Summary
In this study, we investigate a Laval rotor dynamics scenario where the damping properties remain elusive. To address this challenge, we employ an artificial neural network to model the damping force, seamlessly integrating it into the governing equations of motion. Through direct time integration techniques, we analyse the system’s behaviour and delve into the frequency response function to elucidate the impact of incorporating data-driven models into simulations. This approach sheds light on the efficacy of leveraging neural networks to capture unknown physical phenomena, enhancing the accuracy and predictive capabilities of dynamics analyses. The case study showcases the potentials, but also pitfalls, of the direct integration of learning models into knowledge-based system descriptions.
