Uncertainty Quantification of the Goland+ Wing's Flutter Boundary

Kurdi, Mohammad; Lindsley, Ned; Beran, Philip S.

doi:10.2514/6.2007-6309

Cited by 36 publications

(23 citation statements)

References 16 publications

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“…This second reduction of degrees-of-freedom is accompanied by the matrix (20) and thus, the stiffness and mass matrices of the free-free structure associated with the variables  …”

Section: A Modeling Strategymentioning

confidence: 99%

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Nonparametric Stochastic Modeling of Structures with Uncertain Boundary Conditions/Coupling Between Substructures

2013

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The focus of this investigation is on the formulation and validation of a novel approach for the inclusion of uncertainty in the modeling of the boundary conditions of linear structures and of the coupling between linear substructures. This work is particularly relevant to complex structures assembled from simpler substructures as in aerospace applications. First, a mean structural dynamic model that includes boundary condition/coupling flexibility is obtained using classical substructuring concepts. The application of the nonparametric stochastic modeling approach to this mean model is next described and thus permits the consideration of both model and parameter uncertainty.

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“…This second reduction of degrees-of-freedom is accompanied by the matrix (20) and thus, the stiffness and mass matrices of the free-free structure associated with the variables  …”

Section: A Modeling Strategymentioning

confidence: 99%

“…to the flutter boundary. To exemplify this application, the Goland+ wing [18][19][20], see Fig. 11, was considered.…”

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confidence: 99%

Nonparametric Stochastic Modeling of Structures with Uncertain Boundary Conditions/Coupling Between Substructures

2013

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“…The components u wnew and u θnew were previously defined by Kurdi (2007). Its details are given in Appendix.…”

Section: Robust Aeroelastic Analysis Considering Aerodynamic Variationmentioning

confidence: 99%

“…In the varying Mach number mu analysis, a statespace model was suggested by using a rational function approximation (RFA) [5] and in that paper, the DLM and RFA results were compared with respect to the Mach number. However, this approach showed a slight difference between the two results for large values of reduced frequency.…”

Section: Aerodynamic Modelingmentioning

confidence: 99%

“…A frequency domain analysis was performed by Borglund [3,4], who presented the mu value and reduced frequency results. Moreover, an uncertainty quantification method has been suggested by Kurdi and Lindsley [5]. Aerodynamic uncertainty was considered because accurate prediction capability was needed under the varying aerodynamics environment.…”

Section: Introductionmentioning

confidence: 99%

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Validation of a Robust Flutter Prediction by Optimization

Chung¹,

Shin²

2012

International Journal of Aeronautical and Space Sciences

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In a modern aircraft, there are many variations in its mass, stiffness, and aerodynamic characteristics. Recently, an analytical approach was proposed, and this approach uses the idea of uncertainty to find out the most critical flight flutter boundary due to the variations in such aerodynamic characteristics. An analytical method that has been suggested to predict robust stability is the mu method. We previously analyzed the robust flutter boundary by using the mu method, and in that study, aerodynamic variations in the Mach number, atmospheric density, and flight speed were taken into consideration. The authors' previous attempt and the results are currently quoted as varying Mach number mu analysis. In the author's previous method, when the initial flight conditions were located far from the nominal flutter boundary, conservative predictions were obtained. However, relationships among those aerodynamic parameters were not applied. Thus, the varying Mach number mu analysis results required validation. Using an optimization approach, the varying Mach number mu analysis was found out to be capable of capturing a reasonable robust flutter boundary, i.e., with a low percentage difference from boundaries that were obtained by optimization. Regarding the optimization approach, a discrete nominal flutter boundary is to be obtained in advance, and based on that boundary, an interpolated function was established. Thus, the optimization approach required more computational effort for a larger number of uncertainty variables. And, this produced results similar to those from the mu method which had lower computational complexity. Thus, during the estimation of robust aeroelastic stability, the mu method was regarded as more efficient than the optimization method was. The mu method predicts reasonable results when an initial condition is located near the nominal flutter boundary, but it does not consider the relationships that are among the aerodynamic parameters, and its predictions are not very accurate when the initial condition is located far from the nominal flutter boundary. In order to provide predictions that are more accurate, the relationships among the uncertainties should also be included in the mu method.

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