Loren Garrison Purdue University School of Aeronautics and Astronautics W.N. Dalton

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 Purdue University School of Aeronautics and Astronautics

2. 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 Outline Purdue University School of Aeronautics and Astronautics

3. Secondary / Ambient Shear Layer Primary / Secondary Shear Layer Vs Vp Vs Initial Region Interaction Region Mixed Flow Region Four-Source Coaxial Jet Noise Prediction Purdue University School of Aeronautics and Astronautics

4. Four-Source Coaxial Jet Noise Prediction • Secondary Jet: • Effective Jet: • Mixed Jet: • Total noise is the incoherent sum of the noise from the three jets Purdue University School of Aeronautics and Astronautics

5. Forced Mixer H H: Lobe Penetration (Lobe Height) Purdue University School of Aeronautics and Astronautics

6. Nozzle Bypass Flow Mixer Exhaust Flow Core Flow Tail Cone Lobed Mixer Mixing Layer Exhaust / Ambient Mixing Layer Internally Forced Mixed Jet Purdue University School of Aeronautics and Astronautics

7. 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 Noise Prediction Comparisons Purdue University School of Aeronautics and Astronautics

8. Noise Prediction Comparisons Low Penetration Mixer High Penetration Mixer Purdue University School of Aeronautics and Astronautics

9. Noise Prediction Comparisons Low Penetration Mixer High Penetration Mixer Purdue University School of Aeronautics and Astronautics

10. Noise Prediction Comparisons Low Penetration Mixer High Penetration Mixer Purdue University School of Aeronautics and Astronautics

11. Single Jet Prediction Spectral Filter Source Reduction Modified Four-Source Formulation Variable Parameters: Purdue University School of Aeronautics and Astronautics

12. DdB Dfc Dfc DdB Modified Formulation Variable Parameters Purdue University School of Aeronautics and Astronautics

13. 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 Parameter Optimization Algorithm Low Frequency Sub-Domain DdBm ,DdBe fs Mid Frequency Sub-Domain DdBs ,DdBm ,DdBe fs , fm , fe High Frequency Sub-Domain DdBs fm ,fe Purdue University School of Aeronautics and Astronautics

14. Parameter Optimization Algorithm Low Frequency Sub-Domain Mid Frequency Sub-Domain High Frequency Sub-Domain Purdue University School of Aeronautics and Astronautics

15. Parameter Optimization Results Low Penetration Mixer High Penetration Mixer Purdue University School of Aeronautics and Astronautics

16. Modified Method with Optimized Parameters Low Penetration Mixer High Penetration Mixer Purdue University School of Aeronautics and Astronautics

17. Modified Method with Optimized Parameters Low Penetration Mixer High Penetration Mixer Purdue University School of Aeronautics and Astronautics

18. Modified Method with Optimized Parameters Low Penetration Mixer High Penetration Mixer Purdue University School of Aeronautics and Astronautics

19. DdBs (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 Optimized Parameter Trends Purdue University School of Aeronautics and Astronautics

20. DdBm (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 Optimized Parameter Trends Purdue University School of Aeronautics and Astronautics

21. fc (Increased) Influenced by the location where the turbulent mixing layer generated by the lobe mixer intersects the ambient-exhaust shear layer Optimized Parameter Trends Purdue University School of Aeronautics and Astronautics

22. 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 Summary Purdue University School of Aeronautics and Astronautics