High-stealth flying wing layouts must meet the requirements of wideband stealth design.Traditional aerodynamic shape stealth design mainly targets the high-frequency optical range,and there is relatively little research on aerodynamic shape stealth design for wing profiles in the resonance range,making it incapable of providing effective guidance for wideband stealth design of flying wing aerodynamic shapes.To address these issues,research on wideband stealth design of wing layouts was conducted.Firstly,the basic effects method was used to study the sensitivity of wing profile parameters in different frequency bands,and it was found that the requirements for low-frequency stealth design and aerodynamic design have different sensitivity areas to the aerodynamic shape of the airfoil,and there are complex contradictions in stealth design in different frequency bands.This is the basis for a wideband stealth design model for wing layout,and NACA65,3-018 airfoils are used for aerodynamic optimization design that takes wideband stealth into account.The results show that the established wideband stealth design model can improve the aerodynamic performance of wing layout airfoils,simultaneously achieving wideband high stealth characteristics.

Modern supersonic aircraft with low aspect ratio must achieve favorable subsonic and supersonic cruise efficiency and high maneuverability. However, conventional aerodynamic optimization has primarily focused on cruise lift-to-drag characteristics, with limited attention paid to maneuverability at high angles of attack, yielding designs that fail to satisfy practical engineering requirements. To address this gap, a discrete adjoint aerodynamic optimization design method based on an upwind scheme is developed. A high-fidelity adjoint-gradient optimization framework is established based on the free form deformation (FFD) method and the sequential quadratic programming (SQP) algorithm. This framework is applied to the maneuverability optimization of a low-aspect-ratio wing at high angles of attack under supersonic conditions, and the resulting configurations are evaluated for both cruise aerodynamic performance and overload characteristics across subsonic and supersonic flight regimes. The results indicate that the optimized configuration achieves an increase in normal overload of approximately 0.1 g at Ma = 0.9 and 0.27 g at Ma = 1.5. The maneuverability of the aircraft is improved under transonic and supersonic conditions, while the cruise lift-to-drag characteristics are essentially preserved. These findings demonstrate the applicability of the proposed method to complex engineering problems and provide valuable guidance for practical design applications.

Aerial Multi-object tracking is hindered by coupled dynamics in which rapid UAV ego-motion disrupts inter-frame alignment and stochastic target motion amplifies non-linear observation noise, jointly breaking temporal consistency—especially for tiny objects. Existing trackers struggle in this compound regime because convolutional backbones suppress small-target cues under clutter, while constant-velocity motion models are brittle to maneuver-induced jitter and intermittent detections. We propose ADAPT-Tracker, a unified framework that couples scale-aware representation learning with high-order geometric trajectory refinement. On the perception side, Motion-Aware Receptive Field Modulation (MRFM) decomposes features into complementary granularity components to preserve fine-grained target details while suppressing distractors, and performs structure-preserving channel mixing to strengthen small-object representations. On the motion side, High-Order Geometric Stabilized Trajectory Refinement (H-GSTR) conducts bi-directional trajectory inpainting to restore continuity under occlusion and missed detections, and applies curvature-regularized kinematics to stabilize maneuver-induced jitter. Beyond overall metrics, a sequence-level recovery study on UAVDT shows that H-GSTR recovers 8.66% additional valid detections on average, peaking at 27.27% in high-viewpoint small-target scenes. Extensive experiments on UAVDT and VisDrone-MOT demonstrate robustness under scale variation and abrupt motion, improving over the strongest competing method by approximately +2.00 (HOTA), +3.00 (MOTA), and +1.50 (IDF1) percentage points on VisDrone-MOT. Consistent superiority is observed on the UAVDT benchmark.

The application of learning-based multiview stereo (MVS) depth estimation methods has achieved significant results in large-scale 3-D reconstruction benchmarks. However, adjacent terrains in the aerial image interfere with depth estimation along building edges during the matching process, leading to inaccurate results. To address these challenges, we propose a new end-to-end MVS network, named FuS-MVSNet, which fuses monocular depth probability as a semantic guidance into the multiview geometry-based MVS framework. By combining the strengths of geometric consistency and local semantics, the FuS-MVSNet achieves notable enhancements in both accuracy and robustness. Specifically, we first construct a monocular branch based on the pretrained Depth Anything model to perform monocular metric depth estimation. The nonshared parameters ensure that the depth estimation process is independent of the multiview branch, focusing exclusively on semantic depth inference. Subsequently, to incorporate monocular features into the multiview network, we introduce a volume adaptive fusion module, which adaptively integrates monocular feature volumes into the standard cost volume via an attention mechanism and guides the cost volume regularization. Finally, confidence-based dynamic selection between the two prediction branches ensures the selection of the more robust branch result under challenging conditions. Qualitative and quantitative results indicate that we achieve competitive performance on multiple benchmarks, including the WHU and LuoJia-MVS datasets.

Reliable estimation of actuator faults is critical to unmanned aerial vehicle (UAV) safety and health management. The conventional two-stage Kalman filter (TSKF) is effective for fault diagnosis but sensitive to process and measurement noise covariances Q and R. We propose a reinforcement-learning-enhanced two-stage Kalman filter (TSKF-RL) that uses data-driven learning to provide the key quantities required by the update, reducing dependence on Q/R and improving robustness and accuracy. Under two actuator fault scenarios and three noise levels, TSKF-RL reduces the fault-estimation RMSE relative to TSKF by 23–82% for actuator 2 and 46–64% for actuator 1.

Shape stealth is the primary factor determining the stealth performance of an aircraft. In the shape design of modern military aircraft, both aerodynamic performance and stealth performance need to be comprehensively considered. Aerodynamic stealth optimization is a complex multimodal problem. Given the high computational cost of global optimization and the tendency of gradient optimization to fall into local optima, an optimal design method combining global multimodal optimization for airfoils and gradient optimization for layouts is proposed. To address the insufficient local search capability of the classical multimodal particle swarm optimization algorithm, the fitness-euclidean distance ratio ring topology local-best particle swarm optimization (FER-R3PSO) algorithm is introduced by combining the ring topology particle swarm optimization algorithm with the fitness-euclidean distance particle swarm optimization algorithm, which enhances the local search capability of the classical multimodal particle swarm optimization algorithm. To combine the multimodal search algorithm with surrogate models and reduce the computational burden of using the multimodal algorithm in engineering applications, a surrogate model point addition strategy and a peak extraction method suitable for the multimodal search algorithm are proposed. Function tests, airfoil stealth optimization, and standard aerodynamic examples are used to verify the effectiveness of the multimodal algorithm. The global/gradient coupling design for the flying wing layout is proposed, and aerodynamic stealth optimization based on the adjoint method is carried out using the three-dimensional shape obtained through global multimodal algorithm optimization of the airfoil assembly as the initial stage. The optimization results show that, compared to gradient optimization, the proposed method can achieve a shape with superior aerodynamic and stealth characteristics at a lower computational cost.

The primary concern in stealth aircraft design is the very large electrical size objects. However, the computational and storage requirements of these objects present significant obstacles for current high-fidelity design methods, particularly when addressing high-dimensional complex engineering design problems. To address these challenges, we developed a surface sensitivity technique based on the multilevel fast multipole algorithm (MLFMA). An access and storage of sparse partial derivative tensor was improved to significantly enhanced the computation performance. The far-field interactions of the surface sensitivity equation were realized by differential the multipole expansion. In addition, we proposed a fast far-field multiplication method to accelerate the multiplication process. The surface mesh derivative with respect to the design variables was calculated by analytical and complex variable methods, substantially improving computational efficiency. These advancements enabled the MLFMA-based surface sensitivity method to millions meshes and large-scale gradients, extending gradient-based optimization for very large electrical size problems. Test cases have verified the effectiveness of this method in optimizing very large electrical objects in terms of both accuracy and efficiency.

The design of the flying wing airfoil must consider aerodynamic stealth and trim requirements, with their coupling exacerbating the complexity of the design problem by introducing multiple local minimum points in design space. In addition, it would need a broad space with high dimensionality to obtain the ideal result, and expansion of design space could lead to more local minimum points, causing significant challenges to traditional optimization design methods. A Two-Stage Global–Local Constrained Optimization Method (TGLCOM) was proposed to address these issues. The parametric space was divided into a large-scale global space and a high-dimensional local space. A surrogate-based global constrained optimization method was applied in the large-scale global space, followed by a gradient-based algorithm in the high-dimensional local space to refine the design and obtain the global optima. The efficiency and robustness of the proposed TGLCOM were verified through the airfoil and flying wing layout aero/stealth design. The results indicated a minor conflict between the RCS drag and pitch moment performance. Moreover, the stealth design of the airfoil improved the stealth performance of the flying wing layout in both the yaw and pitch directions.

Research on system dynamics of friction stir welding (FSW) of aluminum alloy is of great significance to optimize the welding process and improve welding quality. In this paper, the aluminum alloy FSW is taken as the object, considering the influence of the arc value of the stirring pin, the feed quantity and the equivalent friction coefficient, the nonlinear dynamic model of the aluminum alloy FSW system is established, and the influence of different factors on the vibration characteristics of the system in x direction and y direction is explored. Time domain diagram, phase diagram, Poincare diagram, bifurcation diagram and Lyapunov exponent are used to reveal the vibration response characteristics of the aluminum alloy FSW system. The time delay multi-scale method was used to study the main resonance characteristics of the aluminum alloy FSW system, and the effects of feed quantity and time delay parameters on the main resonance characteristics of the system were explored. The results show that the system shows strong nonlinearity when considering the value of the arc of the stirring pin, the feed quantity and the equivalent softening friction coefficient. With the increase of the feed quantity and the a<^>#mount of stirring pin, the vibration characteristics of the system change from single-cycle to multi-cycle to chaos. The system will be in a complex nonlinear state when the stirring pin is rotated into the radian value and the feed quantity is large. The system can be kept stable by adjusting the feed quantity and time delay parameters. The welding effect is good and the welding quality is high when the speed of FSW of aluminum alloy is about [800 (r/min),900 (r/min)] under the lower feed quantity and the arc value of the stirring pin.

Compared with conventional aircraft, distributed electric propulsion (DEP) aircraft are recognized for their potential to enhance aerodynamic performance and propulsive efficiency, positioning them as one of the most promising advancements in future aviation. This paper explores the aeropropulsive coupling effects in boundary layer-ingesting DEP aircraft through numerical simulation and ground mobile testing. It employs two computational techniques, i.e., the actuator disk boundary condition and the full blade model, to assess the DEP's impact on wing aerodynamics and to evaluate the influence of the propulsors' shroud and design parameters. Ground mobile testing and numerical simulations are conducted on a DEP aircraft. The findings indicate that, with DEP thrust, the wing reduces the drag coefficient by 16% compared with a conventional wing, over a range of small-to-medium angles of attack. This reduction is attributed to the DEP's enhancement of the suction peak at the wing's leading edge and the extension of the plateau in pressure distribution. Additionally, incorporating a shroud around the distributed propulsors leads to a 17% increase in mass flow and a 40% rise in net thrust. However, as fan speed increases, while lift and net thrust on the DEP wing increase, the lift-to-drag ratio and overall propulsive efficiency of the system diminish.

The economy of aircraft has received more and more attention, and the influence of the constant viscosity assumption in discrete adjoint system on the optimization would no longer be ignored. In this paper, we studied the influence of constant viscosity assumption on the stability of the adjoint equation and gradient accuracy. In the viscosity coefficient variation, the laminar viscosity was treated by the Sutherland criterion and the turbulence viscosity was studied by the SA and SST turbulence model, respectively. The ONERA M6 case was adopted to make comparisons of the convergence history of the adjoint equation and the gradient accuracy. The convergence history showed that the viscosity coefficient variation did not significantly affect the stability of the adjoint equation. The gradient variation due to the viscosity coefficient variation at the shock wave separation region was very distinct from the shock-free region, indicating that the viscosity variation could not be ignored in the design problems with large viscous effects, such as shock wave boundary layer interference separation. To furtherly research the influence of the constant viscosity assumption on the aerodynamic optimization design, the optimization benchmark CRM was carried out firstly with the constant viscosity assumption, then restarted with the viscosity variation in second stage. It was presented that the optimization results in second stage could furtherly improve the aerodynamic performance of the aircraft, demonstrating that the variation of the viscosity coefficient also had a significant effect on the shock-free region. From the assessment above, the viscosity variation could significantly improve the accuracy in gradient computation, maintaining the computational stability and convergence. Therefore, the assumption of constant viscosity was not suitable for the high-fidelity aircraft design.

Nonconforming grids, which can simplify the generation of meshes for complex shapes and reduce the number of meshes, are commonly employed in addressing multiscale aeroacoustics numerical simulation challenges. This approach allows for the simulation of sound propagation processes at a reasonable cost. By utilizing the sixth-order weighted compact nonlinear hybrid scheme (WCNHS) with a piecewise exponential mapping function (WCNHS_Pe) and incorporating ghost grid blocks, a method has been developed to reconstruct interface flux for nonsingle-scale multiblock structured grids. The variable values of the ghost grid at the grid boundary are determined through high-order optimization interpolation methods. Furthermore, different time steps can be implemented for each subdomain to accommodate corresponding grid scales. To address coupling across multiple grid sizes, an interface flux reconstruction method is applied to numerically simulate various scenarios, including Sod shock tube problem, the Shu-Osher problem, Gaussian pulse propagation on two-dimensional nonconforming grids, sound propagation on discontinuous interfaces, sound radiation from three cylinders, sound generated by weak shock-vortex interaction, Gaussian pulse propagation on three-dimensional nonconforming grids, and slat noise prediction. The results demonstrate that the high-order finite difference method established in this study exhibits superior capability in capturing discontinuities as well as higher resolution and lower dispersion on nonconforming multisize meshes, making it suitable for simulating linear and nonlinear acoustic problems.

Flying wing configuration fighter is very promising for the advanced fighter in the future. This paper studies the aerodynamic and stealth characteristics of flying wing fighter. Firstly, the effect of sharp leading edge on the subsonic, transonic lift and drag characteristics and stealth characteristics of the fighter are studied. The results show that the sharpen of leading edge of the inboard wing could greatly improve the lateral stealth characteristics while maintaining the subsonic and transonic lift characteristics. In order to furtherly improve its aerodynamic and forward stealth characteristics, the aerodynamic/stealth collaborative design optimization method based on discrete adjoint is carried out. And the results presented that there are obvious contradictions in the subsonic, transonic and supersonic lift characteristics of the flying wing fighter, and the improvement of the forward stealth characteristics will also damage the subsonic lift characteristics. Therefore, it is necessary to make a compromise between the subsonic, transonic and supersonic lift characteristics, drag characteristics and stealth characteristics in the shape design of flying wing fighter.

It is a major challenge for the airframe-inlet design of modern combat aircrafts, as the flow and electromagnetic wave propagation in the inlet of stealth aircraft are very complex. In this study, an aerodynamic/stealth optimization design method for an S-duct inlet is proposed. The upwind scheme is introduced to the aerodynamic adjoint equation to resolve the shock wave and flow separation. The multilevel fast multipole algorithm (MLFMA) is utilized for the stealth adjoint equation. A dorsal S-duct inlet of flying wing layout is optimized to improve the aerodynamic and stealth characteristics. Both the aerodynamic and stealth characteristics of the inlet are effectively improved. Finally, the optimization results are analyzed, and it shows that the main contradiction between aerodynamic characteristics and stealth characteristics is the centerline and cross-sectional area. The S-duct is smoothed, and the cross-sectional area is increased to improve the aerodynamic characteristics, while it is completely opposite for the stealth design. The radar cross section (RCS) is reduced by phase cancelation for low frequency conditions. The method is suitable for the aerodynamic/stealth design of the aircraft airframe-inlet system.

In this article, the large-angle attitude maneuver control problem for liquid-filled spacecraft systems with external disturbance and nonlinear uncertainties is considered, while the angular velocity and control torque constraints are taken into account to suppress the liquid sloshing amplitude. Firstly, the rigid-liquid coupling system is modeled based on the existing pulsating ball model. Different from the existing fuzzy disturbance observer, an adaptive fuzzy neural network disturbance observer is proposed to estimate the lumped disturbance consisting of the nonlinear certainties and external disturbance, which can track the lumped disturbances quickly and has better observation performance. Then, in order to handle the constraints on angular velocity and control torque, two auxiliary systems are developed to stabilize the rigid-liquid coupling systems in finite time under the backstepping algorithm. Finally, numerical simulation results are presented to demonstrate the effectiveness of the proposed disturbance observer and control law.

This paper investigates an affine formation tracking control approach for multiple quadrotor unmanned aerial vehicles (UAVs). The control scheme can make those quadrotors not only achieve the desired formation pattern, but also track any time-varying affine formation, for example, a rotation, scaling, shearing of a nominal configuration. The leader-follower strategy is adopted to solve the affine formation problem, in which the target formation is only known by the leader quadrotors, and the follower quadrotors are solely required to track the leaders. The nonlinear model of each quadrotor is considered, and a sliding mode control law is developed to achieve various affine formation patterns. The proposed control law is proven to be globally stable by Lyapunov stability theory. Finally, the effectiveness of the proposed control law for tracking affine formations is corroborated by the simulation results.

This paper proposes a nonlinear space dimension reduction method named Optimized Generative Topographic Mapping (OGTM). The Generative Topographic Mapping (GTM) method relies on the training sample set to capture the manifold of objective functions, and the generation of the training sample set causes an enormous computational burden. The choice of GTM hyperparameters has a significant influence on the design results. Traditional research has generally adopted the "cut-and-try" method to determine the corresponding hyperparameters and the best design, leading to wasted computational cost. The proposed OGTM overcomes this issue by minimizing the fitting error between the low-dimensional and high-dimensional samples, and the suitable hyperparameters are directly obtained by minimizing the fitting. In addition, the paper adopts a variable-fidelity sample filtration method to extract the promising regions with fewer sample points. To test and verify the effectiveness of the proposed method, it was then compared with the PCA and EGO methods in RAE2822 airfoil and ONERA M6 wing aerodynamic designs. The results demonstrate that the proposed method could capture the effective design space and generally take less computational cost to find the ideal results in all design optimizations.

Dynamic stall often leads to unsteady load and performance degradation in horizontal axis wind turbines. Therefore, accurate modeling of dynamic stall is crucial. However, due to the large variations of the blade aerodynamic profiles and the complexity of dynamic stall flow, numerical simulation and wind tunnel experiment are costly. On the other hand, widely used semi-empirical models have limited accuracy. Hence, this paper proposes a data-knowledge fusion method that incorporates physical knowledge into a neural network to improve its accuracy and generalization ability. Firstly, the force components of the Leishman- Beddoes model are incorporated into the network. An efficient dynamic stall model for the S809 airfoil is thus established with a small amount of high-precision experimental data. It achieves extrapolation predictions of reduced frequency and angle of attack with only 1/5 of the samples in the database to train. Moreover, to make full use of the accumulated existing airfoil data to assist in modeling other airfoils, the obtained S809 model is fused in the network to predict the aerodynamics of S810 and S814. The average relative error of the prediction cases is nearly 10%. Comprehensively, this paper provides a new paradigm for assessing the dynamic stall of the wind turbine blade.

In engineering application, there is only one adaptive weights estimated by most of traditional early warning radars for adaptive interference suppression in a pulse reputation interval（PRI）. Therefore, if the training samples used to calculate the weight vector does not contain the jamming, then the jamming cannot be removed by adaptive spatial filtering. If the weight vector is constantly updated in the range dimension, the training data may contain target echo signals, resulting in signal cancellation effect. To cope with the situation that the training samples are contaminated by target signal, an iterative training sample selection method based on non-homogeneous detector（NHD） is proposed in this paper for updating the weight vector in entire range dimension. The principle is presented, and the validity is proven by simulation results.