A high-aspect-ratio wing is the key to improving the lift-to-drag ratio of an aircraft. However, a high-aspect-ratio wing leads to a large wingspan, which affects the take-off and landing of the aircraft in many aspects, such as runway width, structure safety and crosswind effects. This paper introduces a monoplane-biplane morphing aircraft, which cruises as a monoplane and takes off and lands as a biplane, switching between the two modes during flight. In addition, its morphing does not rely on any active drive mechanism, but relies on ailerons to change lift to drive morphing. This design can change wingspan to a large extent, take into account the different wingspan requirements of take-off, landing and cruising, and save the structure weight, space and complexity of the morphing drive mechanism, thereby improving maintainability. However, as a new morphing mode, this design also brings challenges in many aspects, such as aerodynamic design, multi-body dynamics and control, structural reliability, and experimental verification.

Morphing technology is considered a crucial direction for the future development of aircraft. However, conventional morphing aircraft often employ complex actuation mechanisms and actuators to drive the morphing process. The associated costs in terms of structural weight increase and space occupancy are prohibitively high, even exceeding the benefit of morphing. Especially for high aspect ratio aircraft with large root bending moments, it is very difficult for actuators to directly drive wing deformation. To address this issue, aerodynamic forces generated by control surface deflection can be utilized as an alternative to actuator-driven morphing. This approach reduces the overall cost of morphing while enhancing its benefits. This novel aerodynamic-driven morphing technique imposes new requirements and challenges on the aerodynamic design of aircraft. With a combination of flight experiments and numerical simulations, this article analyzes the variations in aerodynamic forces during the aerodynamic-driven process. Using a high aspect ratio long-endurance UAV as the design baseline, the design method of the control surface for aerodynamic-driven morphing is also discussed.

Fixed-wing long-endurance aircraft play an important role in many fields. However, to reduce drag, these aircraft often have an enormous aspect ratio and wingspan, leading to challenges such as high requirements for takeoff and landing sites and poor wind resistance. Morphing may be able to solve this problem, but conventional morphing aircraft often employ complex actuation mechanisms and actuators to drive the morphing process. The associated costs in terms of structural weight increase and space occupancy are prohibitively high. First, this article develops a high-aspect-ratio aircraft with aerodynamic-driven morphing and validates the rationality and feasibility of this concept through flight tests. Then, focusing on the RQ-4 “Global Hawk” as the design baseline, the article explores multidisciplinary overall design methods for the aircraft, analyzing the comprehensive impact of morphing on aerodynamic, structural, and flight control design. Finally, the article elaborates on the benefits and costs associated with aerodynamic-driven morphing.

To improve the lift-to-drag ratio of hypersonic vehicles, an important idea is to construct favorable interactions between shock waves and expansion waves forming on different parts of the vehicle. In the existing design methods based on this idea, the geometric parameters are calculated by the shock-wave and expansion-wave relationships of two-dimensional or axisymmetric inviscid flow fields. Due to the influence of three-dimensional effect and viscosity, the high lift-to-drag ratio configurations designed by existing methods shows significant performance degradation compared with ideal design performance. To solve this problem, an optimization design method derived by shock-wave morphology is proposed. This method takes the target shock-wave morphology instead of the aerodynamic performance as the objective to guide the optimization direction of geometric parameters. The method is applied to a three-dimensional wave cancellation biplane where the main wing and the upper wing have favorable interference. The optimized configuration outperforms the initial configuration designed by the two-dimensional inviscid method in terms of both shock-wave morphology and aerodynamic performance, which proves the effectiveness of the proposed optimization design method. Compared with the diamond wing, the wave cancellation biplane has the advantage in the lift-to-drag ratio under the design condition.

Machine learning has been widely utilized in flow field modeling and aerodynamic optimization. However, most applications are limited to two-dimensional problems. The dimensionality and the cost per simulation of three-dimensional problems are so high that it is often too expensive to prepare sufficient samples. Therefore, transfer learning has become a promising approach to reuse well-trained two-dimensional models and greatly reduce the need for samples for three-dimensional problems. This paper proposes to reuse the baseline models trained on supercritical airfoils to predict finite-span swept supercritical wings, where the simple swept theory is embedded to improve the prediction accuracy. Two baseline models are investigated: one is commonly referred to as the forward problem of predicting the pressure coefficient distribution based on the geometry, and the other is the inverse problem that predicts the geometry based on the pressure coefficient distribution. Two transfer learning strategies are compared for both baseline models. The transferred models are then tested on complete wings. The results show that transfer learning requires only approximately 500 wing samples to achieve good prediction accuracy on different wing planforms and different free stream conditions. Compared to the two baseline models, the transferred models reduce the prediction error by 60% and 80%, respectively.

Almost half of all flight accidents caused by inflight icing occur at the approach and landing phases when high-lift devices are deployed. The present study focuses on the optimization of an ice-tolerant multi-element airfoil. Dual-objective optimization is carried out with critical horn-shaped ice accumulated during the holding phase. The optimization results show that the present optimization method significantly enhances the iced-state and clean-state performance. The optimal multi-element airfoil has a larger deflection angle and wider gap at the slat and the flap compared with the baseline configuration. The sensitivity of each design parameter is analyzed, which verifies the robustness of the design. The design is further assessed when ice is accreted during the approach and landing phases, which also shows performance improvement.

In the field of supercritical wing design, various principles and rules have been summarized through theoretical and experimental analyses. Compared with black-box relationships between geometry parameters and performances, quantitative physical laws about pressure distributions and performances are clearer and more beneficial to designers. With the advancement of computational fluid dynamics and computational intelligence, discovering new rules through statistical analysis on computers has become increasingly attractive and affordable. This paper proposes a novel sampling method for the statistical study on pressure distribution features and performances, so that new physical laws can be revealed. It utilizes an adaptive sampling algorithm, of which the criteria are developed based on Kullback–Leibler divergence and Euclidean distance. In this paper, the proposed method is employed to generate airfoil samples to study the relationships between the supercritical pressure distribution features and the drag divergence Mach number as well as the drag creep characteristic. Compared with conventional sampling methods, the proposed method can efficiently distribute samples in the pressure distribution feature space rather than directly sampling airfoil geometry parameters. The corresponding geometry parameters are searched and found under constraints, so that supercritical airfoil samples that are well distributed in the pressure distribution space are obtained. These samples allow statistical studies to obtain more reliable and universal aerodynamic rules that can be applied to supercritical airfoil designs.

Natural ice accretion on the lifting surface of an aircraft is detrimental to its aerodynamic performance, as it changes the effective streamlined body. The main focus of this work considers the optimization design of airfoils under atmospheric icing conditions for the Unmanned Aerial Vehicle (UAV). The ice formation process is simulated by the Eulerian approach and the three-dimensional Myers model. A three-equation turbulence model is implemented to accurately predict the stall performance of the iced airfoil. In recognition of the real atmospheric variability in the icing parameters, the medium volume diameter of supercooled water droplets is treated as an uncertainty with an assumed probability density function. A technique of polynomial chaos expansion is used to propagate the input uncertainty through the deterministic system. The numerical results show that the multipoint/multiobjective optimization strategy can efficiently improve both the ice tolerance and the cruise performance of an airfoil. The reason for the focus on robust optimization is that the ice angle of the optimized airfoil becomes less critical to the incoming flow. The optimized airfoils are applied to a UAV platform, in which the performance improvement and the relevant key flow feature are both preserved.