Aerodynamic and flow behaviors of axisymmetric bodies with blunt-based and boattail configurations are investigated under different angles of attack. Particular emphasis is placed on the influence of 20° boattail modifications incorporating longitudinal grooves on wake structure and drag behavior. The angle of attack was adjusted systematically from 0° to 25°, with data acquired through aerodynamic experiments and computational flow analysis. Experimental methods provided lift and drag data, while turbulence modeling in the numerical simulations utilized a two-equation k-ω SST approach within the RANS framework. The results show that longitudinal grooves promote flow reattachment and enhance base-pressure recovery at low angles of attack, yielding up to a 20
We present a necessary and sufficient condition for approximate proper efficiency in vector optimization problems with the ordering cone being a nonnegative orthant. Although the criterion can be established by Benson’s approach (J. Math. Anal. Appl. 71, 232–241, 1979), detailed proofs are given for the first time here. The criterion is a strong motivation to introduce the concept of e-properly efficient solution, where e is any nonzero vector taken from the closed pointed ordering cone. For an arbitrary linear vector optimization problem, we show that either the e-properly efficient solution set is empty or it coincides with the e-efficient solution set. Several illustrative examples are provided.
The integration of unmanned aerial vehicles (UAVs) as aerial base stations (ABSs) is a pivotal enabler for ubiquitous connectivity in 6G networks. Multiple UAVs have been proposed to serve multiple terrestrial users in a small-cell mobile system. However, optimizing multi-UAV 3D placement to maximize the multi-user system throughput remains a challenging non-convex problem. Existing meta-heuristic algorithms like particle swarm optimization (PSO) offer high precision but suffer from prohibitive computational latency. Conversely, deep learning approaches promise rapid inference but often fail to achieve precise coordinate localization. To bridge this gap, this paper proposes a novel hybrid AI-driven framework combining an adaptive multi-start PSO (AMS-PSO) for high-fidelity data generation and a hybrid U-Net with local refinement for real-time placement. Extensive simulations reveal that the performance of the proposed framework depends on the UAV-to-User density ratio. In scenarios with adequate resources, the hybrid U-Net achieves near-optimal performance, closely matching the AMS-PSO benchmark and significantly outperforming standard heuristics. Even in resource-constrained scenarios, it maintains competitive performance. Furthermore, at high UAV densities, where optimization gains saturate, our method retains a critical advantage in computational speed. Overall, the proposed framework reduces inference time by orders of magnitude compared to iterative heuristics, making it highly viable for dynamic, real-time network orchestration.
The main goal of this study is to investigate the dynamic response of honeycomb core sandwich (HCS) plates assuming sinusoidal top face sheet subjected to a half-car moving load resting on an elastic foundation (EF). The HCS plate is composed of a lightweight honeycomb core and two functionally graded (FG) face layers. The half-car model represents a two-axle vehicle with four degrees of freedom (DOFs), including front and rear wheel displacements, body bounce, and pitch motion. The EF is modeled using the Pasternak foundation with two parameters: spring stiffness and shear stiffness. An efficient finite element formulation is developed based on four-node (Q4) elements and the new first-order shear deformation theory (n-FSDT), which accurately captures transverse shear effects while maintaining computational efficiency. The proposed formulation is validated through comparison with established benchmark solutions. A parametric study is subsequently performed to assess the influence of geometric configuration, material properties, foundation stiffness, and sinusoidal surface parameters on the dynamic response of HCS plates. The proposed method provides an efficient tool for analyzing vehicle interaction in lightweight honeycomb sandwich plates. Its findings are directly applicable to the design of high-performance structures in transportation, defense, and civil engineering, where accurate prediction of dynamic response under moving loads is essential.
Electromagnetic pollution driven by 5G/6G technologies calls for absorbers that are lightweight, thin, broadband, and low-cost. In this work, Fe3O4/rGO@SiO2 nanocomposites were prepared by two simple routes: mechanical grinding and a chemical Stober process. Both approaches deliver excellent performance, with a minimum reflection loss (RLmin) =-60.11 dB and an effective absorption bandwidth (EAB) up to 10.65 GHz at only 1.7 mm thickness and 15 wt% filler loading. Despite similar efficiencies, the underlying mechanisms differ: mechanical grinding enriches rGO defects, enhancing dipolar polarization, while chemical synthesis ensures uniform SiO2 coating and abundant heterointerfaces that promote interfacial polarization and suppress Fe3O4 agglomeration. This defect-interface trade-off rationalizes the convergence of performance and provides dual advantages: cost-effective processing flexibility and mechanistic insight for absorber design. These results highlight Fe3O4/rGO@SiO2 as a promising candidate for ultrathin, lightweight broadband EM absorbers in civilian and defence applications.
