The precision of shape representation and the dimensionality of the design space significantly influence the cost and outcomes of aerodynamic optimization. The design space can be represented more compactly by maintaining geometric precision while reducing dimensions, hence enhancing the cost-effectiveness of the optimization process. This research presents a new point cloud Autoencoder Based on Uncertainty Feature Enhancement (AE-BUFE) architecture, designed to attain efficient and precise generalized representations of 3D aircraft through uncertainty analysis of the deformation relationships among surface grid points. The deep learning architecture consists of two components: the uncertainty index-based feature enhancement module and the point cloud autoencoder module. It learns the shape features of the point cloud geometric representation to establish a low-dimensional latent space. To assess and evaluate the efficiency of the method, a comparison was conducted with the prevailing point cloud autoencoder architecture and the proper orthogonal decomposition linear dimensionality reduction method under conditions of complex shape deformation. The results show that the new architecture significantly improves the extraction effect of the low-dimensional latent space. Then, this paper developed the surrogate-based optimization framework based on the AE-BUFE parameterization method and completed a multi-objective aerodynamic optimization design for a wide-speed-range vehicle considering volume and moment constraints. While ensuring the take-off and landing performance, the aerodynamic performance is improved under transonic and hypersonic conditions, which verifies the efficiency and engineering practicability of this method.

The hose-drogue system is a common method for soft aerial refueling, whereby the refueling tanker tows the drogue through the hose. In this paper, a mathematical-physical model of the hose-drogue system is developed and simulated using the Absolute Nodal Coordinate Formulation (ANCF) finite element method. A numerical solution program based on ANCF and ALE (Arbitrary Eulerian-Lagrange)-ANCF method was developed to simulate and analyze the horizontal and elongation release processes of the hose-drogue system at different towing points (underneath the wing and the belly of the aircraft). This program was developed by introducing an ALE description. The numerical solution program, developed based on the ANCF and ALE-ANCF methods, represents a significant advancement in computational efficiency for the rigid-flexible coupled multibody system of the air refueling hose-drogue system. This program can provide a valuable reference for the qualitative design of the hose-drogue multibody system in soft air refueling, while maintaining the necessary accuracy.

The ice accretion on the wing surface of aircraft significantly impacts flight safety. Providing a precise safety assessment by examining flight characteristics and meteorological conditions is challenging. Based on different swept angles, the experimental data from the icing wind tunnel establish the geometric link between the position of the wingspan and the shape of ice accretion at the leading edge. The correlation analysis and Sobol sensitivity are used to study the uncertainty of single variable. Simultaneously, the polynomial chaos method is employed to study the uncertainty of multiple variables. The results indicate significant correlation between the angle of attack and lift and drag coefficients. The influence of height and velocity on sensitivity is negligible, with the aerodynamic characteristics mostly dependent on the geometric attributes of the ice structure. The uncertainty propagation framework established can accurately assess the impact of swept angle on the aerodynamic parameters of the icing wing, and the predicted findings fall within a 95% confidence interval.

In the context of increasing dimensionality of design variables and the complexity of constraints, the efficacy of Surrogate-Based Optimization (SBO) is limited. The traditional linear and nonlinear dimensionality reduction algorithms are mainly to decompose the mathematical matrix composed of design variables or objective functions in various forms, the smoothness of the design space cannot be guaranteed in the process, and additional constraint functions need to be added in the optimization, which increases the calculation cost. This study presents a new parameterization method to improve both problems of SBO. The new parameterization is addressed by decoupling affine transformations (dilation, rotation, shearing, and translation) within the Grassmannian submanifold, which enables a separate representation of the physical information of the airfoil in a high-dimensional space. Building upon this, Principal Geodesic Analysis (PGA) is employed to achieve geometric control, compress the design space, reduce the number of design variables, reduce the dimensions of design variables and enhance predictive performance during the surrogate optimization process. For comparison, a dimensionality reduction space is defined using 95% of the energy, and RAE 2822 for transonic conditions are used as demonstrations. This method significantly enhances the optimization efficiency of the surrogate model while effectively enabling geometric constraints. In three-dimensional problems, it enables simultaneous design of planar shapes for various components of the aircraft and high-order perturbation deformations. Optimization was applied to the ONERA M6 wing, achieving a lift-drag ratio of 18.09, representing a 27.25% improvement compared to the baseline configuration. In comparison to conventional surrogate model optimization methods, which only achieved a 17.97% improvement, this approach demonstrates its superiority.