For the purpose of high-fidelity aircraft cabin noise simulations during early design phases, we study three efficient solving approaches for the fully coupled finite element model of an aircraft fuselage segment. Obtaining an efficient solution with respect to consumed computational time and resources is challenging within a conventional simulation pipeline, as large-scale and complex vibroacoustic models demand crucially high computational costs with increasing frequency. In this contribution, we adopt (1) frequency and domain-adaptive discretisation, (2) domain-decomposition techniques, and (3) model order reduction with rational Arnoldi Krylov subspace methods for an aircraft fuselage model. The three approaches have shown remarkable advantage thereby reducing the solving time as well as the memory requirement that are essential when solving large-scale models. While the discretisation and the model order reduction approaches accelerate the solving process by efficiently handling the complexity of the system to be solved, domain-decomposition techniques further handle the aspect of reducing the overall memory consumption. Finally with the help of active research aircraft models, we implement and showcase the achieved efficiency.

翻译：为了在早期设计阶段实现高保真度的飞机舱内噪声模拟，我们研究了飞机机身段全耦合有限元模型的三种高效求解方法。在传统的仿真流程中，由于大规模且复杂的振动声学模型随着频率升高对计算成本的要求极为严苛，在计算时间和资源消耗方面实现高效求解颇具挑战。本文针对飞机机身模型，采用了(1)频域与区域自适应离散化、(2)区域分解技术以及(3)基于有理Arnoldi Krylov子空间方法的模型降阶。这三种方法在降低求解时间和内存需求方面展现出显著优势，而这正是求解大规模模型时的关键所在。离散化与模型降阶方法通过高效处理待求解系统的复杂性来加速求解过程，而区域分解技术则进一步解决了降低整体内存消耗的问题。最后，借助活跃的研究型飞机模型，我们实现并展示了所取得的效率提升。