Jet Noise Research

High-Speed Jet-Noise Modeling

Unheated Mach 0.9 Jet

Accurate modeling of high-speed flow-induced noise is of importance in the aerospace industry. However, there is a lack of implementation of modeling algorithms suitable for such high speed noise problems in open-source computational fluid dynamic (CFD) codes. The main aim of this investigation is to model the high-speed flow-induced noise such as jet noise, using the open source tools . Accurate far-field noise computation requires resolution of fine scale turbulence structures in the near field, which act as aeroacoustic sources. Efficient resolution of the fine turbulence structures in the smooth regions of the flow requires the use of accurate high resolution schemes. However, dealing with high speed flows requires an additional consideration of high gradients and discontinuities which can cause numerical oscillations during computation. In this study, a hybrid approach is implemented wherein the flow field is partitioned into two domains corresponding to diffusive and high resolution schemes. Ffowcs William-Hawkings acoustic formulation is implemented as a post processing utility within the computational framework to compute the far-field noise based on the compressible flow data generated. A Mach 0.9 jet is simulated using this setup and the results were validated with experimental data. The aerodynamic and acoustic results are found to be in good agreement with existing experimental data suggesting the adequacy of selected setup for modeling high speed flow induced noise.

[Left] Comparison of the center line values with the experimental data .

[Right] Velocity profiles at various axial location depicting the spatial evolution of the

jet

Three-dimensional spatial evolution of the Jet

Q Criterion Iso-surfaces and Radial Mach contours at various axial locations

Aeroacoustic Results

Sound Pressure level at Microphone - A

Sound Pressure level at Microphone - B

Parametric Analysis of Downstream Fluidic Injection

Design and Operational Parameters

Number of injection ports (n)
Injection pressure (IPR)
Angle of injection ( θ )
Injection location (X)

Each combination of design and operational parameters represents a parameter case. Figure on the left shows the steps needed for analyzing a single parameter case. A CAD geometry is created based on the parameters, which is used for generating the computational mesh. The mesh, along with the initial and boundary conditions is used for the aerodynamic simulation. Once the simulation achieves a statistical steady state, the aeroacoustic measurements are made. The generated acoustic results are then post-processed (for e.g. FFT, outliers, averaging filter etc.). Thus, in order to perform a large parametric study covering a wide spectrum of parameters with the aforementioned approach demands the need of a robust framework which can efficiently and effectively model the aerodynamic and aeroacoustic behavior for these parameters. The numerical framework used in this study is based on the model of distributed components built on a distributed objects architecture. Two main levels can be distinguished as shown in figure below.

This figure shows the Mean Mach number contours for a DFI microjet injection setup with 4 microjets.

3D streamlines and vorticity contours along the cross plane are shown for interpreting the flow setup arising form such a nozzle-injector setup.

Effect of Number of microjets

In this case we vary the number of microjets being injected in the main flow while keeping all other parameters same.

Mean velocity contours for a Mach 0.9 jet treated with downstream fluidic injection.

Mach contours at the injection location showing the barrel shock formation.

Vorticity evolution for the case of four microjets as one moves downstream from the injection location.

n = 2

n = 3

n = 4

n = 8

Stream-wise vorticity at different axial planes for above mentioned configurations

x/D = 1.0

x/D = 1.2

x/D = 1.5

x/D = 1.8

x/D = 1.0

x/D = 1.2

x/D = 1.5

x/D = 1.8

x/D = 1.0

x/D = 1.2

x/D = 1.5

x/D = 1.8

x/D = 1.0

x/D = 1.2

x/D = 1.5

x/D = 1.8

Results related to other parameters to be uploaded soon.

Dual Stream Nozzle Implementation with Chevrons and External Injectors

Associated Publications

Rajput, Pankaj, and Sunil Kumar. "Directionally Targeted Jet Noise Suppression: Benefits of Asymmetric Downstream Fluidic Injection." 2018 AIAA/CEAS Aeroacoustics Conference. 2018.

Rajput, Pankaj, and Sunil Kumar. "Jet Noise Reduction by Downstream Fluidic Injection: Effect of Injection Pressure Ratio and Number of Injection Ports." 2018 AIAA Aerospace Sciences Meeting. 2018.

Rajput, Pankaj, Sunil Kumar, and Iraj Kalkhoran. "Noise reduction for an unheated Mach 0.9 jet by fluidic injection." 23rd AIAA/CEAS Aeroacoustics Conference. 2017.

Rajput, Pankaj, and Sunil Kumar. "Directional Noise Reduction via Asymmetric Downstream Fluidic Injection." ASME 2017 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2017.

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