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Iasted Open Loop
 

Iasted Open Loop

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    Iasted Open Loop Iasted Open Loop Presentation Transcript

    • T.L. Grigorie, A.V. Popov, R.M. Botez, Y. Mébarki, and M. Mamou (Canada) The International Association of Science and Technology for Development (IASTED) Identification, Control and Applications Conference ~ICA 2009~ Honolulu, Hawaii August 17 – 19, 2009
    • SUMMARY
      • Project context
      • Experimental setup description
      • Open loop control
      • Experimental results
      • Real time signal acquisition
      • Infrared visualization
      • Conclusion
    • Project context
      • CRIAQ Project 7.1
      • Laminar Flow Improvement on an Aeroelastic Research Wing
      • to reduce the operating costs for the new generation of aircrafts through a fuel economy in flight, and also to improve aircraft performances, expand its flight envelope, replace conventional control surfaces, reduce drag to improve range and reduce vibrations and flutter.
      • Partners
      • Objectives
      • To develop a system for active control of a morphing wing during flight to maintain laminar flow over the upper surface of the wing
      • To detect the airflow characteristics using pressure sensors installed on the upper surface of a morphing wing
    • Project context
      • Among others our team (LARCASE) designing, developing, integrating and validating a controller for the transition control on the Morphing Wing , equipped with intelligent actuators (Smart Material Actuators) and pressure sensors.
      • The controller has been validated by wind tunnel testing at Institute for Aerospace Research, National Research Council NRC, Ottawa, Ontario, Canada.
      • This paper presents the modeling and the experimental testing of our morphing wing aerodynamic performances in open loop architecture.
      • Shown are:
        • the method used to acquire the pressure data from the external surface of the flexible wing skin, using incorporated Kulite pressure sensors;
        • the instrumentation of the morphing controller.
    • Experimental setup description
      • Wing model:
        • 0.5 m x 0.9 m
      • Lower part – aluminium block
      • Upper part :
        • aluminium structure
        • composite materials flexible skin (carbon-kevlar)
        • Shape memory actuators (Ni-Ti)
      From the initial studies related to the optimal configuration of the flexible structure, two actuation lines , positioned at 25.3% and 47.6% of the chord from the airfoil leading edge.
    • Model of the flexible structure The reference airfoil is the NLF airfoil, WTEA.
    • Experimental setup description
      • The flexible skin is required to morph its shape through the two actuation points in order to achieve an optimized airfoil shape according to the theoretical flow conditions similar to those tested in the wind tunnel.
      • Two shape memory alloy actuators, having a non-linear behavior, drive the displacement (dY1, dY2) of the two control points of the flexible skin towards the optimized airfoil shape.
      • Each of the shape memory actuators is activated by a power supply unit and controlled by Simulink/Matlab software through a PID-ON/OFF or a self tuning fuzzy controller.
      • A number of 35 optimized airfoils were designed for the airflow cases combinations of Mach number and angle of attack.
      C135 C128 C121 C114 C107 2 o 7 C134 C127 C120 C113 C106 1.5 o 6 C133 C126 C119 C112 C105 1 o 5 C132 C125 C118 C111 C104 0.5 o 4 C131 C124 C117 C110 C103 0 o 3 C130 C123 C116 C109 C102 -0.5 o 2 C129 C122 C115 C108 C101 -1 o 1 0.3 0.275 0.25 0.225 0.2 α [ o ] 5 4 3 2 1 Mach
    • Experimental setup description
      • SMA’s control working modes :
        • Heating for SMA increasing length
        • Cooling for SMA decreasing length
        • Desired parameters – dY 1 & dY 2
      • Input signal
        • position of actuators from LVDT
        • (dL = 3·dY)
      • Output signal
        • control voltage to power supplies 0-2 V
        • control current for SMA’s 0-10 A
    • Open loop control
      • Control algorithm:
        • Inputs from sensors:
          • M (speed), a (trajectory attitude),
          • Re (atm. cond’s - altitude)
        • Search in database stored in memory
          • Optimum airfoil (criterion CL constant, CD minim etc. ) for given conditions
          • 35 cases (5 Mach x 7 alpha)
          • Calculate actuator displacements (Y1, Y2)
        • Execute the morphing command
      • Advantages :
        • can be used any criterion in optimization process,
        • can be used multiples database for different situations/atm. cond’s
      • Disadvantages :
        • no feed back,
        • no adaptation to change
    • Experimental results PID controller Self tuning fuzzy controller Ladder command self tuning fuzzy controller
    • Experimental results
      • From the self tuning fuzzy versus PID open loop control analysis it was found that the self tuning fuzzy controller needed less power than the PID controller for the same displacements, which was due to its in-built optimization algorithm.
      • The PID controller uses a switch ON/OFF which connected and disconnected the power sources but gave the saw teeth behavior in the temperature time history plots, while the fuzzy controller kept a narrow control over the temperature variations in the SMA’s wires.
      • The time-response of the fuzzy controller is much better than that of the classical PID controller.
      • NI-DAQ USB 6210 card acquisition
        • Total sampling rate : 250 kS/sec
        • 16 analog channels
        • (15 Kulites + 1 dynamic pressure)
      • Simulink analysis
        • Sampling rate : 15.625 kHz per channel
        • Frame based acquisition : 1024 points/frame
        • Frame rate : 15.25 Frames/sec
        • FFT spectra : Frequency: 7.8125 kHz
      Real time signal acquisition
      • The acquired pressure data were analyzed through Fast Fourier Transforms in order to detect the magnitude and frequency of the noise in the air flow and high-pass filtered at 1 kHz.
      • Subsequently, the data were processed by calculating the RMS of the signal in order to obtain a plot diagram of noise in the air flow.
      • These processing were necessary to distinguish the Tollmien-Schlichting waves that were responsible for triggering transition from laminar to turbulent flow in the high frequency band from the low frequency inherent electronically induced noise from the wind tunnel equipments (fan and power supply).
      Data acquisition
    • Infrared visualization
      • In support of the discrete pressure instrumentation, infrared thermography (IR) visualization was performed to detect the transition location on the morphing wing upper surface and validate the pressure sensor analysis.
      M = 0.3,  = -1° M = 0.275,  = 0° Morphing Rigid Rows of pressure sensors Lines of SMA actuators 45% 66% a) b) 33% 57% a) b)
    • Conclusion
      • SMA does respond only to heating or cooling, i.e. pass current or no current (8 A/12 V DC or 0 A/0
        • Very responsive in heating (~10 sec)
        • Slow response in cooling (~30 to 60 sec)
      • Precision of setting point ~0.02 mm due to LVDT accuracy
      • The control is improved by a fuzzy controller that will provide the exact current required to keep the SMA heated as needed instead of cycling ON/OFF
      • The wind tunnel test was a success and the model demonstrated the validity of the concept