AbstractThe investigation of technologies that can improve the sustainability of the air transport system requires not only the development of alternative fuel concepts and novel vehicle technologies but also the definition of appropriate assessment strategies. Regarding noise, the assessment should reflect the situation of communities living near airports, i.e., not only addressing sound levels but also accounting for the annoyance caused by aircraft noise. For this purpose, conventional A-weighted sound pressure level metrics provide initial but limited information as the level- and frequency-dependency of the human hearing is accounted for in a simplified manner. Ideally, subjective evaluations are required to adequately quantify the perceived short-term annoyance associated with aircraft noise. However, listening tests are time-consuming and not suitable to be applied during the conceptual aircraft design stage, where a large solution space needs to be explored. Aiming at bridging this gap, this work presents a methodology for the sound quality assessment of computational aircraft noise predictions, which is hereby conducted in terms of objective psychoacoustic metrics. The proposed methodology is applied to a novel medium-range vehicle with fan noise shielding architecture during take-off and landing procedures. The relevance of individual sound sources, i.e., airframe and engine noise contributions, and their dependencies on the aircraft architecture and flight procedures are assessed in terms of loudness, sharpness, and tonality. Moreover, the methodology is steered towards community noise assessment, where the impacts on short-term annoyance brought by the novel aircraft design are analysed. The assessment is based on the modified psychoacoustic annoyance, a metric that provides a quantitative description of human annoyance as a combination of different hearing sensations. The present work is understood as an essential step towards low-annoyance aircraft design.
Validation data for aircraft noise shielding prediction
