Direct computation of nonlinear mapping via normal form for reduced-order models of finite element nonlinear structures
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Abstract (arXiv)
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 nonresonant 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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Sep 2020.
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Citing papers per year: 2022: 3; 2023: 0; 2024: 0; 2025: 3.
6 papers.
- 2022Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutionscited by 29
- 2022Data-driven reduced order models using invariant foliations, manifolds and autoencoderscited by 7
- 2022Virtual twins of nonlinear vibrating multiphysics microstructures: physics-based versus deep learning-based approachescited by 1
- 2025Nonlinear Model Order Reduction of Dynamical Systems in Process Engineering: Review and Comparisoncited by 0
- 2025Invariant Manifolds of Discrete-time Dynamical Systems with Nonlinear Exosystems via Hybrid Physics-Informed Neural Networkscited by 0
- 2025Real-Time Structural Health Monitoring with Bayesian Neural Networks: Distinguishing Aleatoric and Epistemic Uncertainty for Digital Twin Frameworkscited by 0
- 2025Real-Time Structural Health Monitoring with Bayesian Neural Networks: Distinguishing Aleatoric and Epistemic Uncertainty for Digital Twin Frameworks2 Dec 2025
- 2025Invariant Manifolds of Discrete-time Dynamical Systems with Nonlinear Exosystems via Hybrid Physics-Informed Neural Networks16 Jun 2025
- 2025Nonlinear Model Order Reduction of Dynamical Systems in Process Engineering: Review and Comparison15 Jun 2025
- 2022Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutions13 Nov 2022
- 2022Data-driven reduced order models using invariant foliations, manifolds and autoencoders24 Jun 2022
- 2022Virtual twins of nonlinear vibrating multiphysics microstructures: physics-based versus deep learning-based approaches12 May 2022