Flutter Tests of the Pazy Wing

Drachinsky, Ariel; Avin, Or; Raveh, Daniella E.; Ben-Shmuel, Yaron; Tur, Moshe

doi:10.2514/1.j061717

Cited by 20 publications

(9 citation statements)

References 9 publications

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“…These subcritical behaviours have been observed experimentally from high-aspect-ratio flexible wings [9,10]. Recently, Drachinsky and Raveh [11,12] also discovered subcritical LCO formation in experimental testing of a highly flexible wing in low-speed flutter experiments. However, the effects of nonlinearities on aeroelasticity are often neglected in computational analysis due to the added complexity and high computational cost during the design process, which greatly limits the design space of these aerospace systems.…”

Section: Introductionmentioning

confidence: 60%

Data-Driven Bayesian Inference for Stochastic Model Identification of Nonlinear Aeroelastic Systems

McGurk,

Lye,

Renson

et al. 2024

AIAA Journal

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The objective of this work is to propose a data-driven Bayesian inference framework to efficiently identify parameters and select models of nonlinear aeroelastic systems. The framework consists of the use of Bayesian theory together with advanced kriging surrogate models to effectively represent the limit cycle oscillation response of nonlinear aeroelastic systems. Three types of sampling methods, namely, Markov chain Monte Carlo, transitional Markov chain Monte Carlo, and the sequential Monte Carlo sampler, are implemented into Bayesian model updating. The framework has been demonstrated using a nonlinear wing flutter test rig. It is modeled by a two-degree-of-freedom aeroelastic system and solved by the harmonic balance methods. The experimental data of the flutter wing is obtained using control-based continuation techniques. The proposed methodology provided up to a 20% improvement in accuracy compared to conventional deterministic methods and significantly increased computational efficiency in the updating and uncertainty quantification processes. Transitional Markov chain Monte Carlo was identified as the optimal choice of sampling method for stochastic model identification. In selecting alternative nonlinear models, multimodal solutions were identified that provided a closer representation of the physical behavior of the complex aeroelastic system than a single solution.

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Section: Introductionmentioning

confidence: 60%

Data-Driven Bayesian Inference for Stochastic Model Identification of Nonlinear Aeroelastic Systems

McGurk,

Lye,

Renson

et al. 2024

AIAA Journal

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“…A very significant advantage in performing flutter tests with a very flexible wing is that the flutter vibrations often turn into limit-cycle oscillations (LCO) without damaging the test model structure, as happened in the presented test and in Drachinsky et al [18]. Therefore, to be able to correlate the PFM results to the actual flutter velocities of the wing, direct flutter tests have been performed with the nominal configuration for 𝛼 = 0 • , 2 • , 4 • and 6°.…”

Section: A Direct Flutter Testsmentioning

confidence: 88%

Safe Flutter Determination for Wings Undergoing Large Deflections

Boer

Karpel

Sodja

2023

AIAA SCITECH 2023 Forum

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A new flutter test version of the Parametric Flutter Margin (PFM) method, specifically applied to wings undergoing large deflections is presented. The PFM method adds a stabilising parameter, such as a stabilising mass, to the model such that the flutter velocity is increased. By exciting the stabilising mass in one of the primary (i.e., x, y and z) directions while simultaneously measuring the response in these directions and repeating the excitation in other directions, the flutter margins that are associated with the original model can be determined. To demonstrate the method a wind tunnel test campaign was performed at TU Delft using the Delft Pazy Wing which can exhibit large nonlinear deflection, onto which a flutter pod consisting of a shaker and stabilising mass was placed at the mid-span position at the leading edge of the wing. During the test campaign, three test series were performed. The first identified the flutter boundary through direct flutter tests, with the flutter onset and offset velocities being determined by actually hitting flutter that turned into an LCO. SISO and MIMO PFM tests were then performed to obtain the nominal flutter boundaries without actually hitting flutter, showing a maximum difference of 4.4 % between each other at an angle of attack of 6°. The difference between the MIMO and SISO PFM was found to be increasing with increasing angle of attack, which was as expected. Compared to the directly measured flutter tests, the MIMO PFM results identified a flutter velocity of 4.8 % lower than the directly measured flutter offset velocity at an angle of attack of 4°, and the SISO PFM results identified the flutter velocity to be 8.2 % lower than the offset velocity at 4°angle of attack. The PFM-identified flutter frequencies showed a difference of less than 2 % compared to the direct flutter test, with the difference in identified flutter frequency between the SISO and MIMO PFM reaching a maximum of 2 %. The acquired data shows the potential of the PFM method for performing safer, shorter and consequently cheaper flight tests for the certification procedure of new aircraft configurations. Nomenclature

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“…In order to validate such models, the Pazy wing experiment (Avin et al, 2022) was conducted as part of the Aeroelastic Prediction Workshop (AePW) Large Deflection Working Group (Ritter and Hilger, 2022;Drachinsky et al, 2022;Riso and Cesnik, 2023). The Pazy wing was created at Technion with the intent to be used for validation of aeroelastic models.…”

Section: Phase Between Wing Tip Displacement and Gust Vertical Velocitymentioning

confidence: 99%

“…The Pazy flutter boundaries were measured by recent experiments (Drachinsky et al, 2022) where two instability regions could be extracted: a smaller onset region and a larger offset region. For example, at static root angle of attack α = 3 • , if the flow speed was continuously increased, the wing would start vibrating at 49 m/s.…”

Section: Aeroelastic Simulations Validationmentioning

confidence: 99%