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Noise predictions for a supersonic business jet using advanced take-off procedures

Part of: ISABE 2017 Sustainable Aerospace

Published online by Cambridge University Press:  19 February 2018

J. J. Berton ,
S. M. Jones ,
J. A. Seidel  and
D. L. Huff
J. J. Berton*
Affiliation:
National Aeronautics and Space Administration, John H. Glenn Research Center, Cleveland, Ohio, US
S. M. Jones
Affiliation:
National Aeronautics and Space Administration, John H. Glenn Research Center, Cleveland, Ohio, US
J. A. Seidel
Affiliation:
National Aeronautics and Space Administration, John H. Glenn Research Center, Cleveland, Ohio, US
D. L. Huff
Affiliation:
National Aeronautics and Space Administration, John H. Glenn Research Center, Cleveland, Ohio, US
*
[email protected]
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Abstract

Supersonic civil aircraft present a unique noise certification challenge. High specific thrust required for supersonic cruise results in high engine exhaust velocity and high levels of jet noise during take-off. Aerodynamics of thin, low-aspect-ratio wings equipped with relatively simple flap systems deepen the challenge. Advanced noise reduction procedures have been proposed for supersonic aircraft. These procedures promise to reduce certification noise levels, but they may require departures from normal reference procedures defined in noise regulations. The subject of this article is a take-off performance and noise assessment of a notional supersonic business jet. Analytical models of an airframe and a supersonic engine derived from a contemporary subsonic turbofan core are developed. These models are used to predict take-off trajectories and certification noise levels. Results indicate advanced take-off procedures are helpful in reducing noise along lateral sidelines.

Keywords

Noiseaircraft performancesupersonic transports
Type
Research Article
Information
The Aeronautical Journal , Volume 122 , Issue 1250 , April 2018 , pp. 556 - 571
Copyright
Copyright © Royal Aeronautical Society 2018 

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Footnotes

A version of this paper was presented at the ISABE 2017 Conference, 3-8 September 2017, Manchester, UK.

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