Wall-pressure fluctuations in turbulent pipe flow

Lauchle, Gerald C.; Daniëls, Mark A.

doi:10.1063/1.866080

Cited by 41 publications

(18 citation statements)

References 16 publications

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“…It is usually possible to refer measurements to some point defined as a constant, but the definition varies among experiments, and complicates comparisons. It may not be a coincidence, for example, that the lowest fluctuations in figure 8(a) correspond to a pipe in which the experimenters took special care to remove low-frequency components and spurious vibrations (Lauchle & Daniels 1987).…”

Section: 'Sterile' Eddies and The Fluctuations Of The Outer Layermentioning

confidence: 99%

Turbulent fluctuations above the buffer layer of wall-bounded flows

2008

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The behaviour of the velocity and pressure fluctuations in the logarithmic and outer layers of turbulent flows is analysed using spectral information and probability density functions from channel simulations at Re τ 6 2000. Comparisons are made with experimental data at higher Reynolds numbers. It is found, in agreement with previous investigations, that the intensity profiles of the streamwise and spanwise velocity components have logarithmic ranges that are traced to the widening spectral range of scales as the wall is approached. The same is true for the pressure, both theoretically and observationally, but not for the normal velocity or for the tangential stress cospectrum, although even those two quantities have structures with lengths of the order of several hundred times the wall distance. Because the logarithmic range grows longer as the Reynolds number increases, variables which are 'attached' in this sense scale in the buffer layer in mixed units. These results give strong support to the attached-eddy scenario proposed by Townsend (1976), but they are not linked to any particular eddy model. The scaling of the outer modes is also examined. The intensity of the streamwise velocity at fixed y/ h increases with the Reynolds number. This is traced to the large-scale modes, and to an increased intensity of the ejections but not of the sweeps. Several differences are found between the outer structures of different flows. The outer modes of the spanwise and wall-normal velocities in boundary layers are stronger than in internal flows, and their streamwise velocities penetrate closer to the wall. As a consequence, their logarithmic layers are thinner, and some of their logarithmic slopes are different. The channel statistics are available electronically at

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Section: 'Sterile' Eddies and The Fluctuations Of The Outer Layermentioning

confidence: 99%

Turbulent fluctuations above the buffer layer of wall-bounded flows

2008

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“…Acceleration is shown on the left and wall pressure is shown on the right. In addition, two reference pressure sensors were installed in the same axial plane as the most upstream TBL sensor, and were installed 120 ı apart (similar to those of Lauchle and Daniels [2]). The separation distance between the reference pressure sensors and the all TBL pressure sensors results in uncorrelated TBL pressures for all frequencies of interest.…”

Section: Measured Datamentioning

confidence: 99%

“…Lauchle and Daniels [2] employed a related subtraction technique using multiple sensors measured simultaneously to remove noise signals from their measurements of turbulent boundary layer (TBL) wall pressure. Naguib, Gravante, and Wark [3] discuss the advantages of an optimal filtering approach compared to a difference approach.…”

Section: Introductionmentioning

confidence: 99%

Removing Unwanted Noise from Operational Modal Analysis Data

Bonness

Jenkins

2015

Topics in Modal Analysis, Volume 10

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Operational modal analysis data includes the measurement of dynamic signals such as structural vibration data and the corresponding excitation force or pressure. In addition to the desired information, measured structural vibration data can include unwanted electrical noise and vibration energy from adjacent structures. Measured dynamic pressures can contain unwanted signals such as acoustic and vibration induced pressures. In this paper, a noise removal technique is presented in which an unlimited number of unwanted correlated signals can be removed from a set of measured data. In its simplest form, this technique is related to coherent output power (COP). However, unlike COP noise removal, multiple signals can be removed from measured data while retaining the magnitude and phase of the original data required for modal analysis processing. This technique is demonstrated using vibration data and dynamic wall pressure measurements from a thin-walled aluminum cylinder filled with water flowing at 20 ft/s. IntroductionOperational modal analysis involves measuring the vibration of a system during operation either because the desired operational excitation is not appropriately represented by traditional impulsive modal impact testing or because the system cannot be conveniently taken off-line. Transfer function data between vibration and input force are traditionally measured and used as input for an experimental modal analysis. However, when the input force cannot be measured, cross-spectrum measurements between accelerometers can be used in place of the transfer function data. Resonance frequency, damping, and operational modes shapes can all be obtained from cross-spectrum measurements between accelerometers placed at locations required to appropriately resolve the structural mode shapes. Occasionally, measurements of dynamic force or pressure can also be made to help determine modal mass.Measurements of structural vibration or pressure often include unwanted signals not associated with the signals of interest -especially in an operational setting where other machinery or processes are often running. The unwanted signals may be associated with electrical noise, vibration energy from adjacent structures, vibration induced pressures, and acoustic pressures. While various noise removal techniques are available to "clean up" measured data, a conditioned spectral density technique [1] is presented which can remove multiple signals of unwanted noise from measurements of cross-spectra. This technique can be applied to any dynamic signal measurements which meet necessary criteria.Lauchle and Daniels [2] employed a related subtraction technique using multiple sensors measured simultaneously to remove noise signals from their measurements of turbulent boundary layer (TBL) wall pressure. Naguib, Gravante, and Wark [3] discuss the advantages of an optimal filtering approach compared to a difference approach. In their work, the optimal filtering provides a better estimate of the noise than the difference approach. The ...

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“…At low frequencies part of the source signal was cancelled along with the background noise if insufficient transducer spacing was used. It was shown that this undesired source signal cancellation [7] was an order of magnitude smaller than that removed using the aforementioned methods [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…One technique used the assumption that if the background noise at two different transducers was uncorrelated yet statistically equal, the frequency spectrum due to only the model source could be recovered from the difference between the two time-series. Improvements were made on the technique over the next two decades [2][3][4][5]. In 1989, a technique for capturing model noise from turbulent boundary layer wall-pressure fluctuations was investigated [6].…”

Section: Introductionmentioning

confidence: 99%

A Background Noise Reduction Technique using Adaptive Noise Cancellation for Microphone Arrays

Spalt

Fuller

Brooks

et al. 2011

17th AIAA/CEAS Aeroacoustics Conference (32nd AIAA Aeroacoustics Conference)

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Background noise in wind tunnel environments poses a challenge to acoustic measurements due to possible low or negative Signal to Noise Ratios (SNRs) present in the testing environment. This paper overviews the application of time domain Adaptive Noise Cancellation (ANC) to microphone array signals with an intended application of background noise reduction in wind tunnels. An experiment was conducted to simulate background noise from a wind tunnel circuit measured by an out-of-flow microphone array in the tunnel test section. A reference microphone was used to acquire a background noise signal which interfered with the desired primary noise source signal at the array. The technique's efficacy was investigated using frequency spectra from the array microphones, array beamforming of the point source region, and subsequent deconvolution using the Deconvolution Approach for the Mapping of Acoustic Sources (DAMAS) algorithm. Comparisons were made with the conventional techniques for improving SNR of spectral and Cross-Spectral Matrix subtraction. The method was seen to recover the primary signal level in SNRs as low as -29 dB and outperform the conventional methods. A second processing approach using the center array microphone as the noise reference was investigated for more general applicability of the ANC technique. It outperformed the conventional methods at the -29 dB SNR but yielded less accurate results when coherence over the array dropped. This approach could possibly improve conventional testing methodology but must be investigated further under more realistic testing conditions. = data window weighting constant x n = reference input at step n y n = FIR filter output at step n z n = ANC output at step n = coherence ε n = ANC error at step n μ = LMS algorithm step size τ = time delay τ m = propagation time from grid point to microphone m

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Wall-pressure fluctuations in turbulent pipe flow

Cited by 41 publications

References 16 publications

Turbulent fluctuations above the buffer layer of wall-bounded flows

Turbulent fluctuations above the buffer layer of wall-bounded flows

Removing Unwanted Noise from Operational Modal Analysis Data

A Background Noise Reduction Technique using Adaptive Noise Cancellation for Microphone Arrays

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