# Experimental/Numerical Acoustic Assessment of Aircraft Beat Headsets Based on Electrospupn Mats
## 1 Introduction  
Acoustic computation and noise prediction, accounting for its generation and transmission, is a challenging task with several applications in different industrial sectors. A concern of increasing interest in the aerospace and automotive industries is the acoustic comfort of passengers [1], thus highlighting the need for noise prediction at early design stages. Indeed, noise or, more generally, unwanted sound, has become a growing problem for human health, leading to several adverse effects, including hearing loss, cardiovascular problems, sleep disturbance and psychological harms [2, 3]. The accurate evaluation of noise generation and propagation is nowadays a key concern, especially in the areas in which the comfort of end users has become a turning point. The focus of the present work is the mitigation of noise inside vehicle cabins, in particular in the case of aircraft fuselages. Several classical deterministic and statistical approaches exist for the prediction and analysis of noise in such scenarios, and each of them bears advantages and limitations. For instance, deterministic methods such as multibody simulation [4], Finite Element Method (FEM) [5], Boundary Element Method (BEM) [6] but also hybrid methods [7, 8] are computationally expensive due to geometrical complexity and inherent frequency limitations. As a matter of fact, owing to the large dimensions of aeronautical structures, accurate acoustical computations in the range of human hearing often require the resolution of hundreds of natural frequencies. As a result, reduced approaches [9] and algorithms to lighten the computational requirements [10] often become mandatory. Finally, Statistical Energy Analysis (SEA) can be preferable athigher frequencies for which deterministic approaches are no longer viable due to the high modal densities [11]. In the field of linear acoustics, the BEM is a suitable alternative to FEM and stands as a good compromise between the frequency range of applicability and the prediction of deterministic complex quantities. This is especially true for exterior problems, where the acoustic domain can be modeled as infinite in extent. Applying domain discretization methods such as FEM to such a problem clearly requires careful thought (an example of the infinite radiation condition application with FEM is available in [12]). Instead, it is more advantageous to use the BEM for this kind of application, since only the surface mesh of the vibrating/scattering bodies is required, as the Sommerfeld radiation condition is naturally satisfied at infinity [13]. In this way, both preprocessing times and runtimes can be reduced. In the last decades, BEM has become an established computational method for acoustics, widely adopted for noise calculations in the low and mid frequency range [14]. To solve small to mid-sized scattering, radiation, or coupled problems, the use of a direct BEM solver is recommended as, in comparison with the iterative solver, it will be more accurate and not limited by possible convergence issues. The main limitation to the size of the problem related to the direct BEM solver is in terms of memory to store and to invert the BEM matrices (full and complex). Aeronautical applications require the use of lightweight structures and low-energy consumption. Despite the advent of active noise cancellation in aircraft [15; 16], passive noise control by means of lightweight sound-absorbing materials remains a viable solution [17]. Within the field of these absorbing materials, nanofibers have recently attracted great attention for sound absorption [18; 19]. The thin fiber diameter (less than a micrometer) gives a high surface-to-volume ratio, which is an ideal property for lightweight and porous materials. When a layer of nanofibers is exposed to incident sound waves, the viscous and thermal dissipation inside the nano-pores, and the scattering of waves interacting with fibers, dissipate the sound energy.