1. ANOPP landing-gear noise prediction with comparison to model-scale data.
- Author
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Burley, Casey L., Brooks, Thomas F., Humphreys Jr., William M., and Rawls Jr., John W.
- Subjects
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LANDING of jet transports , *AIRCRAFT noise , *BOEING airplanes , *AERODYNAMICS , *JET transports , *AERONAUTICS - Abstract
The NASA Aircraft NOise Prediction Program (ANOPP) offers two empirically based methods to predict landing gear noise: the "Fink" method and the "Guo" method. The "Guo" method is the most recent and was developed almost exclusively using Boeing full-scale landing gear data. The "Fink" method was developed over 25 years ago, using both model and full-scale data. The details of the two methods are compared and contrasted. The Fink method is found to follow Strouhal scaling, and hence predictions are made with the scale model geometry as input. The Guo method was found not to scale for arbitrary sized landing gear and hence the method required full-scale geometry inputs and the resulting predictions required scaling in order to compare with the measured model results. Application of these methods to a model-scale landing gear is investigated by comparing predicted results from each method with measured acoustic data obtained for a high-fidelity, 6.3%-scale, Boeing 777 main landing gear. The measurements were obtained in the NASALangley Quiet Flow Facility for a range of Mach numbers at a large number of observer angles. Noise spectra and contours as a function of polar and azimuthal angle characterize the directivity of landing gear noise. The measured spectra and contours are compared to predictions made using the Fink method and to scaled predictions from the Guo method. This is the first time an extensive set of landing gear noise directivity data are available to compare and assess predictive capabilities. Both methods predict comparable amplitudes and trends for the flyover locations, but deviate at sideline locations. Neither method fully captures the measured noise directivity. [ABSTRACT FROM AUTHOR]
- Published
- 2009
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