Transport Aircraft System Identification from Wind Tunnel Data

Murphy, Patrick C.; Klein, Vladislav

doi:10.2514/6.2008-6202

Cited by 17 publications

(17 citation statements)

References 6 publications

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“…This approach allows for the inclusion of nonlinear behaviors and unsteady behaviors. The method takes advantage of indicial models that have been successfully applied to both transports and fighter aircraft over many years [5][6][7][8][9][10][11][12][13][14][15] . An advantage of the method is that the models retain conventional static and dynamic terms but replace conventional acceleration terms with indicial functions.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Wing Body Model Identification Using Forced-Oscillation Water Tunnel Data

Murphy

Vicroy

Kramer

et al. 2014

AIAA Atmospheric Flight Mechanics Conference

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Static and dynamic testing of the NASA 0.7 percent scale Hybrid Wing Body (HWB) configuration was conducted in the Rolling Hills Research Corporation water tunnel to investigate aerodynamic behavior over a large range of angle-of-attack and to develop models that can predict aircraft response in nonlinear unsteady flight regimes. This paper reports primarily on the longitudinal axis results. Flow visualization tests were also performed. These tests provide additional static data and new dynamic data that complement tests conducted at NASA Langley 14-by 22-Foot Subsonic Tunnel. HWB was developed to support the NASA Environmentally Responsible Aviation Project goals of lower noise, emissions, and fuel burn. This study also supports the NASA Aviation Safety Program efforts to model and control advanced transport configurations in loss-of-control conditions. SubscriptsA = amplitude a = aero forces and moments: N, Y, l, m, or n E = measured value Superscripts ^ = estimated value ~ = mean value A j , B j = Fourier coefficients a, b 1 = deficiency function parameters b = wing span, ft C N , C Y = normal and side force coefficients C l , C n = rolling and yawing-moment coefficients C m = pitching-moment coefficients c = mean aerodynamic chord, ft a F α , a F β = deficiency functions f = frequency, Hz k = reduced f, / bf V π , or / cf V π m = no. of harmonics in Fourier expansion N = number of data points p, q, r = roll, pitch, and yaw rates, rad/sec R 2 = coefficient of determination S = reference area, ft 2 T = dimensional time constant, sec t = time, sec V = velocity, fps α = angle-of-attack, rad or deg α 0 = initial angle-of-attack in forced oscillation experiments, rad or deg β = sideslip angle, rad or deg η = state variable σ = standard error τ = dummy integration variable τ 1 = non-dimensional time constant, 1 1 2V b b       ω = angular frequency, rad/sec a C γ = in-phase coefficients, γ = α or β a C ζ = out-of-phase coefficients, ζ = p, q, or r , E r SS SS = residual and total sum of squares American Institute of Aeronautics and Astronautics 1 Downloaded by NANYANG TECHNICAL UNIVERSITY on October 1, 2015 | http://arc.aiaa.org |

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Section: Introductionmentioning

confidence: 99%

Hybrid Wing Body Model Identification Using Forced-Oscillation Water Tunnel Data

Murphy

Vicroy

Kramer

et al. 2014

AIAA Atmospheric Flight Mechanics Conference

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“…Results from the dynamic 14X22 wind tunnel data [9] are presented in Fig. 7 for α 0 =24 deg with Δα=+/-5 deg of pitch oscillation.…”

Section: Characterization Of Gtm Aerodynamicsmentioning

confidence: 99%

“…8 depicts a dynamic pitching moment coefficient, C m , that exhibits hysteresis as the GTM oscillates +/-5 deg about its pitch axis at α 0 =24 degrees. (The black dashed line is "mean" wind tunnel data from [9].) The arrow leaning to the right denotes the upswing, and the arrow leaning to the left denotes the downswing.…”

Section: Approach To Numerical Guideline Studymentioning

confidence: 99%

“…3) is a representative twin-engine commercial transport configuration called the Generic Transport Model (GTM) that has received extensive static and dynamic force and moment testing in the NASA Langley 14-by 22-Foot Subsonic Tunnel (14X22) [7][8][9] 6 . Boundary layer transition strips of width 0.1 inch and number 36 grit were applied at 10% chord on the upper and lower surfaces of the wing, horizontal and vertical tails.…”

Section: Geometry and Experimentsmentioning

confidence: 99%

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Guidelines for Computing Longitudinal Dynamic Stability Characteristics on Subsonic Transport

Thompson

Frank

Murphy

2010

28th AIAA Applied Aerodynamics Conference

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A systematic study is presented to guide the selection of a numerical solution strategy for URANS computation of a subsonic transport configuration undergoing simulated forced oscillation about its pitch axis. Forced oscillation is central to the prevalent wind tunnel methodology for quantifying aircraft dynamic stability derivatives from force and moment coefficients, which is the ultimate goal for the computational simulations. Extensive computations are performed that lead in key insights of the critical numerical parameters affecting solution convergence. A preliminary linear harmonic analysis is included to demonstrate the potential of extracting dynamic stability derivatives from computational solutions. = VGRID "viscous" grid spacing normal to surface at node i+1, see Eq. 1, inches δ 1 = spacing of first node off of surface (i=1) in "viscous" grid layers, see Eq. 1, inches

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“…A number of aerodynamic model identi¦cation methods in the context of faults/failures have been proposed in the literature, like the use of indicial functions for unsteady aerodynamic e¨ects [1], real-time identi¦cation in the frequency domain [2,3], and modi¦ed sequential least squares [4]. These publications demonstrate speci¦c advantages for each identi¦cation method.…”

Section: Introductionmentioning

confidence: 99%

Model-based fault detection and identification with online aerodynamic model structure selection

Lombaerts

2013

Progress in Flight Dynamics, Guidance, Navigation, Control, Fault Detection, and Avionics

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This publication describes a recursive algorithm for the approximation of time-varying nonlinear aerodynamic models by means of a joint adaptive selection of the model structure and parameter estimation. This procedure is called adaptive recursive orthogonal least squares (AROLS) and is an extension and modi¦cation of the previously developed ROLS procedure. This algorithm is particularly useful for model-based fault detection and identi¦cation (FDI) of aerospace systems. After the failure, a completely new aerodynamic model can be elaborated recursively with respect to structure as well as parameter values. The performance of the identi¦cation algorithm is demonstrated on a simulation data set.

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Transport Aircraft System Identification from Wind Tunnel Data

Cited by 17 publications

References 6 publications

Hybrid Wing Body Model Identification Using Forced-Oscillation Water Tunnel Data

Hybrid Wing Body Model Identification Using Forced-Oscillation Water Tunnel Data

Guidelines for Computing Longitudinal Dynamic Stability Characteristics on Subsonic Transport

Model-based fault detection and identification with online aerodynamic model structure selection

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