Optimal Design of Composite Shells with Multiple Cutouts Based on POD and Machine Learning Methods

Abstract

Due to the high specific stiffness and strength, composite shells have been widely used in fuel tanks of launch vehicles. The buckling analysis of composite shells with cutouts based on the finite element (FE) method is too time-consuming. From the point-of-view of model size reduction, a novel Proper Orthogonal Decomposition (POD)-based buckling method is proposed in this paper, which can significantly increase the computational efficiency of buckling analysis. In order to improve the efficiency and effectiveness of prediction and optimization of composite shells with multiple cutouts, the POD method is integrated into an optimization framework that uses Gaussian process (GP) machine learning method. First, the training set used for the machine learning training is generated efficiently by means of the POD method. Then, the obtained set is trained and tested based on the Gaussian process method. The inputs are ply angles of the composite shell and the output is the buckling load of the composite shell containing cutouts. In order to maximize the buckling load of the composite shell against cutouts, the Genetic Algorithm is combined with the trained Gaussian process method to search for the optimal ply angles. Finally, an illustrative example is carried out to demonstrate the effectiveness of the proposed prediction and optimization framework.