Wave-Resolving Numerical Prediction of Passenger Cabin Noise Under Realistic Loading

Blech, Christopher; Appel, Christina; Ewert, Roland; Delfs, Jan; Langer, Sabine

doi:10.1007/978-3-030-52429-6_15

“…The segment is situated right before the wings, as to not have the TBL influenced by the aircraft's wings. However, even though a segment of 5 seating rows is chosen, previous evaluations by the authors show that this is sufficient for high frequencies and the gain in computational speed heavily outweighs the drawbacks of losing sound pressure level accuracy [5]. Finally, the aircraft segment model can be seen in Figure 2 and it is made up of four major domains, each influencing the resulting sound pressure level in their own way.…”

Section: Aircraft Modelmentioning

confidence: 99%

“…The following contribution presents an approach for the simulative cabin noise assessment of a novel electrically propelled regional aircraft. To obtain the sound pressure distribution inside the passenger cabin, the Finite-Element-Method (FEM) is used to solve a wave-resolving large-scale vibroacoustic model [4][5][6]. The focus of this contribution is laid on the implementation of methods that allow the examination of the resulting sound pressure level inside the cabin due to two dominant noise sources -the turbulent boundary layer (TBL) and the propeller excitation.…”

Section: Introductionmentioning

confidence: 99%

Comparison of different noise sources for the simulative cabin noise assessment of an electrically propelled regional aircraft

Hüpel,

Blech,

Franco

et al. 2023

inter noise

0

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The rising number of passengers transported by aircraft leads to more flight traffic, further increasing the environmental impact of the aviation sector. In order to combat the growing environmental impact, the Cluster of Excellence Sustainable and Energy Efficient Aviation of TU Braunschweig aims to advance research towards a climate neutral aviation industry, especially with the design of an electrically propelled short-range regional aircraft, among others. In the conscience of passengers, the focus is also shifted towards a healthy and comfortable travel experience. One of the main factors influencing these aspects is noise inside the aircraft cabin. A lower noise impact can help increase the technology acceptance and further push towards more sustainable airborne transport solutions. This contribution aims to simulatively assess and compare the sound pressure levels inside the passenger cabin of an electric propeller aircraft. The focus is laid on two of the most important noise sources: the tonal propeller excitation as well as the sound field beneath the turbulent boundary layer. The paper presents a wave-resolving FE model considering both sources and shows, which sound pressure levels can be expected, while also comparing the frequency spectra separately, therefore enabling early design changes to help reduce the cabin noise.

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“…However, the present situation is that while cabin noise is an important factor in aircraft design it is considered quite late, which makes taking countermeasures expensive. Therefore, the aim is to strive toward a more holistic early design approach [3,4], in which acoustics is considered early on and design change suggestions can also be derived on the basis of predicted sound pressure levels. At an early design phase, prototypes are not built yet, which means that this cabin noise assessment has to be conducted simulatively.…”

Section: Introductionmentioning

confidence: 99%

Efficient solutions of preconditioned large‐scale systems for simulative aircraft cabin noise assessment

Hüpel,

Blech,

Sreekumar

et al. 2023

Proc Appl Math and Mech

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The rising number of passengers transported by aircraft leads to more flight traffic, further increasing the environmental impact of the aviation sector. In order to combat the growing environmental impact, the Cluster of Excellence Sustainable and Energy Efficient Aviation of TU Braunschweig aims to advance research toward climate neutral aviation, especially with the design of newly developed aircraft. In the conscience of passengers, the focus is also shifted toward a healthy and comfortable travel experience. One of the main factors influencing these aspects is noise inside the aircraft cabin. A lower noise impact can help increase the technology acceptance and further push toward more sustainable airborne transport solutions. In order to assess cabin noise with moderate effort in an early design stage, simulative methods such as the finite element method (FEM) are deployed. These have the advantage of yielding important insights into the noise distribution inside the aircraft cabin without having to have a built prototype present. Since methods like the FEM are of a wave‐resolving nature, results contain high‐resolution wave propagation information of the sound waves, created, for example, by the excitation of the turbulent boundary layer on the aircraft's outer skin. Additionally, a direct auralization at seat positions is possible. More powerful computers have further improved the usage of models for an early design noise assessment. However, the more elaborate a model is, the more degrees of freedom (DOFs) it yields as final system of equations. Finally, since the FEM is of wave‐resolving nature and in order to depict sound waves accurately, a fine discretization of 10 nodes per frequency dependent wave length is needed. This leads to a massive increase in DOFs for higher frequencies and finally yielding a large‐scale model, which entails huge computational efforts. This makes the usage of adaptive grids very convenient. In this contribution, adaptive grids entail two main aspects: frequency and domain adaptive discretization. Since higher frequencies lead to shorter wavelengths and therefore finer mesh sizes, it is feasible to adaptively discretize the model in the frequency domain, while also adaptively discretizing the different domains of the models itself, because different materials lead to different wavelengths. However, the resulting system of equations might be ill‐posed. Therefore, this contribution aims to shed light on an efficient solving process of these adaptively discretized large‐scale models with preconditioning and adequate solver choice.

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