# Aerodynamic Optimization and Characterization of a Ducted Tail for a Box-Launched Aircraft
## 1 Introduction  
Box-launched aircraft have the advantages of storage and transportation integration and modularization, which can greatly save storage space. A single launch platform can load multiple aircraft for flexible launch, which greatly improves the combat effectiveness of the aircraft. However, in order to be stored in the launch box, the aircraft's main and tail wings need to be folded, and they need to be quickly unfolded after launch to provide sufficient lift and control torque. The main and tail wings are prone to mechanical failure during the unfolding process, and the aerodynamic force on the aircraft exhibits a nonlinear change process, which is not favorable to flight control of the aircraft. At the same time, there is a step gap at the joint of the folding structure, which is not conducive to the stealth characteristics of the aircraft [1; 2; 3]. Therefore, in order to improve the comprehensive performance of folding-wing aircraft, it is of great significance to optimize the design of aerodynamics, structure and control of folding-wing aircraft. Folded-wing aircraft often exhibit non-constant and non-linear aerodynamic characteristics during morphing, and traditional computational fluid dynamics (CFD) methods are unable to quickly obtain accurate aerodynamic parameters of the aircraft, which causes difficulties in flight control [4; 5]. Ref. [4] takes a folded-wing aircraft with variable sweep-back angle as the research object, and proposes a cross-prediction model based on the fusion of deep learning and multi-task optimization methods, which takes the angle of attack, time, and sweep-back angle as the input variables, and outputs the non-constant force and moment coefficients through multi-layer nonlinear mapping, so that the non-constant aerodynamic parameters can be obtained in a few seconds for any angle of attack and any morphing state. Ref. [5] takes the coupling effect between aircraft aerodynamic performance and control capability as a design constraint, takes a barrel-launched aircraft as aresearch object, and proposes an optimal design method for the aerodynamic layout of a folded-wing aircraft considering the coupling of multiple control surfaces and control capability. Multi-objective optimization based on the alternative model matrix was carried out using the neighborhood cultivation algorithm, which resulted in a 28.36% increase in the lift-to-drag ratio of the aircraft and a 164.29% increase in the space of handling moments, and provided a reference for the aerodynamic layout design of barrel-launched aircraft. Ref. [6] designed an aircraft with simultaneously varying wingspan and swept-back angle, investigated the lift-drag characteristics of the aircraft in five different flight modes, performed finite element analysis of the approximate pressure on the aircraft wing under load and specific boundary conditions, and made a comprehensive assessment of aerodynamic performance and structural reliability in the transition phase. Refs. [7; 8; 9; 10; 11] proposed a reinforcement learning (RL)-based strategy for autonomous morphing aircraft. The proposed morphing strategy focuses on the sparse reward unreferenced decision problem induced by terminal performance goals and remote tasks, and establishes a two-layer variant flight control framework that decouples the design of the morphing strategy from the flight controller, resulting in an optimal morphing decision for the aircraft. Ref. [12] proposed a nonlinear control model for a morphing aircraft based on an adaptive neural network. A nonlinear model with six degrees of freedom is established, and a first-order smooth mode differentiator (FSMD) is used in the control scheme to avoid the \"differential explosion\" problem. A radial basis function neural network is introduced to estimate the model's uncertainty and external disturbances, and an adaptive neural network controller is proposed based on this design.