Stochastic dynamic model updating of aerospace thermal structure with a hierarchical framework. (November 2021) - He, Cheng; Li, Zhonghua; He, Huan; Wang, Jizhen
Highlights: A inverse uncertainty propagations of mechanical systemunder thermal fields with probability distributions is given. The thermal parameters and mechanical characteristics are considered as temperature dependent to be identified. Demonstrates a method to adjust design parameter means and covariance matrix from experimental data based on surrogate model. Polynomial is employed to define the mechanical characteristics and thermophysical parameters' variation with temperature. Abstract: Most physical properties and boundary conditions of complex aerospace mechanical systems are temperature dependent. Hence, the test data are exposed to uncertainty in high surface temperature or large temperature gradient environments. However, existing deterministic model updating methods are not applicable under complicated multi-physics fields. Hence, dynamic model updating under a thermal environment from the stochastic perspective is vital. In this study, a dynamic model updating approach that considers thermal effects and uncertainties by using a hierarchical strategy is proposed, and a method for adjusting design parameter means and their covariance matrix from experimental data is demonstrated, which is based on a surrogate model. In contrast to the previous method, an inverse uncertainty propagation of mechanical system parameters under multiple physical fields with probability distributions is conducted, and the thermal parameters and mechanical characteristics to be identified are regarded as temperature dependent. Numerical simulations are used to demonstrate the stochastic model updating procedure, and their associated response prediction variabilities are explored using a fuselage skin structure.