Uncertainty Analysis of the Grazing Flow Impedance Tube

Brown, Martha C.; Jones, Michael G.; Watson, William

doi:10.2514/6.2012-2296

Cited by 11 publications

(9 citation statements)

References 9 publications

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“…Although most previous studies in impedance eduction focused on the minimization of a cost function [6,8], representing the mismatch between numerical and experimental fields (pressure and velocity), little effort has been dedicated to the evaluation of uncertainties in the results [14,15], associated with the measurement uncertainties inherent to the experimental observation. The method developed in the present study relies on a statistical approach to inverse problems, using Bayes's theorem.…”

Section: Bayesian Inference Applied To Liner Eductionmentioning

confidence: 99%

“…It can be separated from the extraneous noise (due to both the turbulent flow and the measurement noise) by a technique similar to the threemicrophone signal enhancement technique [33,38]. It consists in calculating the cross-spectral density function GŨ ;ls (respectively, GṼ ;ls ) between the velocity signalŨ (respectively,Ṽ) and the loudspeaker signal lst (see [35,39,40] (14) in which G ls is the autospectral density function of the loudspeaker signal. The acoustic velocities in the frequency domain are then given by…”

Section: Velocity Signal Postprocessingmentioning

confidence: 99%

“…Only a few studies have considered the issue of impedanceeduction uncertainty and accuracy. In Brown et al [14], the authors performed a Monte Carlo sensitivity analysis to study the influence of the Mach number, static temperature, and pressure on the impedance-eduction process. They showed the relative insensitivity of these parameters to the eduction outcome.…”

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confidence: 99%

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Statistical Inference Method for Liner Impedance Eduction with a Shear Grazing Flow

et al. 2019

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This paper presents a statistical inference method for impedance eduction in a flow-duct facility. The acoustic impedance is recast into a random variable, and Bayes's theorem is used to obtain the posterior probability density function of both its real and imaginary parts, thus expressing the knowledge/uncertainty one has on the impedance value, given a certain experimental data uncertainty. An evolutionary Markov chain Monte Carlo technique is selected to explore the probability space, and a surrogate model based on the method of snapshots is employed to speed up the calculations. The linearized Euler equations are solved using a two-dimensional discontinuous Galerkin scheme, accounting for the presence of a grazing flow. The inference process is first validated on published NASA Grazing Incidence Tube results, in which acoustic-pressure measurements on the wall opposite the liner are used as inputs. Then, the same procedure is applied to educe the impedance of a conventional single degree-of-freedom liner in the ONERA-The French Aerospace Lab B2A acoustic bench, in which a laser Doppler velocimetry (LDV) technique is used to measure the two components of the acoustic-velocity fields above the liner. The primary conclusion of the study is that the Bayesian inference method allows for consistent impedance eductions, as compared to a classical deterministic eduction approach, for both microphone and LDV measurements. Furthermore, it yields the credibility intervals of the identified impedance, which represent the uncertainty on the identified impedance values, given an uncertain measurement. The identified parameters are less correlated using an LDV-based inference than a microphone-based inference, which might be due to the more limited number of data.

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Section: Bayesian Inference Applied To Liner Eductionmentioning

confidence: 99%

Section: Velocity Signal Postprocessingmentioning

confidence: 99%

mentioning

confidence: 99%

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Statistical Inference Method for Liner Impedance Eduction with a Shear Grazing Flow

et al. 2019

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“…Clearly the quality of the impedance eduction process is critically dependent on the propagation of the above uncertainties. However the uncertainty analysis has rarely been considered except for some initial work by Brown [23], where uncertainties in Mach number, static temperature and pressure have been analyzed with a numerical Monte-Carlo approach. Unfortunately these efforts did not provide a systematic method for the propagation from component uncertainties to the overall uncertainty in the identified impedance results.…”

Section: Introductionmentioning

confidence: 99%

A systematic uncertainty analysis for liner impedance eduction technology

Zhou¹,

Bodén

2015

Journal of Sound and Vibration

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“…One part of this cooperation addresses the issue of liner impedance eduction in the presence of grazing flow. For more than ten years, continuous effort has been provided by NASA to develop reliable impedance eduction methods (see for instance [1][2][3][4] ). A finite-length acoustic liner is mounted on a wall of a flow duct, currently the grazing flow impedance tube (GFIT), and acoustic pressure is measured by microphones flush-mounted in the wall opposite the liner.…”

Section: Introductionmentioning

confidence: 99%

ONERA-NASA cooperative effort on liner impedance eduction

Primus

Piot²,

Simon³

et al. 2013

19th AIAA/CEAS Aeroacoustics Conference

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As part of a cooperation between ONERA and NASA, the liner impedance eduction methods developed by the two research centers are compared. The NASA technique relies on an objective function built on acoustic pressure measurements located on the wall opposite the test liner, and the propagation code solves the convected Helmholtz equation in uniform flow using a finite element method that implements a continuous Galerkin discretization. The ONERA method uses an objective function based either on wall acoustic pressure or on acoustic velocity acquired above the liner by Laser Doppler Anemometry, and the propagation code solves the linearized Euler equations by a discontinuous Galerkin discretization. Two acoustic liners are tested in both ONERA and NASA flow ducts and the measured data are treated with the corresponding impedance eduction method. The first liner is a wire mesh facesheet mounted onto a honeycomb core, designed to be linear with respect to incident sound pressure level and to grazing flow velocity. The second one is a conventional, nonlinear, perforate-over-honeycomb single layer liner. Configurations without and with flow are considered. For the nonlinear liner, the comparison of liner impedance educed by NASA and ONERA shows a sensitivity to the experimental conditions, namely to the nature of the source and to the sample width.

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Uncertainty Analysis of the Grazing Flow Impedance Tube

Cited by 11 publications

References 9 publications

Statistical Inference Method for Liner Impedance Eduction with a Shear Grazing Flow

Statistical Inference Method for Liner Impedance Eduction with a Shear Grazing Flow

A systematic uncertainty analysis for liner impedance eduction technology

ONERA-NASA cooperative effort on liner impedance eduction

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