arXiv:2605.00950 · v3
Equation-Free Digital Twins for Nonlinear Structural Dynamics
摘要
Monitoring floating offshore wind turbines (FOWTs) under stochastic excitation and sensor failure requires computationally efficient and physically interpretable estimators. This paper introduces an equation-free, output-only digital twin framework combining Hankel dynamic mode decomposition (Hankel-DMD), sparse-sensor reconstruction, and a stochastic Kalman extension. A sufficient identifiability condition is derived for modal amplitudes recovered from delay-embedded measurements, while Tikhonov regularization addresses ill-conditioned sensor layouts. Correlation-domain identification improves damping estimates under stochastic forcing and distinguishes structural modes from rotor harmonics. The framework is validated using OpenFAST simulations of an NREL 5-MW spar turbine and experimental basin-test measurements from a 1:70-scale IEA 15-MW semi-submersible. In simulations, the identified tower modes agree with established operational modal analysis methods (MAC at least 0.99), while the Kalman estimator achieves R-squared values of 0.91 to 0.99 under severe sensor sparsity. Experimental results identify tower modes at 0.430 and 0.415 Hz with MAC of 1.00 against SSI-COV and reconstruct tower-base bending moments on unseen runs with R-squared values of 0.83 to 0.89 using three of four sensors. The framework provides a computationally efficient, sensor-failure-resilient structural-state estimation layer for digital twins.
作者备注：Added more details of the theory as well as real experimental validation also included in the results. Now the manuscript is more defensible