Vision based reduced order modeling approach for operational parameter identification of nonlinear dynamic finite element models KU Leuven
Accurate dynamic identification of mechanical components is key to fully exploit the potential of Digital Twins of mechanical systems. However, current state-of-the-art dynamic parameter identification methods do not allow the use of high-spatial density measurements for components under operational conditions. The focus of this project is to develop a framework for identifying these operational parameters for detailed nonlinear dynamic ...