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Benchmarking CEASIOM Software to Predict Flight Control and Flying Qualities of the B-747

Benchmarking CEASIOM Software to Predict Flight Control and Flying Qualities of the B-747 A. Da Ronch The University of Liverpool, UK C. McFarlane, C. Beaverstock Bristol University, UK J. Oppelstrup, M. Zhang, A. Rizzi Royal Institute of Technology, Sweden. Introduction

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Benchmarking CEASIOM Software to Predict Flight Control and Flying Qualities of the B-747

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  1. Benchmarking CEASIOM Software to Predict Flight Control and Flying Qualities of the B-747 A. Da Ronch The University of Liverpool, UK C. McFarlane, C. Beaverstock Bristol University, UK J. Oppelstrup, M. Zhang, A. Rizzi Royal Institute of Technology, Sweden

  2. Introduction • Contemporary aircraft conceptual design • Handbook methods, semi-empirical theory • Need to recalibrate these empirical methods • Augmented-stability & extended flight envelope • More accurate flight dynamics modelling • Computational methods based on first principle • First-Time-Right with the FCS design architecture

  3. CEASIOM • Computerized Environment for Aircraft Synthesis and Integrated Optimization Methods • SimSAC project under the European Commission 6th Framework Programme • Integrates discipline-specific tools for conceptual design to predict flying & handling qualities • http:/www.simsacdesign.eu • http:/www.ceasiom.com

  4. Objectives

  5. CEASIOM main GUI

  6. Test Case: Boeing 747 • Large 4-engined turbofan; 350+ pax • Multiple control surfaces: Krueger LE flaps, triple-slotted TE flaps • Flight dynamics with FCSDT to evaluate different fidelity-level approx

  7. Adaptive Fidelity CFD • DATCOM • Semi-empirical • TORNADO • Vortex-Lattice method • EDGE • CFD solver Fidelity CPU Time Geometry

  8. Adaptive Fidelity CFD • DATCOM • Semi-empirical • TORNADO • Vortex-Lattice method • EDGE • CFD solver • For conventional aircraft, estimate aero derivatives based on geometry details and flight conditions • Suspect results for new configuration • Handbook

  9. Adaptive Fidelity CFD • DATCOM • Semi-empirical • TORNADO • Vortex-Lattice method • EDGE • CFD solver • http:/www.redhammer.se/tornado/ • Modified horse-shoe vortex singularity method • Steady & low reduced-freq harmonic unsteady flows • Prandtl-Glauert similarity role for compressibility • Fuselage can be modelled

  10. Adaptive Fidelity CFD • DATCOM • Semi-empirical • TORNADO • Vortex-Lattice method • EDGE • CFD solver • 3D NS/Euler, compressible flow solver from FOI, Sweden • Unstructured grids with arbitrary elements; node-centred FV • Explicit Runge-Kutta integration to steady state • Semi-implicit, dual-time method for unsteady problem • Acceleration techniques, turbulence models, parallel implementation

  11. CFD Code - EDGE • Deflection of control surfaces • Generation of a new grid for every new configuration of deflected control surfaces • clean geometry • tens of grids needed • Transpiration BCs • only one single grid needed • limits on min/max deflection

  12. Challenges How to automate grid generation for CFD? How to do 100k CFD? How to do S&C analysis early in design phase? “...whether CFD can participate in the design process with sufficient speed to drive down the design cycle time”, Dawes et al.

  13. Challenges How to automate grid generation for CFD? How to do 100k CFD? How to do S&C analysis early in design phase?

  14. From Geometry to CFD Grid (1) • AcBuilder: sketch-pad • Edit XML file to match new design • Visual interpretation

  15. From Geometry to CFD Grid (2) • SUMO* (SUrfaceMOdeler) • Rapid generation of 3D water-tight geometry • Automated generation of unstructured surface mesh • Triangulation based on in-sphere criterion, better than Delaunay, for skewed surfaces • Volume mesh using TetGen • *http:/www.larosterna.com/dwfs.html

  16. From Geometry to CFD Grid (2) SUMO surface grid

  17. From Geometry to CFD Grid (2) SUMO volume grid

  18. From Geometry to CFD Grid

  19. TORNADO Geometry

  20. TORNADO Geometry Munk’s theory Sink/source distribution

  21. TORNADO Geometry

  22. Challenges How to automate grid generation for CFD? How to do 100k CFD? How to do S&C analysis early in design phase?

  23. Aerodynamic Table Format Non-conventional controls

  24. Brute Force Approach • Simple example; let’s assume: • 10 values for AoA, Mach, Beta, Elev, Rud, Ail • More than 100k entries needed in table • 10 seconds each calculation using TORNADO

  25. Brute Force Approach • Simple example; let’s assume: • 10 values for AoA, Mach, Beta, Elev, Rud, Ail • More than 100k entries needed in table • 10 seconds each calculation using TORNADO • 106 / (24 * 60 * 60) > 10 days • Brute force approach not feasible to fill-in aero tables!

  26. Sampling & Data Fusion Flight Dynamics Database Existing Table Increments to Design Aerodynamic Tables Data Fusion for Aerodynamic Increments Kriging Sampling New Design Journal of Aircraft, 46 (3), 2009

  27. Sampling & Data Fusion • STATIC effects: • Sampling for M-α-β dependence • Co-Kriging to calculate increments (controls) • DYNAMIC effects: • No frequency dependence • Alpha dependence only • Replace unsteady time-accurate with HB method? * • Stability derivatives from DATCOM * AIAA Journal, 47 (4), 2009

  28. Challenges How to automate grid generation for CFD? How to do 100k CFD? How to do S&C analysis early in design phase?

  29. FCSDT • FCSDT (Flight Control System Design Toolkit) • Design of the FCS, FCS architecture design • Reliability analysis, failure mode analysis • Control allocation, response simulation • S&C analysis, HQ assessment, control laws design, control laws definition, flight simulation

  30. Aerodynamic Predictions • Low speed aerodynamics • Transonic regime • DATCOM • TORNADO • TORNADO with compressibility correction • EDGE in Euler mode • More comparisons in the paper; exp data from Rodney, C.H., Nordwall, D.R., 1970

  31. CL vsα, Mach = 0.80

  32. CD vs CL, Mach = 0.80

  33. Cm vsα, Mach = 0.80

  34. Mach = 0.80 AoA = 1.0 deg Positive elev deflection

  35. Results • Cruise condition • Trim & Stability analysis • Eigen-structure assignment for feedback controller A + B *K • Flight Handling Qualities • Failed lower rudder segment • Trim & Stability analysis

  36. Trimmed AoA

  37. Trimmed elevator

  38. Pole plot, Mach = 0.8 • Short Period • Dutch-Roll • Phugoid

  39. Results • Cruise condition • Trim & Stability analysis • Eigen-structure assignment for feedback controller A + B *K • Flight Handling Qualities • Failed lower rudder segment • Trim & Stability analysis • Eigen value: -2 ± i *2 for Short Period mode

  40. Kα: gain value of feedback AoA to elevator Kq: gain value of feedback pitch rate to elevator

  41. Results • Cruise condition • Trim & Stability analysis • Eigen-structure assignment for feedback controller A + B *K • Flight Handling Qualities • Failed lower rudder segment • Trim & Stability analysis

  42. Short Period mode • Eigenvalue: ƞ + i *ω T1/2 = ln(2) / |ƞ|

  43. Phugoid mode • ξ: damping ratio ωn: undamped circular freq

  44. Dutch Roll mode • ξ: damping ratio ωn: undamped circular freq

  45. Results • Cruise condition • Trim & Stability analysis • Eigen-structure assignment for feedback controller A + B *K • Flight Handling Qualities • Failed lower rudder segment • Trim & Stability analysis • Lower rudder segment failed at -10o for range of Mach numbers

  46. Conclusions • Aero tables for flight mechanics • Automated generation of CFD grid • From low-fidelity methods to CFD • Multiple control surfaces • Smart procedure to fuse data • Test case: Boeing 747, trim analysis & poles plot • Cruise condition • Failure analysis: lower rudder segment jammed • Demonstrated • Robust process for S&C analysis in early design • CFD needed for good prediction for a realistic test case

  47. Future Works • Flight manoeuvre replay • Aero table with dynamic derivatives from HB • Replay with CFD • When does prediction fail? * Unsteady effects? • Need to review model for flight mechanics • System ID • Indicial (successfully used in gust analyses) • State Space • Towards modelling of unsteady effects * AIAA-2009-6273

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