Spinning mode analysis of the acoustic field generated by a turboshaft engine

Blacodon, Daniel; Léwy, S.

doi:10.2514/3.46287

Cited by 6 publications

(3 citation statements)

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“…Microphone arrays are heavily used to produce acoustic source images [1,2] and acoustic modes [3] in experimental examinations and evaluations. The mode detection, also known as the mode decomposition, is not only a commonly used method in analysing the near-field sound propagation and scattering, but also very useful for the far-field sound field directivity predictions [4] and duct acoustic impedance tests [5]. A new measurement method is proposed here by using compressive sensing that could extensively facilitate the mode detection tests and, therefore, constitutes the main contribution of this paper.…”

Section: Introductionmentioning

confidence: 99%

“…The conventional mode detection methods (for the azimuthal mode decompositions in particular) [4,[6][7][8][9][10] conduct spatial Fourier series decompositions by using circular sensory arrays [3,4,[11][12][13][14], which usually consist of a large number of equally spaced microphones to satisfy the famous Shannon-Nyquist sampling theorem, i.e. the array should have at least 2m microphones to capture an azimuthal mode of the mth order.…”

Section: Introductionmentioning

confidence: 99%

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Compressive sensing based spinning mode detections by in-duct microphone arrays

Wang

Huang

2016

Meas. Sci. Technol.

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This paper presents a compressive sensing based experimental method for detecting spinning modes of sound waves propagating inside a cylindrical duct system. This method requires fewer dynamic pressure sensors than the number required by the Shannon–Nyquist sampling theorem so long as the incident waves are sparse in spinning modes. In this work, the proposed new method is firstly validated by preparing some of the numerical simulations with representative set-ups. Then, a duct acoustic testing rig with a spinning mode synthesiser and an in-duct microphone array is built to experimentally demonstrate the new approach. Both the numerical simulations and the experiment results are satisfactory, even when the practical issue of the background noise pollution is taken into account. The approach is beneficial for sensory array tests of silent aeroengines in particular and some other engineering systems with duct acoustics in general.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Compressive sensing based spinning mode detections by in-duct microphone arrays

Wang

Huang

2016

Meas. Sci. Technol.

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“…The gains in (S/N) of several methods have been compared in a previous work using numerical simulations and mode measurements in a nozzle of a Turbomeca engine. 4 The aim of this article is to derive a new analysis technique from method 4 in order to solve the above problem. The article begins with a brief theoretical review of methods 1-4 (Sec.…”

Section: Introductionmentioning

confidence: 99%

Acoustic spinning-mode analysis by an iterative threshold method

Blacodon

1995

Journal of Aircraft

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Spinning-mode analysis methods are commonly used for studying the spatial structure of the acoustic field inside ducts. In some applications, the background noise is very high, so that the acoustic modes may be overwhelmed by spurious modes in the modal spectra. The problem may be solved by methods that greatly reduce the background noise. Four methods studied in a previous work are briefly summarized in this article. The method providing the best reduction of the background noise is inaccurate in certain cases. An iterative threshold technique is proposed to eliminate this drawback. It is compared to the four other methods using numerical simulations and mode measurements in the nozzle of a Turbomeca TM333 turboshaft engine. Nomenclature/ = frequency G, = gain in signal to noise ratio of method / = 10 log[(S/N) OUT /(S/N) IN ] / = unit matrix Im[w(/, m)] = imaginary part of o>(/, m) J = number of microphones L = number of data blocks M N = set of noise modes M s = set of acoustic modes, signal m = angular wave number of spinning mode order N(f, 0) = frequency Fourier transform of Ai(r, 0) n(t, 6) = background noise Re[o>(/, m)] = real part of w(f, m) 5(/, 0) = frequency Fourier transform of s(f, 0) s(t, 0) = acoustic pressure, signal T Vmin = the smallest power spectral density on a noise mode Tr[V(f)] = trace of matrix T(/) t = time y(/, 0) = frequency Fourier transform of y(t, 0) y(t, 0) = time history of acoustic pressure (signal -+ noise) measured by a microphone at angle 0 T/v(/) = cross-spectral matrix of N(f, 0) r s (/) = cross-spectral matrix of S(f, 0) r y (f) = cross-spectral matrix of Y(f, 0), see Eq. (3) 8 = Kronecker symbol 0 = angle 6j = location angle of microphone y [ = 277(y -!)//],/= 1,2, . . . ,7 cr 2 (/) = noise power spectral density <£,(/, m) = wave number spectrum at frequency/by method / o>(/, m) = angular Fourier transform of Y(f, 0) * = complex conjugate + = transposed complex conjugate Presented as Paper 92-02-041 at

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