Aeroelastic flutter analysis considering modeling uncertainties

Lokatt, Mikaela

doi:10.1016/j.jfluidstructs.2017.06.017

Cited by 13 publications

(7 citation statements)

References 28 publications

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“…Also, L and M are aerodynamic lift and moment, respectively. The aerodynamic lift and moment based on Peters unsteady aerodynamic theory (50) are used in Equations (30) and (31). The aeroelastic governing equations are given by: By discretizing the above equations with the Galerkin method, and solving the final eigenvalue problem, the flutter airspeed and frequency based on the Goland wing parameters are given in Table 2.…”

Section: Example 2: Clean Wingmentioning

confidence: 99%

“…Using Equations (31) and (32) at each α-cut, the flutter airspeed membership function can be obtained, as shown in Fig. 17.…”

Section: Example 2: Clean Wingmentioning

confidence: 99%

“…A probabilistic flutter analysis utilising a meta-modeling technique to evaluate the effect of parameter uncertainty on the flutter speed was conducted by Abbas and Morgenthal (30) . Lokatt (31) approximated the aerodynamic model using a piece-wise continuous rational polynomial function. They proposed this method for efficient flutter analysis of aeroelastic systems including modelling uncertainties.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fuzzy uncertainty analysis and reliability assessment of aeroelastic aircraft wings

Rezaei¹,

Fazelzadeh²,

Mazidi³

et al. 2020

Aeronaut. j.

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In the present study, fuzzy uncertainty and reliability analysis of aeroelastic aircraft wings are investigated. The uncertain air speed and structural parameters are represented by fuzzy triangular membership functions. These uncertainties are propagated through the wing model using a fuzzy interval approach, and the uncertain flutter speed is obtained as a fuzzy variable. Further, the reliability of the wing flutter is based on the interference area in the pyramid shape defined by the fuzzy flutter speed and air speed. The ratio between the safe region volume and the total volume of the pyramid gives the reliability value. Two different examples are considered—a typical wing section, and a clean wing—and the results are given for various wind speed conditions. The results show that the approach considered is a low-cost but suitable method to estimate the reliability of the wing flutter speed in the presence of uncertainties.

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Section: Example 2: Clean Wingmentioning

confidence: 99%

“…Using Equations (31) and (32) at each α-cut, the flutter airspeed membership function can be obtained, as shown in Fig. 17.…”

Section: Example 2: Clean Wingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fuzzy uncertainty analysis and reliability assessment of aeroelastic aircraft wings

Rezaei¹,

Fazelzadeh²,

Mazidi³

et al. 2020

Aeronaut. j.

View full text Add to dashboard Cite

show abstract

“…However, the aforementioned studies of flutter speed were performed under the assumptions of deterministic situations where all the structural and aerodynamic parameters were known. As a matter of fact, in real aeroelastic systems there are multiple sources of uncertainties including but not limited to the modelinginduced uncertainties [8,9], numerical uncertainties, and parametric uncertainties [10]. The above uncertainties have a significant impact on the flutter speed boundary calculation.…”

Section: Introductionmentioning

confidence: 99%

“…Jia et al [20] developed an uncertainty propagation analysis method which was based on an extended sparse grid technique and maximum entropy principle to fit accuracy of the probability density function of the system response. The Non-intrusive Polynomial Chaos (NIPC) derived from the polynomial chaos expansion which is proposed by Ghanem and Spanos [21], has been widely used in the uncertainty propagation in the aerospace area [8][9][10]. For epistemic uncertainty which stems from the insufficient information about the input parameters, the non-probabilistic methods including the interval theory [22], the fuzzy theory [23], the evidence theory [24], and the structured singular value (μ) method [12,23], have been prevalently employed to quantify the propagation of uncertainties in the aeroelastic analysis.…”

Section: Introductionmentioning

confidence: 99%

A Two-Phase Monte Carlo Simulation/Non-Intrusive Polynomial Chaos (MSC/NIPC) Method for Quantification of Margins and Mixed Uncertainties (QMMU) in Flutter Analysis

Zhang

Yang

2020

IEEE Access

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A two-phase Monte Carlo Simulation/Non-intrusive Polynomial Chaos (MCS/NIPC) method for quantification of margins and mixed uncertainties (aleatory and epistemic uncertainties) is proposed in this paper for the flutter speed boundary analysis. Compared with the traditional MCS/MCS method which needs lots of numerical simulations, the MCS/NIPC method can reduce the computational cost without losing accuracy due to the use of point collocation non-intrusive polynomial chaos in the inner loop. Based on the results of uncertainty quantification, a novel practical quantification of margins and mixed uncertainties (QMMU) framework is proposed considering both aleatory and epistemic uncertainties such as the parametric uncertainties in complicated models. A two-dimensional airfoil and a three-dimensional benchmark wing, the AGARD 445.6 wing, are both employed to illustrate the practical application of the proposed methods for flutter speed computation in the presence of mixed uncertainties arising from the material properties and flight conditions. Within the analysis, aleatory and mixed uncertainty quantification are conducted to prove the effectiveness and efficiency of the MCS/NIPC method. Then the QMMU metrics are accomplished for the flutter speed boundary analysis of the two airfoil models. The results demonstrate the potential of the proposed QMMU framework to analyze the stability of engineering systems. INDEX TERMS Flutter, non-intrusive polynomial chaos, probability bounds, quantification of margins and mixed uncertainties, uncertainty quantification

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A successive robust flutter prediction technique for aeroelastic systems using µ method

Onkar

2021

Meccanica

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Aeroelastic flutter analysis considering modeling uncertainties

Cited by 13 publications

References 28 publications

Fuzzy uncertainty analysis and reliability assessment of aeroelastic aircraft wings

Fuzzy uncertainty analysis and reliability assessment of aeroelastic aircraft wings

A Two-Phase Monte Carlo Simulation/Non-Intrusive Polynomial Chaos (MSC/NIPC) Method for Quantification of Margins and Mixed Uncertainties (QMMU) in Flutter Analysis

A successive robust flutter prediction technique for aeroelastic systems using µ method

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