Dataset on permeability of wings from owls and non-silently flying birds.

The very soft and flow-permeable plumage is among the special adaptations of the owl that the silent flight is attributed to. Using a specially designed apparatus that provides a low-speed volume flow of air through a small sample of porous material, measurements of the air flow permeability were performed in accordance to ISO 9053 on a total of 39 prepared wing specimen from six different bird species, including three species of silently flying owls and three non-silently flying bird species. The resulting data set described in the present paper contains the static airflow resistance measured at different positions on the wing.

A fast data-driven method for inverse microphone array signal processing.

Microphone arrays have long been used to characterize and locate sound sources. However, existing algorithms for processing the signals are computationally expensive and, consequently, different methods need to be explored. Recently, the trained iterative soft thresholding algorithm (TISTA), a data-driven solver for inverse problems, was shown to improve on existing approaches. Here, a more in-depth analysis of its robustness and frequency dependence is provided using synthesized as well as real measurement data. It is demonstrated that TISTA yields favorable results in comparison to a covariance matrix fitting inverse method, especially for large numbers of sources.

Fast grid-free strength mapping of multiple sound sources from microphone array data using a Transformer architecture.

Conventional microphone array methods for the characterization of sound sources that require a focus-grid are, depending on the grid resolution, either computationally demanding or limited in reconstruction accuracy. This paper presents a deep learning method for grid-free source characterization using a Transformer architecture that is exclusively trained with simulated data. Unlike previous grid-free model architectures, the presented approach requires a single model to characterize an unknown number of ground-truth sources. The model predicts a set of source components, spatially arranged in clusters. Integration over the predicted cluster components allows for the determination of the strength for each ground-truth source individually. Fast and accurate source mapping performance of up to ten sources at different frequencies is demonstrated and strategies to reduce the training effort at neighboring frequencies are given. A comparison with the established grid-based CLEAN-SC and a probabilistic sparse Bayesian learning method on experimental data emphasizes the validity of the approach.

Material Selection Process for Acoustic and Vibration Applications Using the Example of a Plate Resonator.

In this work, a new method for selecting suitable materials is presented. This method has a high potential for a variety of engineering applications, such as the design of sound-absorbing and vibration-loaded structures, where a large number of different requirements have to be met. The method is based on the derivation of functional dependencies of selected material parameters. These dependencies can be used in parameter studies to consider parameter combinations that lie in the range of real existing and targeted material groups. This allows the parameter space to be reduced, the calculation to be accelerated, and suitable materials to be (pre-)selected for the respective application, which contributes to a more target-oriented design. The method is applied to the example of a plate resonator. For this purpose, a semi-analytical model is implemented to calculate the transmission loss as well as the reflected and dissipated sound power of plate silencers, taking into account the influence of flow velocity and fluid temperature on the performance of plate silencers.

A deep learning method for grid-free localization and quantification of sound sources.

In this contribution it is examined whether the use of deep neural networks can lead to an accurate characterization of single point sources from microphone array data. Based on conventional beamforming maps, the proposed method aims at estimating the source coordinates and the strength. The residual network architecture, a well-established model in the field of image recognition, is successfully applied to this task. The investigation reveals a method that fast and accurately renders the position and strength of an unknown source. Moreover, the accuracy of the position estimation is higher than the grid resolution of the beamforming map.

Surface curvature effects on the tonal noise of a wall-mounted finite airfoil.

This paper is concerned with the influence of camber on the noise of a wall-mounted finite airfoil with natural boundary layer transition. Tonal noise measurements taken in an aeroacoustic wind tunnel are presented for airfoils with aspect ratio of 2, NACAxx12 profile and camber between 0 and 6% at 40% chord. The results show camber is an important parameter that determines the operating conditions for which acoustic tone generation occurs and the number and intensity of the tones produced. Airfoils with 0%-2% camber have an acoustic signature that is dominated by a high amplitude primary tone, whereas the spectra of airfoils with higher camber of 4%-6% feature a more pronounced side tone structure. Tonal noise production does not collapse with lift coefficient, demonstrating that the local flow conditions influence the noise source. Tonal noise production is explained in terms of changes to mean flow topology, namely the location of flow separation, which is linked to tonal noise generation. Scaling of airfoil tonal noise is found to vary with angle of attack and pressure gradient. Empirical scaling laws for the primary tone frequency dependence on velocity are also derived for the cambered airfoils.