Metamodel-Based Uncertainty Propagation for Frequency Responses of Non-Proportionally Damped Composite Structures with Material Variability and Model-Form Uncertainty
Keywords:
Composite Structures, Uncertainty Propagation, Non-Proportional Damping, Metamodeling, Model-Form UncertaintyAbstract
The reliable prediction of dynamic characteristics in composite structures requires precise modeling of both material properties and damping mechanisms. However, practical engineering applications often involve significant uncertainties arising from manufacturing inconsistencies, environmental conditions, and inherent limitations in idealized mathematical representations. This study presents a comprehensive metamodel-based framework designed for the uncertainty propagation in the frequency responses of non-proportionally damped composite structures. The methodology rigorously accounts for both aleatory uncertainties, represented by material property variability, and epistemic uncertainties, characterized by model-form discrepancies. Due to the complex nature of non-proportional damping, the structural dynamic equilibrium cannot be decoupled using classical real-valued modal transformation, necessitating computationally expensive complex eigenvalue analysis or direct frequency response solutions. To alleviate the computational burden typically associated with high-fidelity finite element simulations, a Gaussian process metamodel is constructed and adaptively trained. The incorporation of a discrepancy function allows the framework to compensate for structural simplifications inherent in the baseline finite element model. The proposed methodology is systematically evaluated through the analysis of a multi-layered composite plate subjected to localized damping treatments. The results demonstrate that the metamodel accurately constructs the probabilistic frequency response envelopes and captures resonance shifts while significantly reducing the computational cost compared to traditional Monte Carlo simulations. The findings provide critical insights into the coupled effects of material randomness and modeling simplifications on structural reliability.
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