Most existing aerodynamic shape optimization (ASO) studies do not take the balanced pitching moment into account and thus the optimized configuration has to be trimmed to ensure zero pitching moment, which causes additional drag and reduces the benefit of ASO remarkably. This article proposes an efficient global ASO method that directly enforces a zero pitching moment constraint. A free-form deformation (FFD) parameterization combing Laplacian smoothing method is implemented to parameterize a full aircraft configuration and ensure sufficiently smooth aerodynamic shapes. Reynolds-averaged Navier–Stokes (RANS) equations are solved to simulate transonic viscous flows. A surrogate-based multi-round optimization strategy is used to drive ASO towards the global optimum. To verify the effectiveness of the proposed method, we adopt two design optimization strategies for the NASA Common Research Model (CRM) wing–body–tail configuration. The first strategy is to optimize the configuration without considering balance of pitching moment, and then manually trim the optimized configuration by deflecting the horizontal tail. The second one is to directly enforce the zero pitching moment constraint in the optimization model and take the deflection angle of the horizontal tail as an additional design variable. Results show that: (1) for the first strategy, about 4-count drag-reducing benefits would be lost when manually trimming the optimal configuration; (2) the second strategy can achieve 3.2-count more drag-reducing benefits than the first strategy; (3) compared with gradient-based optimization (GBO), surrogate-based optimization (SBO) is more efficient than GBO for ASO problems with around 80 design variables, and the benefit of ASO achieved by SBO is comparable to that obtained by GBO.

The wing kinematics of birds plays a significant role in their excellent unsteady aerodynamic performance. However, most studies investigate the influence of different kinematic parameters of flapping wings on their aerodynamic performance based on simple harmonic motions, which neglect the aerodynamic effects of the real flapping motion. The purpose of this article was to study the effects of wing kinematics on aerodynamic performance for a pigeon-inspired flapping wing. In this article, the dynamic geometric shape of a flapping wing was reconstructed based on data of the pigeon wing profile. The 3D wingbeat kinematics of a flying pigeon was extracted from the motion trajectories of the wingtip and the wrist during cruise flight. Then, we used a hybrid RANS/LES method to study the effects of wing kinematics on the aerodynamic performance and flow patterns of the pigeon-inspired flapping wing. First, we investigated the effects of dynamic spanwise twisting on the lift and thrust performance of the flapping wing. Numerical results show that the twisting motion weakens the leading-edge vortex (LEV) on the upper surface of the wing during the downstroke by reducing the effective angle of attack, thereby significantly reducing the time-averaged lift and power consumption. Then, we further studied the effects of the 3D sweeping motion on the aerodynamic performance of the flapping wing. Backward sweeping reduces the wing area and weakens the LEV on the lower surface of the wing, which increases the lift and reduces the aerodynamic power consumption significantly during the upstroke, leading to a high lift efficiency. These conclusions are significant for improving the aerodynamic performance of bionic flapping-wing micro air vehicles.

Forward-swept wings are more suitable for natural laminar flow than backward-swept wings. However, in order to reduce the difficulty of optimization, most aero-structural optimization studies of forward-swept wings do not consider the automatic laminar–turbulent transition, discrete variables, or large-scale constraints, which may result in undesirable optimization results. In this article, an efficient aero-structural optimization method for the composite forward-swept natural laminar flow (FSNLF) wing is proposed, which can solve MDO problems with those issues. Reynolds-averaged Navier–Stokes (RANS) equations coupled with the dual e^N transition method are used to simulate subsonic viscous flows. A surrogate-based optimization (SBO) algorithm combining a discrete variable handling method is developed to solve the multidisciplinary design optimization (MDO) problem involving many discrete ply thickness variables of predefined angles (0°/±45°/90°). The Kreisselmeier–Steinhauser (KS) method is employed to handle large-scale geometric constraints, ply fraction constraints and material failure constraints. To verify the effectiveness of the proposed method, we perform the aero-structural optimization of an A320-class composite FSNLF wing. Results show that the proposed method offers great potential in the aero-structural optimization of the composite FSNLF wing. It can handle 32 discrete variables and 11,089 constraints, the drag coefficient and mass of the wing are reduced significantly, and the area of the laminar flow region on the wing upper surface is increased by 24.3% compared with the baseline.

For composite wing optimization based on surrogate models, there is currently no effective method to handle 10% ply fraction constraints. Traditional methods that fix the ply fraction in each design region rely heavily on design experience and often fail to achieve ideal optimization results. This paper proposes a ply fraction constraint handling method based on the Kreisselmeier-Steinhauser (KS) method within a surrogate-based optimization framework. It can aggregate many ply fraction constraints into one, enabling design results to satisfy the ply fraction design criteria automatically. To validate its effectiveness, the uCRM9 wing structure optimization case is performed to compare the proposed method with traditional methods. Results show that the proposed method achieves the most significant mass reduction, it can handle 32 ply fraction constraints, and has great potential in handling more constraints.