Wind Tunnel Database Development Using Modern Experiment Design and Multivariate Orthogonal Functions

Morelli, Eugene A.; DeLoach, Richard

doi:10.2514/6.2002-2795

Cited by 32 publications

(46 citation statements)

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“…This approach is described in Refs. [4] and [14], which are the basis for the material presented here.…”

Section: Generating Orthogonal Modeling Functionsmentioning

confidence: 99%

“…Figure 5 depicts this graphically, using actual modeling results from Ref. [4]. The figure shows that after the first 6 modeling functions, the added model complexity associated with an additional orthogonal modeling function is not justified by the associated reduction in mean squared fit error.…”

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confidence: 99%

“…(22) depends on the mean squared fit error, 2ĴN , and a term proportional to the number of terms in the model, n . The latter term prevents over-fitting the data with too many model terms, which is detrimental to model prediction accuracy 4,14,23 . While the mean squared fit error 2ĴN must decrease with the addition of each orthogonal modeling function to the model (by Eq.…”

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confidence: 99%

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Efficient Global Aerodynamic Modeling from Flight Data

Morelli

2012

50th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

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A method for identifying global aerodynamic models from flight data in an efficient manner is explained and demonstrated. A novel experiment design technique was used to obtain dynamic flight data over a range of flight conditions with a single flight maneuver. Multivariate polynomials and polynomial splines were used with orthogonalization techniques and statistical modeling metrics to synthesize global nonlinear aerodynamic models directly and completely from flight data alone. Simulation data and flight data from a subscale twin-engine jet transport aircraft were used to demonstrate the techniques. Results showed that global multivariate nonlinear aerodynamic dependencies could be accurately identified using flight data from a single maneuver. Flight-derived global aerodynamic model structures, model parameter estimates, and associated uncertainties were provided for all six nondimensional force and moment coefficients for the test aircraft. These models were combined with a propulsion model identified from engine ground test data to produce a high-fidelity nonlinear flight simulation very efficiently. Prediction testing using a multi-axis maneuver showed that the identified global model accurately predicted aircraft responses.

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“…This approach is described in Refs. [4] and [14], which are the basis for the material presented here.…”

Section: Generating Orthogonal Modeling Functionsmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Efficient Global Aerodynamic Modeling from Flight Data

Morelli

2012

50th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

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“…11 Aerodynamic force and moment coefficients are generated using a multivariate orthogonal function method as described in. 21,22 In the original NASA model several regions of angle of attack were used to capture severe nonlinearity. These models were blended using Gaussian weighting.…”

Section: The Generic Transport Modelmentioning

confidence: 99%

Aircraft Accident Prevention: Loss-of-Control Analysis

Kwatny

Dongmo

Chang

et al. 2009

AIAA Guidance, Navigation, and Control Conference

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“…A number of approximation based methods have been presented for the aerodynamic-coefficient prediction including least squares regression (Morelli and DeLoach, 2003), artificial neural network (Norgaard et al, 1997;Rajkumar and Bardina, 2002) and maximum likelihood method (Lee et al, 2009), and extrapolation (Peterson et al, 1980;Nicolì et al, 2006). We have also suggested an adaptive surrogate model (Luo et al, 2011) to improve the accuracy of approximation.…”

Section: Introductionmentioning

confidence: 99%