Characteristics of Control Laws Tested on the Semi-Span Super-Sonic Transport (S4T) Wind-Tunnel Model

Christhilf, David M.; Moulin, Boris; Ritz, Erich; Chen, P. C.; Roughen, Kevin M.; Perry, Boyd

doi:10.2514/6.2012-1555

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…6 For the S 4 T aeroservoelastic wind-tunnel model, both simulation and wind-tunnel data were available from previous development and testing of active controls systems for various purposes such as Gust Load Alleviation (GLA), Ride Quality Enhancement (RQE), and Flutter Suppression (FS). 7 It was of interest to animate the motion of the wind-tunnel model using an available desktop tool, namely MATLAB with handle graphics. The simplest approach that seemed likely to work for both simulation and wind-tunnel data was to take accelerometer time histories and apply two time integrations to get estimates of displacements at accelerometer locations, while connecting the accelerometers with a network of line segments.…”

Section: Introductionmentioning

confidence: 99%

Visualizing a Flutter Mechanism as a Traveling Wave Through Animation of Simulation Results for the Semi-Span Super-Sonic Transport Wind-Tunnel Model

Christhilf

2014

55th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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It has long been recognized that frequency and phasing of structural modes in the presence of airflow play a fundamental role in the occurrence of flutter. Animation of simulation results for the long, slender Semi-Span Super-Sonic Transport (S 4 T) wind-tunnel model demonstrates that, for the case of mass-ballasted nacelles, the flutter mode can be described as a traveling wave propagating downstream. Such a characterization provides certain insights, such as (1) describing the means by which energy is transferred from the airflow to the structure, (2) identifying airspeed as an upper limit for speed of wave propagation, (3) providing an interpretation for a companion mode that coalesces in frequency with the flutter mode but becomes very well damped, (4) providing an explanation for bursts of response to uniform turbulence, and (5) providing an explanation for loss of low frequency (lead) phase margin with increases in dynamic pressure (at constant Mach number) for feedback systems that use sensors located upstream from active control surfaces. Results from simulation animation, simplified modeling, and wind-tunnel testing are presented for comparison. The simulation animation was generated using double timeintegration in Simulink of vertical accelerometer signals distributed over wing and fuselage, along with time histories for actuated control surfaces. Crossing points for a zero-elevation reference plane were tracked along a network of lines connecting the accelerometer locations. Accelerometer signals were used in preference to modal displacement state variables in anticipation that the technique could be used to animate motion of the actual wind-tunnel model using data acquired during testing. Double integration of wind-tunnel accelerometer signals introduced severe drift even with removal of both position and rate biases such that the technique does not currently work. Using wind-tunnel data to drive a Kalman filter based upon fitting coefficients to analytical mode shapes might provide a better means to animate the wind tunnel data. NomenclatureM = Mach number q = dynamic pressure TDT = Transonic Dynamics Tunnel HSR = High Speed Research S 4 T = Semi-Span Super-Sonic Transport RCV = All-moving Ride Control Vane FLAP = Wing trailing edge control surface (flap/aileron) HT = All-moving Horizontal Tail

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Section: Introductionmentioning

confidence: 99%

Visualizing a Flutter Mechanism as a Traveling Wave Through Animation of Simulation Results for the Semi-Span Super-Sonic Transport Wind-Tunnel Model

Christhilf

2014

55th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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“…The control law used for these analytical CPE results is a MIMO control law (Zona v448) 17 . This classic MIMO 2x2 control law uses the HT and the FLAP control surfaces, and the nacelle inboard aft accelerometer (NIBAFTZ) and the inboard mid wing accelerometer (IBMID15I).…”

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confidence: 99%

Analytical and Experimental Evaluation of Digital Control Systems for the Semi-Span Super-Sonic Transport (S4T) Project

Wieseman¹,

Christhilf²,

Perry³

2012

53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials ConferenceSelf Cite

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An important objective of the Semi-Span Super-Sonic Transport (S 4 T) wind tunnel model program was the demonstration of Flutter Suppression (FS), Gust Load Alleviation (GLA), and Ride Quality Enhancement (RQE). It was critical to evaluate the stability and robustness of these control laws analytically before testing them and experimentally while testing them to ensure safety of the model and the wind tunnel. MATLAB based software was applied to evaluate the performance of closed-loop systems in terms of stability and robustness. Existing software tools were extended to use analytical representations of the S 4 T and the control laws to analyze and evaluate the control laws prior to testing. Lessons were learned about the complex windtunnel model and experimental testing. The open-loop flutter boundary was determined from the closed-loop systems. A MATLAB/Simulink Simulation developed under the program is available for future work to improve the CPE process. This paper is one of a series of that comprise a special session, which summarizes the S 4 T wind-tunnel program. Nomenclature CLO = control law output CPE = controller performance evaluation FRF = frequency response function or frequency responses det = determinant G = open-loop plant FLAP = wing trailing edge control surface FLAPCOM = command to FLAP FLAPEXC = excitation to FLAP HT = horizontal tail HTCOM = command to HT HTEXC = excitation to HT IBMID15I = accelerometer located on the inboard middle section of the wing LTI = linear time invariant state-space representation of a system. MIMO = multi-input multi-output NIBAFTZ = nacelle inboard aft accelerometer in the z direction ! = dynamic pressure, psf RCV = ride control vane SISO = single-input single-output ! ! = time history of ith excitation to control surface ! ! = time history of ith control law output ! ! = time history of ith plant output ! ! = matrix of frequency responses of control law outputs to excitations ! ! = matrix of frequency responses of plant outputs to excitations σ = singular value Subscripts f = flutter

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An extrapolation approach for aeroengine’s transient control law design
Kong¹,
Wang²,
Tan³
et al. 2013
Chinese Journal of Aeronautics
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Characteristics of Control Laws Tested on the Semi-Span Super-Sonic Transport (S4T) Wind-Tunnel Model

Cited by 4 publications

References 13 publications

Visualizing a Flutter Mechanism as a Traveling Wave Through Animation of Simulation Results for the Semi-Span Super-Sonic Transport Wind-Tunnel Model

Visualizing a Flutter Mechanism as a Traveling Wave Through Animation of Simulation Results for the Semi-Span Super-Sonic Transport Wind-Tunnel Model

Analytical and Experimental Evaluation of Digital Control Systems for the Semi-Span Super-Sonic Transport (S4T) Project

An extrapolation approach for aeroengine’s transient control law design

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