Predicting the effect of electric and hybrid-electric aviation on acoustic pollution

In the quest for the reduction of noise pollution, novel hybrid-electric or fully-electric power-trains promise to provide a substantial contribution. Especially closer to airfields, where acceptability issues tend to limit air operations with conventional fuel-burning engines, such novel power-trai...

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Bibliographic Details
Main Authors: Riboldi Carlo E.D., Trainelli Lorenzo, Mariani Luca, Rolando Alberto, Salucci Francesco
Format: Article
Language:English
Published: De Gruyter 2020-04-01
Series:Noise Mapping
Subjects:
Online Access:http://www.degruyter.com/view/j/noise.2020.7.issue-1/noise-2020-0004/noise-2020-0004.xml?format=INT
Description
Summary:In the quest for the reduction of noise pollution, novel hybrid-electric or fully-electric power-trains promise to provide a substantial contribution. Especially closer to airfields, where acceptability issues tend to limit air operations with conventional fuel-burning engines, such novel power-trains allow to fly terminal maneuvers with a dramatically reduced impact on pollution. Considering the General Aviation (GA) field, where such new types of propulsion are more likely to gain a significant market share thanks to their favorable characteristics for this weight category, the reduction of the noise impact on ground may increase the infrastructural value of smaller airfields, often located in densely populated areas. This in turn would help in making novel power-train technologies economically advantageous at a system level. Despite these evident advantages, a methodology to quantify noise emissions of a novel type of power-train has not been identified yet – a fundamental step towards the assessment of the potential contribution of hybrid-electric or fully-electric aircraft to the global scenario of future aviation. This work introduces and discusses a possible procedure to provide such estimation. While mainly focused on the field of propeller-driven GA aircraft, the procedure presented herein can be easily scaled to cope with the specific features of heavier categories.
ISSN:2084-879X