Jet Noise Source Distribution from Far-Field Cross Correlations

AIAA Journal ◽  
1977 ◽  
Vol 15 (6) ◽  
pp. 771-772 ◽  
Author(s):  
L. Maestrello ◽  
Chen-Huei Liu
Keyword(s):  
Noise Source ◽  
Jet Noise ◽  
Source Distribution ◽  
Far Field ◽  
Cross Correlations

Effects of Jet Noise Source Distribution on Acoustic Far-Field Measurements

2012 ◽  
Vol 11 (7-8) ◽  
pp. 885-915 ◽  
Author(s):  
Ching-Wen Kuo ◽  
Jérémy Veltin ◽  
Dennis K. McLaughlin
Keyword(s):  
Noise Source ◽  
Jet Noise ◽  
Field Measurements ◽  
Source Distribution ◽  
Far Field

scholarly journals The sources of jet noise: experimental evidence

2008 ◽  
Vol 615 ◽  
pp. 253-292 ◽  
Author(s):  
CHRISTOPHER K. W. TAM ◽  
K. VISWANATHAN ◽  
K. K. AHUJA ◽  
J. PANDA
Keyword(s):  
High Speed ◽  
Noise Source ◽  
Jet Noise ◽  
Polar Angle ◽  
Sound Field ◽  
Source Distribution ◽  
Far Field ◽  
Noise Sources ◽  
Directional Information ◽  
Jet Mixing

The primary objective of this investigation is to determine experimentally the sources of jet mixing noise. In the present study, four different approaches are used. It is reasonable to assume that the characteristics of the noise sources are imprinted on their radiation fields. Under this assumption, it becomes possible to analyse the characteristics of the far-field sound and then infer back to the characteristics of the sources. The first approach is to make use of the spectral and directional information measured by a single microphone in the far field. A detailed analysis of a large collection of far-field noise data has been carried out. The purpose is to identify special characteristics that can be linked directly to those of the sources. The second approach is to measure the coherence of the sound field using two microphones. The autocorrelations and cross-correlations of these measurements offer not only valuable information on the spatial structure of the noise field in the radial and polar angle directions, but also on the sources inside the jet. The third approach involves measuring the correlation between turbulence fluctuations inside a jet and the radiated noise in the far field. This is the most direct and unambiguous way of identifying the sources of jet noise. In the fourth approach, a mirror microphone is used to measure the noise source distribution along the lengths of high-speed jets. Features and trends observed in noise source strength distributions are expected to shed light on the source mechanisms. It will be shown that all four types of data indicate clearly the existence of two distinct noise sources in jets. One source of noise is the fine-scale turbulence and the other source is the large turbulence structures of the jet flow. Some of the salient features of the sound field associated with the two noise sources are reported in this paper.

scholarly journals Jet-Surface Interaction Test: Phased Array Noise Source Localization Results

2012 ◽  
Author(s):  
Gary G. Podboy
Keyword(s):  
Source Localization ◽  
Phased Array ◽  
Noise Source ◽  
Jet Noise ◽  
Companion Paper ◽  
Far Field ◽  
Sound Waves ◽  
Axial Distribution ◽  
Noise Sources ◽  
Interaction Test

An experiment was conducted to investigate the effect that a planar surface located near a jet flow has on the noise radiated to the far-field. Two different configurations were tested: 1) a shielding configuration in which the surface was located between the jet and the far-field microphones, and 2) a reflecting configuration in which the surface was mounted on the opposite side of the jet, and thus the jet noise was free to reflect off the surface toward the microphones. Both conventional far-field microphone and phased array noise source localization measurements were obtained. This paper discusses phased array results, while a companion paper discusses far-field results. The phased array data show that the axial distribution of noise sources in a jet can vary greatly depending on the jet operating condition and suggests that it would first be necessary to know or be able to predict this distribution in order to be able to predict the amount of noise reduction to expect from a given shielding configuration. The data obtained on both subsonic and supersonic jets show that the noise sources associated with a given frequency of noise tend to move downstream, and therefore, would become more difficult to shield, as jet Mach number increases. The noise source localization data obtained on cold, shock-containing jets suggests that the constructive interference of sound waves that produces noise at a given frequency within a broadband shock noise hump comes primarily from a small number of shocks, rather than from all the shocks at the same time. The reflecting configuration data illustrates that the law of reflection must be satisfied in order for jet noise to reflect off of a surface to an observer, and depending on the relative locations of the jet, the surface, and the observer, only some of the jet noise sources may satisfy this requirement.

Simultaneous Noise and Performance Measurements for High-Speed Jet Noise Reduction Technologies

2009 ◽  
Author(s):  
Dean Long ◽  
Steven Martens
Keyword(s):  
Noise Reduction ◽  
High Speed ◽  
Near Field ◽  
Jet Noise ◽  
Source Distribution ◽  
Test Facility ◽  
Wave Radiation ◽  
Far Field ◽  
Shock Cell ◽  
Field Noise

Model scale tests are conducted to assess the Noise/Performance trade for high speed jet noise reduction technologies. It is demonstrated that measuring the near field acoustic signature with a microphone array can be used to assess the far field noise using a procedure known as acoustic holography. The near field noise measurement is mathematically propagated producing an estimate of the noise level at the new location. Outward propagation produces an estimate of the far field noise. Propagation toward the jet axis produces the source distribution. Tests are conducted on convergent/divergent nozzles with three different area ratios, and several different chevron geometries. Noise is characterized by two independent processes: Shock cell noise radiating in the forward quadrant is produced when the nozzle is operated at non-ideally expanded conditions. Mach wave radiation propagates into the aft quadrant when the exhaust temperature is elevated. These results show good agreement with actual far field measurements from tests in the GE Cell 41 Acoustic Test Facility. Simultaneous performance measurement shows the change in thrust coefficient for different test conditions and configurations. Chevrons attached to the nozzle exit can reduce the noise by several dB at the expense of a minimal thrust loss.

Rapid Global Finite-Frequency Ambient Noise Source Inversion

2020 ◽  
Author(s):  
Jonas Igel ◽  
Laura Ermert ◽  
Andreas Fichtner
Keyword(s):  
Ambient Noise ◽  
Noise Source ◽  
Computational Cost ◽  
User Interaction ◽  
Real Data ◽  
Source Distribution ◽  
Source Inversion ◽  
Finite Frequency ◽  
Noise Sources ◽  
Cross Correlations

<p>Common assumptions in ambient noise seismology such as Green’s function retrieval and equipartitioned wavefields are often not met in the Earth. Full waveform ambient noise tomography methods are free of such assumptions, as they implement knowledge of the time- and space-dependent ambient noise source distribution, whilst also taking finite-frequency effects into account. Such methods would greatly simplify near real-time monitoring of the sub-surface. Additionally, the distribution of the secondary microseisms could act as a new observable of the ocean state since its mechanism is well understood (e.g. Ardhuin et al., 2011).</p><p>To efficiently forward-model global noise cross-correlations we implement (1) pre-computed high-frequency wavefields obtained using, for example, AxiSEM (Nissen-Meyer et al., 2014), and (2) spatially variable grids, both of which greatly reduce the computational cost. Global cross-correlations for any source distribution can be computed within a few seconds in the microseismic frequency range (up to 0.2 Hz). Similarly, we can compute the finite-frequency sensitivity kernels which are then used to perform a gradient-based iterative inversion of the power-spectral density of the noise source distribution. We take a windowed logarithmic energy ratio of the causal and acausal branches of the cross-correlations as measurement, which is largely insensitive to unknown 3D Earth structures.</p><p>Due to its parallelisation on a cluster, our inversion tool is able to rapidly invert for the global microseismic noise source distribution with minimal required user interaction. Synthetic and real data inversions show promising results for noise sources in the North Atlantic with the structure and spatial distribution resolved at scales of a few hundred kilometres. Finally, daily noise sources maps could be computed by combining our inversion tool with a daily data download and processing toolkit.</p>

A Study of the Correlation of Large-Scale Structure Dynamics and Far-Field Radiated Noise in an Excited Mach 0.9 Jet

2009 ◽  
Vol 8 (3) ◽  
pp. 231-259 ◽  
Author(s):  
Jeff Kastner ◽  
Jin-Hwa Kim ◽  
Mo Samimy
Keyword(s):  
Large Scale ◽  
Noise Source ◽  
Microphone Array ◽  
Polar Angle ◽  
Streamwise Velocity ◽  
Source Distribution ◽  
Far Field ◽  
Potential Core ◽  
Velocity Fluctuations ◽  
Field Noise

The main goal of the present work is to excite various instabilities of an axisymmetric Mach 0.9 jet with a ReD of 0.76 × 106, track the ensuing large-scale structures/instability waves, and investigate relations between the dynamics of these structures and the far-field sound. The jet was excited over a large range of Strouhal numbers and several azimuthal modes by eight localized arc filament plasma actuators, equally spaced around the circumference of the nozzle, near the nozzle exit. The flow field and far-field noise were investigated using particle image velocimetry and a three-dimensional array of 12 microphones at 30° polar angle to the downstream jet axis. The microphone array results show that the high amplitude noise radiated to 30° polar angle is originated just downstream of the end of the potential core, in agreement with our previous results and the results in the literature. The streamwise noise source distribution was only sensitive to azimuthal modes around the jet preferred mode. Otherwise, the general trend was that forcing the jet at low Strouhal numbers moves the distribution upstream compared to the baseline jet, and at high Strouhal numbers results in a source distribution similar to the baseline jet. ***Conditionally-averaged PIV data were used to relate the flow dynamics and noise sources. The growth, saturation, and decay of the conditionally-averaged velocity fluctuations along the jet centerline correlate well with the far-field noise and the noise source distribution estimated using the microphone array. For m = 0 mode excitation around the jet column Strouhal number, the conditionally-averaged streamwise velocity fluctuations correlate well with the noise source distribution. While for m = 1, the correlation is best with the conditionally-averaged cross-stream fluctuations.

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