Enhancing vibroacoustic reduced order models for digital twin development in industrial applications

In recent years, the growing interest towards the Digital Twin has prompted extensive exploration in both academic and industrial domains. To realize this technology, the development of simulation models that seamlessly run parallel to the physical assets is necessary. Model Order Reduction (MOR) plays a pivotal role, significantly easing the computational demands of 3D models while maintaining high fidelity. Krylov-based methods have proven successful in various vibroacoustic applications. However, state-of-the-art contributions employ suboptimal modeling strategies, leading to computationally inefficient offline phases and limited scalability. The linear Finite Element Method (FEM) exhibits lower efficiency than high-order FEM; Astley-Leis infinite elements, used to enforce non-reflecting conditions, constrain models to spherical domains, proving inefficient for elongated structures. In this work, advanced techniques-FEM Adaptive Order (FEMAO) and flexible infinite elements- are employed to alleviate the computational burden of the MOR process. The advantages of combining MOR, FEMAO, and flexible infinite elements are explored through various numerical examples. Specifically, we highlight the efficiency of output-based MOR in handling models with a high number of inputs, showcasing practical applications, especially in electric motor noise analysis. In conclusion, these numerical techniques reduce the computational burden of the offline phase, unlocking the Digital Twin for large-scale industrial models.