Annoyance to Noise Produced by a Distributed Electric Propulsion High-Lift System

Results of a psychoacoustic test performed to understand the relative annoyance to noise produced by several configurations of a distributed electric propulsion high lift system are given. It is found that the number of propellers in the system is a major factor in annoyance perception. This is an intuitive result as annoyance increases, in general, with frequency, and, the blade passage frequency of the propellers increases with the number of propellers. Additionally, the data indicate that having some variation in the blade passage frequency from propeller-to-propeller is beneficial as it reduces the high tonality generated when all the propellers are spinning in synchrony at the same speed. The propellers can be set to spin at different speeds, but it was found that allowing the motor controllers to drift within 1% of nominal settings produced the best results (lowest overall annoyance). The methodology employed has been demonstrated to be effective in providing timely feedback to designers in the early stages of design development.

[1]  Mark D. Moore,et al.  Misconceptions of Electric Aircraft and their Emerging Aviation Markets , 2014 .

[2]  Daniela M. Witten,et al.  An Introduction to Statistical Learning: with Applications in R , 2013 .

[3]  Nicholas K. Borer,et al.  High-Lift Propeller System Configuration Selection for NASA's SCEPTOR Distributed Electric Propulsion Flight Demonstrator , 2016 .

[4]  Stephen A. Rizzi,et al.  Psychoacoustic Analysis of Synthesized Jet Noise , 2013 .

[5]  Kent L. Gee,et al.  Implementing sharpness using specific loudness calculated from the "Procedure for the Computation of Loudness of Steady Sounds" , 2017, Proc. Meet. Acoust..

[6]  Joseph F. Horn,et al.  Near Real-Time Simulation of Rotorcraft Acoustics and Flight Dynamics , 2003 .

[7]  F. Farassat Linear Acoustic Formulas for Calculation of Rotating Blade Noise , 1981 .

[8]  Pieter G. Buning,et al.  High-Lift Propeller Noise Prediction for a Distributed Electric Propulsion Flight Demonstrator , 2017 .

[9]  Stephen A. Rizzi Toward Reduced Aircraft Community Noise Impact Via a Perception-Influenced Design Approach , 2016 .

[10]  Stephen A. Rizzi,et al.  Acoustic Performance of a Real-Time Three-Dimensional Sound-Reproduction System , 2013 .

[11]  G. Von Bismarck,et al.  Sharpness as an attribute of the timbre of steady sounds , 1974 .

[12]  F. Farassat Derivation of Formulations 1 and 1A of Farassat , 2007 .

[13]  E. Terhardt,et al.  Algorithm for extraction of pitch and pitch salience from complex tonal signals , 1982 .

[14]  R. H. Myers Classical and modern regression with applications , 1986 .

[15]  Arthur A. Regier,et al.  The problem of noise reduction with reference to light airplanes , 1946 .

[16]  Takashi Yano,et al.  STANDARDIZED GENERAL-PURPOSE NOISE REACTION QUESTIONS FOR COMMUNITY NOISE SURVEYS: RESEARCH AND A RECOMMENDATION , 2001 .

[17]  Mark D. Moore,et al.  Misconceptions of Electric Propulsion Aircraft and Their Emergent Aviation Markets , 2014 .

[18]  Jeffrey J. Kelly,et al.  A Users Guide for the NASA ANOPP Propeller Analysis System , 1997 .

[19]  G. A. Marcoulides Multilevel Analysis Techniques and Applications , 2002 .

[20]  Nicholas K. Borer,et al.  Drag Reduction Through Distributed Electric Propulsion , 2014 .

[21]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.

[22]  D. Wilson,et al.  Spherical wave propagation through inhomogeneous, anisotropic turbulence: log-amplitude and phase correlations. , 2004, The Journal of the Acoustical Society of America.

[23]  W. E. Zorumski Aircraft noise prediction program theoretical manual, part 1 , 1982 .