Search Results (2,661)

Next-generation unmanned aerial vehicles require compact propulsion systems capable of providing efficient vertical lift, rapid thrust vectoring, and improved maneuverability. Cyclorotors represent a promising alternative to conventional propellers, but their aerodynamic behavior is governed by highly unsteady blade–wake interactions, making performance prediction challenging. This study investigates a four-bladed cyclorotor equipped with NACA 0012 airfoils using transient computational fluid dynamics simulations and a calibrated semi-analytical blade-element model. The numerical analysis was performed over a rotational-speed range of 368–2305 rpm and for several pitch-amplitude configurations, including 5°, 7.5°, 10°, 12.5° and 15°. The results showed that the favorable pitch amplitude decreases with increasing rotational speed, shifting from larger amplitudes at low RPM to approximately 5° at higher RPM values. The semi-analytical model reproduced the main CFD trends for lift, drag, moment, and power, providing a reduced-order tool for preliminary cyclorotor performance estimation. The comparison confirmed that pitch-amplitude selection strongly influences aerodynamic loading and efficiency and should therefore be adapted to the operating regime. The proposed CFD-based methodology, supported by semi-analytical modelling, provides a useful framework for the aerodynamic characterization and early-stage optimization of cyclorotor propulsion systems for UAV applications. Full article

Accurately obtaining aircraft aerodynamic parameters is essential for improving flight performance, optimizing design and control strategies, and ensuring flight safety. In this study, the improved Northern Goshawk Optimization (SPNGO) algorithm is used to optimize the kernel parameters and regularization coefficients of the Kernel Extreme Learning Machine (KELM). To address the defects of the original NGO algorithm, such as insufficient global optimization ability and being prone to falling into local optimums, two improvement strategies are proposed. The enhanced SPNGO algorithm is verified by 14 benchmark test functions, and the proposed SPNGO-KELM model is evaluated using open-source F-16 nonlinear simulation data for longitudinal aerodynamic parameter identification. The results demonstrate its effectiveness under the considered simulation conditions, while further validation with real flight-test data is required before application to actual flight environments. Comparative analysis with KELM, NGO-KELM, SSA-KELM, and WOA-KELM models shows that a single KELM is difficult to achieve high-precision aerodynamic parameter identification, and other comparison models have obvious fitting deviations in non-steady-state and strong nonlinear regions. Notably, the SPNGO-KELM model achieves the best identification performance, with a determination coefficient (R²) of 0.96537 and a mean absolute percentage error (MAPE) as low as 3.1574%. Its comprehensive identification accuracy is 1.81% to 37.98% higher than that of the comparison models, and it can effectively suppress error oscillations in nonlinear regions. Experimental results show that the proposed algorithm has excellent identification accuracy, generalization ability, and anti-interference performance. Full article

This study evaluates the aerodynamic performance of a modified Archimedes Spiral Wind Turbine (ASWT) using Computational Fluid Dynamics (CFD). A baseline model was compared with different designs, including surface dimples and a trailing-edge flap. Simulations were carried out in SolidWorks Flow Simulation 2025 under a constant inlet velocity of 12 m/s and rotational speeds ranging from 50 to 500 RPM. The performance of the modified ASWTs was evaluated using key parameters, including the power coefficient ($C_p$), torque, and tip speed ratio ($TSR$). The obtained results follow the expected $C_p-TSR$ behavior, with a peak of $C_p=0.24277$ for the smooth blades and $C_p=0.2565$ for the blades with the flap at $TSR=1.63625$. While the addition of dimples along the surface of the blades resulted in reduced $C_p$ values, the trailing-edge flap consistently improved performance, yielding increased $C_p$ values in comparison to the baseline configuration. Overall, the flap modification highlighted higher aerodynamic efficiency, recognizing it as the most successful improvement among all the tested configurations. These findings shed light on the relevance of geometry-specific optimization in improving ASWT productivity for small-scale wind energy applications. Full article

Micro quadrotor unmanned aerial vehicles (UAVs) are highly sensitive to external disturbances and model uncertainties because of their small mass, low moment of inertia, and limited onboard computational resources. To improve the disturbance rejection and trajectory tracking performance of micro quadrotor UAVs, this paper proposes an event-triggered extended state observer (ET-ESO)-based prescribed-time funnel control (PTFC) method. First, a control-oriented dynamic model of the micro quadrotor is established, in which wind disturbances, unmodeled aerodynamic effects, damping uncertainties, and parameter perturbations are represented as lumped disturbances in the translational and rotational subsystems. Then, two event-triggered ESOs are designed to estimate the lumped disturbances of the velocity and angular velocity channels. Compared with conventional continuously sampled ESO schemes, the proposed event-triggered mechanism reduces the frequency of sensor-to-controller information transmission while preserving disturbance estimation capability. Furthermore, a prescribed-time funnel control law is developed to constrain the position and attitude tracking errors within predefined performance boundaries and ensure convergence to the desired accuracy region within a user-specified time. Lyapunov-based stability analysis is provided to prove the boundedness of all closed-loop signals and the validity of the prescribed funnel constraints. Finally, MATLAB/Simulink simulations based on the Parrot Mambo mini-drone parameters are conducted to verify the effectiveness of the proposed method. The results demonstrate that the proposed controller achieves robust trajectory tracking, effective disturbance compensation, improved transient performance, and reduced control update frequency. Full article

The dynamic characteristics and buffeting response of long-span single-pylon cable-stayed bridges are not fully understood after cable failure occurs in coastal wind environments. This study investigates how the location, number, and pattern of cable failures affect structural performance. A three-dimensional finite element model of a 280 m main-span bridge was established using the aerodynamic coefficients extracted from wind tunnel tests. Modal analyses and nonlinear time-domain simulations were conducted. The results show that frequency reduction concentrates in lower-order vertical bending modes, with the first and second modes being the most sensitive. Variations in frequency are closely related to the failure location of stay cables, with the largest reduction at the mode antinode. Unilateral multiple failures induce bending–torsion coupling, whereas symmetric bilateral failures only lower frequencies. Under wind loads, the failure of stay cables results in the redistribution of static internal forces, primarily to the adjacent stay cables on the same side. This phenomenon is enhanced as the number of failed cables increases. The change in buffeting internal forces results in a non-monotonic trend, and the shorter cables near the pylon are more sensitive. Cable failure, which occurs at different phases of the buffeting process, significantly influences the structure's transient response. The scenario in which the structure is subjected to wind loads after cable failure results in the largest variation amplitude. Full article

This study investigates the integration of hybrid-electric distributed propulsion (DEP) systems in aviation to improve environmental sustainability. It aims to develop practical and integrated aircraft solutions by addressing the architectural complexity of hybrid-electric systems through a concurrent design approach. This approach is crucial due to the strong interdependence between aircraft performance and the size of the hybridized propulsion system. The research utilizes a multi-disciplinary Design and Optimisation (MDO) framework, built around GEMSEO, to support aero-structural and system design for a hybrid-electric regional aircraft configuration. The framework combines aerodynamics, structural, and on-board system design using a multi-fidelity approach, facilitating the integration of different design disciplines. Key findings highlight the sensitivity of overall aircraft design to on-board system sizing. We conclude that a concurrent MDO design approach effectively captures the sensitivity of the design to on-board systems sizing. Full article

This paper introduces a refined criterion for evaluating and optimizing the aerodynamic efficiency of compound planform wings, specifically those whose half span is formed by multiple trapezoidal segments. While elliptical lift distribution is known to minimize induced drag, practical manufacturing constraints have led to widespread adoption of tapered wings. However, conventional single-trapezoid planforms deviate significantly from the ideal elliptical distribution, resulting in increased induced drag and reduced fuel efficiency. This study proposes an adjustment to the ellipticity factor, enabling quantitative assessment of how well a multi-trapezoid wing approximates elliptical chord distribution. The methodology is validated through analysis of existing transport aircraft, identifying configurations with ellipticity factors below 5% (e.g., Lockheed C-5A, Antonov An-124) that achieve near-optimal induced drag performance. A comparative case study of a virtual 40-ton aircraft with a 100 m² wing area quantifies trade-offs between three planform configurations. Computational fluid dynamics simulations confirm that increasing trapezoidal segmentation improves spanwise loading and delays flow separation. Results demonstrate that two-trapezoid configurations with total inverse taper ratios of 3.3–4.2 and break coordinates at 35–45% half span achieve ellipticity factors under 7%, offering an optimal balance between aerodynamic efficiency, structural feasibility, and tail surface requirements. The proposed criterion provides aircraft designers with a rapid, computationally efficient tool for planform optimization at the conceptual design stage. The proposed criterion is valid for subsonic cruise conditions (M ≤ 0.85) and does not account for wave drag or aeroelastic effects. Full article

This paper presents an integrated optimal preview control strategy where an active suspension system (AAS) collaborates with an active aerodynamic control surface (AACS), utilizing the information of incoming road disturbance. The optimal preview controller utilizes a feedforward and feedback controller to anticipate future road disturbances while addressing the conflicting objectives of passenger comfort and road-holding attributes. The active aerodynamic surface generates a desired lift or downward force to change the sprung mass vertical load distribution, further improving the ultimate target indices. The preview-based optimal controller was synthesized by optimizing and tuning two sets of weighting factors, each based on passenger comfort and road-holding preferences. A numerical simulation study was performed for a 2-DOF quarter-of-vehicle (QoV) model in MATLAB^® (R2025b). Detailed time- and frequency-domain analyses were performed to validate the performance of the proposed scheme. The mean squared values of the total performance measure, vertical sprung mass acceleration, suspension travel, and road-holding indices were calculated and compared with the passive, active, active suspension with preview controller, and active suspension with an active aerodynamic surface (AAS). From the numerical results, it can be concluded that the proposed control strategy extraordinarily improves both ride comfort and road-holding capabilities of the vehicle model while maintaining the suspension rattle space requirements within the bounds and ensuring the dynamic stability of the vehicle. Full article

To overcome the computational bottlenecks of iterative Computational Fluid Dynamics (CFD) in turbomachinery design, this study introduces a real-time, data-driven inverse design framework for 2D uncooled, high-Reynolds turbine blades. The novelty of this work lies in the application of Kolmogorov–Arnold Networks (KAN), a distinct deep-learning architecture, to predict blade geometry and performance metrics from aerodynamic loading inputs. The foundation of the model is a comprehensive database of approximately 30,000 blade profiles, generated through an automated optimization pipeline coupled with the MISES solver. This dataset explores an extensive design space, covering inlet flow angles from $-{50}^{\circ }$ to $0^{\circ }$ and outlet angles from ${50}^{\circ }$ to ${75}^{\circ }$, with flow turning up to ${125}^{\circ }$. A rigorous benchmarking campaign compares KAN against Multi-Layer Perceptrons (MLPs) and Gaussian Process Regression (GPR), highlighting KAN’s capability to overcome the scalability bottlenecks of Gaussian Process Regression to enable real-time performance while achieving MLP-level accuracy with significantly fewer parameters. A further analysis regarding the trade-off between database size and filtration of unfeasible designs indicates that an optimal data filtration threshold exists, balancing noise reduction with model robustness. The final KAN tool achieves real-time inference speeds (∼0.1 s), reducing the design cycle by four orders of magnitude compared to traditional solvers, while maintaining high accuracy (mean outlet angle error of ${0.086}^{\circ }$ and Mach profile RMS error of $0.004$). Furthermore, the model’s predicted RMS error is exploited as a quantitative proxy for aerodynamic feasibility, identifying ill-posed inverse problems where the target loading cannot be physically realized. This metric enables the generation of comprehensive maps that rigorously delineate the boundaries of the viable design space across arbitrary aerodynamic loading styles, providing physics-aware guidelines for preliminary design. Full article

Accurate performance prediction for large offshore wind turbines requires a principled treatment of uncertainty in both the wind resource and the rotor design parameters. In the present work, we develop a surrogate-based, multi-level uncertainty quantification (UQ) framework coupling a physics-based Blade Element Momentum (BEM) solver with a spectral Polynomial Chaos Expansion (PCE) surrogate that replaces the expensive Monte Carlo loop and apply it to the IEA 15 MW offshore reference wind turbine. The framework is completed by Sobol variance-based global sensitivity analysis. The contribution is methodological rather than algorithmic: although each individual ingredient (PCE, Sobol, BEM, and Jensen) is well established, their joint deployment in a single, internally consistent, end-to-end probabilistic workflow that simultaneously delivers (i) aerodynamic–structural UQ with analytical Sobol ranking, (ii) a like-for-like cross-comparison of three reference turbines, (iii) a quantitative leading-edge icing degradation study, and (iv) a farm-level wake-steering optimization on the same IEA 15 MW reference rotor yields a unified probabilistic envelope from which manufacturing tolerances, cold-climate investment thresholds, and farm-layout/control trade-offs can be read off consistently. Five input parameters are treated as random variables: hub-height wind speed (Weibull, k = 2.2, c = 9.8 m/s), air density, blade chord length, twist angle, and rotor speed. A degree-4 sparse PCE is built by non-intrusive spectral projection using N = 5000 Sobol quasi-random realizations, which allows the Sobol indices to be recovered analytically from the expansion coefficients at essentially no extra cost. Three parallel engineering studies complement the core UQ analysis: (A) a head-to-head comparison of the NREL 5 MW, DTU 10 MW, and IEA 15 MW reference turbines; (B) a quantitative assessment of leading-edge ice accretion at four severity levels; and (C) a Jensen-based wake optimization for a 25-turbine offshore array with static wake steering. The main results are as follows: the turbine reaches C_p,_max = 0.480 at λ_opt = 8.51, and an annual energy production (AEP) of 71,261 MWh/year (PCE: 70,840 ± 2,140 MWh/year, 95% CI). Wind speed emerges as the dominant driver of C_p variance (S₁ = 0.412), followed by blade twist (0.198) and chord (0.143). Severe icing (30 kg/m) reduces C_p by 18.2% and increases the blade-root Damage Equivalent Load (DEL) by 18.5%. For the array, the optimal spacing (s_x = 8D, s_y = 6D) gives a farm efficiency of 89.6% and 1296 GWh/year, and a 15° wake-steering offset adds a further +3.2% to farm AEP. Compared with plain Monte Carlo, the sparse PCE delivers the same statistics with about 36% fewer model evaluations and a relative error below 0.8%. Full article

To investigate the flow characteristics of three-dimensional swept interactions, 3D steady Reynolds-averaged Navier–Stokes (RANS) simulations are conducted at an incoming Mach number of 3.5 and a Reynolds number of 30,955 based on the incoming boundary-layer thickness δ₀. Three independent compression configurations with a total compression angle of 18° are analyzed and compared: planar swept shocks, curved swept shocks featuring an initial 2° deflection step followed by a continuously curved compression surface, and continuous isentropic compression waves. The results demonstrate that, unlike the baseline planar case, the interactions induced by both curved swept shocks and isentropic compression waves depart from the canonical quasi-conical similarity and transcend existing topological classification frameworks. These non-planar interactions are characterized by large-scale primary vortices and small-scale corner vortices that evolve along curved trajectories downstream. Quantitatively, the curved shock interaction yields maximum normal scales of 5.4δ₀ for the primary vortex and 1.8δ₀ for the corner vortex—significantly more compact than the 6.7δ₀ and 7.5δ₀ observed in the planar-shock interaction. Furthermore, the specific modality of compression—whether by discrete shock or continuous wave—exerts a profound effect on aerodynamic performance. Under the present conditions, while isentropic compression achieves the highest compression efficiency and planar shocks provide superior mass flow capture, curved shock compression strikes a favorable balance between these competing metrics. Curved shock configurations may offer potential for improving integrated inlet performance through appropriate adjustment of the initial shock strength. Full article

Diffusers in diffuser-augmented wind turbines (DAWTs) require high-camber airfoils operating at low Reynolds numbers (Re), and their laminar separation bubbles (LSB) significantly complicate aerodynamic predictions. No prior study has experimentally validated XFOIL, k-ω SST, and γ-Re_θ models against simultaneous lift, drag, and chord-wise pressure coefficient (Cp) measurements for the customized high-camber airfoil at Re = 68,000 (68k), 118,000 (118k), and 159,000 (159k). Lift, drag, and Cp distributions were measured experimentally. The γ-Re_θ model demonstrated superior performance, achieving a lift maximum absolute percent error of 1.6–3.4%, near-zero bias, and a coefficient of determination >0.99. It accurately captured the LSB pressure plateau at mid-chord, with mean gross-averaged Cp percent errors of 8.1% and 2.1% for upper and lower surfaces, respectively. The k-ω SST model overpredicted lift by up to +9.8% at Re = 68k and underpredicted drag by up to 66%. XFOIL is unreliable specifically for separated transitional flows at Re < 118k, but improves at Re = 159k. The experimental dataset and validated transition-sensitive RANS approach provide a foundation for low-Re airfoil and DAWT diffuser design. Future work should extend measurements below Re = 50k and above 200k, including post-stall conditions, and system-level design of DAWT. Full article

Aerospace canopies require both high impact resistance and optical transparency for pilot safety and aerodynamic shielding. While polycarbonate (PC) and poly(methyl methacrylate) (PMMA) are widely utilized, their vulnerability to strain-rate-dependent failure during high-velocity bird strikes necessitates advanced reinforcement strategies. This study presents a multiscale computational framework for nanoparticle-reinforced PC nanocomposites. To circumvent the prohibitive computational costs of atomistic simulations, a novel Strain-Rate Dependent Micromechanics (SRDM) framework is proposed for silica-, alumina-, and zirconia-reinforced PC systems, integrating the Goldberg constitutive model with Halpin–Tsai micromechanics to generate rate-dependent stress–strain responses and calibrate Johnson–Cook (J-C) parameters for impact-scale simulations. Unlike conventional approaches relying on atomistic simulations or empirical fitting, the proposed framework directly links micromechanical nanocomposite modeling with finite element bird-strike simulations. Bird-strike analyses were performed in LS-DYNA on a generic fighter canopy model. The framework further incorporates literature-based optical transparency criteria considering nanoparticle size and refractive-index compatibility. Among the investigated nanofillers, silica-reinforced PC provided the most favorable response. At the most critical impact location, the maximum canopy deformation decreased from 118.6 mm for neat PC to 61.9 mm, corresponding to an approximately 48% reduction. Although the reinforced canopy exhibited a reduction in peak internal energy absorption from approximately 10 kJ to 5 kJ due to its increased stiffness and reduced plastic deformation, it provided improved deformation resistance and structural stability under impact loading. Overall, this work provides a computationally efficient framework for designing bird-strike-resistant transparent nanocomposite canopy structures using nanofiller systems previously reported in the literature to preserve optical transparency. Full article

Optimal sensor placement is a crucial issue in scientific and engineering research. This study proposes an autoencoder-based deep learning framework for automated optimal sensor layout and flow field reconstruction. A dataset is established based on transient Computational Fluid Dynamics (CFD) simulation results of a three-dimensional finite-span airfoil under various Reynolds numbers and angles of attack, enabling high-precision reconstruction of airfoil surface pressure distribution using sparse pressure coefficient data. The multilayer perceptron of both the encoder and decoder adopts an optimal five-layer structure with 200 nodes per layer, and the ReLU activation function delivers superior performance with a training loss reduction of over 45%. When using 50 sensors, the proposed architecture determined detailed placement and obtained a reconstruction error of 0.0604, which outperforms traditional manual sensor placement. The reconstruction accuracy of aerodynamic loads improves with increasing sensor count, but exhibits diminishing returns beyond the optimal threshold 50, necessitating a balanced selection that optimizes performance-to-cost ratio. The proposed method adaptively captures critical flow regions with high gradients, cutting sensor quantity by 60–80% versus grid-based placement. This method can flexibly use either CFD or experimental data in practical applications, offering an efficient solution for aerodynamic field reconstruction. Full article

A novel semi-analytical computational approach is formulated and assessed for the dynamic aeroelastic stability analysis of flexible slender thin wings in incompressible flow, which can boost the preliminary airframe design and optimisation of lightweight aircraft, offering both theoretical and practical insights. Hencky’s bar-chain model is explicitly adopted as a discrete numerical implementation of the Euler–Bernoulli continuous beam idealisation for the flexible wing structure and its deformation, resulting in a linear system of coupled ordinary differential equations for its bending and torsion dynamics. Modified strip theory is employed for the unsteady sectional airload, where approximate yet effective analytical expressions are efficiently adopted for its build-up and distribution, combining two- and three-dimensional effects in subsonic potential flow. Once the natural vibration modes of the wing are obtained from its physical model, a reduced set is selected, and a modal approach is then employed to perform its aeroelastic stability analysis with either “p-k” or “p” method, depending on the aerodynamic model. Numerical results from such a reduced-order model are critically assessed for the flutter analysis of Goland’s, Loring’s, and Pazy wings and demonstrate excellent agreement with literature results for two- and three-dimensional airflow, also for the case of the swept wing. Full article