Thermo-mechanical optimization of metallic thermal protection system under aerodynamic heating

This paper details the application of the finite element model, Bayesian regularized neural network, and genetic algorithm for metallic thermal protection system parameter optimization. The object is to minimize the structure weight and satisfy multiple performance constraints which consist of the deformation of the top face sheet, the stress around the lug hole, the stress of the honeycomb core, and the inner temperature. Firstly, a high-fidelity thermo-mechanical coupled finite element model was established to investigate the thermal and mechanical properties of the metallic thermal protection system. Then, surrogate models were constructed based on the Bayesian regularized neural network which assigns a probabilistic nature to the network weights and biases and allows the network automatically and optimally penalize complex models. To guarantee full exploration of the design space, an optimal orthogonal-maximin Latin hypercube design was adopted and modified to generate preliminary sampling points. Finally, an approach for genetic algorithm constraint handling, stochastic ranking, was introduced and modified by multiple constraint ranking to handle constraints. In addition, a sensitivity analysis was performed to disclose the effects of individual design variables on the thermal and mechanical responses. The results indicate that the structure mass is decreased by 41.2 % when compared to the initial design and all the constraints are satisfied.