Designing Clean Jet-Noise Facilities and Making Accurate Jet-Noise Measurements

Ahuja, K. K.

doi:10.1260/147547203322986188

Cited by 50 publications

(40 citation statements)

References 38 publications

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“…As a jet noise testing rig, the SHJAR was designed to minimize rig noise sources, incorporating the recommendations of Viswanathan [17] and Ahuja [18] to achieve this goal. The rig is a single flow jet rig that used 150-psi air supplied by several remotely located compressors.…”

Section: A Facility Descriptionmentioning

confidence: 99%

Establishing Consensus Turbulence Statistics for Hot Subsonic Jets

Bridges

Werner

2010

16th AIAA/CEAS Aeroacoustics Conference

114

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Many tasks in fluids engineering require knowledge of the turbulence in jets. There is a strong, although fragmented, literature base for low order statistics, such as jet spread and other meanvelocity field characteristics. Some sources, particularly for low speed cold jets, also provide turbulence intensities that are required for validating Reynolds-averaged Navier-Stokes (RANS) Computational Fluid Dynamics (CFD) codes. There are far fewer sources for jet spectra and for space-time correlations of turbulent velocity required for aeroacoustics applications, although there have been many singular publications with various unique statistics, such as Proper Orthogonal Decomposition, designed to uncover an underlying low-order dynamical description of turbulent jet flow. As the complexity of the statistic increases, the number of flows for which the data has been categorized and assembled decreases, making it difficult to systematically validate prediction codes that require high-level statistics over a broad range of jet flow conditions. For several years, researchers at NASA have worked on developing and validating jet noise prediction codes. One such class of codes, loosely called CFD-based or statistical methods, uses RANS CFD to predict jet mean and turbulent intensities in velocity and temperature. These flow quantities serve as the input to the acoustic source models and flow-sound interaction calculations that yield predictions of far-field jet noise. To develop this capability, a catalog of turbulent jet flows has been created with statistics ranging from mean velocity to space-time correlations of Reynolds stresses. The present document aims to document this catalog and to assess the accuracies of the data, e.g. establish uncertainties for the data. This paper covers the following five tasks:• Document acquisition and processing procedures used to create the particle image velocimetry (PIV) datasets.• Compare PIV data with hotwire and laser Doppler velocimetry (LDV) data published in the open literature.• Compare different datasets acquired at roughly the same flow conditions to establish uncertainties.• Create a 'consensus' dataset for a range of hot jet flows, including uncertainty bands.• Analyze this consensus dataset for self-consistency and compare jet characteristics to those of the open literature.One final objective fulfilled by this work was the demonstration of a universal scaling for the jet flow fields, at least within the region of interest to aeroacoustics. The potential core length and the spread rate of the half-velocity radius were used to collapse of the mean and turbulent velocity fields over the first 20 jet diameters in a highly satisfying manner. Nomenclature ! "shear layer thickness, U j /|dU/dr| max # shear layer coordinate, = (r -r 0.5 )/! " , # $ shear layer growth rate coordinate, = (r -r 0·5

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Section: A Facility Descriptionmentioning

confidence: 99%

Establishing Consensus Turbulence Statistics for Hot Subsonic Jets

Bridges

Werner

2010

16th AIAA/CEAS Aeroacoustics Conference

114

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“…As a jet noise testing rig, the SHJAR was designed to minimize rig noise sources, incorporating suggestions of Viswanathan [5] and Ahuja [6] to achieve this goal. The single flow jet rig uses 1 MPa (150psi) air supplied by several remotely located compressors.…”

Section: Test Facilities and Instrumentationmentioning

confidence: 99%

Assessment of Current Jet Noise Prediction Capabilities

Bridges

Khavaran

Hunter

2008

14th AIAA/CEAS Aeroacoustics Conference (29th AIAA Aeroacoustics Conference)

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“…In (24) and (25), f is the temporal frequency in Hz. It should be noted that in this case the eigenvalues and eigenfunctions are now functions of temporal frequency f, as well as streamwise location and azimuthal mode number m. A summary of the solutions to this problem can be found in Ukeiley et al" for the high Mach number subsonic jet.…”

Section: A Proper Orthogonal Decompositionmentioning

confidence: 99%