Direct computation of nonlinear mapping via normal form for reduced-order models of finite element nonlinear structures

The direct computation of the third-order normal form for a geometrically nonlinear structure discretised with the finite element (FE) method, is detailed. The procedure allows to define a nonlinear mapping in order to derive accurate reduced-order models (ROM) relying on invariant manifold theory. The proposed reduction strategy is direct and simulation free, in the sense that it allows to pass from physical coordinates (FE nodes) to normal coordinates, describing the dynamics in an invariant-based span of the phase space. The number of master modes for the ROM is not a priori limited since a complete change of coordinate is proposed. The underlying theory ensures the quality of the predictions thanks to the invariance property of the reduced subspace, together with their curvatures in phase space that accounts for the non-resonant nonlinear couplings. The method is applied to a beam discretised with 3D elements and shows its ability in recovering internal resonance at high energy. Then a fan blade model is investigated and the correct prediction given by the ROMs are assessed and discussed. A method is proposed to approximate an aggregate value for the damping, that takes into account the damping coefficients of all the slave modes, and also using the Rayleigh damping model as input. Frequency-response curves for the beam and the blades are then exhibited, showing the accuracy of the proposed method. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper details the direct computation of the third-order normal form for a geometrically nonlinear structure discretised with the finite element method. By defining a nonlinear mapping, accurate reduced-order models can be derived based on invariant manifold theory. The method allows for a direct transition from physical coordinates to normal coordinates in the phase space, without the need for simulation, ensuring quality predictions.