Numerical Scalability of Model Order Reduction Techniques for Very Large, Strongly Coupled Vibroacoustic Problems

Abstract Model order reduction (MOR) can significantly reduce the computational cost of vibroacoustic simulations. While most MOR studies focus on single-domain problems, such as structural dynamics or acoustics, this work compares MOR techniques for large coupled heavy-fluid-loaded vibroacoustic systems and evaluates their efficiency, accuracy, and scalability. Harmonic response simulations of vibroacoustic fluid–structure coupled systems used to compute frequency response functions from an input force to either structural acceleration or pressure in the heavy fluid domain are of particular interest. To assess the feasibility and accuracy of these techniques for different system sizes, a scalable model of a water-filled Plexiglass (PMMA) cylinder is developed, with mesh sizes from 10,000 to 1,000,000 Degrees of Freedom (DOF). The quality of the models is assessed through validation against experimental data and convergence studies within the frequency range of interest. The geometry, model data, and experimental results are made available so that they can be used as a benchmark for further studies. For medium-sized models up to approximately 100,000 DOFs, both modal and Krylov-subspace-based reduction methods are feasible. Modal approaches are advantageous because they are independent of a specific excitation and, therefore, provide a more general approximation of the physical system. However, for larger systems, the investigated modal methods become impractical due to memory limitations. In contrast, a Krylov-subspace two-level orthogonal Arnoldi method (TOAR), combined with matrix symmetrization and conditioning, provides an accurate and efficient approximation for a prescribed excitation scenario. For 1000 frequency steps, this approach achieves speedups of up to 600 $$\times $$ × compared with the full-order model.