A High-Performance Computing Strategy for Acoustic Encapsulation Modeling of Electric Vehicle Subsystems Using Coupled BEM–PEM–FEM Methods

Electric vehicle subsystems, including powertrains, electric motors, and gearboxes, pose new challenges in achieving stringent acoustic performance targets for both interior and exterior noise. These challenges are intensified by increasingly demanding customer expectations regarding interior acoustic comfort, which encompasses the reduction of intrusive noise sources and the enhancement of overall sound quality across a broad frequency spectrum. A primary concern associated with electric vehicles subsystems is the generation of high-frequency tonal noise, commonly referred to as whine noise, which can significantly impact acoustic performance and passenger comfort. High-frequency whine noise propagates through multiple transmission paths and can be effectively attenuated at the source through encapsulation strategies, which also contribute to broadband noise reduction across a wide frequency spectrum. To predict the acoustic performance of encapsulation, a coupled simulation approach combining the Boundary Element Method (BEM), the Finite Element Method (FEM) and the Poroelastic Finite Element Method (PEM) has been developed. This methodology has been already presented and validated through experimental measurements, demonstrating its acoustic effectiveness in the encapsulation of a generic electric motor housing. While BEM is well-suited for modeling exterior acoustic propagation, standard implementations encounter limitations at high frequencies due to mesh density requirements and computational cost. This work presents hybrid parallelization strategies that integrate frequency-domain decomposition with multi-threading to accelerate BEM H-matrix computations. Frequency decomposition enables parallel processing by distributing independent frequency tasks across multiple processes, while multi-threading enhances performance for fine-grained operations such as matrix assembly and H-matrix compression within each frequency. The processes and improvements enabled by these strategies are discussed and presented within an adapted high-performance computing (HPC) environment.