Multidisciplinary optimization design of high dimensional target space for flying wing airfoil

There are many difficulties when traditional optimization methods are used to deal with high-dimensional multi-objective problems. For the high-dimensional multi-objective optimization problem, it is a practical and effective method to reduce dimensionality. In this paper, we first discuss the idea of principal component analysis (PCA) target dimension reduction method in high-dimensional multi-objective applications. The effectiveness of the dimension reduction method is verified by the application of the test function of typical high dimensional objects. After that, the method is applied to the aerodynamics and stealth multidisciplinary integrated design of airfoils, and principal component analysis is carried out on the integrated design of the high dimensional target space. The PCA is used to extract principal components and identify redundant or unimportant targets. Redundant targets are removed or added as constraints to the optimization process of important targets. The results show that the optimal design results of the target space after dimension reduction satisfy the requirements of torque, drag divergence, cruise lift-drag ratio, and low-speed lift characteristics as well as stealth characteristics. The application prospect of this method in the multi-objective and multi-disciplinary optimization design of aircrafts is further discussed.