A piloted flight simulator study of conflict of interest wind modelling techniques

A PILOTED FLIGHT SIMULATOR STUDY

OF CONFLICT OF INTEREST WIND MODELLING TECHNIQUES

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UTIAS Technica1 Note No. 238

CN ISSN 0082-5263

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A PILOTED FLIGHT SIMULATOR STUDY

OF CONFLICT OF INTEREST WIND MODELLING TECHNIQUES

by

R. B. MacKenzie

November, 1982

UTIAS Technical Note No. 238

•

ACKNOWLEDGEMENTS

The author would like to thank his supervisor, Dr. L. D. Reid, for his support of this work and his willing availability for consultation. The author also wishes to express his appreciation to Wolf Graf, group engineer, for the many patient hours he spent explaining the inner workings of the simulation facility and helping track down hardware and software bugs.

Special recognition must go to Alex Markov in whose work "conflict of interest" wind modelling was conceived and whose enthusiasm for the concept sparked the author's own enthusiasm.

Lastly, the author gratefully acknowledges the contribution of the two pilots, Mr. R. Fowler and

Mr. D. Oswald, who gave unselfishly of their evenings to fly over 70 difficult ILS approaches at a consistently high level of performance.

The study was financially supported by grants from the Natural Sciences and Engineering Research Council. Partial personal support for the period 1980-81 was provided

through a University of Toronto Connaught Scholarship, and for 1981-82 through an Ontario Graduate Scholarship.

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ABSTRACT

Hazardous wind models synthesized using techniques based on differential games theory formalization of the conflict of interest between the wind and an aircraft on landing approach have been implemented and evaluated on a fixed-base flight simulator. This approach yields wind inputs that are dependent up on the aircraft state vector. Four wind modeIs, formulated using two distinct techniques arising from the theory, were tested in a series of steep ILS approaches flown by two pilots. Evaluations were made of pilot performance and the severity and realism of the generated winds, then compared to results obtained in the presence of two reference wind profiles. The study indicated that one of the techniques, which utilized a "wind controller" in a feed-back loop to generate winds on a real-time basis, had several advantages over existing simulator wind modelling methods and should be assessed further.

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TABLE OF CONTENTS ACKNOWLEDGEMENTS ABSTRACT TABLE OF CONTENTS LIST OF SYMBOLS Chapter . 1. INTRODUCTION 11. THEORETICAL BACKGROUND

2.1 Differential Games Theory

2.2 Landing Approach Formulation 2.3 Conflict of Interest Formulations

2.3.1 2.3.2 2.3.3

Indirect Method . • • . Direct Method . . . . Differential Games Method 2.4 Application to Flight Simulator Wind

Mode11ing • • • . . . 2.4.1

2.4.2

Indirect Technique . . • Wind Controller Technique 111. AIRCRAFT SIMULATOR . . • 3.1 Simulator Facility 3.2 Simulator Configuration 3.3 Analog Simulation . 3.4 Digital Simulation 3.5 Simulation Validation iv Page i i i i i iv vi 1 5 5 8 9 10 10 10 11 11 12 14 14 14 16 17 18

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Chapter IV. EXPERIMENT 4.1 Task 4.2 Subject Pilots 4.3 Organization 4.4 Wind Models . 4.5 4.4.1 4.4.2 4.4.3 Reference Profiles Wind Controller Models Indirect Technique Model Wind Model Software

V. RESULTS AND DISCUSSION 5.1 Data Presentation 5.2 Wind Controller Wind 5.3 Indirect Technique Model 5.4

5.5

Control Over Shear Encounters Pilot Adaptation to Wind Controller Models VI. CONCLUSIONS REFERENCES APPENDICES TABLES FIGURES v Page 20 20 21 21 22 22 23 25 26 29 29 30 38 39 42 44 46 A-l

A b ~l

f3

d E F Ql Q2 g H h h_G Iyy J M m n P LIST OF SYMBOLS

Aircraft system matrix

Solution to the differential games auxiliary equation (2.6b)

Aircraft control distribution matrix

Aircraft system wind rate distribution matrix

Glidepath deviation normal to glidepath plane,

positive above, m

Isoparametric constraint General linear system matrix

General linear system control distribution matrix

General linear system disturbance distribution matrix

Acceleration due to gravity, m.s -2 Disturbance vector space

Height of aircraft's centre of mass above ground level, m

Height of top of wind speed envelopes, m

Aircraft pitching moment of inert ia

Payoff function

Aerodynamic pitching moment

Aircraft mass, kg

Longitudinal wind speed envelope power-law

coefficient

Solution to the differential games matrix

Riccati equation vi

ç

q ~l' ~2 S S Sws T Tl / 2 (2) t U ~ u, w V ~E V app W W1 W3 W1G , W3G X, Z x ~ xI' zI

"

Weighting matrix

Pitching rate, nose-up positive. rad .s-l Weighting matrices Weighting matrix Sws/trun Isoperimetric constraint E 2in 5erms of wind velocity ra te of change, m

.s-Phugoid mode period, s

Time to half (double) amplitude, s Time, s

Control vector space General control vector

Components of ~ in stability axes frame Airspeed vector

Velocity of aircraft centre of mass through

inertial space

Approach airspeed

Wind velocity matrix, components in inertial

axes frame

Tailwind velocity, m.s- l -1 Downdraft velocity, m.s

Wind velocities at top of respective wind speed envelopes

Components of aerodynamic force in stability axes frame

General linear system state vector or aircraft

state vector

Specified perverse state

Components of position of aireraft's centre of mass in inertial axes frame

Aircraft con trol vector

Elevator position, rad

Throttle fraction Damping ratio

General disturbance vector

Euler pitch angle, positive up, rad Wind energy adjustment parameter Mean value of S

Standard deviation of S

Damped_~atural frequency of phugoid mode,

rad .s

Undamped natural frequency of phugoid mode, rad .s-l

Denotes perturbation quantity Denotes reference equilibrium value Denotes value at terminal time Denotes value at initial time

Denotes quantity to be used in optimization process

Denotes value set by wind controller Denotes vector

Denotes matrix or column vector Denotes transpose of matrix

Denotes root mean squared value (time-averaged) Denotes mean value (time-averaged)

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CHAPTER I

INTRODUCTION

A number of recent aircraft accidents for which certain types of wind conditions have been found to be major contributing factors (e.g. the well-documented JFK accident) have focused attent ion on the problem of flight through variabIe winds, particularly in the context of the landing approach. While one may proceed with the analysis of many facets of this problem using stochast ic techniques, certain aspects are best treated using determinist ic models which represent one realization of the wind field. This is true when considering flight through the atmospheric

boundary layer where the turbulence properties are functions of at least height and wind probability models are not weIl established, and is especially true when considering the effects of winds that lie in the tails of the probability curves whose contribution to the expecta-tion values of the aircraft response may be small, but which nevertheless may create significant safety problems when they do occur.

In view of these remarks i t is not surprising that there is an ongoing research effort aimed at developing techniques of generating deterministic wind modeIs. These

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techniques include those that reflect the hazards of flight through low altitude variabIe winds, and that may be conveniently implemented on flight simulators. The modelling methods include the following important ones: 1. Models based on meteorological data.

2. Models based on data obtained in atmospheric boundary layer wind tunnels.

3. Situation specific dynamic models of the physics of the atmospheric flow (e.g. thunderstorm outflow models [1]1.

4. Arbitrary profiles intended to stimulate aircraft dynamic response.

5. Mathematically optimal "worst-case" wind modeIs.

The last group of methods are of interest because they pose the worst-case concept in a formal mathematical frame-work while, at the same time, avoiding the difficult task of modelling complex atmospheric flows. They seek to create wind disturbances constrained in a specified manner that maximize a functional of the state of the aircraft. Such techniques have been used in the past to find worst-case wind time histories (e.g. van der Vaart's method [2]).

For certain types of formulations i t is possible to specify the worst-case solutions in terms of the aircraft

state. The theoretical development of this concept has been carried out by Markov at UTIAS based on differentiai games theory formalization of the basic conflict of interest present between the wind and an aircraft on

3

landing approach [3]. The resulting techniques are equivalent to closing the loop on the wind, a

character-istic that has interesting implications for the

imple-mentation of these wind models on flight simulators. The level of control difficulty caused by the presence of the variable winds may be adjusted by setting parameters within the wind control-loop. More importantly, because the human

pilot introduces randornness and because the gross features

of the control-loop signals can be removed by linearizing about a reference equilibrium, the pilot will never see the identical wind profile twice, and thus, as in the real world, each encounter will represent a new experience.

In the following, the results of a preliminary assess-ment of the conflict of interest wind modelling concept of Markov for flight simulation applications are presented. The study had three main objectives:

1. To determine the best way of implementing the wind models in an aircraft flight simulator.

2. To obtain objective and subjective evaluations of the wind model severity and realism, and to assess their usefulness for flight training purposes.

3. To compare these models to an existing severe wind profile currently approved for training purposes by the FM.

Four different wind models, synthesized using two of Markov's techniques, were implemented on a manned three degree-of-freedom fixed-base simulation of a light STOL

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transport. Data was collected for steep ILS approaches, flown by two pilots, in the face of these four models, the severe wind profile and a reference profile.

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CHAPTER II THEORETICAL BACKGROUND

2.1 Differential Games Theory

Two-sided optimization problems defined by systems of

differential equations are the subject of the branch of

optimal control theory known as differential games. The

detailed development of the application of differential

games theory to the wind versus aircraft on landing approach problem has been carried out by Markov at UT lAS

(see Reference 3). Key points of this development will be

summarized for the reader.

The conflict of interest between the wind and

air-craft or airair-craft controllers is an example of the

minimax problem in optimal control. With the exception of

the class of linear quadratic systems, analytical feedback

solutions to minimax problems are generally difficult to

obtain. Fortunately, the aircraft-wind system equations

can be put into a linear formulation of the form

~(t)

!

~(t) + ~l ~(t) + ~2 ~(t) (2.1a)

~(to) ~o (2.1b)

where ~(t) corresponds to the aircraft state vector, ~(t)

to the aircraft controller input vector and ~(t) to the 5

~

6

wind controller input vector.

In a differential games formulation the conflict between aircraft and wind controller is then formalized

through aquadratic payoff function J(~, ~) to be maximized

by the wind vector ~ E Hand minimized by the control

vector u E U. The minimax solution satisfies the

inequality

J(~*, ~) ~ J(~*, ~*) ~ J(~, ~*) (2.2)

The superscript asterisk denotes the minimax solution. Note

that the payoff function and the admissible control and

disturbance spaces U and H must constrain u and ~

sufficiently to make the problem meaningful, i.e., so that arbitrarily large control and disturbance input energies are not allowed. Because the payoff function is the same

for the maximizing and minimizing controllers, the game is also zero sumo

The payoff function for the aircraft-wind linear

quadratic problem can be formulated as

J ~ T _{(t f )} ~ ~(tf) - ~(tf) ~ ~(tf) T

J

tf T T T

+ to [~ (t)

g

x(t) - ~(t)

g

~d(t) + ~ (t) ~l ~(t)

T

+ ~ ~ (t) ~2 ~(t)ldt (2.3)

where ~(t) may be included as a specified perverse state

matrices with the following sign definiteness properties

on [to, t

f]:

~,

Q

are positive semidefinite

~l is positive definite

~2 is negative definite

The properties of these matrices make the problem

meaning-ful in the minimax context. The positive parameter lJ may

be adjusted so that the disturbance "energy"

J

tf T

E - to ~ (t) ~2 ~(t)dt (2.4)

is within a desired range.

The minimax feedback solution to the linear quadratic

problem presented above can be shown to be

u*

_~~l

Qi

[~(t) ~(t)

+

~(t)]

(2. Sa) ~*

!

R;l GI [Ptt) x(t) + bIt)] l J - - - (2.Sb) where ~(t) -P(t)F - F Ptt) _{- - - -}T + _{Ptt) [G1Rl G1 - - -}-1 T _- + -_{l J - -}1 G2R-1 T 2 _-G2]P(t)

-- Q

(2.6a) ~(t) -F T _-bIt) + _-Ptt) [G1Rl G1 _{- - -}-1 T + -1 _{lJ - - - -}G2R-1 T 2 G2]b(t) +

.Q

~(t) (2.6b)

with terminal conditions

~(tf) S (2.7a)

0(

8

~(tf) -~ ~(tf) (2.7b)

Equation (2.6a) is the generalized matrix Riccati equation,

from optimal control theory, for the system, and (2.6b) an

auxiliary equation arising due to the formulation of the problem.

2.2 Landing Approach Formulation

In order to conduct a simulation of an aircraft on approach through variabIe winds involving the differential

games technique, i t is necessary to have the aircraft

longitudinal dynamics in linear form. This can be done by

utilizing perturbation equations, written in stability axes, linearized about a reference equilibrium consisting of flight on a rectilinear glide slope in the presence of

a constant headwind. The development of the perturbation

equations used for this work is presented as Appendix A.

Their matrix form is

6 ~ A 6 ~ +

fl

6

f

+

f

2

6 W +

f

J

6 W (2.8)

In order to apply the differential games theory, equation

(2.8) must be rewritten in the form of (2.1a). This may be

accomplished by augmenting the state vector with 6 Wand

treating the disturbance vector, 6 ~, to be the wind

controllers input, i.e., 6 W _{~wc' It also places the}

problem in a form in which 6 W may be weighted in the

payoff function. Similarly, to permit weighting of control

ra te in the payoff function (for smoothness of control

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response) and to allow formulations where the wind controller strategy includes feedback based on the

air-craft control position vector, 6

i,

the state vector is

also augmented with 6 0 and the aircraft controller's inputs are taken to be control rate 6

o.

Trajectory perturbations 6 xI and 6 h can be combined to provide the controllers with glidepath deviation 6 d

6 d 6 xI sin Y_e + 6 h cos Y_e (2.9) where Y

e is the equilibrium glidepath angle. The resulting

augmented equations of mot ion are

6 ~p ~p 6 ~p + flop 6 ~ + f 3_{0p ~wc} (2.10)

where now

6 x_-op T

=

[6u 6w 6q 68 6d 60E 60_T 6Wl 6W31 (2.11a)

W

T

[W_IWC W_3WC]

-wc (2.11b)

Here, 6 ~ _-vp , 6 _-0, _-wc W ,A ,Cl _{-op - op} and C_- 3 _op have a one-to-one correspondence with the terms of equation (2.1a). The optimization problem need not be carried out with the

full dynamic system of equations, i.e., one may define

6 x , 6 0 or 6 W to be any suitable subset of themselves.

-op

-2.3 Conflict of Interest Formulations

The conflict of interest concept is summarized in the feedback block diagram of Figure 2.1. A number of formula-tions are possible as determined by the position of

10 switches A, Band C.

2.3.1 Indirect Method

For these formulations, switch A is closed and

switches Band C are open. The wind inputs generated by the wind controller attempt to make the aircraft track a specified state trajectory, ~(t), which is known to be particularly perverse. Such state trajectories may be based on recorded trajectories in aircraft incidents and accidents. The payoff function to be minimized by the wind controller is of the form of equation (2.3) but naturally does not contain the ~T(t) ~l ~(t) term.

2.3.2 Direct Method

For these formulations switch B is closed and

switches A and C are open. The wind controller attempts to maximize the aircraft deviation from the desired state trajectory in the sense of the quadratic payoff function. The payoff function, this time, does not contain either the

~T(t) ~l ~(t)

or the

~(t)

terms. 2.3.3 Differential Game Method

For these formulations switches Band Care closed and switch A is open, i.e., both wind and aircraft controllers are to be optimized. The objective is to find a best-case

aircraft controller and a worst-case wind controller that

minimaximize the resulting payoff function.

method formulations discussed above did not include a

feedback aircraft controller. Such an aircraft controller,

for example, to fly the approach may be readily incorporat-ed in these formulations by defining i t a priori and

including it in the airframe dynamics. Note that this is

not an optimal controller as will be produced by the differential games method.

2.4 Application to Flight Simulator Wind Modelling

2.4.1 Indirect Technique

The indirect method is useful for flight simulator

applications when i t is desired to cause the aircraft to

follow a prescribed state trajectory. The optimal wind

controller, however, cannot be determined in real-time. This necessitates initially running the simulation off-line, then using the resulting wind altitude profile or wind-rate time history as a disturbance input in the

flight simulator. Note that these wind inputs during the

flight simulation will not be optimal as the aircraft system now includes the hurnan pilot's control inputs. There is no guarantee that the pilot will attempt to minimize the same payoff function that the wind controller

is trying to maximize, or th at he will even perform

optimally. From the practical point of view, however, the

differences between the dynamic system for which the winds

were optimize~ and the actual dynamic system to which they will be applied should not reduce the perversity of the

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winds to such an extent as to preclude their use for

flight training purposes.

2.4.2 Wind Controller Technique

Direct and differential games formulations have a particular appeal in flight simulator applications because advantage is taken of the closed-loop nature of the

worst-case wind inputs. These inputs become specified as a

function of state and, thus, once the wind control law is determined, they are available for all possible states. Again, the problem arises that the wind control law cannot

be determined in real time. A simulation must be run

off-line where the solution to the Riccati equation is

deter-mined and the wind con trol law in the form of the matrix

- l

_~

_-

R;l

_{- -}

G;

PIt) stored. This matrix may then be used in

conjunction with equation (2.5b) to determine wind inputs

in real time in the flight simulator. The vector ~(t) does

not appear in these cases as the perverse state trajectory

vector ~d(t), of course, does not exist.

The problem of the wind controller not being optimal

against the pilot, discussed in the previous section, is alleviated by the fact that the wind con trol law still penalizes departures of the aircraft state from the desired

trajectory, regardless of the control technique i t is

operating against. In fact, a time-invariant control law,

based on a value of ~ at a suitably chosen time, can be

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computer memory for storing a time-varying wind control law. A further loss of optimality ensues, but not of utility.

t

CHAPTER III AIRCRAFT SIMULATOR 3.1 Simulation Facility

The piloted landing approaches for this study were conducted in the UTIAS multi-purpose fixed-base simulation facility. This facility consists of a Hewlett-Packard HP2l00 mini-computer, an EAI PACE TR-48 analog computer, a "vector-line" display system and an aircraft cockpit work station. The HP2l00 mini-computer interfaces with the analog computer through an HP56l0A l6-channel analog to digital converter, an HP6940A Multiprograrnrner device with 5 channels of digital to analog conversion available and with the display system via its data and control buses. An HP7970B Digital Tape Unit is available to store simulation variables on

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magnetic tape.

3.2 Simulator Configuration

For this study the facility was configured to provide a three-degree of freedom simulation of a typical light twin-turboprop STOL transport (see schematic Figure 3.1). The simulated aireraft's geometrie and dynamic character-istics are surnrnarized in Table 3.1.

The cockpit was provided with conventional controls

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and throttle. In addition, manual elevator trim is available on the throttle quadrant. Only the elevator, its trim and the throttle provided inputs to the aircraft equations of motion in this simulation.

Cockpit instrumentation (see Figure 3.2) consisted of an airspeed indicator (ASI), altimeter, percent engine power indicator (single needle indicating cornbined percent engine power) and an electronic attitude indicator (EAI) with fast/slow and glidepath deviation bugs. The fast/slow bug was referenced to the approach speed of the aircraft, 78 KTS, each dot equa1 to 5 KTS deviation. The glidepath deviation indicator did not emp10y an expanded sca1e presentation; therefore, on this instrument, 2 dots equal the standard 0.70 _{full-scale deviation. Hatch marks on the}

attitude indicator were at 50

intervals.

A low-speed warning system was avai1able to provide a visual and aural warning if the a ir speed fell below a pre-selected value (nominally 15 KTS below the approach speed). No out-the-window visual cues were provided to the pilot.

Arnbient cockpit noise was e1ectronica11y generated to respond to the throttle setting in pitch and volume.

The hybrid digital-analog simulation was conducted by an operating program resident in the HP2100 mini-computer. This program samples the aircraft state vector present on the analog computer, calculates the aircraft's position in space, records the simulation variables on magnetic tape, calculates and outputs the instrument readings and

co-ordinates of the EAI display; then computes the desired wind derivative terms and inputs these back into the air-craft dynamic equations on the analog computer.

3.3 Analog Simulation

The simulated aircraft's dynamics were prograrnrned on the analog computer to provide rea1-time solutions of the aircraft's state vector to the simulation program.

The form of the linearized longitudinal equations of mot ion used in this three-degree-of-freedom simulation was that developed in Reference 4 (see Appendix A). The reference equilibrium conditions used for the approach task were: YG 0.122 rad (3.1) V e 40.0 ms -1 (3.2) Wie 0.00 ms -1 (3.3)

These correspond to the conditions used in the digital simulations from which the wind controller wind matrices were obtained. The resu1ting aircraft 1inearized dynamic equations are:

llu -0.0990 llu + 0.150 llw - 9.74 118 + 4.86 llÖ T

- 0.993 llWi - 0.122 llW3 (3.4)

17 /::,.w= -0.456 /::"u - 1.15 /::"w + 38.3 /::"q + 1.18 M - 3.97 /::"0E - 3.20 /::"0T + 0.121 /::"W1 - 0.983 /::"W3 /::,.q 0.00862 /::"u - 0.106 /::,.w - 2.19 /::,.q - 0.0151 /::,.8 - 4.87 /::"0E - 0.301 /::"0T M /::,.q /::"x I = 0.993 /::"u - 0.122 /::,.W + 4.87 /::,.8 + /::"W1 /::,.h -0.122 /::"u - 0.993 /::,.W + 39.7 /::,.8 + /::"W3 (3.5) (3.6) (3.7) (3.8) (3.9)

They were programmed on the analog computer using standard techniques. The scaling factors employed appear in

Table 3.2. The circuit schematic and corresponding potentiometer coefficients are given in Figure 3.3 and Table 3.3, respectively.

3.4 Digital Simulation

All user simulation routines were written in HP Fortran and deve10ped on the HP2l00 system (for program listings see Appendix B). Extensive use was made of resident system subroutines for operation of the peripherals, e.g., A to D, D to A conversions, vector image generation.

The simulation (see flowchart Figure 3.4) was

conducted by an overall operating program ACSIM which calls

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user routines DISPL, MTFSU, RECD and WIND, and system routines to perform specialized tasks as needed. A cycle time of 40 ms was selected in order to have time available for future expansion of the wind generating subroutine. In order to avoid flicker in the EAI display produced by DISPL, it is necessary to refresh the image at a rate in excess of the 40 ms cycle time. Inherent in the system software driving the vector generator is an image "refresh" operation whereby the image is redrawn, using the existing coordinates, at a specified time in the cycle, independent of any computations presently under way in the main

program. The refresh was programmed to occur at 20 ms so that the image refresh ra te would be 50 Hz.

The recording capability is optional on a per run basis. Up to 18 variables can be recorded. Each recorded run is prefaced on the magnetic tape by an identifying header record created by the magnetic tape file system utility (MTFSU) subroutine which is responsible for all magnetic tape manipulations with the exception of the simulation data recording performed by the subroutine REeD.

Subroutine WIND is described in detail in Section 4.5. 3.5 Simulation Validation

The hybrid simulation was validated by comparing the open-loop response of the aircraft state vector to sinu-soidal wind and control inputs with the response of an available digital simulation of the aircraft to the same

inputs. Throttle, elevator, Wl and W3 inputs into the aircraft equations of mot ion were checked with the following forcing functions:

MT 0.2 sin (0.293l7t)

ME 0.02 sin (0.29317t) rad

W1 0.5 sin (0.29317t) m.s -2

W3 1.0 sin (0.293l7t) m.s -2

The frequency was chosen to stimulate the aireraft's phugoid mode.

(3.l0a)

(3.10b) (3.10c)

(3.l0d)

Same-scale plots of the aircraft response for the hybrid and digital simulations were obtained and compared directly. In all cases, plots matched to within the digitization error of the A to 0 converter (for error particulars, see Table 3.4).

CHAPTER IV

EXPERIMENT 4.1 Task

The simulated task consisted of intercepting a 7° ILS glidepath from level flight at 457 m (1500 ft) and flying an approach to a 30 m (100 ft) decision height (see Figure 4.1). The simulation began with the aircraft at 610 m (2000 ft), 5765 m from the touchdown point in its nominal descent configuration (full flaps, 16% power, Vapp

=

78 KTS). This configuration corresponds to all perturbations about the reference equilibrium conditions equal to zero.

Subject pilots were instructed to level-off at 457 m (1500 ft), maintaining V to intercept the glide slope.

app

Upon interception, the pilots were to fly the approach using whatever techniques they felt were appropriate, but in all circumstances to continue to the decision height. At the decision height, a go-around was to be commenced and continued until the simulation ended.

Note that the aircraft was assumed to maintain the localizer at all times, regardless of pilot aileron or rudder activity, as the simulation had only longitudinal freedom of motion. The pilots were also not provided with

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any means to ascertain ground position (i.e., marker beacons or OME).

4.2 Subject Pilots

Two pilots participated in the formal portion (phase 2) of the study. They are denoted as pilots 1 and 2. Pilot 1 was an experienced (13000 hours total time) test pilot with over 1000 hours IFR and 400 hours in flight simulators. Pilot 2 was a civilian flying instructor

(950 hours total time) with 29 hours IFR and 9 hours in flight simulators.

4.3 Organization

Upon arrival at the simulation facility, the pilots were briefed on the exercise in general and shown the simulation facility. They then flew a series of

familiarization runs without any wind inputs to acquaint themselves with simulator instrumentation and its handling qualities. When they felt comfortable with the simulation, a variable wind, consisting of a Wl time-referenced

sinusoid at the aircraft phugoid frequency superimposed over a decreasing linear wind gradient (Wl

=

sin (0.293l7t) + 0.025h' m.s-2), was introduced for practice in handling the simulator in strong wind-shear conditions (Figure 4.2). This warm-up procedure was repeated at the beg inning of each session that each pilot attended.

Product ion runs were flown in blocks of 5 approaches, each block utilizing the same wind model, for a total of 10

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runs per wind model per pilot. The order of blocks was randomized for each pilot (Table 4.1). Prior to flying the production runs, each pilot was told that he would encounter variable wind conditions but was not informed as to the type or degree of severity of the winds.

Following each approach, the pilot was asked to fill out a questionnaire (see Appendix C). Af ter each block of 5 approaches, he was interviewed by the experimenter to elicit any further comments regarding the winds encountered and the simulation itself.

The recording of each simulator run began when the aircraft was 3725 m behind the glidepath ground inter-ception point and ended 500 m back. For this study, ~u, ~w, ~q, ~e, ~8T' ~8E' ~xI' ~h, d, t, W1 , W3 , W1 , W3 , xI and h were recorded at 40 ms intervals.

4.4 Wind Models

Six wind models were employed in this study. Two were fixed reference wind profiles, an additional three were wind controller models based on the conflict of interest concept and synthesized using linear quadratic differential games theory, while the final model was based on a fixed time-referenced wind-rate history from an indirect method formulation. No turbulence inputs were introduced in any of these models.

4.4.1 Reference Profiles

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representative of light wind shear conditions in which

W3 = O. W1 is constant at 10 ms-l above 400 m where i t

begins to linearly decrease until reaching 0 ms-l at

ground level (see Figure 4.3). It was included to obtain

baseline data.

Model 3 (see Figure 4.3) is based on the winds

estimated to have been present at the time of the JFK

Eastern Airlines accident (Reference 5). The downdraft

velocities were reduced by a factor of 0.75 to make them

more compatible with the climb capabilities of the

simulated STOL transport. This model was used to obtain

data representative of flight in acknowledged extreme wind shear and downdraft conditions.

4.4.2 Wind Controller Models

Models 1, 2 and 4 are the wind controller modeIs. The

payoff function weighting matrices under which they were

obtained were selected 50 that technically significant

deviations in the aircraft state from the reference state

were weighted approximately equally and 50 that the phugoid

mode of the aircraft was destabilized (see Table 4.2). The wind control law for Model 1 given by

W1_WC -0.299 L1u - 0.00722 L1w + 0.326 L1q + 1.08 L16 + 0.821 L1e E - 0.730 LIeT (4.1a) W_3WC 0.0136 L1u - 0.040 L1w + 0.143 L1q + 1.46 L16 - 0.834 L1Ö E - 0.0344 LIeT (4.1b)

produces both W1 and W3 winds. It is based on a steady-state* differential games solution which includes

contri-butions dependent upon L1e_E and LIeT perturbations in its

formulation (details may be found in Reference 3, Example 1 of Section 5.5.4).

The wind control laws for Models 2 and 4 were based on a direct method (no LIOT or L1e_E feedback) worst-case

solution** producing only a longitudinal wind component (for particulars, see Reference 3, Section 5.5.3).

Model 2 was chosen because i t strongly destabilized

the open-loop phugoid mode of the aircraft but still was judged, during initial trials, to produce wind encounters

that were "flyable" in the simulated aircraft. Its control

law was given by

W_1WC -0.365 L1u - 0.195 L1N + 2.19 L1q + 12.9 L16 (4.2)

Model 4 was of the same type as Model 2, but somewhat

less destabilizing of the phugoid mode (see Table 4.2).

*

Wind control law (2.5b) with Ptt) replaced by

pCt).

-lim t .... -00

** Because of the presence of conjugate points (see

Reference 3, Section 5.5.3), this model did not have a

steady-state solution. The wind control law was

arbitrar-ily chosen to be that control law which results from (2.5b)

25

W_1WC 0.244 ~u - 0.106 ~w + 1.20 ~q + 7.08 ~e (4.3)

The wind inputs generated by these three models were introduced at an altitude of 400 m (1312 ft) and were

superimposed on the wind profile of Model O. The latter

was done for two reasons. Firstly, i t was done to produce

an offset in the aircraft state vector from equilibrium on which the wind controller models could begin to operate. That is, while tracking the ILS glidepath in the presence of longitudinal wind not accounted for in the linearization of the equations of motion, the aircraft will need power and pitch altitude adjustments producing perturbations in

the aircraft state. The continuously decreasing headwind

of Model 0 necessitates several throttle adjustments to

maintain glidepath while making an approach. Secondly, the

superposition of the models facilitated comparison with the baseline data.

4.4.3 Indirect Technique Model

Model 5 was a fixed time-referenced wind-ra te history

obtained using the indirect technique. The perverse state

trajectory vector and the weighting vector were based on the

frequency, magnitude and phase relationships implied by the open-loop phugoid mode eigenvalues and eigenvectors of the STOL transport (for particulars, see Reference 3, Section

5.5.2, Example 1). The wind-rate time-history that results

destabilizes the phugoid mode of the aircraft (see

26 Figure 4.4).

The wind derivative terms, due to the time-history,

were introduced at an altitude of 250 m (820ft). The

typical wind profile which resulted appears as Figure 4.5.

Note that the baseline wind profile was introduced prior

to the time-history to again facilitate comparisons.

4.5 Wind Model Software

The subroutine WIND called by the simulation operating

program determines the appropriate wind rates. WIND exists

in three distinct versions corresponding to the three types of models used in this study:

1. Fixed Wind Profile Generator;

2. Wind Controller Wind Derivative Generator;

3. Fixed Time-Referenced Wind Derivative History

Generator.

Listings appear in Appendix B.

Version 1 accepts an arbitrary Wl and W3 versus

altitude profile pre-punched on paper tape. The program

will move the wind velocities from their initial value of

zero to the values specified at the top of the wind

profile, during the first 30 seconds of the simulation

run, according to a square-law tracking routine. Once the

altitude of the top of the profile is reached, the program interpolates through the profile calculating the wind rates required to reproduce the profile on the basis of the aircraft's latest calculated descent rate.

Vers ion 2 accepts the wind controller matrix pre-punched on paper tape. Af ter requesting the longitudinal

(Wll wind component at 400 m to establish the linear wind gradient onto which the wind controller wind rate contri-butions are to be superimposed, the program will again move the longitudinal wind velocity to the specified value according to the tracking algorithm. The wind controller is phased-in on top of the linear gradient at 400 m over 10 seconds to avoid discontinuities.

For certain situations, the wind inputs generated by the wind controller may become unrealistically large. This problem was avoided by specifying wind speed envelopes, as shown in Figure 4.3.

The Wl envelope is a power-law profile described by

Wl WlG(ilh n ' G where WlG 24.5 ms -1 h G 400 m n 0.28

It was chosen as an approximation to an atmospheric boundary-layer type profile.

The W3 envelope is a linear profile described by

W3 W2G h G where W3G 14.5 ms -1 h G 400 m 28

This profile arises from the "90o-Corner" flow problem in incompressible fluid mechanics.

The winds present in the simulation were matched smoothly with these envelopes using an algorithm, based on the ratio of the square of the actual wind speed to the square of the wind envelope speed, that was applied continuously to Wl and W3 •

Version 3 accepts an arbitrary Wl and W3 versus time-history pre-punched on paper tape. It requests the

longitudinal (Wll wind component at 400 m to establish the linear wind gradient present prior to introducing the wind derivative time-history. The longitudinal velocity is brought to the specified value as previously described. At 250 m, the linear wind algorithm is replaced by a

routine which interpolates through the time-history profile, specifying Wl and W3 • At the end of the time profile, the program takes the present winds and decreases them linearly to zero at ground level.

CHAPTER V RESULTS AND DISCUSSION

5.1 Data Presentation

Data recorded during product ion runs was analyzed

off-line on the HP2l00 computer to produce time-history plots of aircraft state variables and summary statistics for each run. The data analysis program is included as

Appendix D. Figures 5.la to 5.le and Table 5.1 present

output for a representative run.

The quantity Sws appearing in Figure 5.lb is a

measure of the wind variability defined as

Sws

J:f

.

.

[W12(t) + W/(t)

1

dt (5.1)

It is analogous to the integral E of equation (2.4).

Normalizing Sws with respect to run time, the quantity S

is obtained

S _{Sws/trun (5.2)}

which is just the time-averaged value of the sum of the

squares of the wind rates, i.e.

.. ..

S W12 +

wl

(5.3)

Note that S depends on the time rate of change of the wind

29

30

velocity as seen by the aircraft, and will thus change

from run to run, even for runs in the presence of the reference profiles of Models 0 and 3.

The run summary sheet, Table 5.1, provided the

root-mean-square (rms) deviation in various aircraft state

variables for the runs and categorization and zone

boundaries of the longitudinal wind shears encountered

during the run. The categorization criteria are discussed

in the next section.

Tables 5.2a-d summarize the state variable rms

deviations, S values, maximum longitudinal wind shears

encountered and pilot questionnaire responses analyzed in the following sections, for all production runs.

Figures 5.2a-h contain the wind profiles resulting from the approaches flown through the three wind-controller models.

5.2 Wind Controller Wind Models

Other than the W3 component of Model 1, the profiles

of Figures 5.2a-5.2h change markedly from run to run and

pilot to pilot. The Sparameter also shows considerable

variation between wind models as well. This is apparent

from Table 5.3. The quantity as/~s is several times

larger for the wind controller models than for the fixed profiles of Models 0 and 3. This is indicative of the

large variation between winds produced during different

are generally larger for Pilot 1 than for Pilot 2. This corresponds to the generally stronger wind shears

experienced by Pilot 1. Figure 5.3 presents the range

and Figure 5.4 presents the relative frequency distribution

of the severest longitudinal wind shear encountered during

each run. The categorization of the wind shear was

according to the leAO interim classification scheme [6] of

Table 5.4. The longitudinal wind velocity changes tend to

be low frequency in nature, a characteristic which is to be expected from such wind models as they stimulate the more

weakly damped phugoid mode of the pilot-aircraft system. The relatively minor changes observed in the

characteristics of the W3 profiles of Model 1 may be

explained by considering in detail the way in which the

wind controllers were synthesized and then implemented in

the simulation. In the optimal wind controller synthesis

process, a linear aircraft dynamics model with Wl

e = 0 and

time-invariant reference equilibrium conditions was used. In the simulation, the same aircraft dynamics model was used, but the wind inputs generated by the wind controller

were superimposed onto the linear wind profile of Model O.

In general, the presence of the linear wind results in ~e

and ~oT offset components that feed back through the wind

controller model to generate Wl and W3 offsets. This

effect was most prominent for W3 in Model 1, a consequence

of the particular values of the ~e and ~oT gains in that

model, and resulted in a significant contribution to W3

32

that was unchanged from run to run.

This characteristic can be avoided by eliminating the linear wind or by considering a more sophisticated simula-tion in which the state variables that produce these offset effects are passed through a suitably defined low pass

filter. The perturbation quantities ~e, ~oT' and so forth, on which the wind controller operates, can then be defined

with respect to the filtered quantities rather than with respect to their reference equilibrium values, thus eliminating the unwanted offsets in the wind controller output.

t-tests were employed to examine the differences between the results produced by wind Models 1-4 and the

baseline (Model 0 ), and the differences between the two

pilots. The tests were performed on the run simulation

variable rms deviation and S values presented in Tables

5.2a-d. Results are surnrnarized in Tables 5.5 and 5.6.

From Table 5.5 i t can be seen that,generally, significantly

larger values of the rms deviations and S were obtained for

wind Models 1-4 than for Model O. Throttle rate activity

showed the least change in rms level from the baseline for

the wind controller models. This is probably indicative of

the fact that the pilots were attempting to track the glidepath with elevator during the shear encounters, only adjusting power when i t became apparent that a long term deviation in flight path angle was occurring.

33

pilots for the wind controller Models 1 and 2 are significant at the 5% confidence level (see Table 5.6). This suggests that some fundamental differences existed in pilot control strategy. Since the wind controllers

determine wind inputs based on a subset of the aircraft state and control inputs, certain control strategies will, therefore, produce different wind characteristics. This is also suggested by a comment, repeated several times by Pilot 1, that he had concentrated on obtaining good glide-path tracking and was willing to tolerate a "few knots" of airspeed deviation. Since none of the wind controller models contain glidepath deviation in their wind generation algorithms while they all contain airspeed deviation, they would tend to produce less severe winds for pilots who adopted a tighter airspeed tracking strategy. Conversely, a pilot who adopts a tight glidepath tracking strategy at the expense of airspeed tracking might ultimately find himself in a divergent situation where both a ir speed and

glidepath tracking performance are degraded markedly. Such an effect was seen for Pilot 1 during many of his approaches involving Model 2 where the wind inputs would oscillate from one side of the limiter envelope to the other.

With this said, i t is interesting to note that no significant differences exist between pilot ~s values for Model 4 which is of the same form as Model 2. This might suggest that the pilot-wind controller system may be very sensitive to the amount of ÁU feedback in the loop for this

34

particular model formulation. The phugiod damping does not seem to be the appropriate factor to look at as Modellis more negatively damped (see Table 4.1) than Model 2, but even including the W3 contribution i t does not produce as large S values for either pilot. However, its longitudinal ÁU feedback coefficient is some 20% less than that of Model 2's. It may not be correct to make this direct comparison of ÁU across types of wind model formulation, though, due to the different feedback elements involved. Certainly, within a given formulation, phugoid damping will be a function of ÁU coefficient value and, hence, weighting strategy during model formulation. It may be possible to lessen W1 sensitivity to Áu while still maintaining the level of destabilization through careful selection of weights. Further study should be made of the relationships involved in this process as they pertain to the results which will occur with the pilot in the control loop.

The hazard posed by the wind controller models can be evaluated directly through the categorization of the wind shear encounters according to the ICAO classification mentioned earlier, and subjectively through the pilot's response to the questionnaire.

The S value for a run did not turn out to be a very good measure of the strength of the shears encountered by the aircraft. In general, the S values obtained for the wind controller models were smaller than those obtained for the reference severe shear condition Model 3. However,

35

this did not imply that the winds generated by these models

were not hazardous. As S represents an average value of

wind rate, large shears may still have existed even in

runs where S was not large. An example of this is the

wind profile of Figure 5.le which has been categorized

according to the lCAO scheme. From Figure 5.5 i t is seen

that, despite the relatively small value of S, there are a

number of strong and severe shear encounters. The

relationship between S and shear encounters will be discussed further in Section 5.4.

Pilot assessments of the shears encountered during the experiment are presented in Figures 5.6 and 5.7 which correspond to the earlier Figures 5.3 and 5.4 detailing

actual shear values. The assessments seem to follow the

actual shear values reasonably closely for the wind

controller models, indicating that both pilots were fairly "well-calibrated" in terms of the lCAO classification

scheme.

The pilots had difficulty determining the type of wind disturbance (i.e. Wl, W3, or both) which they had

encountered on a given approach (see Table 5.7). This is

consistent with a similar finding by Bisgood et al at the

Royal Aeronautical Establishment (Reference 7) who also

noted that pilots tended to imagine W3 components in the

presence of strong longitudinal winds when, in fact, none

existed. Pilot 1 commented that he would have been able to

make a better assessment of the type of wind inputs if

inertial acceleration cues had been available.

Pilot task ratings (Figures 5.8 and 5.9) again tended to follow the severity of the wind shear encounter with the possible exception of Pilot 2's ratings for Model 2 versus

the severe profile of Model 3. Model 3 was rated from

"very demanding of pilot skill" to "uncontrollable" for all

runs, whereas the four severe shear encounters during

Model 2 runs were rated at worst only as "demanding of

pilot skill". This may be just a function of the workload

added due to Model 3's downdraft, for this pilot as he

successfully recognized in 80% of the runs that Model 2

contained only the Wl component. Pilot 2 rated all his

severe wind encounters as "nearly uncontrollable" or "uncontrollable".

The response to the question on whether the winds

encountered would have prompted a go-around in normal circumstances did produce some interesting differences

between pilots. Figure 5.10 shows the number of go-arounds

that each pilot felt should have been executed. Both would

have aborted for all approaches into the downburst Model 3, but Pilot 1 seemed to be more cautious in his assessment,

electingtogo-around several times in the face of only

moderate shears.

Both pilots generally found the wind controller models

useful for training purposes (see Table 5.8), although they found some of the severe wind encounters to be unrealistic

37

Model 3, which is based on an estimate of the severe

variabIe wind conditions which existed during the JFK

incident, was also rated as being unrealistic for many of

the runs. Pilot 1 also complained that, during those

Model 2 runs where the wind oscillated from one side of

the limiting envelope to the other, in addition to being

too severe, the frequency and nurnber of wind changes were

much too high.

It is interesting to compare the wind controller wind profiles obtained to profile shapes estimated to occur in

nature. In addition to the JFK accident profile of

Figure 4.3, Figure 5.11 presents two other profiles approved by the FAA for use in flight simulators [8). While there is no a priori reason for similarity, many of the wind

controller profiles have characteristic shapes strongly in

cornrnon with the naturally occurring profiles. Model 1

profiles of ten mimic the thunderstorm model with its strong

downdraft and headwind then severe tailwind shear. Models

2 and 4 produce profiles with shear regions looking like

those of the inversion and frontal wind shear profiles.

To obtain a profile similar to that of the frontal wind

shear, one could superimpose the wind controller winds over

a tailwind to headwind linear wind gradient.

The fact that these similarities do occur and are in

some measure a function of wind model, adds greatly to the

credibility of th is modelling technique as one appropriate

for flight simulator applications.

38

5.3 Indirect Technique Model

The indirect technique appears to be a useful way to

generate hazardous wind profiles which are aircraft

specific. For training purposes, however, i t suffers from

the same drawbacks as all other fixed wind models.

All approaches through Model 5 wind were categorized

as either encountering moderate or strong shear. Again,

the differences in S values and shear categorization for the fixed profile result from variations in rate of descent for the aircraft from run to run. Pilot 1 showed some variability in his assessments of the shear encountered,

which ranged from light to severe. This interesting

tendency was not confined solely to one block of runs, but

occurred both times. He was, however, correct in his

assessment, this time, of the presence of astrong W3

component. Pilot 2 assessed all encounters as being strong

and indicated in the debriefing that he recognized that the winds encountered were not changing from run to run. He

did not cite a W3 component in his assessment of shear

content.

From Table 5.5, i t can be seen that Pilot 1 showed

only a significant rise in airspeed deviation over the

baseline for this model, while Pilot 2 did show an increase

in glidepath deviation and elevator activity. The only

difference between pilots was in the latter's elevator inputs (see Table 5.7).

assessments generally indicating that the approaches were "mildly demanding" to "demanding of pilot attention, skill,

or effort". All Model 5 wind encounters, with the exception of Pilot l ' s run 4 assessed as producing a

severe shear, were felt to be realistic and useful for

training purposes by both pilots.

5.4 Control Over Shear Encounters

In order to implement wind-controller wind models in a

training environment, i t is necessary to have some degree of

control over the severity of the winds generated. The gross

features of the wind encounters can be, to some extent,

regulated by correct selection of the wind controller with

an eye to aircraft-wind controller open-loop damping

characteristics or the weighting of feedback elements.

However, i t has been se en that, within given wind models,

the actual shears encountered may vary from light to severe,

depending upon how the approach was flown. This very large degree of variability may not be totally desirable from a

training point of view. For example, consider the cases where an instructor has selected a wind controller model which, on the average, tends to produce strong shear

encounters. The pilot flying the approach is "right on the

money" with respect to the equilibrium conditions and,

hence, only experiences light shear conditions. The

training value of this approach has then been lost.

Disruption of the aircraft from its equilibrium by

40

superimposing baseline winds or turbulence over the wind

controller winds is one solution, but i t mayalso have the

opposite effect of initiating wind shear con~itions which

become too severe.

A better answer might be to actually directly control

the severity of the winds generated through the wind control law. One natural parameter through which to

exercise this control is Wl , the wind velocity rate of change (c.f. S in (5.3».

W1 is really the shear magnitude and can be connected

to an equivalent spatial shear with the relationship

Wl dW_dh l dh _dt (5.4)

Assuming that

~~

is nominally the equilibrium descent

speed dh

dt '" he Ve sin Se (5.5)

the Wl equivalent to a given spatial shear is

W1 Ve sin Se _{. dh} dWl (5.6)

With the reference equilibrium conditions of

(3.1 - 3.3), the above relationship may be used to loosely

define the ICAO shear categories in terms of Wl for the

aircraft flown in·this study (see Table 5.9). Once this

relationship is calculated for any aircraft, i t then

becomes possible to select the wind shear severity through Wl •

"

41

Figure 5.12 shows the relationships that were

A

actually obtained between maximum shear encountered and

Wl

(the rms wind rate averaged over the time of the run) for the wind controller models. The numbers, of course, do not correspond to those given in Table 5.9, because the

averaging has been done over the entire run time but,

nevertheless, the relationship between shear strength and

W1 is readily apparent.

When designing a controller to select the gains of another controller i t is evident that care must be taken

to ensure a stable system. One way of alleviating possible

problems may be to feed back, instead of WI ' a mean-squared wind rate averaged over a "suitable" period of time. Since the wind-controllers are primarily designed to

destabilize the aircraft's phugoid mode, one choice of time

might be the period of the open-loop aircraft-wind

controller system phugoid mode, as given in Table 4.1.

Figure 5.13 presents the rms value of W1 averaged over the corresponding phugoid period for a sample run from each wind controller model. The runs chosen were those having multiple shear encounters of approximately the same

magnitude so as best to illustrate the point that a

relatively constant value of rms wind rate results. The danger in the approach of controlling shear through variabIe gain wind controller laws may be in a

resulting tendency to promote unrealistic multiple shears at high ga in settings.

42

Along similar lines pilot performance could be fed back to control wind severity. This would be advantageous

in situations where the pilot ended up in a condition with

the aircraft far from the reference equilibrium; however, the ability to select a desired level of shear for a given approach would be lost. Some combination of wind rates and pilot performance feedback might prove to provide

accept-able control characteristics.

5.5 Pilot Adaptation to Wind Controller Models

In the straightforward implementation of the wind

controller models in this study, the pilot is, in effect, flying an aircraft with modified stability, where the wind

controller will, in general, be dest~bilizing. With enough

runs against a given wind controller, the pilot might be expected to adapt to the model in the same manner as he would learn to compensate for an aircraft with poor

stability characteristics, and improve his performance. He

will, however, obtain this improved performance at some

increase in workload.

Such learning of the model is at a different level

from learning to recognize a discrete wind input. Since these wind controller models are intended to produce hazardous conditions, the pilot will be confronted with them only occasionally (say once or twice in a given

session), rather than continuously, where he would

Since, for each encounter, the winds are different (versus

a discrete wind profile), there is a lower likelihood that

the pilot would recognize a particular wind model than

there is of him recognizing a deterministic wind model.

Further study should be made to determine the degree to

which this holds true. Variable wind controller laws, as

discussed in the previous section, would alleviate the problem greatly as the pilot would be forced to try to adapt to a system which was continuously varying.

CHAPTER VI CONCLUSIONS

Hazardous wind models,synthesized using techniques

based on differential games theory formalization of the

conflict of interest between the wind and an aircraft on

landing approach, have been implemented and evaluated on a

fixed-base flight simulator. The wind controller models

studied showed a major advantage over pre-recorded determinist ic models in that they produced wind inputs that changed from run to run and pilot to pilot. These low

frequency wind inputs tended to excite the more weakly

damped low frequency modes of the pilot-aircraft system,

of ten producing hazardous flight conditions. The wind

controller models were generally considered by the pilots

to produce disturbances that were realistic and useful for training purposes. Furthermore, some of the results

suggest that these models may be formulated in a manner

that favour certain pilot control strategies over others.

This characteristic may prove to be useful as a training

tooI. There are also indications that i t may be useful to

exercise real-time control over the magnitude of the wind

shear encountered through a feedback loop involving the

longitudinal wind ra te averaged over some period of time 44

45 and/or pilot performance parameters.

The indirect technique wind model appeared to be a useful way to generate aircraft specific hazardous wind profiles, but suffers from the limitations of all fixed profiles.

It is recommended that future work in this area should

be undertaken to study:

1) the implementation and assessment of the wind controller technique on more sophisticated six degree-of-freedom moving base flight simulators in an airline training environment;

2) the development of real-time techniques for controlling the strength of shear encountered; and

3) the level of pilot adaptation to wind controller models as compared to pre-recorded deterministic models.

REFERENCES

1. Williamson, G. G., Lewellen, W. S., and Teske, M. E., Model Predictions of Wind and Turbulence Profiles Associated with an Ensemble of Aircraft Accidents, NASA CR 2884, July 1977.

2. van der Vaart, J. C., Worst-Case Wind Time Histories Causing Largest Deviations from a Desired Flight Path, Report LR-267, Delft University of Technology,

April 1978.

3. Markov, A. B., The Landing Approach in Variable Winds:

Curved Glidepath Geometries and Worst-Case Wind Modelling, Report No. 254, University of Toronto Institute for Aerospace Studies, December 1981. 4. Reid, L. D., Markov, A. B., and Graf, W.O.,

The Application of Techniques for Predicting STOL Aircraft Response to Wind Shear and Turbulence During the Landing Approach, Report No. 215, University of Toronto Institute for Aerospace Studies, June 1977.

5. Anonymous, USAF Wind Shear Conference, Travis AFB,

California, 3-4 May 1978, Appendix IV.

6. Fichtl, George H., "Wind Shear Near the Ground and Aircraft Operations," Journalof Aircraft, Vol. 9, No. 11, November 1972.

7. Bisgood, P. L., Britton, J. W., and Ratcliffe, H. Y., Wind-Shear Encounters During Visual Approaches at Night. A Piloted Simulator Study., RAE TR-79l26, September 1979.

8. Gartner, W. B. and McTee, A. C., Piloted Flight Simulator Study of Low-Level Wind Shear, Phase 1, Report No. FAA-RD 77-166, SRI International, Menlo Park, California, May 1977.

9. Etkin, Bernard, Dynamics of Atmospheric Flight, John Wiley & Sons, Inc., Toronto, 1972.

10. Solowka, E. N., The Effect of a Predictive Wind Shear Chart on Pilot Landing Performance, Technical Note No. 220, University of Toronto Institute for Aerospace Studies, April 1980.