Additive manufacturing (AM), widely known as 3D-printing, builds parts by adding material in a layer-by-layer process. This tool-less procedure enables the manufacturing of porous sound absorbers with defined geometric features, however, the connection of the acoustic behavior and the material’s micro-scale structure is only known for special cases. To bridge this gap, the work presented here employs machine-learning techniques that compute acoustic material parameters (Biot parameters) from the material’s micro-scale geometry. For this purpose, a set of test specimens is used that have been developed in earlier studies. The test specimens resemble generic absorbers by a regular lattice structure based on a bar design and allow a variety of parameter variations, such as bar width, or bar height. A set of 50 test specimens is manufactured by material extrusion (MEX) with a nozzle diameter of $0.2$ mm and a targeted under extrusion to represent finer structures. For the training of the machine learning models, the Biot parameters are inversely identified from the manufactured specimen. Therefore, laboratory measurements of the flow resistivity and absorption coefficient are used. The resulting data is used for training two different machine learning models, an artificial neural network and a k-nearest neighbor approach. It can be shown that both models are able to predict the Biot parameters from the specimen’s micro-scale with reasonable accuracy. Moreover, the detour via the Biot parameters allows the application of the process for application cases that lie beyond the scope of the initial database, for example, the material behavior for other sound fields or frequency ranges can be predicted. This makes the process particularly useful for material design and takes a step forward in the direction of tailoring materials specific to their application.

The 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.