Proper Orthogonal Decomposition-Based Surrogate Modeling Approximation for Aeroengines Nonlinear Unbalance Responses

Abstract

Abstract Finite element (FE) models to simulate aeroengines rotordynamics become more and more complex thanks to the improvement of computational resources. Numerical simulations for industrial scale applications are nevertheless expensive in terms of computational effort because of their size and the presence of nonlinearities. In many-query frameworks (sensitivity or reliability analysis, model calibration, etc.), parametric studies have to be performed and the FE models are generally simplified or strongly reduced with a consequent loss of accuracy. Recent machine learning methods can be employed to build faster-to-evaluate surrogates of the original model. Nevertheless, this type of offline approach can be very demanding in terms of number of full order evaluations to reach a sufficient accuracy. The challenge is therefore to control the burden associated with the evaluation of the design of experiments (DoE) and the surrogate model training phase. This work presents a surrogate modeling technique to quickly and accurately reproduce the nonlinear unbalance responses of industrial scale rotor-dynamical models submitted to rotor–stator contact. About 20DOFs of the industrial FEM are considered in this work and a proper orthogonal decomposition (POD)-based approximation (surrogate modeling technique known as POD-SM or nonintrusive POD in the literature) of their frequency dependent unbalance response are presented. In order to analyze the modeling uncertainties of a specific shaft's support of a modern aeroengine, its dynamical parameters (stiffness, damping, nonlinear contact parameters) are studied within a specific variation range, defining the design space covered by this study.