Simulation of a hot coaxial jet: Direct noise prediction and flow-acoustics correlations

Bogey, Christophe; Barré, Sébastien; Juvé, Daniel; Bailly, Christophe

doi:10.1063/1.3081561

Cited by 69 publications

(33 citation statements)

References 58 publications

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“…59 This does not appear necessary for jetT3, because of the very strong resemblance between the flow fields of jetT2 and jetT3, which will be shown in sections III.B and III.C. The extrapolation is performed from fluctuating velocities and pressure recorded in the LES on a surface at r = 7.5r 0 as mentioned above.…”

Section: Far-field Extrapolationmentioning

confidence: 99%

Influence of nozzle-exit boundary-layer profile on high-subsonic jets

Bogey

Marsden

2014

20th AIAA/CEAS Aeroacoustics Conference

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The influence of the nozzle-exit boundary-layer profile on high-subsonic round jets is investigated by performing compressible large-eddy simulations of four jets using lowdissipation numerical schemes. The jets are isothermal, and have a Mach number of 0.9 and a diameter-based Reynolds number of 5 × 10 4 . They originate from a pipe nozzle in which a trip-like forcing is applied. In that way, they exhibit, at the exit section, around 6% of peak turbulence intensity and boundary-layer velocity profiles characterized by a momentum thickness of about 2.8% of the nozzle radius, yielding a Reynolds number around 700, and by shape factors equal to 1.68, 1.77, 2.01 and 2.36. The results from the fourth case with a laminar velocity profile differ significantly from those from the three first cases with transitional profiles, whose accuracy is shown by a grid refinement study. Clear trends are thus identified when the shape of the exit boundary-layer profile changes from laminar to turbulent. Higher azimuthal modes and higher Strouhal numbers are found to predominate, respectively, at the pipe exit close to the wall and early on in the mixing layers. The latter appear to develop more slowly, leading to a longer potential core, and weaker velocity fluctuations are obtained in the shear layers and on the jet axis. Finally, lower noise levels are generated in the acoustic field.

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Section: Far-field Extrapolationmentioning

confidence: 99%

Influence of nozzle-exit boundary-layer profile on high-subsonic jets

Bogey

Marsden

2014

20th AIAA/CEAS Aeroacoustics Conference

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“…; , x, experimental data given in Ref. 9 (ws/wp = 0.759). The reference data has been shifted in the streamwise direction to account for differences in the initial flow development that are due to different disturbance forcing methods at the inflow.…”

Section: Mean Flowmentioning

confidence: 99%

“…Experimental results given in Ref. 9 are included. Our own simulation data is given as a function of the shifted streamwise coordinate z p , which is defined as z p = z + (Z c − z c ), where z c is the length of the potential core in the original coordinate z and Z c = 15 is the (arbitrarily chosen) position of the potentialcore end in the shifted coordinate z p .…”

Section: Mean Flowmentioning

confidence: 99%

Flow Dynamics and Aeroacoustics of Heated Coaxial Jets at Subsonic Mach Numbers

Gloor

Buehler

Kleiser

2014

20th AIAA/CEAS Aeroacoustics Conference

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The flow field and the noise radiation of coaxial jet flows with a hot primary stream are investigated numerically by Large-Eddy Simulation and Direct Noise Computation (DNC). The core-stream velocity is subsonic based on local conditions but mostly supersonic with respect to the ambient speed of sound. The flow field and the associated acoustic radiation are analyzed systematically for a range of velocity and temperature ratios between the primary (core) and the secondary (bypass) stream. Strong mixing of cold and hot fluid and the intermittent character of the flow field close to the end of the potential core lead to a pronounced noise radiation at an aft angle of approximately 35 • . The unsteady pressure field within the jet flow is analyzed and radiating contributions are extracted using a Fourier-filtering technique. NomenclatureMach number Ma p,0 ≡ Ma 0 primary jet Mach number M a p,∞ = w * 0 /a * ∞ ambient Mach number (core) M a s,∞ = w * s /a * ∞ ambient Mach number (bypass) N number of grid points P power spectral density estimate p pressure ∆r, ∆θ, ∆z grid spacings ∆t simulation time step (r, θ, z) cylindrical coordinates R 1,2 primary / secondary jet radius r mic distance of virtual microphonesradial, azimuthal, axial velocity w 0 core-jet velocity z c length of potential core α axial wavenumber κ heat conductivity (ξ, η, ζ) computational coordinates µ dynamic viscosity ω circular frequency φ radiation angle ρ density Subscripts 0 reference quantity at r = z = 0 ∞ ambient conditions cl centerline quantity (r = 0) p primary (core) stream s secondary (bypass) stream rms root-mean-square Superscripts ′′ Favre fluctuation quantity b base flow * dimensional quantityConventions · temporal and azimuthal average

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“…With simple tools lacking, efforts have focused on detailed simulations, which skirt explanation of how a jet makes sound by brute-force representation of the process in sufficient detail. Initial direct numerical simulations, which represented all turbulence scales in low-Reynolds-number model jets [12,13], have advanced toward largeeddy simulations [14,15,16,17,18,19,20], which promise to be more efficient by representing only the range of scales that make the most significant acoustic radiation. When exactly such simulations will yield sufficiently accurate predictions in engineering geometries at engineering Reynolds numbers can be argued, but given current effort and the ever-increasing availability of massive computational resources, this time will come soon if it has not already.…”

Section: Introductionmentioning

confidence: 99%

Adjoint-based optimization for understanding and suppressing jet noise

Freund

2010

Procedia Engineering

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Advanced simulation tools, particularly large-eddy simulation techniques, are becoming capable of making quality predictions of jet noise for realistic nozzle geometries and at engineering relevant flow conditions. Increasing computer resources will be a key factor in improving these predictions still further. Quality prediction, however, is only a necessary condition for the use of such simulations in design optimization. Predictions do not of themselves lead to quieter designs. They must be interpreted or harnessed in some way that leads to design improvements. As yet, such simulations have not yielded any simplifying principals that offer general design guidance. The turbulence mechanisms leading to jet noise remain poorly described in their complexity. In this light, we have implemented and demonstrated an aeroacoustic adjoint-based optimization technique that automatically calculates gradients that point the direction in which to adjust controls in order to improve designs. This is done with only a single flow solutions and a solution of an adjoint system, which is solved at computational cost comparable to that for the flow. Optimization requires iterations, but having the gradient information provided via the adjoint accelerates convergence in a manner that is insensitive to the number of parameters to be optimized.Keywords: jet noise, optimal control, adjoint-based optimization, computational aeroacoustics BackgroundAircraft jet exhausts remain loud, which continues to motivate efforts to suppress their noise. Chevroned nozzle lips [1, 2], nozzle-exit plasma actuators [3, 4], fluidic actuators [5], and many other techniques are currently being explored for this objective. Seemingly without exception, a significant degree of parametric exploration is employed in these efforts. This is because jet noise, unlike many flow phenomena, has no readily harnessed this-does-that mechanistic description at fixed global flow conditions. We have Lighthill's famous strong power-law sensitivity of radiated power to velocity ("a high power, near the eighth" [6]), but no correspondingly simple guidelines exist for what changes to make at fixed flow condition to suppress noise. The complex interplay between the jet turbulence and radiated sound, added to the underlying complexity of the turbulence itself, is the root cause of this. It is a problem of describing this complexity in a useful way. Acoustic analogy formulations, in which acoustic sources are crafted based upon estimates of turbulence statistics [7,8,9,10], have increased in their robustness to errors in these estimates [11], but such formulations have not yet yielded a flexible general method because they depend upon describing the complexity of the underlying turbulence in an accessible fashion.

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Simulation of a hot coaxial jet: Direct noise prediction and flow-acoustics correlations

Cited by 69 publications

References 58 publications

Influence of nozzle-exit boundary-layer profile on high-subsonic jets

Influence of nozzle-exit boundary-layer profile on high-subsonic jets

Flow Dynamics and Aeroacoustics of Heated Coaxial Jets at Subsonic Mach Numbers

Adjoint-based optimization for understanding and suppressing jet noise

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