9 th AIAA/CEAS Aeroacoustics Conference Purdue University School of Aeronautics and Astronautics 1 An Investigation of Extensions of the Four- Source Method for Predicting the Noise From Jets With Internal Forced Mixers Loren Garrison Purdue University School of Aeronautics and Astronautics W.N. Dalton Rolls-Royce Corporation A.S Lyrintzis and G.A. Blaisdell Purdue University School of Aeronautics and Astronautics

9 th AIAA/CEAS Aeroacoustics Conference Purdue University School of Aeronautics and Astronautics 2 Outline Summary of the Four-source coaxial jet noise prediction method Internally forced mixed jet configurations Comparisons of mixer experimental data to coaxial and single jet predictions Modified four-source formulation Modified Method Parameter optimization Modified Method Results

9 th AIAA/CEAS Aeroacoustics Conference Purdue University School of Aeronautics and Astronautics 3 Four-Source Coaxial Jet Noise Prediction VsVs VsVs VpVp Initial Region Interaction Region Mixed Flow Region Secondary / Ambient Shear Layer Primary / Secondary Shear Layer

9 th AIAA/CEAS Aeroacoustics Conference Purdue University School of Aeronautics and Astronautics 4 –Secondary Jet: –Effective Jet: –Mixed Jet: –Total noise is the incoherent sum of the noise from the three jets Four-Source Coaxial Jet Noise Prediction

9 th AIAA/CEAS Aeroacoustics Conference Purdue University School of Aeronautics and Astronautics 5 Forced Mixer H Lobe Penetration (Lobe Height) H:

9 th AIAA/CEAS Aeroacoustics Conference Purdue University School of Aeronautics and Astronautics 6 Internally Forced Mixed Jet Bypass Flow Mixer Core Flow Nozzle Tail Cone Exhaust Flow Exhaust / Ambient Mixing Layer Lobed Mixer Mixing Layer

9 th AIAA/CEAS Aeroacoustics Conference Purdue University School of Aeronautics and Astronautics 7 Noise Prediction Comparisons Experimental Data –Aeroacoustic Propulsion Laboratory at NASA Glenn –Far-field acoustic measurements (~80 diameters) Single Jet Prediction –Based on nozzle exhaust properties (V,T,D) –SAE ARP876C Coaxial Jet Prediction –Four-source method –SAE ARP876C for single jet predictions

9 th AIAA/CEAS Aeroacoustics Conference Purdue University School of Aeronautics and Astronautics 8 Noise Prediction Comparisons Low Penetration MixerHigh Penetration Mixer

9 th AIAA/CEAS Aeroacoustics Conference Purdue University School of Aeronautics and Astronautics 9 Noise Prediction Comparisons Low Penetration MixerHigh Penetration Mixer

9 th AIAA/CEAS Aeroacoustics Conference Purdue University School of Aeronautics and Astronautics 10 Noise Prediction Comparisons Low Penetration MixerHigh Penetration Mixer

9 th AIAA/CEAS Aeroacoustics Conference Purdue University School of Aeronautics and Astronautics 11 Modified Four-Source Formulation Variable Parameters: Single Jet Prediction Source Reduction Spectral Filter

9 th AIAA/CEAS Aeroacoustics Conference Purdue University School of Aeronautics and Astronautics 12 Modified Formulation Variable Parameters  dB fcfc fcfc

9 th AIAA/CEAS Aeroacoustics Conference Purdue University School of Aeronautics and Astronautics 13 Parameter Optimization Algorithm Frequency range is divided into three sub-domains Start with uncorrected single jet sources Evaluate the error in each frequency sub-domain and adjusted relevant parameters Iterate until a solution is converged upon Low Frequency Sub-Domain  dB m,  dB e f s Mid Frequency Sub-Domain  dB s,  dB m,  dB e f s, f m, f e High Frequency Sub-Domain  dB s f m,f e

9 th AIAA/CEAS Aeroacoustics Conference Purdue University School of Aeronautics and Astronautics 14 Parameter Optimization Algorithm Mid Frequency Sub-Domain High Frequency Sub-Domain Low Frequency Sub-Domain

9 th AIAA/CEAS Aeroacoustics Conference Purdue University School of Aeronautics and Astronautics 15 Parameter Optimization Results Low Penetration Mixer High Penetration Mixer

9 th AIAA/CEAS Aeroacoustics Conference Purdue University School of Aeronautics and Astronautics 16 Modified Method with Optimized Parameters Low Penetration MixerHigh Penetration Mixer

9 th AIAA/CEAS Aeroacoustics Conference Purdue University School of Aeronautics and Astronautics 17 Modified Method with Optimized Parameters Low Penetration MixerHigh Penetration Mixer

9 th AIAA/CEAS Aeroacoustics Conference Purdue University School of Aeronautics and Astronautics 18 Modified Method with Optimized Parameters Low Penetration MixerHigh Penetration Mixer

9 th AIAA/CEAS Aeroacoustics Conference Purdue University School of Aeronautics and Astronautics 19 Optimized Parameter Trends  dB s (Increased) –Influenced by the convergent nozzle and mixing of the secondary flow with the faster primary flow –The exhaust jet velocity will be greater than the secondary jet velocity resulting in a noise increase

9 th AIAA/CEAS Aeroacoustics Conference Purdue University School of Aeronautics and Astronautics 20 Optimized Parameter Trends  dB m (Decreased) –Influenced by the effect of the interactions of the mixing layer generated by the mixer with the outer ambient-exhaust shear layer –The mixer effects cause the fully mixed jet to diffuse faster resulting in a larger effective diameter and therefore a lower velocity, resulting in a noise reduction

9 th AIAA/CEAS Aeroacoustics Conference Purdue University School of Aeronautics and Astronautics 21 Optimized Parameter Trends f c (Increased) –Influenced by the location where the turbulent mixing layer generated by the lobe mixer intersects the ambient-exhaust shear layer

9 th AIAA/CEAS Aeroacoustics Conference Purdue University School of Aeronautics and Astronautics 22 Summary In general the coaxial and single jet prediction methods do not accurately model the noise from jets with internal forced mixers The forced mixer noise spectrum can be matched using the combination of two single jet noise sources Currently not a predictive method Next step is to evaluate the optimized parameters for additional mixer data –Additional Mixer Geometries –Additional Flow Conditions (Velocities and Temperatures) Identify trends and if possible empirical relationships between the mixer geometries and their optimized parameters