Data-driven and physics-informed approaches for improving the performance of dynamic models of fluid film bearings

Abstract

Conventional numerical models of fluid film bearings (FFB) typically require significant amounts of computations to achieve acceptable calculation accuracy. This may cause many inconveniences, especially for computationally expensive rotor dynamics tasks. The study addresses general issues in building synthetic data-driven dynamic FFB models using machine learning methods, focusing on artificial neural networks (ANNs). In the study, surrogate models predicting journal bearing forces were built using a variety of approaches, including single- and multi-component ANN-based models, as well as so-called physics-informed neural networks (PINN). The work presents a comparative analysis of the considered approaches, primarily in the context of rotor dynamics calculations. It includes an assessment of the accuracy and performance of considered models, as well as the time spent on their creation. The results show that all tested solutions outperform numerical methods by order of magnitude or more, but it is difficult to talk about the overwhelming advantage of any one approach over others. When choosing a methodology for building surrogate FFB models, one should proceed from the requirements for the model, the benefits and shortcomings of certain methods. The work gives an idea of their relationships, including the influence of key hyperparameters of the methods on the properties of the resulting models. The results can be useful both for traditional calculations in the area of rotor dynamics, and for relatively new problems in the rotor systems, such as predictive analytics and optimal design systems, predictive controllers of active components of rotary machines.