Model order reduction for nonlinear dynamics engineering applications

In order to fully exploit available computer aided engineering (CAE) models for modern mechatronic systems they need to be merged with measurement data in order to obtain reliable Digital Twins. These Digital Twins are evolving to become instrumental assets that accompany the product throughout its lifetime, enabling virtual quality assurance, virtual sensing and model-based control. However, in mechatronics, the typical CAE design models are relatively expensive physics based models which cannot be directly used throughout the entire lifecycle as Digital Twins. Model order reduction approaches for these nonlinear dynamic applications allow to reduce the computational cost sufficiently for use in a Digital Twin setting. This work gives an overview of nonlinear dynamics engineering applications in modern mechatronics products design, and demonstrates the role of (novel) MOR techniques in the design engineering process.