Probabilistic Transient Heat Conduction Analysis Considering Uncertainties in Thermal Loads Using Surrogate Model

Abstract

Thermal protection system (TPS) is one of the most important subsystems for vehicle reentry. Because of the uncertainties in thermal loads, material properties, and manufacture tolerances, the thermal response of TPS has strong randomness, which brings great challenges to the engineering design and reliability evaluation. Because the uncertain aerodynamic heating loads are stochastic processes in time history, it is difficult to model them in the conventional surrogate models that usually take scalar random variables as input. This paper thus proposes an effective characterization method that can represent the uncertainties of aerodynamic heating by a set of random variables using Karhunen–Loève expansion. The random variables that describe the uncertainty of thermal loads, material properties, and geometric thickness are taken as inputs for probabilistic analyses. A kriging surrogate model is constructed to solve the transient heat conduction with high accuracy. Taking a typical multilayer TPS as an example, the probabilistic thermal analyses are carried out considering different degrees of heat rate uncertainties. The results show that the uncertainties of aerodynamic heating intensify the randomness of the thermal response of TPS, and thus have a great impact on the thermal reliability. The sensitivities of temperature response to various uncertain parameters are investigated as well, which provide a reference for system design and reliability improvement.

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