Framework for predicting Noise-Power-Distance curves for novel aircraft designs

Abstract

Along with flight profiles, Noise-Power-Distance (NPD) curves are the key input variable for computing noise exposure contour maps around airports. With the development of novel aircraft designs (incorporating noise reduction technologies) and new noise abatement procedures, NPD datasets will be required for assessing their potential benefit in terms of noise reduction around airports. NPD curves are derived from aircraft flyover noise measurements taken for a range of aircraft configurations and engine power settings. Clearly then, empirical NPD curves will be unavailable for novel aircraft designs and novel operations. This paper presents a generic framework for computationally generating NPD curves for novel aircraft and situations. The new framework derives computationally the NPD noise levels that are normally derived experimentally, by estimating noise level variations arising from technological and operational changes with respect to a baseline scenario, where the noise levels are known, or otherwise estimated. The framework is independent of specific prediction methods and can use any potential new model for existing or new noise sources. The paper demonstrates the methodology of the framework, discusses its benefits and illustrates its applicability by deriving NPD curves for an unconventional approach operation and for a future concept blended-wing-body (BWB) aircraft.

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ORCID for Antonio Torija: orcid.org/0000-0002-5915-3736

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