Aerodynamic/stealth optimization is a key issue during the design of a stealth UAV. Balancing the weight of different incident angles of the RCS and combining stealth characteristics with aerodynamic characteristics are hotspots of aerodynamic/stealth optimization. To address this issue, this paper introduces a radar detection probability model to solve the weight balance problem of incident angles of the RCS and a penetration efficiency model to transfer the multi-object optimization into single-objective optimization. In this paper, a parameterized model of a flying-wing UAV is selected as the research object. A gradient-free optimization algorithm based on the genetic algorithm is used for maximizing efficiency. The optimization model balances the influence of the RCS mean value and RCS peak value on stealth performance. Moreover, the model achieves an optimal entire life cycle penetration efficiency coefficient by balancing aerodynamic and stealth optimization. The results show that the optimized model improves the penetration efficiency coefficient by 13.84% and increases maximum flight sorties by 1.8%. These results prove that the model has a reasonable combination of aerodynamic and stealth optimization for UAVs undertaking penetration missions.

The Lambda wing UAV inevitably needs to adjust the lift coefficient and pitch moment by controlling the flap when encountering multiple flight states. Inspired by the natural morphing mechanisms of bird wings, this study draws upon bionic principles to explore a seamless morphing trailing edge (SMTE) design that mimics the smooth and continuous deformation observed in avian flight. A comparative analysis between SMTE flaps and traditional hinge flaps highlights the superior rearward stealth performance of SMTE flaps in the direction angles of 120° to 140°, along with a reduction in aerodynamic drag. During the optimization, a model based on flexible wing ribs and elastic skin was established, and statistical analysis revealed a strong linear correlation between the maximum lift coefficient provided by the flaps and the overall lift-to-drag ratio. Consequently, the optimization process omitted the enhancement of the comprehensive lift-to-drag ratio during the flap deflection. Additionally, a deep Neural Network Surrogate Model was introduced to streamline the computationally intensive aerodynamic calculations. Ultimately, an MOGA-2 optimization model incorporating local deep Neural Network Surrogate Models was utilized to achieve the comprehensive optimization of aerodynamic and stealth characteristics, through an adjustment of the wing rib and skin proportions. The UAV rearward RCS and the maximum lift coefficient provided by the flaps have decreased by 2.8% and increased by 6.6%, respectively.

As a commonly used configuration for advanced unmanned aerial vehicles (UAVs), the flying-wing configuration suffers from pitching moment trimming issues due to the lack of horizontal tail. The UAV either needs to unload lift at the trailing edge or needs to increase the wingtip twist angle at the cost of losing the lift-to-drag ratio. The commonly used methods for solving pitching moment trimming issues are compared and analyzed in this paper, and it is found that the method of trailing-edge twist has advantages under cruising lift coefficient. Furthermore, a trailing-edge twist deformation parameterized model that can deform multiple critical sections is designed with relevant grids. The multi-objective genetic algorithm is used to optimize the parameterized model and obtain the optimized results. Through comparative analysis, it is found that the optimized trailing-edge twist model has an advantage in distributing the pitching moment. By optimizing the distribution of aerodynamic forces and moments, cruise trim is achieved with only a 1.43% cost to the cruise lift-to-drag ratio compared to the initial model.