Single-Sensor Identification of Spinning Mode Noise from Aircraft Engine

Huang, Xun

doi:10.2514/1.j051508

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…In this work, we assume that the fan noise source is at x 0, and the sensory array is installed at x 0.1, where almost all spinning modes, except m −33 and m 22, are cut off and have been largely attenuated [8]. Figure 6 shows the Fourier coefficients of analytical solutions at x 0.1.…”

Section: Compressive Sensing-based Measurementsmentioning

confidence: 99%

“…To resolve this stringent environmental issue, extensive efforts have been invested on silentengine design. Experimental investigation of the propagation of tonal spinning-mode noise is valuable to understand inherent fluid mechanics, to evaluate numerical models, and to verify and validate suitable low-noise design methods [8,13]. In this note, we illustrate that the advanced signal-processing technology holds the potential to extensively simplify experimental apparatus.…”

mentioning

confidence: 96%

“…The Nyquist-Shannon sampling theorem sets the most pessimistic criterion. If some prior knowledge, such as an inherent model of the fluid process of interest, can be incorporated, it is possible to employ fewer sensors [8]. On the other hand, Candes et al [1] proposed that it is possible to perfectly reconstruct a discrete-time signal f ∈ C N with M nonzero sparse items in a Hilbert basis from the randomly and partially accomplished samples.…”

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confidence: 99%

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Compressive Sensing and Reconstruction in Measurements with an Aerospace Application

Huang

2013

AIAA Journal

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Nomenclature a, b = integers arg min = the argument of the minimum B = blade number C 0 = speed of sound, m∕s C M = a constant E = approximation error f = a test function g = approximation of f g min = arg min kĝk 1 J = Bessel function of the first kind k = wave number K = compressive sample number M = number of nonzero entries M j = Mach number m = circumferential mode N = dimension of a signal n = integer p = pressure, Pa r = radial axis u, v, w = velocity, m∕s V = vane number x = axial axis θ = circumferential angle, rad θ K = circumferential position of Kth microphone, rad ρ = density, kg∕m 3 ω = angular frequency, rad∕s Subscripts 0 = stationary value m = variables related to the mth mode Superscriptŝ = Fourier transform 0 = disturbance

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Section: Compressive Sensing-based Measurementsmentioning

confidence: 99%

mentioning

confidence: 96%

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confidence: 99%

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Compressive Sensing and Reconstruction in Measurements with an Aerospace Application

Huang

2013

AIAA Journal

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“…As a first step, one needs to isolate each spinning mode using the so-called mode detection technique [16,17]. Next, sound pressure contours at the region of noise sources can be visualised using beamforming technique [16,18].…”

Section: Introductionmentioning

confidence: 99%

Duct spinning mode’s particle velocity imaging with in-duct circular microphone array

Wei

Bao

Huang

2014

20th AIAA/CEAS Aeroacoustics Conference

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Nowadays, the measurements within a duct have to be conducted using in-duct microphone array, which is unable to provide information of complete acoustic solutions across the test section. In this work, an imaging method of acoustic spinning modes propagating within a circular duct simply with surface pressure information is introduced. The proposed method is developed in a theoretical way and is demonstrated by a numerical simulation case. The fundamental idea behind the testing method was originally developed in control theory for ordinary differential equations. Spinning mode propagation, however, is formulated in partial differential equations. A finite difference technique is used to reduce the associated partial differential equations to a classical form in control theory. The observer method can thereafter be applied straightforwardly. The algorithm is recursive and thus could be operated in real-time. The observer error of the method is analyzed then. A numerical simulation for a straight circular duct is conducted. The acoustic solutions on the test section can be reconstructed with a good agreement to analytical solutions. The results suggest the potential and applications of the proposed method.

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A compressive-sensing-based method for radial mode analysis of aeroengine fan noise

Huang

Zhang

2020

Journal of Sound and Vibration

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Single-Sensor Identification of Spinning Mode Noise from Aircraft Engine

Cited by 6 publications

References 14 publications

Compressive Sensing and Reconstruction in Measurements with an Aerospace Application

Compressive Sensing and Reconstruction in Measurements with an Aerospace Application

Duct spinning mode’s particle velocity imaging with in-duct circular microphone array

A compressive-sensing-based method for radial mode analysis of aeroengine fan noise

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