figure 4.5.3 – PSD of Large Civil Transport ROACs and Aircraft Acceleration (NZ) 

The high frequency content is also apparent in the time response plot of each model. In 

the time response, Figure 4.5.4, the majority of the 20 second sample shows more similarity in 

the frequency content of the APID ROAC and the response of the NLAC (NZ). The ARX 

developed model has very little amplitude in the short period oscillations. This characteristic 

was anticipated by the lack of power in frequencies above 0.2 Hz. 

0 0.5 1 1.5 2

10

-3

10

-2

10

-1

10

0

PSD of ROAC Models

Frequency (Hz)

Power

NZ

ARX ROAC

APID ROAC
43 

Figure 4.5.4 – Time Response of Large Civil Transport ROACs and Aircraft Acceleration (NZ) 

4.6 GHSCT: Reduced Order Aircraft Model Development 

Testing was conducted to see whether or not the Large Civil Transport ROAC could be 

used to represent this aircraft. The results of frequency analysis showed large differences in the 

performance of each model (see Figure 4.6.1). This shows the PSD of the vertical acceleration 

of the GHSCT (NZ) due to severe turbulence (level 6). This is compared to the simulated ROAC 

that was developed for the Large Civil Transport (Eq. 4.5.2). Based on the response in the lower 

frequencies, under 0.15 Hz, the PSD shows a great deal of error between the ROAC and NLAC 

response. For this reason, a new model was developed to better represent the dynamics of the 

GHSCT. 

0 2 4 6 8 10 12 14 16 18 20

-0.15

-0.1

-0.05

0

0.05

0.1

Time Response, ROAC and 1/$&

Time (sec)

Magnitude

NZ

ARX ROAC

APID ROAC
44 

Figure 4.6.1 – PSD of Large Civil Transport ROAC and GHSCT Aircraft Acceleration (NZ) 

In order to capture the aircraft response to turbulence, the GHSCT was flown at 30,000 ft 

ASL with altitude hold on, through severe turbulence (level 6). Since these runs were done at 80 

Hz, 3 minute runs produced datasets of sufficient size to develop the reduced order model. The 

response data used in the parameter identification algorithms is shown below in Figure 4.6.2. 

0 0.5 1 1.5 2

10

-2

10

-1

10

0

PSD of ROAC Models

frequency (Hz)

power

NZ

LCT ROAC
45 

Figure 4.6.2 – Input and Output Data for GHSCT ROAC Development 

The development of a ROAC for the GHSCT used the same parameter identification 

tools as with the Large Civil Transport. As with the Large Civil Transport, ARX and APID were 

employed. Fourth order transfer functions were used as the format for this model, the same as 

with the Large Civil Transport. The frequency response of each new model is shown in Figure 

4.6.3. As with the Large Civil Transport model generated with ARX, there is considerable phase 

shift across all frequencies. 

3 2

4 3 42 5 4

0.4105 26.93 1213 8391

22.31 1.474*10 2.41*10 3.425*10

s ss

s s ss

  

  

AT Z ()

G

s 



Eq. 4.6.1 

0 2 4 6 8 10 12 14 16 18 20

-20

-10

0

10

20

GHSCT Time Response: Wgust and NZ

Time (seconds)

Wgust (m/s)

0 2 4 6 8 10 12 14 16 18 20

0.8

0.9

1

1.1

1.2

1.3

Time (seconds)

NZ (g)
46 

3 2

43 2

0.315 12.25 119.6 11.83

57.8 1121 7473 3463

sss

s ss s

 

  

AT Z ()

G

s 



Eq. 4.6.2 

Figure 4.6.3 – Bode Diagram of GHSCT ROACs 

The model developed using ARX was still not as accurate as APID. Figure 4.6.4 shows 

the most accurate APID model (Eq. 4.6.2) compared against the most accurate model generated 

with ARX (Eq. 4.6.1), the Large Civil Transport ROAC, and the GHSCT simulation output 

(NZ). The bottom plot of Figure 4.6.4 allows the reader to see the PSD of the most accurate 

ROAC (developed with APID) compared with NZ alone. There is minimal error at frequencies 

under 0.1 Hz, and at frequencies greater than this, the PSDs practically overlap. The time 

10-1100101102 -60

-40

-20

0

Bode Diagram: GHSCT ROAC

Magnitude (dB)

10-1100101102 -200

0

200

400

600

Phase (deg)

Frequency (rad/sec)

ARX

APID
47 

response also shows much better performance characteristics than the model developed with 

ARX, in both the short period motion and high frequency characteristics of the signal (Figure 

4.6.5). 

Figure 4.6.4 – PSD of GHSCT ROACs and Aircraft Acceleration (NZ) 

0 0.5 1 1.5 2

10

-2

10

0

PSD of ROAC Models

frequency (Hz)

power

PSD of ROAC Models (only showing NZ and APID ROAC)

NZ

LCT ROAC

ARX ROAC

APID ROAC

0 0.5 1 1.5 2

10

-2

10

0

frequency (Hz)

power

NZ

APID ROAC
48 

Figure 4.6.5 – Time Response of GHSCT ROACs and Aircraft Acceleration (NZ) 

4.7 Turbulence Cueing Filter 

The Reid Robinson algorithm employed a high-pass filter in the turbulence channel that 

was similar to the high-pass filter of the primary flight channel. In the implementation of the 

turbulence channel on the Nonlinear Algorithm, a low-pass filter was employed. This low pass 

filter was designed to attenuate the high frequency content generated by the ROAC, which was 

modeled as a high-pass filter. 

A similar design was employed on Implementation 2 of the ATC. The Turbulence 

Cueing Filter, or TCF, was designed to attenuate high frequencies, out of the range of the aircraft 

response to turbulence. It is also designed to wash out the long period of the signal. Since the 

0 2 4 6 8 10 12 14 16 18 20

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

Time Response, ROAC and 1/$&

Time (sec)

Magnitude

NZ

ARX ROAC

APID ROAC
49 

primary cueing algorithm provides the pilot with long period motion due to turbulence, there is 

no reason to preserve this motion in the ATC. 

A fourth order, band-pass filter design was chosen (Eq. 4.7.1). From 5 rad/s (0.79 Hz) to 

25 rad/s (3.98 Hz), the gain of the filter remains near 0.0 dB (see Figure 4.7.1). Frequencies 

under 0.79 Hz and frequencies greater than 3.98 Hz are attenuated, and subjected to considerable 

phase shift. 

T filtered

T

Z()

Z()

s

s 

2

44 3 2

0.5

2.04*10 0.020 1.025 1.255 0.25

s s

s sss 



  

Eq. 4.7.1 

Figure 4.7.1 – Bode Diagram of Turbulence Cueing Filter 

100101102 -10

-8

-6

-4

-2

0

Bode Diagram: Turbulence Cueing Filter

Magnitude (dB)

100101102 -150

-100

-50

0

50

Phase (deg)

Frequency (rad/sec)
50 

The step response of the filter shows the ability of this filter to wash out cues. The 

settling time is 11.2 seconds, however, 70% of the amplitude is washed out after 2 seconds. 

Figure 4.7.2 – Step Response of Turbulence Cueing Filter 

Later testing with the real-time simulation revealed that the cueing filter in the turbulence 

channel caused the turbulence cue to lose some sharpness. The pilot noted that with the cueing 

filter activated, there was enough of a difference to slightly reduce the intensity rating. Some of 

the longer period turbulence cues appeared to be washed out before the motion was completed. 

The algorithms were tuned with the filter on, but the real-time experiments were all conducted 

without the TCF. 

Step Response: Turbulence Cueing Fitler

Time (sec)

Amplitude

0 2 4 6 8 10 12

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

System: tcf

Rise Time (sec): 0

System: tcf

Settling Time (sec): 11.2
51 

4.8 Integration with Aeroelastic Model 

The GHSCT aircraft is integrated with the Dynamic Aeroservoelastic Model (DASE 

model) and the NASA Adaptive cueing algorithm. As mentioned in Chapter 3, the DASE model 

augments the output of the primary motion cueing algorithm, in a similar fashion to the ATC. 

Figure 4.8.1 shows the block diagram of the integration of the two augmented channels. 

Figure 4.8.1 – Integration of DASE Model and Augmented Turbulence Channel 

The inputs to the DASE Model (q A  and r

A

 ) are directly from the aircraft model state 

vector. The aeroelastic model uses these angular accelerations along with a series of filters to 

calculate bending displacements in the vertical and lateral axis ( M AE Z and M AE Y , 

respectively); see Chapter 3 for more detail on this model. Now that the ATC and DASE models 

q A 

rA

AT Z

M AE Z

M AE Y

G

MT Z

Z
52 

are integrated, there are two augmentation cues in the vertical channel, one from the turbulence 

channel ( M T Z ) and one from the aeroelastic model ( M AE Z ). 

These augment the cueing algorithm in DOF space, the resulting augmentation cue in the 

vertical channel is Z . This is summed with the output of the primary motion cue, X M

 , not 

shown in this figure. 

The combined output of Z and X M

 is then sent to the motion base, presenting the 

pilot with augmented cues representing both atmospheric turbulence and aeroelastic bending. 

See Appendix B for detail on the Fortran code used to execute the turbulence channel and 

integrate the two augmented channels. 

4.9 Testing: Offline Simulation of Algorithms 

Before implementing the Augmented Turbulence Channel on the real-time simulation, 

there must be some offline testing. These tests are designed to examine the effect of the 

augmented cue on the primary motion cue. The NASA Adaptive and SUNY Nonlinear 

algorithms, like most cueing algorithms, attenuate the amplitude and frequency content of 

atmospheric turbulence cues. Testing will show whether or not the frequency content of the 

primary motion cue has improved with the augmented algorithm. 

When running the GHSCT, the cueing algorithm must also contain the output from the 

flexible aircraft model. The offline testing will verify the implementation of the ATC and the 

integration with the DASE model. 
53 

The offline testing is broken down into two sections in this chapter: the Large Civil 

Transport and GHSCT implementations (sections 4.9.1 and 4.9.2, respectively). This was done 

because each aircraft is currently implemented with a different motion cueing algorithm and 

required a separate set of offline runs. Additionally, the integration of the ATC and DASE 

model requires a separate set of analysis, which only relates to the GHSCT. 

4.9.1 Large Civil Transport: Offline Testing 

In the case with the Large Civil Transport, the offline testing was conducted on the 

SUNY Nonlinear Motion Cueing Algorithm. This algorithm is set to run in batch mode using a 

Fortran compiler. The algorithm running on the flight simulator reads aircraft state data from the 

Large Civil Transport in real-time. Using this information, the motion cues are calculated by the 

cueing algorithm, also in real-time. The offline cueing algorithm is executed using an input file 

that contains the output of the nonlinear Large Civil Transport model. 

To generate the cueing algorithm input file, the Large Civil Transport is executed offline. 

This model allows the user to define pilot input and atmospheric disturbances, among other 

things. The model itself cycles at 60 Hz, generating data comprising of every piece of 

information with regard to the aircraft state. Figure 4.9.1 shows the Large Civil Transport 

(NLAC) GUI that is executed to generate the aircraft state information. 
54 

Figure 4.9.1 – Large Civil Transport Model GUI (courtesy of NASA LaRC) 

The offline cueing algorithm treats the run in the same fashion as the real-time algorithm. 

The output of the cueing algorithm is the motion base state, in degree-of-freedom space and 

actuator leg space. To verify the implementation of the turbulence channel, the DOF output is 

evaluated with and without the ATC. When the ATC is activated, there should be a substantial 

increase in the high frequency motion of the final cue. 

The two implementations are shown below in Figures 4.9.2 and 4.9.3. Here, the output 

of the NLAC ( X A



 ) represents the aircraft response to both atmospheric turbulence and pilot 

input. The NLAC that is executed in the turbulence channel produces the aircraft response to 
55 

turbulence alone ( AT Z ). In implementation 1, this output is scaled, producing the turbulence 

augmentation cue ( M T Z ). Implementation 2 calculates M T Z by filtering AT Z . 

Figure 4.9.2 – Large Civil Transport Implementation 1 of Augmented Turbulence Channel 

Figure 4.9.3 – Large Civil Transport Implementation 2 of Augmented Turbulence Channel 

The cueing algorithm was executed using aircraft state data from level flight through 

level 5 turbulence. The first plot (Figure 4.9.4) shows the motion cue generated by the primary 



ZM

Z

MT Z

X A ZM





AT Z

Nonlinear

A/C Model

(LCT)

Nonlinear

A/C Model

(LCT)



Gain

CS

Transformation

Nonlinear

Limiting and

Scaling

Pilot Input

Turbulence (G)

Nonlinear

Motion

Cueing

Algorithm

Turbulence

Cueing Filter

+

+

ZM

Z

MT Z

X A ZM





AT Z
56 

motion cueing channel. This was executed running the modified cueing algorithm and turning 

the ATC off with a gain of 0.0 in the ATC. 

Figure 4.9.4 – Large Civil Transport Testing, Time Response, ACT Off, Vertical Channel Only 

The aircraft vertical acceleration shown in this figure is the vertical component of X A



 . 

The high frequency accelerations of this output are due to turbulence. The next piece of data 

(Zdd) is the output of the cueing algorithm, prior to the integrations (also defined as ZM

 ). Once 

integrated, the high frequency motion that is apparent in the aircraft vertical acceleration and ZM



appear to have no impact on the primary motion cue (ZM ). This is the type of issue that is 

0 5 10 15 20 25

-0.1

-0.05

0

0.05

0.1

0.15

0.2

Time (Seconds)

Aircraft Vertical Acceleration (m/s2)*0.05

Zdd (m/s2)

ZM (m)

LCT Testing: ACT Off
57 

usually faced when trying to simulate atmospheric turbulence, and the main motivation for this 

study. 

The output of implementation 1 of the ATC is shown in Figure 4.9.5. The same data was 

used to execute the cueing algorithm; the only difference here is the addition of the turbulence 

channel. It is clear from this plot that Z contains high frequency motion and some greater peak 

amplitude. The gain used in the turbulence channel is 1.5. Accordingly, there seems to be some 

large amplitude excursions due to the turbulence. Tuning the algorithm using the simulator 

motivated a large reduction in the gain; the cues were amplifying the perceived turbulence level 

substantially, even under light turbulence. 

Figure 4.9.5 – Large Civil Transport Testing, Time Response, Implementation 1 of ATC, 

Vertical Channel Only 

0 5 10 15 20 25 30

-0.1

-0.05

0

0.05

0.1

0.15

Time (Seconds)

Zdd (m/s2)

ZM (m)

Z (m)

LCT Testing: Implementation 1
58 

The time response of Implementation 2 is shown in Figure 4.9.6. There is not a 

substantial difference between the turbulence cues of these two implementations. The peak 

amplitude of Z is reduced slightly. It also appears that Z oscillates about ZM rather than 

slightly above it. In Implementation 1, the augmented cue is usually greater than the output of 

the primary flight channel (ZM ). This does not happen with Implementation 2 because the 

Turbulence Cueing Filter washes out the long period motion. The only portion of the turbulence 

cue left in M T Z is the high frequency component. 

Figure 4.9.6 – Large Civil Transport Testing, Time Response, Implementation 2 of ATC, 

Vertical Channel Only 

0 5 10 15 20 25 30

-0.1

-0.05

0

0.05

0.1

0.15

Time (Seconds)

Zdd (m/s2)

ZM (m)

Z (m)

LCT Testing: Implementation 2
59 

In order to analyze the effects of the TCF more closely, Figure 4.9.7 shows zoomed 

portions of the time response of Implementation 1 and 2. In this plot, it is more noticeable that 

Z Implementation 1 (without the added filter) adds some long period amplitude to the primary 

motion cue. The peak magnitude of Z is greater without the filter. 

Figure 4.9.7 – Large Civil Transport Testing, Time Response, Comparison of Implementations 1 

and 2 

With the results of the offline testing, it was determined that the next step was to test the 

algorithm in real-time. The additional code that was added to the motion cueing algorithm is in 

0 2 4 6 8 10 12

-0.02

-0.01

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

Time (Seconds)

ZM (m)

Z (m) (Implementation 1)

Z (m) (Implementation 2)

LCT Testing: Implementation 1 and Implementation 2
60 

Appendix B. Both implementations were executed on the flight simulators at NASA LaRC. 

Chapter 5 details the real-time tuning of the algorithm and the piloted experiments. 

4.9.2 Generic High-Speed Civil Transport: Offline Testing 

To test the implementation on the GHSCT, the ATC was implemented with the NASA 

Adaptive Motion Cueing Algorithm. Figure 4.9.8 shows Implementation 1 of the Augmented 

Turbulence Channel and DASE Model. Just as the Large Civil Transport testing, only the 

vertical channel is analyzed in this set of tests. The vertical output of the primary cueing channel 

(ZM ) is summed with the augmentation cues produced by the DASE Model ( M AE Z ) and the 

ATC ( M T Z ). Each of these cues represents a position increment designed to provide motion at 

the pilot station that represents atmospheric turbulence and fuselage bending. Implementation 2 

adds the Turbulence Cueing Filter to the algorithm (Figure 4.9.9). 

Figure 4.9.8 – GHSCT Implementation 1 of Augmented Turbulence Channel and DASE 

Aeroelastic Model 

Z

MT Z



X A





M AE Z

ZM ZM

AT Z
61 

Figure 4.9.9 – GHSCT Implementation 2 of Augmented Turbulence Channel and DASE 

Aeroelastic Model 

Aircraft state data were collected from running the rigid body model in real time. In 

order to obtain data that included fuselage bending, the GHSCT was flown with a pilot at the 

controls. The rigid body model could have been executed offline, along with the DASE Model, 

but this would not have generated appropriate aeroelastic bending cues without pilot input. 

The aeroelastic cues are dependent on the size of the angular accelerations (q A  and r

A

 ). 

The turbulence model that is implemented on the GHSCT does not produce rotational turbulence 

gust disturbances. A pure translational turbulence model does not produce much rotation of the 

aircraft, and therefore pilot input is needed to excite large bending modes. 

To gather the data for this model, the GHSCT was flown through turbulence level 6. 

There was no pilot input during the first portion of the run; for this time, the only external 

disturbance was the turbulence wind gust. As the run progressed, the pilot began making inputs 

to compensate for the divergence from altitude. As expected, these inputs induced larger angular 

accelerations which excited the aeroelastic model. The same DASE Model parameters were 

ZM

Z

MT Z

ZM



X A





M AE Z

AT Z
62 

used on the real-time experiments. Parameters for the aeroelastic model are shown in Appendix 

B. 

The Fortran code from the NASA Adaptive algorithm was modified to include a new 

subroutine that executes the augmented turbulence channel during each iteration. With this new 

piece of code, the algorithm can be executed offline with the turbulence channel. The aeroelastic 

output is calculated when running the GHSCT rigid body model and simply summed with the 

primary cue along with the output of the ATC. 

For the purpose of comparing ZM and Z , the static sag offset was removed from the 

aeroelastic cue. The static sag is a constant that is summed with the vertical channel output of the 

DASE Model, more information on this term is provided in Chapter 3. Removing the term 

allowed the DASE Model output to oscillate about zero, rather than at an offset of 0.19 m. 

The original implementation of the GHSCT using the NASA adaptive algorithm is 

plotted in Figure 4.9.10. This time response is from the portion of the run without pilot input. 

The cueing algorithm attenuates the high frequency motion in the same manner as the Large 

Civil Transport run shown earlier in the chapter. The aircraft responds to the turbulence gust 

input (see Aircraft Vertical Velocity, from X A



 ). The cue prior to integration (Zdd) also contains 

this high frequency motion. Just as the other cueing algorithm, once ZM is calculated, the 

turbulence is attenuated. 
63 

Figure 4.9.10 – GHSCT Testing, Time Response, ATC Off, Vertical Channel Only 

Adding the ATC to the algorithm had the same effect as with the Large Civil Transport 

implementations. Implementation 1 and 2 were executed with a gain of 1.5 in the turbulence 

channel. The time response of each implementation was analyzed to ensure the cue was 

produced properly. Plots are shown in Figures 4.9.11 and 4.9.12. 

0 2 4 6 8 10 12

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

GHSCT Testing: ACT Off

Time (Seconds)

Aircraft Vertical Acceleration (m/s2)*0.05

Zdd (m/s2)

ZM (m)
64 

Figure 4.9.11 – GHSCT Testing, Time Response, Implementation 1 of ATC, Vertical Channel 

Only 

The output of the ATC adds considerable high frequency motion to Z. In 

Implementation 1, the peak amplitude of Z is increased more. Applying the cueing filter to the 

turbulence channel manages to reduce the peak amplitude of Z (Figure 4.9.12). One of the other 

advantages of Implementation 2 is the ability to use a higher gain term without excessively 

amplifying the cue. This can be done because any amount of long period motion will be washed 

out from that signal by the filter; the only portion that is intensified by the gain is the short period 

oscillation. 

0 2 4 6 8 10 12

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

GHSCT Testing: Implementation 1

Time (Seconds)

Zdd (m/s2)

ZM (m)

Z (m)
65 

Figure 4.9.12 – GHSCT Testing, Time Response, Implementation 2 of ATC, Vertical Channel 

Only 

The integration of the two augmented channels also needed to be tested. During this run, 

the pilot input began at 8.5 seconds. Figure 4.9.13 shows the effect of adding just the DASE 

Model to the algorithm. The resulting cue (Z) does not contain any high frequency content 

because the turbulence channel is off. According to the plot, there is no cue from the aeroelastic 

model until the pilot begins to make a small input. The resulting motion from the DASE Model (

M AE Z ) is shown in the in the second plot along with the scaled pitch stick input (force). The 

pilot control input also increases the amplitude of the simulator excursion considerably. 

0 2 4 6 8 10 12

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

GHSCT Testing: Implementation 2

Time (Seconds)

Zdd (m/s2)

ZM (m)

Z (m)
66 

Figure 4.9.13 – GHSCT Testing, Time Response, ATC Off, DASE Model On 

The next configuration includes the ATC and DASE Model. Figure 4.9.14 is the same as 

the above figure, however, Z includes the addition of the turbulence channel. This is 

Implementation 1 with the DASE Model active; the TCF was not used. Once the pilot begins to 

make inputs, the output of the DASE Model ( M AE Z ) reaches much higher amplitude than the 

turbulence channel ( M T Z ). The turbulence cue present in Z is much less noticeable when 

combined with the aircraft flexibility. 

0 5 10 15 20

-0.1

0

0.1

GHSCT Testing: DASE Model On

Time (Seconds)

0 5 10 15 20

-0.4

-0.2

0

0.2

Time (Seconds)

ZM (m)

Z (m)

ZM+AE

Pitch Stick Force *0.1
67 

Figure 4.9.14 – GHSCT Testing, Time Response, ATC On, DASE Model On 

 

Early in the run, the turbulence motion added by the ATC is very apparent in the cue. 

Once the flexibility model is excited by pilot input, the motion due to turbulence is not as visible. 

This offline testing simulates the ATC to allow visualization of the full algorithm. Normally the 

pilot would be responding to the cue produced by the ATC, thus changing the dynamics of their 

input. Here, the ATC is added in just to analyze the change in vertical response. 

Offline testing shows distinct motion cues from both the turbulence channel and 

aeroelastic model are now part of the cue that drives the motion base. This output will change 

the dynamics of the motion cueing algorithm and potentially improve the simulation. The real0 5 10 15 20

-0.1

0

0.1

GHSCT Testing: DASE Model On

Time (Seconds)

0 5 10 15 20

-0.1

-0.05

0

0.05

0.1

Time (Seconds)

ZM (m)

Z (m)

ZM+AE

ZM+T
68 

time simulation discussed in Chapter 5 takes into account the response of the pilot with the new 

augmented cues and looks to validate the algorithm.

Modifications were made to the NASA Adaptive Algorithm to include the turbulence 

channel. This version of the cueing algorithm already comprised the aeroelastic model. In order 

to do this, a subroutine needed to be added that calculates the output of the ROAC. This output 

was then scaled by a gain term. This gain term was added to the GUI of the simulation for 

modification between runs when tuning the algorithm. The output of the turbulence channel was 

combined with the DOF output of the primary flight channel. This is the same output that is 

augmented by the DASE Model output. Fortran subroutines that comprise the turbulence 

channel can be found in Appendix B. There, the added code to apply the output of the ATC is 

also shown. 

 
69 

5. Design of Experiments 

5.1 Introduction 

Experiments were designed to demonstrate the efficacy of the augmented cueing channel 

and the effect on pilot control, in order to gain insight into the response of the pilot to the newly 

developed turbulence channel and aeroelastic model. These experiments have been divided into 

two studies: the Large Civil Transport study and the Generic High-Speed Civil Transport study. 

Three experiments were conducted under each study. The Augmented Turbulence Channel was 

implemented on both aircraft; only the GHSCT is outfitted with the aeroelastic channel. 

This chapter contains a detailed description of each study, including the method by which 

the pilot will subjectively evaluate the experiments. Chapter 6 contains the quantitative results 

and subjective data as well as analyses of these real-time experiments. 

5.2 Background Information 

The studies were conducted on two flight simulators, each at NASA Langley Research 

Center. The Visual Motion Simulator, or VMS, is a general-purpose flight simulator with a 

synergistic motion base. It has a 60 inch usable stroke and uses six hydraulic actuators to 

provide the pilot with full six degree-of-freedom motion (Telban et al. (a)). Figure 5.2.1 shows 

the VMS. 
70 

Figure 5.2.1 – Visual Motion Simulator (Courtesy of NASA LaRC) 

The current setup comprises six heads-down CRT displays, providing the pilot with 

aircraft state, engine information, and navigation and map display. There are also numerous 

instruments and gauges on the instrument panel. Figure 5.2.2 shows the inside the cockpit view. 

Figure 5.2.2 – Visual Motion Simulator Flight Deck (Courtesy of NASA LaRC) 

The second flight simulator is part of the newly developed NASA Cockpit Motion 

Facility, or CMF. This facility has one motion system that has the ability to accept 
71 

interchangeable flight decks. There are four fixed-base sites that allow for no-motion 

experiments to be conducted. Figure 5.2.3 shows an artist’s concept of the facility, with three 

fixed-base simulators and one flight deck mounted on the motion system. 

Figure 5.2.3 – Artist Rendition of Cockpit Motion Facility (Courtesy of NASA LaRC) 

The motion system is a six degree-of-freedom, synergistic motion base with six 76 inch 

hydraulic actuators with 72 inch usable stroke. This state of the art motion system utilizes 

advanced control laws that allow for a bandwidth of up to 2.5 Hz. Future advancement of this 

control logic will allow for a bandwidth over 5 Hz. Each of the cockpits is equipped with an 

image generator and display hardware, flight input control loaders, and a full instrument panel. 

The flight deck that was used in this study was the Generic Flight Deck (GFD), shown in Figure 

5.2.4. It uses four CRT/beam splitter/mirror collimated displays to provide the pilots with an out 
72 

the window view. The GFD instrument panel uses LCD panels and gauges to display sensor 

readings and other information. 

Figure 5.2.4 – Generic Flight Deck on CMF motion system (Courtesy of NASA LaRC) 

5.3 Tuning the Augmented Turbulence Channel (ATC) 

The gain in the turbulence channel was tuned subjectively until the pilot was satisfied 

with the turbulence sensation. Each implementation of the ATC had to be tuned separately. The 

Large Civil Transport and GHSCT implementation required multiple tuning sessions, before the 

results were satisfactory. The VMS and CMF have very different control laws, and each motion 

base will filter the commanded motion slightly. Because of this, the algorithms had to be tuned 
73 

separately for each simulator as well. The GHSCT implementation was tuned on the CMF and 

VMS; the Large Civil Transport implementation was only tuned on the VMS. 

The gain tuning sessions were conducted subjectively. The pilot was provided with the 

Turbulence Reporting Criteria Table, from the FAA Airman’s Guide6

. This guide, shown in 

Appendix C, details criteria for each specific intensity: Light, Moderate, Severe, and Extreme. 

The specific criteria are separated into two categories: Aircraft Reaction and Reaction Inside the 

Aircraft. As the aircraft is flown through the turbulence field, the pilot monitors the instruments 

and notes the sensation he feels. The pilot also indicates whether or not there was any portion of 

time in which he/she was not in positive control over the aircraft. With all this information, the 

pilot can accurately assess the turbulence level. The turbulence is then described as Occasional, 

Intermittent, or Continuous. Light and moderate chop are also designated in the guide as 

turbulence that causes rapid and rhythmic bumps, without appreciable changes in altitude. 

According to the Airman’s Guide, if the aircraft is under moderate or severe turbulence, 

the reaction inside the aircraft specifies that unsecured objects are dislodged or tossed about. It 

is also states that occupants feel a certain amount of strain against their seatbelts. Due to the 

limitations of the simulator, enough motion could not be produced to actually displace unsecured 

objects. It was also difficult to produce any substantial strain against the seatbelts. It is possible 

that these criteria could be met using a simulator that can provide longer sustained acceleration, 

such as the Vertical Motion Simulator at NASA Ames. 

The ability to continue food service and for people to walk is also assessed under the 

reaction inside the aircraft category. Under light turbulence, walking and food service should be 

possible. In moderate turbulence, walking and food service should be difficult and they should 
74 

be impossible in severe turbulence. These criteria were met with each turbulence level, when 

tuning the Large Civil Transport. Some of the reaction inside the aircraft criteria could not be 

met when tuning the GHSCT. 

In order to support a rating of extreme turbulence, the aircraft reaction specification 

includes that the aircraft is violently tossed about and that control is practically impossible. It is 

also indicated that the turbulence may cause structural damage. To ensure the safety of the pilot 

and copilot, along with the simulator hardware, the algorithm was not tuned to meet the 

necessary measure for extreme turbulence. 

The Dryden turbulence model is designed to provide the aircraft with severe turbulence at 

level 6. The gain tuning of the algorithm ensured the most realistic severe turbulence cue was 

provided to the pilot at this level and that each possible criterion in the FAA Airman’s Guide was 

met. Since the ATC only supplements the vertical channel, the tuning was also intended to keep 

a balance of motion in each degree of freedom. It is not desirable for the vertical channel of 

turbulence to overshadow the other degrees-of-freedom. If the results of this experiment show 

that more balance between the heave and lateral channels is needed, the turbulence channel can 

be applied to other degrees-of-freedom. 

It was noted that the only way to achieve each possible criterion for moderate and severe 

turbulence intensities, the rotational turbulence model (MIL-F-8785 B) had to be active. When 

just translational turbulence encounters the aircraft, it does not induce as much workload on the 

pilot; he/she compensates for little changes in attitude. It is also noted that even in a severe 

turbulence field, without rotational cues, the aircraft was always in positive control. 
75 

The rotational turbulence model also adds realism to the simulation since, in reality, 

atmospheric turbulence does not have an even distribution; this causes attitude changes to the 

aircraft. The GHSCT had not been outfitted with the rotational turbulence model; consequently, 

most of the turbulence reporting criteria were not met during the tuning of the ATC or the 

experiments. 

Once the channel was tuned to ensure the turbulence met all possible criteria detailed in 

the FAA Airman’s guide, the experiments could be conducted. The resulting gain for the Large 

Civil Transport implementation was tuned to 0.13. The GHSCT implementation gain was tuned 

to 0.015 on the VMS and 0.04 on the CMF. Tuning was done subjectively, also the two 

simulators are very different. The reason the gain was so much lower for the GHSCT 

implementation is because the turbulence gust input to the ATC is in ft/s. For the Large Civil 

Transport implementation, the gust input is in m/s. The pilot considered the visual cues, force 

feel, instruments and many other cues when determining an appropriate gain. The gain tuning 

test plans for each aircraft are shown in Appendix D. 

5.4 Design of Experiments 

The experiments are intended to test the effect of the ATC on the pilot and determine 

whether or not the ATC adds realism to the simulation. Since the turbulence and aeroelastic 

augmentation channels modify the dynamics of the simulation, the pilot should respond by 

changing his/her flying characteristics. It is hypothesized that increasing the motion to the pilot 

will result in an increase in their overall effort, thereby, increasing their workload. The 
76 

frequency of the pilot control will most likely shift as well, as they respond to higher frequency 

motion cues. 

Workload is a human factors concept that is used to describe how difficult a system is to 

control. It is dependent on the amount of work the pilot does and the frequencies at which he/she 

operates. One of the goals of any simulation is to provide an environment where the pilot exerts 

the same workload as they do in an actual flight. 

Three types of experiments were conducted during each study. The first task was to 

maintain straight and level flight at 30,000 feet above sea level for 2 minutes. The second was to 

perform a straight in approach and landing, starting on the glide slope. The third experiment was 

an offset approach and landing. For this experiment, the aircraft was trimmed on the localizer 

and glide slope of the runway. The pilot was then directed to switch to a parallel runway and 

complete the approach. 

The Cooper-Harper Rating Scale was used to rate certain tasks in each experiment. 

Cooper-Harper Ratings (CHR) are a set of criteria used by test pilots and flight test engineers to 

evaluate the handling qualities of an aircraft. The scale ranges from 1 to 10; where 1 indicates 

the best handling characteristics and 10 indicates the worst. 

Cooper-Harper Ratings were given during each of the experiments. For Experiment 1, 

the pilot provided a CHR on the task of maintaining level flight at the prescribed altitude. When 

conducting the two landing experiments (Experiments 2 and 3), the pilot provided a CHR on the 

task of maintaining the glide slope and a CHR on the touchdown. With regard to Experiments 2 
77 

and 3, it is noted that pilots would never be intentionally directed to land in severe turbulence. 

See Appendix E for the Cooper-Harper Rating Table. 

Each experiment was conducted without turbulence and at different turbulence 

intensities. These included light, moderate, and severe; which correspond to Dryden levels 2, 4, 

and 6. Between runs, the structure of the algorithm was changed to represent three different 

configurations. Figure 5.4.1 shows the different configurations for running the experiments with 

the Large Civil Transport. Figure 5.4.2 shows each configuration of the GHSCT. 

Configuration 1 used just the primary flight channel. This did not provide the pilot with 

any augmented cues. Configuration 2 applied the Augmented Turbulence Channel and 

Configuration 3 applied both the ATC and DASE model. Since the DASE model is only 

available on the GHSCT aircraft, Configuration 3 was not applied when running the Large Civil 

Transport. 
78 

Figure 5.4.1 – Large Civil Transport Experiment Configuration Block Diagrams 

Nonlinear

A/C Model

(LCT)

 Modified NASA Adaptive

Motion Cueing Algorithm

Pilot Input

Turbulence (G)

Image Generation

Motion

Base

X A



 X M

 X M





Instruments

Configuration 1: LCT

Nonlinear

A/C Model

(LCT)

Reduced

Order

A/C Model



Gain

Modified NASA Adaptive

Motion Cueing Algorithm

Pilot Input

Turbulence (G) Motion

Base

X A





X AT

 X M T



X M

 X M





+

+

Configuration 2: LCT

Image Generation

Instruments
79 

Figure 5.4.2 – GHSCT Experiment Configuration Block Diagrams 

A/C Model

(GHSCT Rigid Body

Model)



Pilot Input

Turbulence

NASA Adaptive

Motion Cueing

Algorithm

Motion

Base

Reduced

Order

A/C Model

Gain

X A





X AT



X M T



X M

 X M





Image Generation

Instruments

+

+

A/C Model

(GHSCT Rigid Body

Model)



Pilot Input

Turbulence

NASA Adaptive

Motion Cueing

Algorithm

DASE Aeroelastic Model

Motion

Base

Reduced

Order

A/C Model

Gain

X A





X AT



X M T



X M AE



X M

 X M





Image Generation

Instruments

+

+

+

Configuration 1: GHSCT

A/C Model

(GHSCT Rigid Body

Model)



Pilot Input

Turbulence

NASA Adaptive

Motion Cueing

Algorithm

Motion

Base

X A



 X M

 X M





Image Generation

Instruments

Configuration 2: GHSCT

Configuration 3: GHSCT
80 

The runs were randomized based on both the turbulence level, and whether or not the 

ATC or DASE model was turned on. There were seven specific runs for each experiment with 

the Large Civil Transport, making a total of 21 runs. There were 10 runs for each experiment 

with the GHSCT, which had a total of 30 runs. Table 5.4.1 shows the run order and conditions 

for the GHSCT experiments. Appendix F details the run conditions and the randomized order 

that was followed along with the experimental protocol for each implementation. 

 
81 

Table 5.4.1 – Run order and conditions for GHSCT experiments 

Run

Order

Run Number ‐ 

Data file number Configuration Turbulence level

1 1 OFF

2 26

3 14

4 32

5 16

6 12

7 34

8 24

9 22

10 3 6

1 24

2 12

3 14

4 16

5 32

6 36

7 26

8 34

9 1 OFF

10 2 2

1 16

2 34

3 24

4 26

5 1 OFF

6 12

7 22

8 36

9 14

10 3 2

Run Conditions: Generic High Speed Transport

Experiment 1: Maintain straight and level flight at 30000 feet ASL

Experiment 3: Offset approach and landing

Experiment 2: Straight in approach and landing
82 

Data were recorded during the runs, specifically; all pilot control inputs, along with the 

aircraft model state, and simulator sensor outputs. Both simulators have six platform 

accelerometers and a package of sensors at the centroid of the motion base. On the VMS, this 

centroid package contains three translational accelerometers (X,Y,Z) and three rotational 

accelerometers (, ,   ). The CMF has a similar package at the centroid; however there are 

three rotational rate sensors instead of three rotational accelerometers. The simulators also have 

sensors on the actuators. The VMS and CMF have position transducers; the CMF has 

accelerometers as well. Due to control feel system problems on the CMF simulator, the 

experiments for both the Large Civil Transport and the GHSCT were conducted on the VMS. 

The experiments were conducted according to the randomized order shown in Appendix 

F. After each run, the pilot indicated the turbulence intensity sensation along with the turbulence 

reporting criteria from the FAA Airman’s Guide that was fulfilled. Lastly, a Cooper-Harper 

Rating was provided for specific tasks. Chapter 6 contains the subjective analysis along with the 

quantitative results of each experiment, from both studies. 

 
83 

6. Results of Real-Time Experiments 

6.1 Introduction 

This chapter is separated into two main sections: the Large Civil Transport study and the 

GHSCT study. Each study is then broken down into the subjective analysis and quantitative 

results of the experiments. Each study is comprised of three different experiments. The 

experiments include an up and away case, where the pilot maintains altitude through atmospheric 

turbulence, a straight-in approach and landing, and an offset approach and landing. 

As described in Chapter 5, the subjective analysis is performed on the information 

provided by the pilot after each run. The quantitative results are derived from data collected 

during each run. 

Separate conclusions will be drawn from each set of experiments. The next two sections 

(6.2 and 6.3) provide analysis on the Large Civil Transport study. The GHSCT study is shown in 

sections 6.4 and 6.5. Each of these sections is broken down into subsections that describe the 

results of one experiment. This was done to allow separate conclusions to be drawn from each 

set of runs. 

6.2 Large Civil Transport Study – Subjective Analysis 

In order to assess the accuracy of the turbulence channel, a subjective evaluation of the 

turbulence field was conducted after each run. This included an intensity rating (Light, 

Moderate, or Severe) and reporting on all criteria from the FAA Airman’s Guide. As mentioned 

in the Design of Experiments (section 5.4), Cooper Harper Ratings (CHR) were used to rate the 
84 

handling qualities of each run. The results of the Large Civil Transport implementation are 

provided in the following sections. 

6.2.1 Large Civil Transport Experiment 1: Subjective Analysis 

The Large Civil Transport subjective analysis on Experiment 1 provided insight into the 

effect of adding the turbulence channel. Even though the order of runs was randomized, the pilot 

was able to correctly identify the turbulence intensity for all runs. Table 6.2.1 shows the actual 

turbulence level and the perceived intensity by the pilot, for Experiment 1. It also details the 

CHR given by the pilot, on the specific task of maintaining altitude while flying through the 

turbulence field. Configuration 1 represents the simulation without the turbulence channel; 

configuration 2 employs the ATC. 

Table 6.2.1 – Large Civil Transport Experiment 1, Turbulence Intensity and CHR 

Run

Number

Turbulence

Level Configuration ReportedIntensity

14 2 Moderate,

Continuous

6

26 1 Severe,

Continuous

8

32 1 Light,

Continuous

3

4 OFF 1 none 1

52 2 Light,

Continuous

3

64 1 Moderate,

Continuous

5

76 2 Severe,

Continuous

8

CHR:

Maintaining

Altitude

Experiment 1 – LCT
85 

For the case with no turbulence, the pilot gave a CHR of 1. This indicates that pilot 

compensation was not a factor in maintaining altitude during this run. For each of the light 

turbulence runs (3 and 5), the pilot rated the task as requiring minimal pilot compensation to 

achieve desired performance. At the moderate intensity, when adding the turbulence channel 

(configuration 2) the pilot reported an increase in the necessary compensation to perform the 

task, specifically from a CHR of 5 (Run 6) to a CHR of 6 (Run 1). Since the pilot was unaware 

of the specific conditions of each run, this indicates that the pilot perceived a difference when 

adding the channel at this turbulence level and that more compensation was required to complete 

the task. In the two cases with turbulence level 6, the pilot reported the intensity correctly 

(severe), however there was no change in the CHR. 

The next table, Table 6.2.2, shows the pilot’s reporting on the criteria from the FAA 

Airman’s Guide. The Airman’s guide has specific details with regard to the aircraft reaction at 

different turbulence levels. Once the intensity was estimated, the pilot then stated yes or no to 

the different reaction criteria. It was no surprise that the aircraft reaction criteria were met with 

and without the turbulence channel, as the ATC does not affect the instruments or dynamics of 

the aircraft model. 

As mentioned earlier, the simulator was unable to cause object displacement or provide 

the pilot with much seatbelt strain. For this reason, these two “Reaction Inside Aircraft” criteria 

were not met in any runs. For assessing food service and walking, the pilot could respond with 

possible, difficult, or impossible. For Run 1, even though the turbulence intensity was moderate, 

it was determined that food service and walking were not possible. In this case, the ATC was 
86 

turned on. This increase to impossible under moderate turbulence also supports the increased 

CHR given for this run. 

 Table 6.2.2 – Large Civil Transport Experiment 1, FAA Turbulence Reporting Criteria 

6.2.2 Large Civil Transport Experiment 2: Subjective Analysis 

The subjective results of Experiment 2 are shown in Table 6.2.3. The pilot’s task for this 

experiment was to maintain the glide slope and perform a touchdown. During each run, the pilot 

aimed for the same landing zone. For this experiment, a CHR was given for two tasks: 

maintaining the glide slope, and performing the touchdown. During the severe turbulence runs, 

the motion base was prone to tracking errors. From looking at the CHR, the workload of the 

Run

Number Altitude Attitude Airspeed Controllability SeatbeltStrain

Object

Displacement

Food Service

& Walking

1 yes yes yes yes no no impossible

2 yes yes yes yes no no impossible

3 yes yes yes yes no no possible

4 n/a n/a n/a n/a n/a n/a possible

5 yes yes yes yes no no possible

6 yes yes yes yes no no difficult

7 yes yes yes yes no no impossible

Experiment 1 – LCT

Aircraft Reaction Criteria Reaction Inside Aircraft
87 

pilot control input is substantially greater when performing a landing under these extreme 

conditions, when compared to maintaining altitude (Experiment 1). 

Each turbulence intensity was identified correctly by the pilot, just as in Experiment 1. In 

the case with no turbulence (Run 12), the pilot rated the task of maintaining the glide slope with 

a CHR of 2 and the landing task with a CHR of 3. During Run 8 (light turbulence, No ATC), the 

pilot increased his CHR to 4 and 6, for the glide slope and landing rating, respectively. For Run 

13, the light turbulence run with the ATC on, the CHRs were both reduced to 3. 

A possible explanation for this decrease in the necessary pilot compensation is that Run 8 

was the first run of the set of straight-in approach and landing experiments. The pilot was more 

familiarized with the task by the later runs, and this could have contributed to the two decreased 

CHRs of Run 13. Another hypothesis for the decrease in the CHR is that the motion cues 

provided the pilot with better correlation with the visual and instrument cues. With the ATC on, 

the aircraft was easier to control. 
88 

Table 6.2.3 – Large Civil Transport Experiment 2, Turbulence Intensity and CHR 

The aircraft reaction and reaction inside the aircraft were both indicative of the correct 

perceived turbulence level. Table 6.2.4 shows the criteria which were met for each specific run. 

As with Experiment 1, object displacement did not occur and seat belt strain was never great 

enough to meet the criteria. Walking was consistently rated as possible in light turbulence, 

difficult in moderate turbulence, and impossible in severe turbulence. 

Pilot comments indicated increased difficulties with controllability, when performing the 

more control intensive task of landing the aircraft. In each of the severe cases, the pilot would 

remain out of positive control periodically during the runs. This happened more frequently than 

when conducting Experiment 1, and justified an increase of both CHRs to 9 (Runs 10 and 14). 

Run

Number

Turbulence

Level Configuration ReportedIntensity

82 1 Light,

Continuous

4 6

94 1 Moderate,

Continuous

7 7

10 6 1 Severe,

Continuous

9 9

11 4 2 Moderate,

Continuous

7 6

12 OFF 1 none 2 3

13 2 2 Light,

Continuous

3 3

14 6 2 Severe,

Continuous

9 9

CHR:

Maintaining

Glideslope

CHR:

Performing

Landing

Experiment 2 – LCT
89 

For Runs 9 and 11 (moderate turbulence), it appears that adding the ATC decreased the amount 

of compensation required to perform the landing task. The landing task in general induces a 

much greater workload from the pilot; here, the addition of the cue from the ATC resulted in a 

decrease in the CHR from 7 to 6 between those two runs. 

Table 6.2.4 – Large Civil Transport Experiment 2, FAA Turbulence Reporting Criteria 

Run

Number Altitude Attitude Airspeed Controllability SeatbeltStrain

Object

Displacement

Food Service

& Walking

8 yes yes yes yes no no possible

9 yes yes yes yes no no difficult

10 yes yes yes yes no no impossible

11 yes yes yes yes no no difficult

12 n/a n/a n/a n/a n/a n/a n/a

13 yes yes yes yes no no possible

14 yes yes yes yes no no impossible

Experiment 2 – LCT

Aircraft Reaction Criteria Reaction Inside Aircraft
90 

6.2.3 Large Civil Transport Experiment 3: Subjective Analysis 

Experiment 3 required the most workload of all the experiments. This task, in itself, is a 

difficult maneuver; performing it under any level of atmospheric turbulence only increases the 

difficulty. The pilot always began the maneuver of switching runways at the same time during 

each run. This kept the task as consistent as possible. Table 6.2.5 shows the reported intensities 

for each run and the CHR. 

When completing the task without any turbulence (Run 19), the pilot reported a CHR of 3 

for both maintaining the glide slope and touchdown performance. There is an increase in one 

point for the CHR of the glide slope, compared to the on approach landing run (Experiment 2) 

without turbulence. When conducting the offset approach, the pilot has to align the aircraft on 

the glide slope of the parallel runway during the maneuver. 

Table 6.2.5 – Large Civil Transport Experiment 3, Turbulence Intensity and CHR 

Run

Number

Turbulence

Level Configuration ReportedIntensity

15 6 1 Severe,

Continuous

9 9

16 4 1 Moderate,

Continuous

7 7

17 2 1 Light,

Continuous

3 4

18 4 2 Moderate,

Continuous

6 7

19 OFF 1 none 3 3

20 2 2 Light,

Continuous

3 4

21 6 2 Severe,

Continuous

9 9

Experiment 3 – LCT CHR:

Maintaining

Glideslope

CHR:

Performing

Landing
91 

In this experiment, all possible aircraft reaction and reaction inside aircraft criteria were 

met for each run. As with the other experiments, and the tuning sessions, seatbelt strain and 

object displacement criteria were not met. For the light turbulence runs (17 and 20), the pilot 

reported the same level of compensation, with and without the ATC. At this low level, the added 

motion from the ATC doesn’t appear to increase the pilot workload. 

The ATC has a different effect in level 4 turbulence. It appears that performing this 

experiment with the ATC on is slightly easier, specifically maintaining the glide slope. Run 16, 

ACT off, have a CHR of 7 for both ratings. Run 18 have a CHR of 6 and 7, for the glide slope 

and touchdown, respectively. This was also noticed in experiment 2 at level 4 turbulence (Figure 

6.2.3). The CHR of the touchdown decreased from 7 to 6 with the addition of the ATC. 

Similarly to the Experiment 2 runs, the severe turbulence runs (15 and 21) acquired 

ratings of 9 for each task. A CHR of 9 indicates that the strongest level of pilot compensation 

was necessary to complete the run. Even though this task is much more difficult, a CHR of 9 is 

justified because a CHR of 10 would indicate that control was lost during the entire run. 

Momentary lapse in control is a byproduct of severe turbulence, and does not necessarily warrant 

a CHR of 10 on the handling qualities of that run since the task was completed. 

Some of the severe turbulence runs were repeated because of motion base failures. These 

motion base failures were because of tracking errors, or the excursion limits of the motion base 

forced the shutdown. After one or two repeat attempts at this turbulence level, the runs were all 

completed. 
92 

The reaction criteria for Experiment 3 are shown below in Table 6.2.6. When compared 

with the other experiments, the fluctuations in altitude, attitude, airspeed, and controllability 

were larger in all cases. The pilot control input is greater throughout these runs, and is definitely 

contributing to the increase in the amplitude of these measurements. This change in the pilot’s 

dynamics is explained in more detail in the quantitative results, section 6.3. 

Table 6.2.6 – Large Civil Transport Experiment 3, FAA Turbulence Reporting Criteria 

With regard to experiments 2 and 3, the task of landing the aircraft detracts from the 

pilot’s ability to perceive the turbulence level and gauge the control compensation necessary to 

land the aircraft. In both moderate turbulence cases, the pilot reported a decrease in the 

Run

Number Altitude Attitude Airspeed Controllability SeatbeltStrain

Object

Displacement

Food Service

& Walking

15 yes yes yes yes no no impossible

16 yes yes yes yes no no difficult

17 yes yes yes yes no no possible

18 yes yes yes yes no no difficult

19 n/a n/a n/a n/a n/a n/a n/a

20 yes yes yes yes no no possible

21 yes yes yes yes no no impossible

Experiment 3 – LCT

Aircraft Reaction Criteria Reaction Inside Aircraft
93 

necessary compensation to maintain the glide slope when the turbulence channel was added. 

This can also be due to an improvement in the pilot’s understanding of the situation with the 

additional cue. 

6.3 Large Civil Transport Study – Quantitative Results 

The high volume of data being recorded during the experiments should allow formulation 

of conclusions with regard to the effect of the Augmented Turbulence Channel. The ATC adds a 

substantial amount of high frequency motion to the motion base, and the pilot should react to 

this. The majority of the results are shown in the frequency domain. Data from both pilot 

control input and aircraft reaction are analyzed in the following sections. Each section contains 

the results of one experiment. 

6.3.1 Large Civil Transport: Experiment 1 – Quantitative Results 

The task for Experiment 1 was to maintain straight and level flight through various 

turbulence levels. This was the least control intensive task, and resulted in the most conclusive 

data on the effects of the ATC. In order to maintain altitude, the pilot will take note of the 

baseline altitude, and then focus on the vertical velocity of the aircraft. If the aircraft begins to 

diverge from the set altitude, the pilot performs a control input intended to cancel out any 

growing vertical rates. Shortly after, the vertical speed will indicate that aircraft is returning to 

the task defined altitude. Once the aircraft has returned to the prescribed altitude, the pilot will 

compensate if drifting occurs again. 
94 

At mid and high levels of turbulence, this is an extremely intricate task. Large vertical 

wind gusts cause a great deal of fluctuation in altitude and vertical velocity. It requires the pilot 

to constantly focus on various instruments, and remain active in the control loop for the duration 

of the run. 

When conducting frequency analysis, it is desirable to have datasets that are as stationary 

as possible. Since the pilot was constantly monitoring vertical velocity, stationarity was 

evaluated by inspecting the mean square of the aircraft vertical velocity throughout the run. The 

runs from Experiment 1 averaged around 6200 data points; they were cut into 128 point 

segments, producing about 50 windows of data. The mean square was calculated for each 

window. With this information, the portion of the signal with the most stationarity can be 

identified. 

Figure 6.3.1 is a plot of the normalized mean square from Runs 1-7 (Experiment 1). The 

plot was normalized by dividing each separate signal by its maximum value. As the aircraft 

travels through different turbulence levels, the vertical velocity begins to grow. Consequently, 

the mean square of the vertical velocity is also amplified. Normalizing the data allows each run 

to be plotted together, and shows the sections of the runs that have the least variation. 
95 

Figure 6.3.1 – Large Civil Transport Experiment 1, Normalized Mean Square of Vertical 

Velocity 

The beginning of each signal was trimmed by 25 windows, removing 3200 data points. 

The end of each run was also trimmed so each signal was 19 windows long, or 2432 data points. 

Removing this portion of data isolated run information where frequency analysis could be 

performed effectively. Frequency analysis was conducted on the data between windows 26 and 

45. 

Except for a few outlying points in runs 2, 3, and 6, the mean square fluctuates far less 

than it did during other portions of the runs. The reduction of the irregularity of the mean square 

can be attributed to the pilot’s ability to better understand the task towards the end of the run. 

0 10 20 30 40 50

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Experiment 1: Normalized Mean Square, Vertical Velocity

Window Number

Run1

Run 2

Run 3

Run 4

Run 5

Run 6

Run 7
96 

Since the turbulence is continuous, the pilot control response is what changed as the runs 

progressed. Even though this analysis does not prove stationarity, it ensures the most credible 

frequency analysis. 

Another assumption is that the selected portion of data is long enough to be ergodic. The 

simulation was run at 50 Hz, the portion of data that was analyzed includes about one minute of 

data per run. Accurate frequency analysis can be conducted on these data sets. Averaging and 

windowing were also performed to allow for smooth plots. 

The first set of analysis is on the pilot control input. This was done on the pitch stick 

input, in the frequency domain. Pitch stick was chosen because the task of maintaining altitude 

requires compensation primarily in the pitch attitude of the aircraft. The next set of plots show 

the Power Spectrum Density (PSD) of the pilot input for specific runs conducted during 

Experiment 1. In this case, the units of power are

2 (% of stick deflection)

Hz . 

Figure 6.3.2 shows the light turbulence runs with the turbulence channel off (Run 3) and 

on (Run 5). The top plot shows the PSD of the pitch stick input, the bottom plot contains the 

same data, except it is normalized. Without the ATC, the peak power is 0.05 and at about 0.1 

Hz. Turning on the ATC causes the amplitude of this peak to drop to 0.03. There is also an 

increase in the power of most frequencies above 0.5 Hz. Inspecting the normalized PSD plot 

shows that there is almost no shift in frequency domain of the peak power. 
97 

Figure 6.3.2 – Large Civil Transport Experiment 1, Light Turbulence, PSD of Pitch Stick Input 

and Normalized PSD of Pitch Stick Input 

The moderate turbulence runs were plotted next, in Figure 6.3.3. Looking at this 

turbulence level, there is definitely a change in the characteristics of the pilot when adding the 

ATC. The power increases across almost all frequencies. The peak power is located at 0.48 Hz, 

the same frequency for both runs. The maximum power of the pilot input, with the turbulence 

channel on, is more than 4 times greater than with the ATC off. It appears the pilot is responding 

in most of the same frequencies, but increasing his amplitude as a response to the added motion 

cue. 

The peak power of these runs is about 0.3, a substantial increase over the light turbulence 

cases (Figure 6.3.2). The peak frequency of the pilot has also shifted from 0.1 Hz to almost 0.5 

Hz. 

0 0.5 1 1.5 2 2.5 3

0

0.02

0.04

PSD of Pitch Stick Input - Turbulence lvl 2

Power

0 0.5 1 1.5 2 2.5 3

0

0.5

1

Normalized PSD of Pitch Stick Input - Turbulence lvl 2

Power

Frequency (Hz)

Run 3 - ATC OFF

Run 5 - ATC On

Run 3 - ATC OFF

Run 5 - ATC On
98 

Figure 6.3.3 – Large Civil Transport Experiment 1, Moderate Turbulence, PSD of Pitch Stick 

Input and Normalized PSD of Pitch Stick Input 

The response of the pilot under severe turbulence had the most apparent change when 

adding the ATC. Figure 6.3.4 shows the PSD of the pitch stick input as well as the normalized 

PSD. Here, there was a substantial decrease in the power of the pilot input, across all 

frequencies. The peak power without the ATC (Run 2) is 1.3, and drops to 0.1 when the ATC is 

turned on (Run 7). Adding the ATC also causes the peak power of the pilot to shift from 0.7 Hz 

to 0.4 Hz. 

Inspecting the runs with the ATC off, going from moderate to severe turbulence 

increased the peak power from 0.3 to 1.3. In the frequency domain, this peak shifted from about 

0 0.5 1 1.5 2 2.5 3

0

0.1

0.2

0.3

PSD of Pitch Stick Input - Turbulence lvl 4

Power

0 0.5 1 1.5 2 2.5 3

0

0.5

1

Normalized PSD of Pitch Stick Input - Turbulence lvl 4

Power

Run 6 - ATC OFF

Run 1 - ATC On

Run 6 - ATC OFF

Run 1 - ATC On
99 

0.5 Hz to 1.3 Hz. Severe turbulence causes the pilot to respond at higher frequencies along with 

additional amplitude, when compared to both moderate and light turbulence. 

Figure 6.3.4 – Large Civil Transport Experiment 1, Severe Turbulence, PSD of Pitch Stick Input 

and Normalized PSD of Pitch Stick Input 

Another useful piece of information is contained in the mean square of each run. The 

mean square is calculated by integrating the magnitude of the PSD across the frequency 

spectrum. Table 6.3.1 shows the mean square and details the conditions for each run. In this 

table, the run numbers are organized by turbulence level. This is done to allow for comparison 

between the different mean squares when the augmented channel is turned on. Turning on the 

ATC at level two turbulence (Runs 3 and 5) reduces the mean square slightly. This slight change 

0 0.5 1 1.5 2 2.5 3

0

0.5

1

1.5

PSD of Pitch Stick Input - Turbulence lvl 6

Frequency (Hz)

Power

0 0.5 1 1.5 2 2.5 3

0

0.5

1

Normalized PSD of Pitch Stick Input - Turbulence lvl 6

Frequency (Hz)

Power

Run 2 - ATC OFF

Run 7 - ATC On

Run 2 - ATC OFF

Run 7 - ATC On
100 

can be attributed to the fact that at such a low turbulence level, the necessary pilot input to 

perform the task is quite small. 

Table 6.3.1 – Mean Square of Pitch Stick Input, Large Civil Transport Experiment 1 

Under level 4 turbulence (Runs 6 and 1); turning the ATC on increases the mean square 

by a factor of 4.5. This substantial increase was expected; inspecting the PSD of the pilot input 

at turbulence level 4 (Figure 6.3.3) shows a major increase in power between the two runs. 

In the level 6 turbulence runs, the trend shown in the mean square of the level 4 runs is 

reversed. Between runs 2 and 7, the mean square is reduced by a factor of 9 when adding the 

ATC. This ambiguity can be explained by the pilot’s decrease in power across all frequencies. 

Referring back to Figure 6.3.3, the entire Run 7 PSD curve remains at a much lower amplitude 

than the Run 2 plot, which has the ATC off. 

Run

Number

Turbulence

Level Configuration Mean Square

3 2 (1) ACT Off 0.02

5 2 (2) ACT On 0.019

6 4 (1) ACT Off 0.054

1 4 (2) ACT On 0.243

2 6 (1) ACT Off 0.817

7 6 (2) ACT On 0.069

Experiment 1 – LCT
101 

Not many conclusions can be drawn by inspecting the pilot input alone. In order to 

analyze whether or not the pilot has a better understanding of the situation when adding the 

ATC, aircraft response data must also be inspected. 

As specified earlier in this section, the pitch stick input is a direct response to the vertical 

velocity of the aircraft along with the motion the pilot senses in the vertical channel. If the pilot 

is in fact responding to the vertical velocity of the aircraft then there should be some correlation 

between the two. This type of analysis can be done a number of ways. Some methods include 

analyzing the remnant between the two signals, the cross-correlation function, and the coherence 

function. 

The coherence function is a normalized, frequency domain plot that demonstrates 

correlation between two signals at each frequency. A coherence of 1.0 indicates that the two 

signals are highly correlated. It is hypothesized that the addition of the augmented turbulence 

channel will provide the pilot with missing turbulences cues and enhance the correlation between 

his control input and the response of the aircraft. 

The literature indicates that the coherence function is used to validate sets of data and that 

if the data have a coherence less than 0.6, the data are not correlated enough to be evaluated. It 

is noted that in the cases with Experiments 2 and 3, there is difficulty reaching a coherence 

function of this magnitude. The task requires the pilot to focus on more than just atmospheric 

turbulence, causing his control input to be less correlated to the response of the aircraft vertical 

velocity. 
102 

The next set of plots displays the coherence function between the pilot control input 

(pitch stick) and aircraft vertical velocity. As with the PSD plots, the coherence functions are 

plotted against each other based on turbulence level. This is done to analyze whether adding the 

ATC improves the coherence function, or reduces it. 

Figure 6.3.5 shows the two coherence functions from the light turbulence runs along with 

the PSD of the pitch stick control input. Turning the ATC on yields a much higher peak 

coherence, an increase from 0.64 to 0.79. The frequency range with the most correlation is 

between 0.5 Hz and 3 Hz. This frequency range also shows a difference in the pilot dynamics; 

with the ATC on, there is more power in the areas with increased coherence. 

Figure 6.3.5 – Coherence Function of Vertical Velocity and Pitch Stick Input; PSD of Pitch Stick 

Input, Large Civil Transport Experiment 1, Level 2 Turbulence 

10

-1

10

0

10

1 0

0.2

0.4

0.6

0.8

1

Coherence Function: Vertical Velocity and Pitch Stick Magnitude

Frequency (Hz)

10

-1

10

0

10

1 0

0.01

0.02

0.03

0.04

0.05

PSD of Pitch Stick Input

Frequency (Hz)

Power

Run 3 - Turbulence lvl 2, ATC Off Run 5 - Turbulence lvl 2, ATC On
103 

The coherence function is distorted in the higher frequency range, over 5 Hz. Even with 

additional averaging and windowing, this distortion could not be removed from these plots. If a 

higher sampling rate were achieved by the hardware, this noise could be reduced with more post 

processing. Additionally, this portion of the plot does not provide useful information as the 

pilot’s operating frequency peaks near 3 Hz, and the noise doesn’t become overpowering until 5 

Hz. 

Adding the ATC to the moderate turbulence runs also results in an increase of the peak 

coherence (Figure 6.3.6). This increase was from 0.85 to 0.86, much less noticeable than the 

increase seen in the light turbulence runs. One major result of adding the ATC at this level is the 

increase in coherence over almost the entire frequency range. The pilot is much more correlated 

with the aircraft vertical velocity, especially in the high frequencies (1.0 – 3.0 Hz). The max 

coherence of almost 0.9 indicates a highly correlated signal. As with the other coherence 

function plot, there is quite a bit of distortion after about 5 Hz. 
104 

Figure 6.3.6 – Coherence Function of Vertical Velocity and Pitch Stick Input; PSD of Pitch Stick 

Input, Large Civil Transport Experiment 1, Level 4 Turbulence 

Under moderate turbulence, the dynamics of the pilot change substantially throughout the 

entire frequency range. With the ATC on, the pilot exerts more power in higher frequencies. 

This added power also improves the coherence at these frequencies. The only difference 

between these two runs is the augmented channel; therefore, this additional power is a result of 

the additional motion cue the pilot is receiving. 

The cue provides the pilot with more information on the turbulence field, and his reaction 

is more correlated with the vertical velocity, which is what the pilot is compensating for during 

the run. At this turbulence level, the cues from the ATC induce a more correlated reaction by the 

pilot. 

10

-1

10

0

10

1 0

0.2

0.4

0.6

0.8

1

Coherence Function: Vertical Velocity and Pitch Stick Magnitude

Frequency (Hz)

10

-1

10

0

10

1 0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

PSD of Pitch Stick Input

Frequency (Hz)

Power

Run 6 - Turbulence lvl 4, ATC Off Run 1 - Turbulence lvl 4, ATC On
105 

The power of the pilot input was severely attenuated after adding the ATC and running 

with level 6 turbulence. Not only was the peak power of the pilot input much lower; it shifted to 

a lower frequency. Inspecting the coherence function across the same frequency range provides 

some insight into the effect of this change in pilot dynamics (Figure 6.3.7). 

In the case with the ATC off, the pilot is quite correlated with the vertical velocity of the 

aircraft. The maximum coherence is 0.87 with the ATC off; it drops to 0.76 with the ATC on. 

The other insightful characteristic of the coherence functions is the drop in magnitude across the 

entire operating range of the pilot after adding the ATC. The coherence drops to 0.3 at .9 Hz, 

where it once was 0.7 without the ATC. 

Figure 6.3.7 – Coherence Function of Vertical Velocity and Pitch Stick Input; PSD of Pitch Stick 

Input, Large Civil Transport Experiment 1, Level 6 Turbulence 

10

-1

10010

1 0

0.2

0.4

0.6

0.8

1

Coherence Function: Vertical Velocity and Pitch Stick Magnitude

Frequency (Hz)

10

-1

10010

1 0

0.2

0.4

0.6

0.8

1

1.2

1.4

PSD of Pitch Stick Input

Frequency (Hz)

Power

Run 2 - Turbulence lvl 6, ATC Off Run 7 - Turbulence lvl 6, ATC On
106 

It can be seen in the PSD plot that the pilot reduces the power and frequency of his pitch 

stick input when the ATC is added. It is hypothesized that the lack of pilot input across this 

frequency range is because he simply cannot keep up with the added cue. It is possible the ATC 

is producing large amplitude motion cues that the pilot is unable to compensate. Without the 

ATC, the cue is strong enough to induce a reaction, but not overpowering the pilot. Once the 

motion is so large that it cannot be responded to, the pilot remains less active in the control loop. 

Chapter 6 provides possible solutions to rectify this ambiguity between the different turbulence 

levels. 

6.3.2 Large Civil Transport: Experiment 2 – Quantitative Results 

In Experiments 2 and 3, the pilot’s focus is not solely on compensating for disturbances 

due to atmospheric turbulence. In Experiment 2, the pilot has to maintain the glide slope as he 

descends towards the runway. The initial portion of this task is similar to maintaining altitude in 

Experiment 1. 

At the start of the run, the pilot takes note of the altitude, sink rate, and DME (Distance 

Measuring Equipment). The glide slope is then monitored; as glide slope error starts to build, 

pitch attitude is adjusted to change the vertical velocity of the aircraft. This will arrest the rate of 

departure from the glide slope and bring the error down to zero. As the aircraft approaches the 

runway, the corrections become smaller and more precise. At about 200 feet, the tracking task 

becomes purely visual; the pilot focuses on the landing site and ensures a proper touchdown. 

Glide slope error is ignored by the pilot for this portion of the run. 
107 

The task during the early portion of each run is the least control intensive. This is the 

portion of the dataset that will be analyzed. It is quite difficult to support a substantial amount of 

stationarity in these signals; however, the beginning of each run has the least fluctuation in the 

mean square of the vertical velocity. Figure 6.3.8 shows the normalized mean square for each 

128 point window, from the Experiment 2 Runs. 

Figure 6.3.8 – Large Civil Transport Experiment 2, Normalized Mean Square of Vertical 

Velocity 

There is quite a bit of fluctuation of the mean square, but according to this plot, the mean 

square is most stable until about 9 windows of data. Run 14 has some outlying points, but the 

rest remain the most stable during this time period. These runs are quite a bit shorter than the 

0 5 10 15 20 25

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Experiment 2: Normalized Mean Square, Vertical Velocity

Window Number

Run 8

Run 9

Run 10

Run 11

Run 12

Run 13

Run 14
108 

Experiment 1 runs; lasting about one minute. When cut at 9 windows, the runs are trimmed to a 

length of 23 seconds. The following analysis comes from that portion of data. 

The first set of analysis inspects the mean square of the pilot input. There is a definite 

trend in this data; the addition of the turbulence channel at different levels always brings about 

an increase in the mean square (Table 6.3.2). With light turbulence, the mean square went from 

0.191 to 0.319 (Runs 8 and 13). For moderate turbulence, the mean square increased from 0.437 

to 0.467 (Runs 9 and 11). The largest increase was observed when adding the ATC to the level 6 

turbulence run. The mean square increased from 1.039 to 1.69 (Runs 10 and 14). Run 14 also 

had the largest mean square of all the runs. This indicates that the pilot consistently works 

harder with the additional turbulence cue. 

Conditions for Run 12 allowed the pilot to land the aircraft without any turbulence turned 

on. The mean square of the pitch stick input was 0.014; a fraction of the mean square when 

flying through any turbulence intensity. 
109 

Table 6.3.2 – Mean Square of Pitch Stick Input, Large Civil Transport Experiment 2 

The next set of analysis is designed to inspect the correlation between the pilot input and 

the aircraft dynamics. Since the pilot is responding mainly to vertical velocity when performing 

this task just as with Experiment 1, the correlation between these two parameters was evaluated 

by inspecting the coherence function. This next set of plots shows the coherence of the two 

signals and details the PSD of the pilot input. Figure 6.3.9 is from Run 12, where the pilot lands 

the aircraft without any turbulence. 

Run

Number

Turbulence

Level Configuration Mean Square

8 2 (1) ACT Off 0.191

13 2 (2) ACT On 0.319

9 4 (1) ACT Off 0.437

11 4 (2) ACT On 0.467

10 6 (1) ACT Off 1.039

14 6 (2) ACT On 1.69

12 OFF n/a 0.014

Experiment 2 – LCT
110 

Figure 6.3.9 – Coherence Function of Vertical Velocity and Pitch Stick Input; PSD of Pitch Stick 

Input, Large Civil Transport Experiment 2, Turbulence Off 

Out of all the runs for Experiment 2, this produced the highest coherence, with a peak 

amplitude of 0.8 at 2 Hz. This frequency is in the upper range of the spectrum of pilot control 

input. The control input power at this portion is quite low, close to 0.002. The majority of the 

coherence function indicates a strong correlation between the pilot and the aircraft. Without any 

atmospheric turbulence, the pilot can devote all control inputs to countering the deviation from 

the glide slope, by monitoring the vertical velocity of the aircraft. 

At level 2 turbulence, the addition of the ATC causes the pilot to increase his power over 

almost all frequencies. The pilot’s maximum power increases from 0.4 to 0.7, and shifts to a 

10-1 100 101 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Coherence Function: Vertical Velocity and Pitch Stick Magnitude

Frequency (Hz)

10-1 100 101 0

0.005

0.01

0.015

0.02

0.025

0.03

PSD of Pitch Stick Input

Power

Frequency (Hz)

Run 12 - Turbulence Off
111 

higher frequency. Figure 6.3.10 shows the PSD of the pilot control input and the coherence 

function between the vertical velocity of the aircraft and the pitch stick input. The coherence 

function at this turbulence level shows a major improvement in the maximum coherence, from 

0.44 to 0.79. It is also noted that the PSD of the pilot input is much greater than the light 

turbulence runs from Experiment 1. 

Figure 6.3.10 – Coherence Function of Vertical Velocity and Pitch Stick Input; PSD of Pitch 

Stick Input, Large Civil Transport Experiment 2, Turbulence Level 2 

Both light turbulence runs are correlated with the vertical velocity of the aircraft. The run 

with the ATC on (Run 13) maintains a stronger coherence over most of the spectrum. The 

10-1 100 101

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Coherence Function: Vertical Velocity and Pitch Stick Magnitude

Frequency (Hz)

10-1 100 101

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

PSD of Pitch Stick Input

Power

Frequency (Hz)

Run 8 - Turbulence lvl 2, ATC Off Run 13 - Turbulence lvl 2, ATC On
112 

higher power in the pilot control input around 0.7 Hz appears to add to the pilot’s correlation 

with the vertical velocity of the aircraft. The coherence at lower frequencies, under 0.5 Hz, is 

deteriorated by the addition of the augmented channel. Just as the coherence plots from the 

analysis of Experiment 1, the signal is too noisy to be analyzed at frequencies over 5 Hz. 

Figure 6.3.11 shows the coherence function and PSD of the moderate turbulence runs. 

The peak power of the pilot input shifted from 0.37 Hz to 0.63 Hz when the ATC was turned on. 

The maximum power of the signal decreased when the channel was added. 

The coherence function acquires some noticeable changes when running with the ATC. 

There is improvement in the peak coherence, especially between 0.5 and 2.5 Hz. During this 

portion, the Run 11 coherence remains greater than the coherence of Run 9. 
113 

Figure 6.3.11 – Coherence Function of Vertical Velocity and Pitch Stick Input; PSD of Pitch 

Stick Input, Large Civil Transport Experiment 2, Turbulence Level 4 

The pilot’s operating frequencies appear to shift to the right, in doing this; the pilot 

amplifies his coherence with the vertical velocity of the aircraft. This indicates that the change 

in dynamics of the pilot, brought about by the augmented turbulence cues, is improving the 

pilot’s correlation with the motion of the aircraft. 

Results of the severe turbulence runs are also similar to the data from Experiment 1. In 

this case, the pilot increases the power of his control input in the low frequency range. The peak 

power grows from 2.5 to 3.2, and shifts from 0.5 to 0.4 Hz. (Figure 6.3.12). 

10-1 100 101 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Coherence Function: Vertical Velocity and Pitch Stick Magnitude

Frequency (Hz)

10-1 100 101 0

0.2

0.4

0.6

0.8

1

1.2

1.4

PSD of Pitch Stick Input

Power

Frequency (Hz)

Run 9 - Turbulence lvl 4, ATC Off Run 11 - Turbulence lvl 4, ATC On
114 

Figure 6.3.12 – Coherence Function of Vertical Velocity and Pitch Stick Input; PSD of Pitch 

Stick Input, Large Civil Transport Experiment 2, Turbulence Level 6 

For the most part, the coherence function indicates that the addition of the ATC reduces 

the pilot’s correlation with the aircraft vertical velocity. The only portion where the pilot is 

better correlated is in low frequencies, under 0.5 Hz, and the peak coherence of Run 14, which is 

at 2.4 Hz. The rest of the frequency range shows a lack of correlation between the two signals. 

At level 6 turbulence it appears the pilot stops responding to the high frequency cue. His 

peak power always shifts to the lower frequencies when adding the ATC. At all other turbulence 

levels, the pilot increases his power at higher frequencies when the ATC is added. 

10-1 100 101 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Coherence Function: Vertical Velocity and Pitch Stick Magnitude

Frequency (Hz)

10-1 100 101 0

0.5

1

1.5

2

2.5

3

3.5

PSD of Pitch Stick Input

Power

Frequency (Hz)

Run 10 - Turbulence lvl 6, ATC Off Run 14 - Turbulence lvl 6, ATC On
115 

Further conclusions were drawn by comparing the results of Experiments 1 and 2. The 

data indicates that the pilot is always more correlated with the vertical velocity of the aircraft 

when simply maintaining altitude. It appears that the added difficulty of maintaining the glide 

slope while on approach reduces the coherence function under all turbulence intensities. 

The coherence function is usually improved when adding the ATC in both light and 

moderate turbulence. The improvement of the coherence is much more apparent in the moderate 

turbulence cases. When flying through severe turbulence, the pitch stick control is more 

correlated with the aircraft vertical velocity without the augmented channel. 

6.3.3 Large Civil Transport: Experiment 3 – Quantitative Results 

Producing Quantitative results from a very control intensive task is somewhat 

problematic. In order to justify frequency analysis, the simulation must not be dependent on 

time. When performing an offset approach, the entire experiment forces a wide range of 

maneuvers from the pilot. Results of frequency analysis can be useful to calculate the work the 

pilot is inputting to the system. 

The mean square of each run is shown in Table 6.3.3. The overall mean square is much 

greater than what was seen in Experiments 1 and 2. The pilot exercises more work in each of 

these runs. One interesting piece of information is the decrease in the mean square at level six 

turbulence, when the ATC is added. This was also noted in Experiment 1. As mentioned before, 

the evidence suggests that the pilot is simply not responding to the high frequency motion. It is 
116 

possible that the pilot is unable to respond because the increase in magnitude is too large and in 

the higher frequency content. 

Table 6.3.3 – Mean Square of Pitch Stick Input, Large Civil Transport Experiment 3 

6.4 Generic High-Speed Civil Transport Study – Subjective Analysis 

6.4.1 GHSCT Experiment 1: Subjective Analysis 

The generic aircraft model used in this study is still under development, and the current 

dynamics resulted in difficulties assessing the turbulence level and completing the experiments. 

Flying the GHSCT in its current form was a difficult task in itself; the aircraft handling qualities 

Run

Number

Turbulence

Level Configuration Mean Square

17 2 (1) ACT Off 0.478

20 2 (2) ACT On 1.08

16 4 (1) ACT Off 1.487

18 4 (2) ACT On 2.478

15 6 (1) ACT Off 2.635

21 6 (2) ACT On 1.921

19 OFF n/a 0.403

Experiment 3 – LCT
117 

require the pilot to constantly perform control adjustments to keep the aircraft on task. The pilot 

found it very difficult to identify turbulence intensities, even when just maintaining altitude. 

The actual motion of this aircraft when under the influence of turbulence is severely 

lacking as it is only outfitted with a translational turbulence model. Rotational turbulence cues 

produce a more realistic feel, and also require the pilot to compensate for abrupt changes in 

attitude. 

The first set of results is from Experiment 1. As with the experiments conducted on the 

Large Civil Transport, the pilot is to maintain straight and level flight at a set altitude. He then 

gives a CHR rating on the compensation required to perform this task. Configurations 1 and 2 

are the same as the Large Civil Transport experiments. Configuration 3 indicates that both the 

ATC and aeroelastic channel are turned on. Table 6.4.1 shows the Dryden turbulence level, 

configuration, reported turbulence intensity, and the CHR for maintaining altitude. 
118 

Table 6.4.1 – GHSCT Experiment 1, Turbulence Intensity and CHR 

These subjective ratings indicate that the pilot had a very difficult time identifying the 

turbulence level. During the up and away flight, the intensity never actually reached severe. 

Cooper Harper Ratings also show that the pilot never needed to exert much compensation when 

completing this task. The run numbers where the turbulence level was misidentified are marked 

with an asterisk (*). 

Experiment 1 – GHSCT CHR:

Maintaining

Altitude Run

Number

Turbulence

Level Configuration Reported

Intensity

1 OFF 1 none 2

2* 6 2 Light, Continuous 3

3* 4 1 Light, Continuous 3

4 2 3 Light, Continuous 3

5* 6 1 Light, Continuous 4

6 2 1 Light, Continuous 3

7* 4 3 Light, Continuous

(high end) 4

8* 4 2 Light, Continuous

(low end) 2

9 2 2 Light, Continuous 2

10* 6 3 Moderate,

Continuous 5

* Indicates incorrect intensity rating for that run
119 

The aeroelastic channel appears to consistently increase the effects of the turbulence 

intensity. At level 4 turbulence, with the DASE channel turned off, the intensity was reported as 

light turbulence (Runs 3 and 8). Adding the DASE channel caused this to increase to the high 

end of light turbulence (Run 7). The CHR also increased to 4 with the aeroelastic effects on. As 

described in Chapter 4, the aeroelastic channel is excited very little by turbulence. Most of the 

bending is due to pilot control input. When running with Configuration 3, the pilot feels the cues 

added by the turbulence channel and the flexible response of the aircraft as the pilot makes 

control inputs. 

Since some turbulence intensities were reported incorrectly, the aircraft reaction criteria 

that were met are related to the perceived erroneous intensity. For example, if the pilot identified 

a level 4 turbulence run as light turbulence, then the aircraft reaction criteria would have been 

indicative of the light turbulence that was perceived. If level 4 turbulence was observed 

correctly, the aircraft response criteria would represent moderate turbulence. Table 6.4.2 shows 

the reaction criteria from the FAA Airman’s guide which were satisfied for Experiment 1. 
120 

Table 6.4.2 – GHSCT Experiment 1, FAA Turbulence Reporting Criteria 

The reaction inside the aircraft criteria for seatbelt strain and object displacement were 

removed from the tables because these conditions were never met. The pilot still determined 

whether walking and food service was possible, difficult, or impossible. For the Experiment 1 

runs, walking was always possible, except at level 6 turbulence with the ATC and DASE model 

both turned on (Run 10). In this run, the highest CHR for this experiment was given, and 

Experiment 1 – GHSCT

      Aircraft Reaction Criteria Reaction Inside

Aircraft

Run

Number

Base

Failure Altitude Attitude Airspeed Controllability Food Service &Walking

1 no n/a n/a n/a n/a possible

2* no yes yes yes yes possible

3* no yes yes yes yes possible

4 no yes yes yes yes possible

5* no yes yes yes yes possible

6 no yes yes yes yes possible

7* no yes yes yes yes possible

8* no yes yes yes yes possible

9 no yes yes yes yes possible

10* no yes yes yes yes difficult

* Indicates incorrect intensity rating for that run
121 

walking and food service were recorded as difficult. Typically, level 6 turbulence should 

produce a more extreme reaction by the aircraft and the motion base, making the aircraft difficult 

to control and impossible to walk in. 

6.4.2 GHSCT Experiment 2: Subjective Analysis 

Experiment 2 was a more control intensive task than Experiment 1. Since the pilot is 

much more active in the control loop, it is more difficult to identify the turbulence intensity. The 

Dryden turbulence model was turned off during Run 19, however the pilot identified light, 

intermittent, turbulence for that run. A possible explanation for this is that abrupt changes in 

vertical velocity can be due to the dynamics of the aircraft response to pilot input. It is likely that 

the aircraft response is erratic enough that it shares some characteristics with the aircraft 

response to turbulence. Table 6.4.3 shows the CHR for each Experiment 2 run and the perceived 

turbulence intensity. 
122 

Table 6.4.3 – GHSCT Experiment 1, Turbulence Intensity and CHR 

There is a substantial increase in the CHR of the different runs now that the task has 

become more difficult. The pilot still had trouble identifying turbulence levels, but severe 

turbulence was achieved at level six, with the ATC on (Run 17). This run also received the 

highest CHR; 8 for maintaining the glide slope and 9 for the landing. 

There is a consistent trend in these results; the addition of the flexibility channel causes 

the turbulence intensity to be assessed one level higher than the configuration without the 

Experiment 2 – GHSCT CHR:

Maintaining

Glide Slope

CHR:

Performing

Landing Run

Number

Turbulence

Level Configuration ReportedIntensity

11* 4 2 Light, Continuous 4 6

12 2 1 Light, Continuous 3 4

13* 4 1 Light, Continuous 4 5

14* 6 1 Moderate,

Continuous 6 7

15* 2 3 Moderate,

Continuous 5 6

16 6 3 Severe,

Continuous n/a n/a

17 6 2 Severe,

Continuous 8 9

18* 4 3 Severe,

Continuous 7 8

19* OFF 1 Light, Intermittent 3 3

20 2 2 Light, Continuous 3 4

* Indicates incorrect intensity rating for that run
123 

augmentation. Level 2 turbulence is reported as light turbulence with configuration 1 (Run 11). 

Adding the aeroelastic channel causes the rating to go up to moderate (Run 15). The moderate 

turbulence runs feel like light turbulence (Runs 11 and 13) until the DASE model is activated 

(Run 18). This particular run was identified as severe. 

Running turbulence level 6 without any augmented cues, the pilot sensed moderate 

turbulence (Run 14). Adding the ATC increased the accuracy of his judgment, as he identified 

Run 17 as severe. With the flexible model and ATC activated, this level was also correctly 

identified (Run 16). Run 16 could not be fully evaluated as the motion induced by this 

configuration caused the base to receive a tracking error early in the run. Repeat attempts had 

the same problem and criteria with regard to aircraft reaction could not be evaluated, nor could 

CHR be assigned to the different tasks. It was noted that food service and walking would be 

impossible in these conditions (Table 6.4.4). 
124 

Table 6.4.4 – GHSCT Experiment 2, FAA Turbulence Reporting Criteria 

Turbulence levels for Runs 13 and 14 were not identified correctly, but some of the 

aircraft reaction criteria did point to the proper turbulence intensity. During Run 13, the pilot 

noticed that there were periods of time where the aircraft was difficult to control. This is 

specified in the FAA Airman’s guide under moderate turbulence, and that is why the 

controllability rating for Run 13 was not fulfilled. 

Experiment 2 – GHSCT

      Aircraft Reaction Criteria Reaction Inside

Aircraft

Run

Number

Base

Failure Altitude Attitude Airspeed Controllability Food Service &Walking

11* no yes yes yes yes possible

12 no yes yes yes yes possible

13* no yes yes yes no (Moderate) possible

14* no yes yes

no

(Severe) yes difficult

15* no yes yes yes yes difficult

16 yes n/a n/a n/a n/a impossible

17 yes yes yes yes yes difficult

18* yes** yes yes yes yes impossible

19* no yes yes yes yes possible

20 no yes yes yes yes possible

*    Indicates incorrect intensity rating for that run                                                            

** Notes that base failed at or near the touchdown location
125 

Run 14 showed high amplitude fluctuations in indicated airspeed. This is detailed as a 

response to severe turbulence. It is also noted that the only time it was impossible to walk in the 

aircraft was with the flexibility model turned on, in level 4 or 6 turbulence (Runs 16 and 18). 

Since motion base failures were quite common during the runs, a column was added to 

identify these occurrences. In these situations, data was saved from the run; however, the run 

was not completed. If the motion base failed but the data included the touchdown of the aircraft, 

or was very close to a touchdown, it is indicated by a double asterisk (**). Multiple attempts for 

these runs were made; however, it was just not possible to complete some of the runs without 

producing a tracking error or overdriving the motion system. 

6.4.3 GHSCT Experiment 3: Subjective Analysis 

The analysis of Experiment 3 resulted in conclusions similar to Experiment 2. The run 

without turbulence (Run 25) was identified as light, continuous turbulence. A similar result was 

seen in Experiment 2, Run 19, and can be attributed to the aircraft dynamics. Table 6.4.5 shows 

the results of each run and the CHR. 
126 

Table 6.4.5 – GHSCT Experiment 3, Turbulence Intensity and CHR 

Identification of the perceived turbulence level was much more accurate in Experiment 3. 

Experiments 1 and 2 had many incorrectly identified runs; there were only two in Experiment 3. 

The aeroelastic effects caused Run 22 to be reported as severe turbulence even though it was 

only turbulence level 4. Since this is an offset approach, the pilot has to switch to a parallel 

runway and attempt to maintain the glide slope of the new runway once he is aligned with it. 

Experiment 3 – GHSCT CHR:

Maintaining

Glide Slope

CHR:

Performing

Landing Run

Number

Turbulence

Level Configuration Reported

Intensity

21 6 1 Severe,

Continuous 9 9

22* 4 3 Severe,

Continuous 8 8

23 4 2 Moderate,

Continuous 6 6

24 6 2 Severe,

Continuous 8 9

25* OFF 1 Light, Continuous 3 3

26 2 1 Light, Continuous 3 4

27 2 2 Light, Continuous 4 5

28 6 3 Severe,

Continuous n/a n/a

29 4 1 Moderate,

Continuous 6 7

30 2 3 Light, Continuous 5 5

* Indicates incorrect intensity rating for that run
127 

Motion due to the flexibility of the aircraft becomes quite powerful when the pilot is required to 

input this much control, this is why the perceived turbulence intensity increased. 

According to the Cooper Harper Ratings, this set of runs had a significant increase in the 

compensation required to maintain the glide slope and successfully land the aircraft. Even the 

level 4 turbulence case received a CHR of 8 (Run 22); an increase of one point in the glide slope 

rating from Run 18, Experiment 2. Only one run was incomplete (Run 28) and did not receive 

any CHR. The glide slope could not be maintained for a long enough time period to be rated, 

and the touchdown was never completed. 

The task of repositioning the aircraft on approach with the parallel runway led to more 

unavoidable motion base failures. Luckily, most of these occurred when the aircraft was at or in 

close proximity to the touchdown point (indicated by **). This allowed the pilot to rate the glide 

slope task, and in some cases, the touchdown as well. The aircraft reaction and reaction inside 

aircraft criteria are detailed in Table 6.4.6. 
128 

Table 6.4.6 – GHSCT Experiment 3, FAA Turbulence Reporting Criteria 

For all experiments, the reaction criteria correspond to the pilot’s reported turbulence 

intensity. Under reported moderate turbulence, it is indicated that walking and food service is 

difficult (Runs 23 and 29). In light turbulence, food service and walking are possible (Runs 26 

and 27). The resulting motion from the flexibility model always increased the difficulty to walk 

and serve food (see Runs 22, 30). 

Experiment 3 – GHSCT

      Aircraft Reaction Criteria Reaction Inside

Aircraft

Run

Number

Base

Failure Altitude Attitude Airspeed Controllability Food Service &Walking

21 yes** yes yes yes yes impossible

22* yes** yes yes yes yes impossible

23 yes** yes yes yes yes difficult

24 no yes yes yes yes difficult

25* no yes yes yes yes possible

26 no yes yes yes yes possible

27 no yes yes yes yes possible

28 yes yes yes yes yes impossible

29 no yes yes yes yes difficult

30 no yes yes yes yes difficult

*    Indicates incorrect intensity rating for that run                                                            

** Notes that base failed at or near the touchdown location
129 

As with Experiment 2, the addition of the ATC (Configuration 2) had mixed results. In 

the light turbulence case, it increased the compensation necessary to perform the task (Run 27). 

In Run 23 (moderate turbulence), simply adding the ATC reduced the CHR of performing the 

touchdown. In Run 24 (severe turbulence), turning on the ATC caused the pilot to sense that less 

control input is required to maintain the glide slope. 

Adding both the ATC and DASE model (Configuration 3) always resulted in an 

increased CHR for at least one of the ratings. This is attributed to the fact that the flexibility 

model heightens the response of the aircraft to pilot input. The motion cues from this flexibility 

are easily felt by the pilot and may have overpowered the added motion of the ATC in some 

runs. 

Some of the ambiguities from all three GHSCT experiments can be attributed to the 

pilot’s unfamiliarity with aircraft. For a study such as this one, the aircraft must not play a factor 

in the identification of the turbulence level. Runs 26 and 36 were not conducted with an 

atmospheric turbulence model or a flexible aircraft model, however, the pilot perceived the 

dynamics of the simulation to include continuous light turbulence or intermittent light 

turbulence. 

The addition of the ATC seemed to have little effect on the pilot evaluation, and in some 

cases, allowed the pilot to adequately perform the task with reduced compensation. The 

turbulence channel has the potential to improve this simulation. Perhaps when the GHSCT rigid 

body model is complete, more conclusive results can be obtained from a similar set of 

experiments. With a more refined aircraft model, the pilot can better understand the GHSCT 
130 

dynamics. This will allow him to accurately assess the turbulence intensity and the new 

augmentation cues. 

6.5 Generic High-Speed Civil Transport Study – Quantitative Results 

6.5.1 GHSCT: Experiments 1, 2 and 3: Quantitative Results 

Quantitative results of the experiments conducted on the GHSCT were not conclusive. 

The goal of the experiments was to gain insight into the effect of the ATC on the way the pilot 

controlled the aircraft. In order to perform this analysis, the pilot must have sufficient familiarity 

with the aircraft dynamics, and must also have the ability to perform the specific maneuvers. 

It was mentioned in the subjective analysis that a considerable number of runs suffered 

from repeated motion base failures. The added motion from the DASE model tends to drive the 

base at high amplitudes. Performing a landing will also make use of more of the available 

actuator length. This also causes problems with the excursion limits. 

The complexity of the experiments is also increased because of the addition of the 

aeroelastic cues. With both augmented channels, the pilot is receiving a tremendous amount of 

information as he tries to maintain altitude or land the aircraft. 

One main difficulty with the subjective analysis is that the pilot was unable to accurately 

determine the turbulence intensity that the aircraft was subjected to. Without properly sensing 

the aircraft dynamics, analysis of the pilot control input is not applicable. In order to conduct 

quantitative analysis, the perception of the conditions which the aircraft is under must be 

accurate. 
131 

Without subjective analysis to support this data, the results are inconclusive. The mean 

square fluctuates far too much during these experiments to support stationarity and frequency 

analysis. The integration of the two augmented channels was successful; however, results on the 

accuracy of the additional motion cues are mostly indeterminate. Recommendations to improve 

and continue this study are provided in Chapter 7. 

 
132 

7. Conclusions and Future Recommendations 

7.1 Summary and Conclusions 

The goal of this project was to improve the simulation of disturbances above the 

frequency range of most pilot control input. Typically these motion cues are a response to 

atmospheric turbulence or aeroelastic bending of the aircraft. These two concepts were analyzed 

in this report with the intent of creating a more realistic flight simulation experience. 

Using a standard motion cueing algorithm, simulation of the motion environment 

associated with atmospheric turbulence has been less than satisfactory; this results in attenuated 

and misrepresented cues being sent to the pilot. The primary goal of this research was to present 

to pilots a higher fidelity motion environment. There have been previous advancements in this 

area, Reid (1990) and Telban et al. (a). Analysis of their algorithms led to a revised turbulence 

channel implementation. This augmentation channel was designed to supplement the missing 

high frequency motion cues in the vertical degree of freedom. 

The design of the Augmented Turbulence Channel (ATC) was based on the Telban et al. 

(a) implementation of the SUNY Nonlinear Algorithm. This algorithm had a tendency to cause 

the simulator to diverge during long runs. It was concluded that double integrating the signal 

produced by the turbulence channel was the cause of the divergence, and also contributed to the 

attenuation of the frequency content of the signal. 

Further inspection of the original design revealed flaws which led to more changes in the 

architecture. The reduced order aircraft model was redesigned to better represent the dynamics 

of a specific aircraft. The original ROAC was modeled as a high-pass filter. In the original 

implementation, the output of the turbulence channel was sent through low bandwidth filters that 
133 

were not designed for signals with this frequency content. A high bandwidth cueing filter was 

designed to filter the output of the ATC. 

Reduced order models were determined using parameter identification software. APID, 

which was developed at the Man-Machine Systems Research Lab, SUNY Binghamton, 

determines a linear model and verifies it based on PSD. For a single input, single output system, 

the PSD of the model is compared with the PSD of the data provided to the algorithm. This data 

represents the input and output to the system; in this case the input is the vertical turbulence gust 

vector and output is the vertical acceleration of the aircraft. This was chosen to identify the 

model because the frequency content of the ROAC is important, and must reflect the response of 

the NLAC model. 

The ATC was implemented on two different simulated aircraft; the Large Civil Transport 

and the Generic High-Speed Civil Transport. A separate study was conducted on each aircraft. 

The GHSCT is also outfitted with an aeroelastic model. This model generates motion cues 

representing the bending of the fuselage in the vertical and lateral direction. The flexibility 

model was integrated with the turbulence channel to provide the pilot with both cues during the 

experiments. 

Verification of the turbulence simulation was conducted subjectively and quantitatively. 

To evaluate the aircraft response to atmospheric turbulence, the FAA Airman’s Guide to 

Reporting Turbulence was employed by the pilot. This guide has specific criteria for the aircraft 

response to turbulence and the reaction inside the aircraft. After each run, the pilot determined 

which criteria were met. Next, the results of the runs with and without the augmented turbulence 

channel were compared. 
134 

Before experiments could be conducted, the algorithm had to be tuned. The LCT 

implementation was tuned on the VMS; the GHSCT implementation was tuned on both the VMS 

and CMF, each at NASA Langley Research Center. The algorithms were tuned subjectively 

using the FAA Airman’s Guide. The LCT implementation managed to meet almost all criteria at 

the different turbulence levels (light, moderate and severe). The Airman’s Guide indicates that 

there will be a certain amount of seatbelt strain under moderate and severe turbulence. It also 

indicates that lose objects will be dislodged. Due to the motion limitations of the flight 

simulators, these criteria could not be met. With the LCT there were no issues meeting the 

altitude, attitude, airspeed, controllability, walking and food service criteria. 

When tuning the GHSCT implementation, it was difficult to meet certain criteria in the 

Airman’s Guide. The GHSCT is only outfitted with a translational turbulence model; unlike the 

LCT which supplements rotational turbulence motion in the primary cueing channel. This 

rotational response of the aircraft was necessary to meet the criteria for controllability and 

attitude fluctuations. When flying through severe turbulence, without the rotational turbulence 

model, changes in attitude were minimal. The guide also states that in severe turbulence, the 

aircraft may be momentarily out of positive control; this criterion was never met on the GHSCT. 

The reduced ability to walk and serve food was met with moderate turbulence, and rated 

impossible under severe turbulence. 

Both studies included three types of experiments, each flown through different levels of 

turbulence. The first was to maintain straight and level flight, the second was to maintain the 

glide slope and perform a landing. The third experiment was an offset approach; the aircraft was 

trimmed on the glide slope and localizer of a runway and directed to land on a parallel runway. 
135 

Pilots would not normally be directed to perform landings in high intensity turbulence; however, 

training for this event will help prepare them if a situation such as this is unavoidable. 

The LCT study and GHSCT study were both conducted on the VMS. After each run, the 

turbulence intensity was rated, and the pilot indicated which criteria were met from the FAA 

Airman’s Guide. 

The order of the experiments was randomized based on the turbulence level and 

configuration of the algorithm. In the experiments conducted with the LCT, there were two 

configurations: one with the ATC, one without. During these experiments the pilot was able to 

accurately identify the turbulence level for each run. Typically, there was a noticeable difference 

in the runs with and without the ATC. 

The pilot rated his ability to maintain the glide slope using the Cooper Harper Rating 

scale. The touchdown task was also given a CHR. This scale uses specific descriptions with 

regard to the amount of pilot compensation needed to adequately perform the task. In some 

cases, the added motion from the ATC increased the CHR. 

Each task became increasingly difficult under high levels of turbulence. In some cases, 

the added motion of the turbulence channel caused the pilot to rate the task with a lower CHR. 

The motivation for this decrease in the amount of compensation to perform the task is not clear. 

It is possible that the pilot has a better understanding of the aircraft motion with the augmented 

channel. The pilot should only provide control input when it is necessary; it’s possible that the 

added cue allows the pilot to reduce his input and still adequately perform the task. 

In order to conduct the quantitative evaluation of the augmented channel, the pilot control 

input was analyzed along with the response of the aircraft. Vertical velocity was chosen for the 

evaluation because it is a very important piece of aircraft state information during the 
136 

performance of this task. When maintaining altitude, the pilot will assess his deviation from 

altitude by monitoring vertical velocity. If the altitude begins to deviate, control input is applied 

to cancel growing vertical rates. Control input is maintained until the vertical velocity indicates 

the aircraft is returning to the task defined altitude. 

For the first experiment, the pilot was directed to maintain altitude at 30,000 feet ASL. 

According to the quantitative results of the LCT study, this is the task where the pilot was most 

correlated with the aircraft dynamics. The analysis was conducted in the frequency domain. 

Under light and moderate turbulence, the pitch stick input of the pilot was more correlated with 

the aircraft vertical velocity with the ATC on. Inspecting the PSD of the pilot input showed 

changes in the operating frequencies of the pilot. The additional coherence showed that the 

added cue from the ATC increased the pilot’s ability to respond to the turbulence cue. 

In the severe turbulence case, adding the ATC reduced the correlation between the pitch 

stick input and the aircraft vertical velocity. This indicated that the pilot had more difficulty 

controlling the aircraft. When flying through severe turbulence, adding the ATC caused the PSD 

of the pilot to decrease across all frequencies. The peak operating frequency also shifted to a 

lower frequency. It appeared as though the pilot simply could not keep up with the cue and since 

the aircraft response comprised turbulence response, the lacking control input of the pilot 

reduced the coherence. 

The fact that the pilot was less correlated in severe turbulence does not mean that the 

ATC does not add realism. In a real flight through this turbulence intensity, it is possible that the 

pilot has the same reaction. The main goal of a simulation such as this one is to replicate the 

workload of an actual flight, and that can be verified through other means. 
137 

When performing a landing, the pilot compensated for deviation from the glide slope by 

countering changes in the vertical rate of the aircraft. The aircraft sink rate was monitored 

throughout the run; control input was used to cancel building vertical rates and bring the aircraft 

back to the glide slope. Since experiments 2 and 3 were landing tasks, the quantitative results of 

these experiments were also based on the pilot input and the aircraft vertical velocity. 

In experiment 2 of the LCT study, the aircraft started lined up on the approach and 

required the pilot to maintain the glide slope and perform the landing. This task required more 

pilot control input than maintaining altitude, but was still a task based on vertical velocity. The 

pilot also monitored glide slope error, to aid in determining how much input was needed to 

minimize the error. This was also a lateral tracking task where the pilot was directed to aim for 

the center of the runway touchdown zone. Since the ATC only impacted the vertical channel, the 

pilot’s correlation with the lateral channel was not analyzed. Near the end of the approach, the 

pilot completed the landing using mainly visual cues. 

Similar conclusions were formed from both experiments 1 and 2. According to the 

coherence function, the pilot was more correlated with the response of the aircraft at light and 

moderate turbulence. In these cases, the pilot increased the power of his control input over 

almost all frequencies when the ATC is on. The peak power tended to shift to the right. 

Under severe turbulence, the trend in the pilot control input dynamics was reversed. It 

appeared that the addition of the ATC caused the pilot control to shift to lower frequencies and 

drop in power. The result was less correlation between the pilot input and vertical velocity. It 

was possible that the pilot just simply could not keep up with the intensity of the cue. Rather 

than responding to the high frequency motion, the pilot only added control input in lower 

frequencies. Results of experiment 2 did not have as much coherence as the experiment 1 
138 

analysis. This was due to the fact that the pilot was performing a more advanced task, and more 

factors affected his pitch stick input. 

Experiment 3 was the most control intensive task, and led to less conclusive quantitative 

results. It was difficult to perform frequency analysis on these runs because the task required 

various pilot control inputs. Since this was an offset approach, the pilot had to maneuver the 

aircraft to align it with the parallel runway. This entire run is conducted with turbulence and the 

control input during the run is not necessarily a response to changes in the aircraft vertical 

velocity. Because of this, inspecting the coherence function can only provide minimal insight 

into the effect of the ATC. Stationarity of the signal was also a factor, limiting the ability to 

conduct frequency analysis. 

When adding the ATC, the work done by the pilot increased substantially during light 

and moderate turbulence. Just as in experiment 1, there was a decrease in the overall work done 

by the pilot when flying through severe turbulence. 

The same three experiments were conducted during the GHSCT study. There were three 

configurations of the GHSCT; the first configuration had no ATC and no aeroelastic model. The 

second configuration employs the ATC. The third configuration uses both the ATC and 

aeroelastic model. 

Like the LCT study, the run order was randomized based on the configuration of the 

algorithm and turbulence level. These experiments yielded inconclusive results, both 

subjectively and quantitatively. The subjective analysis of experiment 1 indicated that the pilot 

was unable to determine the turbulence intensity for most runs. In moderate and severe 

turbulence, the perceived intensity was always lower than the actual intensity. Also, criteria for 

severe turbulence were never met in this experiment. 
139 

During experiment 2, the pilot incorrectly identified the turbulence intensity for 6 out of 

10 runs. Severe turbulence was achieved during this experiment; an improvement over the 

previous experiment. In most cases, the activation of the aeroelastic channel caused the pilot to 

perceive a higher turbulence intensity, even though the turbulence level was not changed. Level 

4 turbulence (moderate) was identified as light turbulence without the ATC or DASE Model; 

however activating both of the augmentation channels caused the intensity to be reported as 

severe. 

The DASE model response was almost entirely due to pilot control input. As the pilot 

maintained the glide slope and compensated for turbulence, control inputs excited the bending 

modes of the fuselage. The additional motion resulted in an increase in the perceived turbulence 

intensity. 

Another source of ambiguity was from the results of the run without turbulence. During 

this run, the pilot reported light, intermittent turbulence. There was no flexibility model during 

this run. The sensation of turbulence indicated that the aircraft response to the pilot was erratic 

enough to feel like turbulence. 

Experiment 3 required the most work by the pilot. This experiment also had the most 

issues with tracking errors and excursion limits of the simulator motion base. The task required 

a great deal of pilot input which drove the simulator towards its physical limits. In most cases, 

the motion base failures occurred late in the run, allowing the pilot to provide a CHR for both the 

glide slope and the landing. 

Out of all three GHSCT experiments, turbulence intensity was reported with the most 

accuracy on this task. Only two runs were incorrectly identified. Similarly to experiment 2, the 

run with no turbulence was reported to be light, continuous turbulence. During this run, aircraft 
140 

dynamics were not being driven by external gust inputs, or aeroelastic effects. This indicates 

that a portion of the aircraft response to control inputs was not completely understood by the 

pilot. 

Without the ability to support quantitative analysis with pilot perception, the analytical 

results were inconclusive. The frequency analysis was also hindered by the lack of stationarity 

in the signal. The pilot’s response to the aircraft was more erratic and usable portions of data 

could not be isolated. 

Aside from the inconsistencies with the results, the augmented channel was successfully 

implemented along with the aeroelastic channel on the GHSCT. Currently, it appears that the 

aircraft dynamics were affecting the pilot’s ability to analyze the turbulence simulation. Further 

tuning and refining of the channel may lead to more conclusive results. 

7.2 Future Recommendations 

It would be beneficial to continue this study to further investigate some of the ambiguities 

in both the subjective and quantitative results. Based on the Cooper Harper Ratings, there were 

instances when the pilot’s subjective analysis indicated that the addition of the ATC negatively 

affected the handling qualities of the simulation. There are other cases where the opposite 

occurred. A multi-pilot study can determine trends in the CHR and allow for more conclusive 

results. 

Some ambiguities in the quantitative results of the LCT study indicate that there may be a 

need to further refine the augmented turbulence algorithm. During the severe turbulence runs, 

the power of the pilot control input decreased over all frequencies. This reduced the correlation 

between the pilot input and aircraft response. This decrease in correlation does not warrant 
141 

changes to the algorithm if it reflects the true reaction of the pilot in this turbulence intensity. 

One way to assess this is to compare actual flight data to the results of these experiments. If 

modifications are applicable, the algorithm will have to be tuned again, and reevaluated 

subjectively and quantitatively. 

Currently it seems that the most attenuated degree of freedom is the vertical channel; 

however, the addition of another ATC in the lateral degree of freedom could benefit the 

simulation. Lateral cues produced from atmospheric turbulence can be modeled in the same 

fashion; using a reduced order aircraft model and adding a position augmentation cue to the 

lateral motion cue before driving the motion system. This channel could be developed and tested 

by having the pilot perform a task that requires more lateral response compensation. This added 

motion has the potential to allow the simulation to meet more criteria from the FAA turbulence 

reporting standard; such as seat belt strain or object displacement. 

The GHSCT simulation would benefit from the addition of a rotational turbulence model. 

The Mil-F-8785B model has been implemented on the LCT and produces adequate rotational 

cues. These cues allow most of the FAA turbulence criteria to be met during the LCT 

simulation, something that was not the case in the GSCHT runs. If the additional cue is enough 

to meet more of the subjective turbulence criteria, then another GHSCT study should be 

conducted. The ATC has the potential to add realism to the GHSCT simulation and compliment 

the cues produced by the flexible aircraft dynamics. 

Further verification of the turbulence channel is necessary to validate the augmented 

algorithm. In order to do this, results of a multi-pilot study must clearly indicate a more realistic 

simulation is provided to the pilot by the ATC. Pilots should fly different versions of the 
142 

algorithm and rate the realism provided by the simulation. Finally, pilot workload analysis 

should be compared to actual flight data. 

 
143 

Appendices 

Appendix A: Turbulence Spectrums Defined: 

1. Dryden Spectrum Translational Turbulence Model: 

2

2

2 1 ( )

1

u

L

V L

v

  

 



      

 

2

2

2 2

1 3

( )

1

v

L

L V

V L

V



  





       

               

2

2

2 2

1 3

( )

1

w

L

L V

V L

V



  





       

              

2

2

2 1 ( ) 1( ) g

u u u

u

L

L

 



 

 

2

2

2 2

1 3( ) ( )

1( ) g

v v v v

v

L L

L

 



   

     

2

2

2 2

1 3( ) ( )

1( ) g

w w w w

w

L L

L

 



   

     

Variables Defined: 

u Longitudinal Spectrum component of turbulence   2

m

rad s

v Lateral Spectrum component of turbulence   2

m

rad s

w Vertical Spectrum component of turbulence   2

m

rad s

2  Variance of turbulence 

2

2

m

s

 Wave Number rad

m

 Frequency rad

s

V Mean velocity of the mean wind vector relative to the aerospace vehicle m

s

g u Longitudinal Spectrum component of gust   2

m

rad s

g v Lateral Spectrum component of gust   2

m

rad s

wg  Vertical Spectrum component of gust   2

m

rad s

2  u Variance of turbulence in longitudinal direction 

2

2

m

s
144 

2  v Variance of turbulence in the lateral direction 

2

2

m

s

2  w Variance of turbulence in the vertical direction 

2

2

m

s

Lu Length scale of turbulence in longitudinal direction m

Lv Length scale of turbulence in lateral direction m

Lw Length scale of turbulence in vertical direction m

2. MIL-F-8785B Rotational Turbulence Model 

1/3

2

2

.8

4 ( )

4 1

g

w

w p

w

L

b

L b



 



       

       

2

2 ( ) ( )

4 1

g g q w

b

 



   

       

2

2 ( ) ( )

3 1

g g r v

b

 



   

       

g

g

w

p

y

   

g

g

w

q

x



 



g

g

v

v

x

   

Variables Defined: 

Lu Length scale of turbulence in longitudinal direction 

Lv Length scale of turbulence in lateral direction 

Lw Length scale of turbulence in vertical direction 

g p p-rotational Spectrum component of gust 

g q q-rotational Spectrum component of gust 

g r r-rotational Spectrum component of gust 

g p p-rotational velocity component of gust 

g q q-rotational velocity component of gust 

gv Lateral turbulence velocity 

b Wing Span 

 
145 

Appendix B: Fortran code for ATC; DASE model parameters 

Fortran code for augmented turbulence channel implementation: 

c Modified Turbulence Channel 

c Comprising: Reduced Order Aircraft Model (ROAC) 

c and Turbulence Cueing Filter 

 SUBROUTINE TURB_CHANNEL ! added by GAT 

 INCLUDE "comint2.com" ! added by GAT 

 INCLUDE "wcomgust.com" ! added by GAT 

 INCLUDE "optint3.com" ! added by lck 

 

c Define coefficients 

 REAL HT1N4,HT1N3,HT1N2,HT1N1,HT1N0,HT1D3,HT1D2,HT1D1,HT1D0 

 REAL WTN4,WTN3,WTN2,WTN1,WTN0,WTD3,WTD2,WTD1,WTD0 

 REAL KT 

 REAL DT ! added by GAT 

 REAL BTW1 

 REAL BT1,CT1,DT1,BT2,CT2,DT2 ! added by GAT 

 REAL CTW1,DTW1,ETW1,BTW2,CTW2,DTW2,ETW2 ! added by GAT 

c Turbulence Cueing Filter 

 DATA HT1N3,HT1N2,HT1N1,HT1N0, 

 + HT1D2,HT1D1,HT1D0/ 

 + 0.0000,49.35,24.67,0.000, 

 + 50.59,61.94,12.34/ 

c Reduced Order Aircraft Model 

 DATA WTN4,WTN3,WTN2,WTN1,WTN0, 

 + WTD3,WTD2,WTD1,WTD0/ 

 + 0.0000,0.000,7,2.94,0.63, 

 + 28.84,423.6,336.9,12.8/ 

 DT = H ! added by GAT 

C 

C Compute Augmented Acceleration from W-Gust 

C 

 BTW1=DT*(-WTD3*YW1O+YW2O+(WTN3-WTD3*WTN4)*WGUSTO) 

 CTW1=DT*(-WTD2*YW1O+YW3O+(WTN2-WTD2*WTN4)*WGUSTO) 

 DTW1=DT*(-WTD1*YW1O+YW4O+(WTN1-WTD1*WTN4)*WGUSTO) 

 ETW1=DT*(-WTD0*YW1O+(WTN0-WTD0*WTN4)*WGUSTO) 

 

 BTW2=DT*(-WTD3*(YW1O+BTW1)+YW2O+CTW1+(WTN3-WTD3*WTN4)*WGUST) 

 CTW2=DT*(-WTD2*(YW1O+BTW1)+YW3O+DTW1+(WTN2-WTD2*WTN4)*WGUST) 

 DTW2=DT*(-WTD1*(YW1O+BTW1)+YW4O+ETW1+(WTN1-WTD1*WTN4)*WGUST) 

 ETW2=DT*(-WTD0*(YW1O+BTW1)+(WTN0-WTD0*WTN4)*WGUST) 

 
146 

 YYW1=YW1O+0.5*(BTW1+BTW2) 

 YW2=YW2O+0.5*(CTW1+CTW2) 

 YW3=YW3O+0.5*(DTW1+DTW2) 

 YW4=YW4O+0.5*(ETW1+ETW2) 

 ACZT=YYW1+WTN4*WGUST 

 ACZT=ACZT*9.81*KT 

 YW1O=YYW1 

 YW2O=YW2 

 YW3O=YW3 

 YW4O=YW4 

 

 WGUSTO=WGUST 

C 

C APPLY THE FILTER (Remove comments to run with filter) 

C 

c BT1=DT*(-HT1D2*Y1O+Y2O+(HT1N2-HT1D2*HT1N3)*ACZTO) 

c CT1=DT*(-HT1D1*Y1O+Y3O+(HT1N1-HT1D1*HT1N3)*ACZTO) 

c DT1=DT*(-HT1D0*Y1O+(HT1N0-HT1D0*HT1N3)*ACZTO) 

 

c BT2=DT*(-HT1D2*(Y1O+BT1)+Y2O+CT1+(HT1N2-HT1D2*HT1N3)*ACZT) 

c CT2=DT*(-HT1D1*(Y1O+BT1)+Y3O+DT1+(HT1N1-HT1D1*HT1N3)*ACZT) 

c DT2=DT*(-HT1D0*(Y1O+BT1)+(HT1N0-HT1D0*HT1N3)*ACZT) 

 

c YY1=Y1O+0.5*(BT1+BT2) 

c Y2=Y2O+0.5*(CT1+CT2) 

c Y3=Y3O+0.5*(DT1+DT2) 

 

c ACZTF=YY1+HT1N3*ACZT 

 Y1O=YY1 

 Y2O=Y2 

 Y3O=Y3 

 ACZTO=ACZT 

 END ! added by GAT 

 
147 

DASE Model Parameters: 

Note: all experiments used DASE Model 1 

 
148 

Appendix C: FAA Airman’s Guide to Reporting Turbulence Intensity (TBL 7-9-1) 

Intensity Aircraft Reaction Reaction Inside Aircraft Reporting Term‐Definition

Light Turbulence that momentarily causes slight

erratic changes in altitude and/or attitude

(pitch, roll, yaw) Report as Light

Turbulence;*

or

Turbulence that causes slight rapid and

somewhat rythmic bumpiness without

appreciable changes in altitude or

attitude. Report as Light Chop.

Occupants may feel a slight strain against seat

belts or shoulder straps.  Unsecured objects

may be displaced slightyly.  Food service may

be conducted anad little or no difficulty is

encountered in walking.

Occasional –Less than 1/3

ofthe time.

Intermittent – 1/3 to 2/3

Continuous – More than 2/3

Moderate Turbulence that is similar to Light

Turbulence but of greater intensity.  

Changes in altitude and/or attitude occur

but the aircraft remains in positive control

at all times.  It usually causes variations in

indicated airspeed.  Report as Moderate

Turbulence;*

or

Turbulence that is similar to Light Chop but

of greater intensity.  It causes rapid bumps

or jolts without appreciable changes in

aircraft altitude or attitude. Report as

Moderate Chop.*

Occupants feel definite strains against seat

belts or shoulder straps.  Unsecured objects

are dislodged.  Food service and walking are

difficult.

Severe Turbulence that cause large, abrupt

changes in altitude and/or attitude.  It

usually causes large variations in indicated

airspeed.  Aircraft may be momentarily out

of control.  Report as Severe Turbulence.*

Occupants are forced violently against seat

belts or shoulder straps.  Unsecured objects

are tossed about.  Food service and walking

are impossible.

Extreme Turbulence in which the aircraft is violently

tossed about and is practically impossible

to control.  It may cause structural

damage.  Report as Extreme Turbulence.*

*High level turbulence (normally above 15,000 feet) not associated with cumuliform cloudiness, including

thunderstorms, should be reported as CAT (clear air turbulence) preceded by the appropriate intensity, or light or

moderate chop.

NOTE:

1. Pilots should report location(s), time (UTC), intensity, whether in or near clouds, altitude, type of aircraft and, when

applicable, duration ofturbulence

2. Duration may be based on time between two locations or over a single location.  All locations should be readily

identifiable.
149 

Appendix D: Gain Tuning Test Plans 

Test Plan for LCT: 

Record video and audio (pilot comments) during runs 

 Initial Flight Test (without motion) 

o Ensure motion system commands will not overdrive the system 

 Initial Flight Test (with motion) 

o Ensure aircraft reacts properly to pilot input 

o Test data recording of A/C output 

o Test data recording of simulator sensors 

 Turbulence Gain Tuning, Turbulence Cueing Filter OFF: 

o For all runs, trim to 30,000 ft., M=0.8 

o Initialize Gain at 0.05 (tuned gain from last visit) 

o Test flight without motion – look for bump when going to operate (VMS only) 

and ensure turbulence channel is not producing cues that will overdrive the 

motion base 

o Tuning Flights – Start with low turbulence level and adjust gain after all runs 

 Turbulence lvl 1 

 Note if gain needs to be adjusted (only adjust if cues are 

overdriving simulator) 

 Turbulence lvl 4 

 Note if gain needs to be adjusted 

 Turbulence lvl 6 

 Note if gain needs to be adjusted 

 Turbulence lvl 7 

 Note if gain needs to be adjusted 

 After running with turbulence level 7, adjust gain accordingly and test 

lower levels of turbulence until tuning is complete 

 Turbulence Gain Tuning, Turbulence Cueing Filter ON: 

o For all runs, trim to 30,000 ft., M=0.8 

o Initialize Gain at 0.05 

o Test flight without motion – Ensure turbulence channel is not producing cues that 

will overdrive the motion base 

o Tuning Flights – Start with low turbulence level and adjust gain after all runs 

 Turbulence lvl 1 

 Note if gain needs to be adjusted (only adjust if cues are 

overdriving simulator) 

 Turbulence lvl 4 

 Note if gain needs to be adjusted

 Turbulence lvl 6 

 Note if gain needs to be adjusted 

 Turbulence lvl 7 

 Note if gain needs to be adjusted 
150 

 After running with turbulence level 7, adjust gain accordingly and test 

lower levels of turbulence until tuning is complete 

Test Plan for GHSCT: 

Record video and audio (pilot comments) during runs 

 Initial Flight Test (without motion) 

o Ensure motion system commands will not overdrive the system 

 Initial Flight Test (with motion) 

o Ensure aircraft reacts properly to pilot input 

o Test data recording of A/C output 

o Test data recording of simulator sensors 

 Turbulence Gain Tuning, Turbulence Cueing Filter OFF, Aeroelastic Model OFF: 

o For all runs, trim to 30,000 ft., M=0.8 

o Initialize Gain at 0.02 (tuned gain for original ROAC on GHSCT) 

o Test flight without motion – Ensure turbulence channel is not producing cues that 

will overdrive the motion base 

o Tuning Flights – Start with low turbulence level and adjust gain after all runs 

 Turbulence lvl 1 

 Note if gain needs to be adjusted (only adjust if cues are 

overdriving simulator) 

 Turbulence lvl 4 

 Note if gain needs to be adjusted 

 Turbulence lvl 6 

 Note if gain needs to be adjusted 

 Turbulence lvl 7 

 Note if gain needs to be adjusted 

 After running with turbulence level 7, adjust gain accordingly and test 

lower levels of turbulence until tuning is complete 

 Repeat above tuning for each Aeroelastic Model 

 Turbulence Gain Tuning, Turbulence Cueing Filter ON, Aeroelastic Model OFF: 

o For all runs, trim to 30,000 ft., M=0.8 

o Initialize Gain at 0.02 

o Test flight without motion – Ensure turbulence channel is not producing cues that 

will overdrive the motion base 

o Tuning Flights – Start with low turbulence level and adjust gain after all runs 

 Turbulence lvl 1 

 Note if gain needs to be adjusted (only adjust if cues are 

overdriving simulator) 

 Turbulence lvl 4 

 Note if gain needs to be adjusted 
151 

 Turbulence lvl 6 

 Note if gain needs to be adjusted 

 Turbulence lvl 7 

 Note if gain needs to be adjusted 

 After running with turbulence level 7, adjust gain accordingly and test 

lower levels of turbulence until tuning is complete 

 Repeat above tuning for each available aeroelastic model 

 
152 

Appendix E: Cooper-Harper Rating Scale 

 
153 

Appendix F: Experiment Conditions and Run Order 

Large Civil Transport Experiments: Run Order, Configuration, and Turbulence Level 

Initial Test Plan for LCT:

Record video and audio (pilot comments) during runs

 Initial Flight Test (without motion)

o Ensure motion system commands will not overdrive the system

 Initial Flight Test (with motion)

o Ensure aircraft reacts properly to pilot input

o Test data recording of A/C output

o Test data recording of simulator sensors (all accelerometers)

 Begin Experiments
154 

Run order and conditions for LCT experiments 

GHSCT Experiments: Run Order, Configuration, and Turbulence Level 

Initial Test Plan for GHSCT:

Record video and audio (pilot comments) during runs

 Initial Flight Test (without motion)

o Ensure motion system commands will not overdrive the system

 Initial Flight Test (with motion)

Run

Order

Run Number ‐ 

Data file number

Configuration Turbulence level

1 24

2 1 6

3 1 2

4 1 OFF

5 2 2

6 1 4

7 2 6

1 1 2

2 1 4

3 1 6

4 2 4

5 1 OFF

6 2 2

7 2 6

1 1 6

2 1 4

3 1 2

4 2 4

5 1 OFF

6 2 2

7 2 6

Experiment 1: Maintain straight and level flight at 30000 feet ASL

Experiment 2: Straight in approach and landing

Experiment 3: Offset approach and landing
155 

o Ensure aircraft reacts properly to pilot input

o Test data recording of A/C output

o Test data recording of simulator sensors (all accelerometers)

 Begin Experiments
156 

Run order and conditions for GHSCT experiments 

Run

Order

Run Number ‐

Data file number Configuration Turbulence level

1 1 OFF

2 2 6

3 1 4

4 3 2

5 1 6

6 1 2

7 3 4

8 2 4

9 2 2

10 3 6

1 2 4

2 1 2

3 1 4

4 1 6

5 3 2

6 3 6

7 2 6

8 3 4

9 1 OFF

10 2 2

1 1 6

2 3 4

3 2 4

4 2 6

5 1 OFF

6 1 2

7 2 2

8 3 6

9 1 4

10 3 2

Experiment 1: Maintain straight and level flight at 30000 feet ASL

Experiment 3: Offset approach and landing

Experiment 2: Straight in approach and landing
157 

References 

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Models. Journal of Guidance, Control, and Dynamics, Vol. 16, no.1 

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Cueing Algorithm Modification for Improved Turbulence Simulation. AIAA Modeling and 

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Center, Hampton, Va. 

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Assessment (LaRC.1) Pilot Briefing Guide. NASA/TM-1999-209533 

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for Motion Simulators. AIAA Visual and Motion Simulation Conference (1973). No. 73-930 

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Selecting the System Parameters. Utias Report No. 307. 
158 

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University of New York. New York 
REPORT DOCUMENTATION PAGE Form Approved

OMB No. 0704-0188

2. REPORT TYPE 

Technical Memorandum

 4. TITLE AND SUBTITLE

Algorithm for Simulating Atmospheric Turbulence and Aeroelastic Effects 

on Simulator Motion Systems

5a. CONTRACT NUMBER

 6. AUTHOR(S)

Ercole, Anthony V.; Cardullo, Frank M.; Kelly, Lon C.; Houck, Jacob A.

 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

NASA Langley Research Center

Hampton, VA 23681-2199

 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)

National Aeronautics and Space Administration

Washington, DC 20546-0001

 8. PERFORMING ORGANIZATION

 REPORT NUMBER

L-20173

10. SPONSOR/MONITOR'S ACRONYM(S)

NASA

13. SUPPLEMENTARY NOTES

12. DISTRIBUTION/AVAILABILITY STATEMENT

Unclassified - Unlimited

Subject Category 54

Availability: NASA CASI (443) 757-5802

19a. NAME OF RESPONSIBLE PERSON

STI Help Desk (email: help@sti.nasa.gov)

14. ABSTRACT

Atmospheric turbulence produces high frequency accelerations in aircraft, typically greater than the response to pilot input. Motion system equipped flight 

simulators must present cues representative of the aircraft response to turbulence in order to maintain the integrity of the simulation. Currently, turbulence 

motion cueing produced by flight simulator motion systems has been less than satisfactory because the turbulence profiles have been attenuated by the motion 

cueing algorithms. This report presents a new turbulence motion cueing algorithm, referred to as the augmented turbulence channel. Like the previous 

turbulence algorithms, the output of the channel only augments the vertical degree of freedom of motion. This algorithm employs a parallel aircraft model and 

an optional high bandwidth cueing filter. Simulation of aeroelastic effects is also an area where frequency content must be preserved by the cueing algorithm. 

The current aeroelastic implementation uses a similar secondary channel that supplements the primary motion cue. Two studies were conducted using the 

NASA Langley Visual Motion Simulator and Cockpit Motion Facility to evaluate the effect of the turbulence channel and aeroelastic model on pilot control 

input. Results indicate that the pilot is better correlated with the aircraft response, when the augmented channel is in place. 

15. SUBJECT TERMS

Aeroelastic; Cueing algorithms; Flight simulator; Motion systems; Turbulence

18. NUMBER

 OF 

 PAGES

174

19b. TELEPHONE NUMBER (Include area code)

(443) 757-5802

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b. ABSTRACT

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17. LIMITATION OF 

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3. DATES COVERED (From - To)

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

5d. PROJECT NUMBER

5e. TASK NUMBER

5f. WORK UNIT NUMBER

160961.01.01.01 

11. SPONSOR/MONITOR'S REPORT

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NASA/TM-2012-217601

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1. REPORT DATE (DD-MM-YYYY)

01- 08 - 2012