Efficient Computation of Robust Flutter Boundaries Using the mu-k Method

Borglund, Dan; Ringertz, Ulf

doi:10.2514/1.20190

Cited by 29 publications

(26 citation statements)

References 11 publications

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“…Some recent methods have also introduced complex aerodynamic uncertainty [26], which is undoubtedly present in real-world aeroelastic systems. Efficient computation of robust flutter boundaries, using these approaches, has been demonstrated on an F-16 sample test case by Borglund and Ringertz [30]. Extending the -analysis approach here to take advantage of aspects from these recent approaches would most likely decrease the computational burden associated with upper-bound determination.…”

Section: E Comparison Of the Uncertainty Analysis Methodsmentioning

confidence: 89%

Evaluation of Aeroelastic Uncertainty Analysis Methods

Danowsky

Chrstos

Klyde

et al. 2010

Journal of Aircraft

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Flutter is a destructive and potentially explosive phenomenon that is the result of the simultaneous interaction of aerodynamic, elastic, and inertial forces. The nature of flutter mandates that flutter flight testing be cautious and conservative. Because of this, further investigation of uncertainty analysis with respect to the flutter problem is desired and warranted. Prediction of flutter in the transonic regime requires computationally expensive high-fidelity simulation models. Because of the computational demands, traditional uncertainty analysis is not often applied to transonic flutter prediction, resulting in reduced confidence in the results. The work described herein is aimed at exploring various methods to reduce the existing computational time limitations of traditional uncertainty analysis. Specifically, the coupling of design of experiment and response surface methods and the application of analysis are applied to a validated aeroelastic model of the AGARD 445.6 wing. From a high-fidelity nonlinear aeroelastic model, a linear reduced-order model is produced that captures the essential dynamic characteristics. Using reduced-order models, the design of experiment, response surface methods, and -analysis approaches are compared with traditional Monte Carlo-based stochastic simulation. All of these approaches to uncertainty analysis have advantages and drawbacks. Results from these methods and their robustness are compared and evaluated.

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Section: E Comparison Of the Uncertainty Analysis Methodsmentioning

confidence: 89%

Evaluation of Aeroelastic Uncertainty Analysis Methods

Danowsky

Chrstos

Klyde

et al. 2010

Journal of Aircraft

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“…Moreover, both the lower and upper bound flutter speeds can be obtained which represent the worst case and best case, respectively [7]. In literature [8], a new formulation connecting μ-k approach and traditional frequencydomain flutter analysis was established to develop an efficient algorithm for computation of robust flutter boundaries, and an F-16 sample test case with uncertain external stores aerodynamics demonstrated the efficiency of this method. Researches have also explored the dynamic behavior of nonlinear aeroelastic system.…”

Section: Introductionmentioning

confidence: 99%

Robust flutter analysis of a nonlinear aeroelastic system with parametric uncertainties

Yun

Jing

2009

Aerospace Science and Technology

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“…Thus, the result of the mu analysis in the varying Mach number mu analysis work still required validation. The previous studies of other researchers' have attempted to validate the results of the mu analysis by evaluating the eigenvalues at the obtained flutter boundary [4]. However, the validation was done under constant Mach number flight conditions.…”

Section: Introductionmentioning

confidence: 99%

“…1 and this is adapted from Borglund [4]. However, the constant Mach number analysis is limited as it does not consider the structural and aerodynamic force variations within an aircraft.…”

Section: Introductionmentioning

confidence: 99%

“…A robust flutter boundary with varying dynamic pressures was investigated by Lind [1], who found out that the results of the robust flutter margin were about 4% more conservative than the nominal flutter boundary. 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].…”

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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Efficient Computation of Robust Flutter Boundaries Using the mu-k Method

Cited by 29 publications

References 11 publications

Evaluation of Aeroelastic Uncertainty Analysis Methods

Evaluation of Aeroelastic Uncertainty Analysis Methods

Robust flutter analysis of a nonlinear aeroelastic system with parametric uncertainties

Validation of a Robust Flutter Prediction by Optimization

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