Evaluation of Forces in Dynamically Loaded Journal Bearings Using Feedforward Neural Networks

Abstract

This paper explores the usage of artificial neural networks to evaluate forces acting in dynamically loaded finite-length journal bearings. Unlike standard numerical approaches, which require solving a hydrodynamic pressure field, the network predicts the forces directly from relative displacements and velocities of a rotating journal to a stationary bearing shell. This practice can significantly accelerate transient simulations of systems supported on such bearings without compromising their nonlinear properties. The proposed method utilises feedforward neural networks, which use a precomputed database of nondimensional forces for training. This database is generated using a finite difference method and supplemented with the corresponding relative displacements and velocities. The performance of the trained networks is also analysed.