Performance of a Checkerboard Liner with Uncertain Impedances

Robinson, Jay H.; Watson, William

doi:10.2514/6.2005-2850

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…These uncertainties are modeled in the framework of the probability theory using the nonparametric probabilistic approach involving random matrix theory [2] and the parametric probabilistic method. Some works devoted to uncertainty quantification for the liner impedance in presence of a flow have also been carried out in the last decade (see for instance [3][4][5][6][7]).…”

Section: Introductionmentioning

confidence: 99%

Stochastic computational model of 3D acoustic noise predictions for nacelle liners

Dangla

Soize

Cunha

et al. 2020

Aiaa Aviation 2020 Forum

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This paper is devoted to the uncertainty quantification for 3D acoustic performance model of nacelle liners (acoustic treatments). Uncertainties are taken into account in order to increase the robustness of the predictions. A full computational acoustic propagation model based on the convected Helmholtz equation in presence of a non-homogeneous flow velocity field computed by solving the Linearized Euler Equations (LEE) is used. A reduced-order computational model is deduced in order to implement the probabilistic model of uncertainties. The model uncertainties induced by modeling errors have been taken into account for the acoustic propagation model and the liner model, using the nonparametric probabilistic approach. In addition, the uncertainties on the acoustic excitation induced by the fan have been introduced using the parametric probabilistic approach. The developed methodology is applied to a 3D nacelle intake and allows for computing the confidence regions of the random far-field radiated pressure in terms of random SPL (Sound Pressure Level), which are compared to experiments for several flight conditions and frequencies. Nomenclature r(x, y, z), t, ω = Space three-dimensional vector, time scalar variable, and angular frequencỹ X, X 0 , X = Total, static, and fluctuating thermodynamic variable, function of space and time M ∞ , M = Uniform and local Mach number c α , ϕ α (x, y), k z α , k xy α = Amplitude, eigenfunction, axial, and transversal wave numbers of a duct mode α S duct = Cross section of the duct domain Ω duct ρ, ρ 0 , ρ ∞ , ρ = Total, steady-flow, uniform, and fluctuating fluid mass densitỹ v, v 0 , v ∞ , v = Total, steady-flow, uniform, and fluctuating fluid velocitỹ p, p 0 , p = Total, steady-flow, and fluctuating pressure fields Ω i , Ω o , Ω duct = Inner, outer, and duct domains of the intake acoustic problem ∂Ω i , ∂Ω o , ∂Ω duct = Inner, outer, duct domain boundaries Γ io , Γ h , Γ, Γ f , Γ ho , Γ duct , = Interface between inner and outer domains, hardwall, liner, fan, outer hardwall, and duct surfaces n io , n h , n, n f , n ho , n duct = Outward unit normal of surfaces Γ io , Γ

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Section: Introductionmentioning

confidence: 99%

Stochastic computational model of 3D acoustic noise predictions for nacelle liners

Dangla

Soize

Cunha

et al. 2020

Aiaa Aviation 2020 Forum

View full text Add to dashboard Cite

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Design of a low noise turbofan duct via an acoustic gradient-enhanced Kriging method

Qiu¹

2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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The Kriging models which are frequently used in aerodynamic shape optimization may become computationally inefficient when solving problems with large numbers of design variables. One solution to this problem would be the application of gradient-enhanced Kriging model. A gradient-enhanced Kriging and acoustic adjoint-method approach to duct acoustic problems is developed, aimed to improve the efficiency and accuracy of the existing Kriging approach at acoustic problems with many design parameters. To our knowledge, it is the first application of gradient-enhanced Kriging for duct acoustic problem. It employs a Kriging response surface in the parameter space, augmented with gradients obtained from the acoustic adjoint equations efficiently. The present paper aims at describing the potential of the gradient-enhanced Kriging method for low noise turbofan duct design. Prior to the optimization process, the implementation of the unsteady aeroacoustic adjoint method in shape optimization is validated by comparing the gradient values with that obtained by finite differences. In this work, the ordinary Kriging model and gradient-enhanced Kriging method are applied firstly to a benchmark functions and the results show that the additional gradient information can signiﬁcantly enhance the accuracy of Kriging model. And then, the original Kriging-based, adjoint-based and the gradient-enhanced Kriging method are all used to model 50 variable duct acoustic problems, respectively. The test results show that this approach whose gradient information is introduced by using acoustic adjoint method developed from multimode LEE, named as acoustic gradient-enhanced Kriging, can signiﬁcantly enhance the accuracy of Kriging models when the gradient data are available and thus provide an optimized low noise intake while maintaining the aerodynamic performance.

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Robust Three-Dimensional Acoustic Performance Probabilistic Model for Nacelle Liners

et al. 2021

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This paper is devoted to the robust optimization of nacelle liners (acoustic treatments).The full computational aeroacoustic model is based on the convected Helmholtz equation in presence of a non-homogeneous flow velocity field, which is computed by solving the potential Euler equations. Uncertainties are taken into account in order to increase the robustness of the predictions. A reduced-order computational aeroacoustic model is constructed in order to implement the nonparametric probabilistic model of uncertainties induced by the modeling errors in the aeroacoustic model and in the liner model. In addition, the uncertainties on the acoustic excitation induced by the fan are taken into account using the parametric probabilistic approach. The developed methodology is applied to a 3D nacelle intake and allows for computing the confidence regions of the random far-field radiated pressure in terms of random SPL (Sound Pressure Level), which are compared to experiments for one flight condition, one frequency, and for two types of liners.

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Performance of a Checkerboard Liner with Uncertain Impedances

Cited by 3 publications

References 6 publications

Stochastic computational model of 3D acoustic noise predictions for nacelle liners

Stochastic computational model of 3D acoustic noise predictions for nacelle liners

Design of a low noise turbofan duct via an acoustic gradient-enhanced Kriging method

Robust Three-Dimensional Acoustic Performance Probabilistic Model for Nacelle Liners

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