The ACSE Flight Simulator David Allerton Department of Automatic Control and Systems Engineering 24...

The ACSE Flight Simulator

David Allerton

Department of Automatic Control

and Systems Engineering

24th April 2006

2

Overview

• Design objectives

• Organisation

• Capability

• Dynamics and control

• Applications

• Questions

• Demonstration

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ACSE Flight Simulator

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ACSE Flight Simulator

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Aims

• Engineering flight simulator

• Real-time non-linear simulation

• Modular architecture

• Low cost

• Applications: control system design, avionics, displays and modelling

• Accessible to students (iron bird rig)

6

Architecture

• Distributed array of PCs

• Ethernet

• 50 Hz update rate

• Computer graphics

• Off-the-shelf hardware

• Custom software (20,000+ lines of code)

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Architecture

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Modular Architecture

PktRead Flight

C o ntro lS ystem

PktRead

D isplayFlight S im ulato r

Flight S im ulato r PktW rite

PktRead

ParameterIdentif ic ationFlight S im ulato r

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Ethernet Packets

Flight Model Navigation System Visual System

Engine Model Instructor Station

1

2

3 5

4

Ethernet

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I/O Interface

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Flight Computer

• Equations of motion

• Aerodynamic model

• Engine model

• Primary flight display (PFD)

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Boeing 747-400 PFD

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Navigation Computer

• Navigation sensor models

• Navigation equations

• Navigation database of beacons and runways

• Navigation flight display (NFD)

• Soft panels - trackerball pilot input

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Boeing 747-400 NFD with Airbus FCU

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Instructor Station

• Windows-like interface

• Monitoring

• Session management

• Flight data recording

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Instructor Station

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Instructor Station

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Instructor Station

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Visual System

• 3 image generators - PC with nVidia card

• SGI Performer - real-time rendering

• 1024x768 resolution per channel, 50 Hz update rate

• Fully textured anti-aliased display

• Industry standard visual database including dynamic models

• Projection onto a spherical screen 150°x40°

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Visual System

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Visual System

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Visual System

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Visual System

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Mechanisation of the Equations of Motion

compute aerodynamic coefficients

compute aerodynamic

compute aerodynamic

convert axes stability to body

forces

moments convert axes

stability to body

compute linear accelerations

compute angular accelerations

compute

compute Euler compute DCM

convert axes body to Euler

convert axes body to stability

atmospheric model

P',Q',R'

P,Q,R

Ps,Qs,Rs

L,M,N

engine forces

, M P,Q,R

e0,e1, e2,e3

inceptors

,M Xp,Zp

Lp,Mp,Np

Xs,Ys,Zs Xb,Yb,Zb U',V',W'

U,V,W

Ps,Qs,Rs

Vc

inceptors

' '

and moments

U,V,W

Vx,Vy,Vz Pn,Pe,h

Ls,Ms,Ns

,M

Vc, Vc,

parameters

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Model Validation – Boeing 747 Short Period

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Model Validation – Boeing 747 Phugoid

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Model Validation – Boeing 747 Dutch Roll

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Altitude Flight Control Law

Flight Model Navigation System Visual System

Engine Model Instructor Station

1

2

3 5

4

Ethernet

Flight Control System

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hd,h,,,qe

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Octave Altitude Control Law

% Open the socket for reading/writing pktsopenskt;sendskt;while(1) % Loop forever % Get a pkt from the simulator getskt; % Access the simulation variables U = getU; H = getAltitude; Pitch = getPitch; Alpha = getAlpha q = getQ; % Your altitude hold code goes here... % Put the control inputs into the packet setElevator ( de ); % Send the new pkt to the simulator sendskt; % Check for shutdown testskt;endwhile;

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EPSRC Research Grants

• Real-time wake vortex modelling, in

collaboration with Prof Qin’s CFD group in

Mechanical Engineering

• Synthetic vision – radar imaging, in collaboration

with the University of Essex and BAES

(Rochester)

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Wake Vortex Modelling

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Wake Vortex Modelling

• CFD methods to generate vortex flows

representative of large transport aircraft

• 4-5 days computation on the Bluegrid cluster

(15 dual processors) to produce 3 minutes of

vortex data (30 Gbytes)

• Unstructured grids of spatial and time varying

flow field data

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Real-time Wake Vortices

• Compress and organise very large vortex fields

• Extract vortex flow components from spatial data

• Compute interaction between a vortex and an

aircraft

• Develop flight control laws to increase safety in

the presence of vortices

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Wake Vortex Visualisation

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Synthetic Vision

• BAES radar penetrates cloud and rain (92 GHz)

• Cluttered radar image displayed on a HUD

• Real-time radar model developed

• Real-time imaging detection algorithms to

locate the runway in a cluttered image

• Failure detection algorithms

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Synthetic Vision

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Applications

• Air traffic management (ATM) – conflict

detection, conflict resolution, datalink modelling,

situation awareness

• Sensor modelling – GPS, INS, radar, IR,

Doppler

• Displays – Head-Up Display guidance

• Terrain-following and Mission Management

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Applications

• Novel actuation – electrical actuation systems,

flow control (e.g. MEMs actuation), load

alleviation

• Novel configurations – vectored thrust, rotary

wing, UAVs, active reverse thrust

• Novel sensors – terrain reference navigation,

sensor fusion, FDI

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Applications

• Modern control system design – certification, real-time code generation, health and usage monitoring

• Environmental models – air traffic, winds, turbulence

• Image detection – targets, obstacles, feature extraction

• Human factors – pilot models, pilot work load