1. The ACSE Flight Simulator David Allerton Department of Automatic Control and Systems Engineering 24 th April 2006

2. 2 Overview Design objectives Organisation Capability Dynamics and control Applications Questions Demonstration

3. 3 ACSE Flight Simulator

4. 4

5. 5 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. 6 Architecture Distributed array of PCs Ethernet 50 Hz update rate Computer graphics Off-the-shelf hardware Custom software (20,000+ lines of code)

7. 7 Architecture

8. 8 Modular Architecture

9. 9 Ethernet Packets Flight ModelNavigation SystemVisual System Engine ModelInstructor Station 1 2 35 4 Ethernet

10. 10 I/O Interface

11. 11 Flight Computer Equations of motion Aerodynamic model Engine model Primary flight display (PFD)

12. 12 Boeing 747-400 PFD

13. 13 Navigation Computer Navigation sensor models Navigation equations Navigation database of beacons and runways Navigation flight display (NFD) Soft panels - trackerball pilot input

14. 14 Boeing 747-400 NFD with Airbus FCU

15. 15 Instructor Station Windows-like interface Monitoring Session management Flight data recording

16. 16 Instructor Station

17. 17 Instructor Station

18. 18 Instructor Station

19. 19 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°

20. 20 Visual System

21. 21 Visual System

22. 22 Visual System

23. 23 Visual System

24. 24 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, parameters

25. 25 Model Validation – Boeing 747 Short Period

26. 26 Model Validation – Boeing 747 Phugoid

27. 27 Model Validation – Boeing 747 Dutch Roll

28. 28 Altitude Flight Control Law Flight ModelNavigation SystemVisual System Engine ModelInstructor Station 1 2 35 4 Ethernet Flight Control System 6 h d,h, , ,q ee

29. 29 Octave Altitude Control Law % Open the socket for reading/writing pkts openskt; 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;

30. 30 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)

31. 31 Wake Vortex Modelling

32. 32 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

33. 33 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

34. 34 Wake Vortex Visualisation

35. 35 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

36. 36 Synthetic Vision

37. 37 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

38. 38 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

39. 39 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