Search Results (316)

Accurate yaw alignment is critical for maximizing power capture in horizontal-axis wind turbines, as even moderate yaw misalignment leads to significant aerodynamic losses, increased actuator usage, and accelerated mechanical wear. This research paper proposes a hybrid smart yaw control system for small-scale wind turbines that combines real-time measurements with short-term wind direction prediction to improve alignment accuracy, operational reliability, and energy efficiency under realistic operating conditions. The system integrates four wind direction information sources, such as physical wind vane sensing, live online weather data, forecast data, and a data-driven prediction module within a structured priority framework (VANE → LIVE → FORECAST → AI), to ensure continuous yaw control during sensor or communication unavailability. The prediction module is based on a long short-term memory (LSTM) neural network trained in MATLAB using live data from an online platform, with sine–cosine encoding employed to address the circular nature of directional data. The yaw controller incorporates a ±15° deadband, dwell-time logic, shortest-path rotation, and cable-safe constraints to reduce unnecessary actuation while maintaining effective alignment. The proposed system is validated through MATLAB/Simulink simulations and real-time microcontroller-based experiments using a stepper motor-driven nacelle. Compared with conventional vane-based yaw control, the hybrid AI-assisted approach reduces the average yaw error by approximately 35–45%, maintains a yaw error within ±15° for more than 90% of the operating time, increases average electrical power output by 3–5%, and reduces yaw motor energy consumption by 10–15%, while decreasing corrective yaw actuation events by 30–40%. These results demonstrate that integrating an LSTM-based wind direction predictor with multi-source wind data provides a robust, low-cost, and practically deployable yaw control solution that enhances energy capture and mechanical durability in small-scale wind turbines. Full article

To ensure the safety and structural integrity of composite flexible blades under strong winds, this study investigates the extreme aeroelastic responses of the IEA 15 MW wind turbine blade during an emergency shutdown with pitch system faults. Existing studies often rely on simplified models or one-way coupling; we adopt a bidirectional computational fluid dynamics–finite element method (CFD–FEM) fluid–structure interaction (FSI) framework to examine how wind speed and pitch system faults affect aerodynamic loads, displacement responses, and structural stresses when the blade is shut down in a parked-upwind condition. The results reveal that, under the no-pitch condition, the blade experiences extreme loading, with thrust being approximately 15 times higher and the peak stress being 8.6 times that of the pitch condition. Furthermore, a high frequency of 1.969 Hz emerges, significantly increasing the risk of aeroelastic instability as the wind speed increases or under the no-pitch condition. A stress analysis identified that high stress is mainly located in the main spar region, with the peak stress location shifting closer to the blade root under the no-pitch condition. This study highlights the potential risks of composite flexible blades during shutdowns and provides a reference for structural safety design and targeted monitoring. Full article

When high-altitude aircraft operate in a low-density environment, the flow instability within their internal ducts poses a severe challenge to aerodynamic design and operational safety. Especially in the intake system of the tandem dual-fan configuration, the asymmetric flow caused by rotating machinery coupled with the low-density effect exacerbates flow distortion, momentum dissipation, and efficiency loss and may even trigger system instability risks such as rotational stall or surge. To address these challenges, this paper establishes a high-fidelity dynamic model of the internal flow field of the aircraft, based on the Reynolds-averaged Navier–Stokes equations and the SST k-ω turbulence model, combined with dynamic mesh technology. It reveals the unstable mechanism caused by angular momentum accumulation under co-rotation conditions and its intrinsic correlation with the degradation of aerodynamic performance. Inspired by the concept of micro-flow regulation, an active flow control strategy integrating discrete auxiliary injection and local geometric shape optimization is proposed. Numerical results show that by reasonably arranging auxiliary injection holes in the intake duct and optimizing local geometric fillets, the uniformity of intake flow can be effectively improved, and the formation of large-scale vortex structures can be suppressed. This method increases the system’s flow capacity by approximately 47.4%, significantly improves the total pressure recovery coefficient and fan aerodynamic efficiency, and reduces the amplitude of low-frequency pressure fluctuations by approximately 23.1%. Research shows that in high-altitude low-Reynolds-number conditions, micro-flow regulation combined with geometric reconstruction can effectively suppress flow instability induced by rotating machinery. This achievement provides a theoretical basis and feasible engineering path for aerodynamic stability design and optimization of key components, such as the aircraft intake and exhaust systems and thermal management systems, and is of significant value for improving the overall performance and reliability of high-altitude long-endurance aircraft. Full article

Mitigating heat stress in high-density historical districts remains a critical challenge in urban renewal due to complex morphological heterogeneity. Existing research often relies on isolated intervention measures, lacking systematic, multi-strategy assessments driven by high-precision spatial data. This study addresses this gap by establishing a quantitative framework that couples thermal infrared remote sensing with Computational Fluid Dynamics (CFD) to optimize microclimate responses in Beijing’s Liulichang Historic District. Remote sensing data were utilized to retrieve high-resolution Land Surface Temperature (LST), providing accurate thermal boundary conditions for micro-scale wind-thermal simulations. A baseline scenario (S0) and seven renewal strategies (S1–S7)—integrating varying configurations of greenery, water bodies, and permeable pavements—were evaluated using pedestrian-level comfort indices. Results reveal that single-factor interventions yield marginal improvements or thermodynamic trade-offs; specifically, adding greenery (S1) in narrow street canyons increased aerodynamic roughness, thereby obstructing ventilation and inducing localized warming. Conversely, composite strategies significantly enhanced microclimatic quality. The “greenery-water-permeable pavement” strategy (S4) achieved optimal synergistic effects, characterized by substantial cooling and spatial homogenization. Regression analysis identified water bodies as the dominant cooling driver, where a 10% increase in water coverage resulted in a temperature reduction of approximately 5.17 °C. Conversely, greenery alone showed no statistically significant cooling contribution (p > 0.05) without the synergistic presence of water or pavement modifications. This research suggests that urban renewal in high-temperature zones (>36 °C) should prioritize composite cooling networks. Furthermore, vegetation layouts near wind corridors must be precisely regulated to prevent ventilation degradation. These findings provide a scientific basis for the climate-adaptive sustainable regeneration of culturally significant, high-density urban blocks. Full article

The aerodynamics of avian flight provides critical inspiration for the design of bioinspired aerial vehicles, yet the quantitative characterization of free-flight wing kinematics remains challenging. This study employs a neural-network-based motion tracking approach (DeepLabCut) to analyze wingbeat kinematics in free-flying birds from video data. We automatically digitize key wing points and reconstruct three-dimensional trajectories to quantify asymmetric flapping patterns. Our analysis reveals that while wing oscillations approximate sinusoidal motion, they exhibit statistically significant velocity differences between upstroke and downstroke phases, confirming the stroke asymmetry of avian flapping. Furthermore, using video of a flying frigatebird (Fregata ariel), we quantify the changes in the effective wing area throughout the wingbeat cycle, showing a ~19% variation that significantly impacts lift generation efficiency. These findings provide quantitative benchmarks for avian-inspired wing design and offer insights for optimizing flapping kinematics in bioinspired aerial systems, particularly for enhancing takeoff and landing capabilities in micro air vehicles. Full article

Owing to its large flexible envelope, an airship is highly sensitive to environmental disturbances, such as wind gusts. Fluid–structure interaction induces structural deformation, which modifies the aerodynamic force distribution and introduces additional coupling effects. Furthermore, servo-elastic deformation alters the position and orientation of actuators mounted on the envelope, resulting in deviations between commanded and actual control forces. To address these issues, a composite control strategy integrating trajectory tracking and active elastic deformation suppression is proposed for a flexible airship equipped with multiple vectored propellers. Structural flexibility is explicitly incorporated into the dynamic model through modal decomposition, where the generalized coordinates and their time derivatives associated with deformation modes are included in the system state vector. A disturbance observer is developed to estimate actuator-level force deviations induced by elastic deformation, and the estimated disturbances are compensated in real time. Based on this formulation, a composite control framework, referred to as servo-elastic control, is established. The framework consists of a trajectory tracking controller and a displacement compensation module to achieve simultaneous motion regulation and structural deflection suppression. Numerical results demonstrate that the displacement at vectored thrust actuator attachment points is reduced to approximately 10% of that obtained using a trajectory tracking controller alone. The proposed method achieves significant deformation suppression without degrading position tracking performance, thereby enhancing control effectiveness and system stability of flexible airships. Full article

Autonomous aerial refueling is a key technology for enhancing the endurance of unmanned aerial vehicles; however, the wingtip vortices generated by the tanker create a strong three-dimensional wake-vortex flow field, whose downwash and lateral airflow can impose significant rolling moments on the follower Unmanned Aerial Vehicle (UAV), posing a serious threat to flight safety. To address this issue, this study proposes an integrated framework that combines wake-vortex risk-field modeling with optimal path planning. The classical Hallock–Burnham (HB) model is first employed to predict vortex descent and lateral transport, while a two-phase model is used to characterize the temporal decay of vortex circulation. The predicted vortex parameters are then coupled with the UAV’s aerodynamic characteristics, and the rolling-moment coefficient (RMC) is introduced as a risk metric to compute its spatiotemporal distribution in three dimensions, thereby transforming the invisible wake-vortex disturbance into a visualizable and quantifiable dynamic three-dimensional risk map. On this basis, a wake-vortex-aware path-planning algorithm based on particle swarm optimization (PSO) is developed, incorporating adaptive weighting and elitist mutation strategies. A multi-objective cost function considering path length, safety, and smoothness is further constructed to search for an optimal safe path under wake-vortex influence. Simulation results indicate that, compared with the classical A* and Rapidly-Exploring Random Tree (RRT) algorithms, the proposed method reduces cumulative risk exposure by approximately 90% and 75%, respectively, while limiting the increase in path length to about 8% (significantly lower than the increases of 40% for A* and 44% for RRT). In addition, the maximum turning angle is constrained within 10°, and the computation time remains around 0.052 s, satisfying real-time requirements. These results demonstrate that the proposed method can generate safe, efficient, and dynamically feasible paths for UAV aerial refueling and provide a valuable reference for wake-vortex avoidance in similar aerospace missions. Full article

The hyperloop transportation system is a promising ultra-high-speed mobility solution operating in a reduced-pressure environment, where pod performance is governed by the coupled behaviour of structural integrity, aerodynamics, and electromagnetic propulsion. This paper presents the design, numerical analysis, and performance evaluation of a lightweight hyperloop pod equipped with a linear induction motor (LIM)-based propulsion and electromagnetic stabilisation system. The pod chassis was fabricated using Carbon Fibre-Reinforced Polymer (CFRP) and Aluminium 6061-T6, achieving a significant weight reduction while maintaining structural safety. Finite Element Analysis reveals a maximum von Mises stress of 82 MPa, which is well below the material yield strength, and a maximum deformation of 0.64 mm under worst-case loading conditions. Modal analysis indicates the first natural frequency at 47.6 Hz, ensuring sufficient separation from operational excitation frequencies. Computational Fluid Dynamics analysis conducted inside a rectangular tube shows a drag coefficient reduction of approximately 18% compared to a baseline blunt design, with stable velocity distribution and no flow choking at operating speeds. The optimised nose geometry enables rapid acceleration, achieving 25 km/h within 1.1 s in prototype testing. The LIM analysis demonstrates a peak thrust of 1.85 kN at an optimal slip range of 6–8%, with operating currents between 35 and 55A and power consumption of 18–25 kW. Thermal analysis confirms a maximum stator temperature of 78 °C, remaining within safe operating limits. The integrated numerical and experimental results confirm the feasibility, efficiency, and stability of the proposed hyperloop pod design. Full article

Fast and reliable aerodynamic predictions are crucial in the early phases of aircraft design, where a quick assessment of various configurations is required. In this context, the Vortex Lattice Method (VLM) is widely adopted due to its computational efficiency; however, its predictive accuracy is highly sensitive to panel discretization strategies, which are often determined heuristically. This study proposes a bio-inspired optimization framework for VLM panel discretization and evaluates it through a systematic case study on a representative wing geometry. A grid-convergence analysis was initially carried out to ensure solution independence across various spanwise-to-chordwise panel ratios. Subsequently, a novel Hybrid Response Surface Methodology (HRSM), integrating Box–Behnken and Central Composite experimental designs, was employed to enable a more comprehensive exploration of the factor space while quantifying the effects of clustering parameters at the leading-edge, trailing-edge, root, and tip regions of the wing. The HRSM dataset was further utilized to train Ensemble Machine-Learning surrogate models, which were coupled with bio-inspired Crayfish and Aquila optimization algorithms, alongside a classical Genetic Algorithm (GA) as a performance benchmark, to identify the optimal discretization strategy and to enable a comparative assessment of their convergence behavior and robustness against the numerical noise of the ensemble-based landscape. Compared to base (i.e., uniform) panel distribution, the optimally clustered discretization enhanced overall aerodynamic prediction accuracy by approximately 33%, particularly at low angles of attack, while maintaining robust performance at higher angles. Both algorithms converged to similar minima; however, the Aquila algorithm achieved higher solution consistency, whereas the Crayfish algorithm exhibited greater dispersion despite faster convergence, revealing a multimodal optimization landscape. The variance decomposition revealed that trailing-edge clustering dominated aerodynamic accuracy at low angles of attack, contributing up to 90% of the total variance, whereas tip clustering became increasingly influential at higher angles, exceeding 30%, highlighting the need for adaptive discretization strategies to ensure reliable VLM-based aerodynamic analyses. Full article

To enhance the aerodynamic performance of organic Rankine cycle (ORC) turbines under increasing energy demands, surrogate-based optimization was applied to a 100 kW ORC turbine rotor. Four representative surrogate models—a radial basis neural network (RBNN), Kriging, response surface approximation (RSA), and a PRESS-based weighted (PBW) ensemble—were comparatively evaluated under identical numerical conditions. Independent optimizations of the first- and second-stage rotors enabled an examination of how different design variable space characteristics influenced surrogate predictive behavior. A fractional factorial sampling strategy was used to construct the training dataset, and learning curve analysis was conducted to assess sample size adequacy. Sensitivity estimation revealed distinct response surface characteristics between stages, allowing the interpretation of variations in surrogate stability. In both stages, geometric modifications were primarily concentrated near the outlet blade angle, identified as a dominant variable influencing efficiency. CFD validation confirmed that surrogate-based exploration successfully identified improved rotor geometries. Flow-field analysis indicated reduced entropy generation near the trailing edge region, suggesting the mitigation of aerodynamic losses. The results demonstrate that surrogate-based optimization can reliably improve turbine performance within a bounded design space, while the relative effectiveness of surrogate models depends on the sensitivity structure of the underlying problem. Full article

Ground vortex formation beneath aeroengine intakes during near-ground operations represents an energy-related aerodynamic issue, as it degrades inlet flow quality, induces pressure distortion, and reduces the effective utilization of incoming kinetic energy. This study investigates the unsteady characteristics of ground vortex flow under headwind conditions and its influence on foreign object ingestion (FOI) in an aeroengine intake. Three-dimensional unsteady Reynolds-averaged Navier–Stokes (URANS) simulations coupled with a Lagrangian Discrete Phase Model (DPM) are employed to resolve the interaction between intake-induced vortices and dispersed particles near the ground. The results indicate that the ground vortex rapidly develops into a quasi-periodic state, generating significant unsteady total pressure distortion at the intake face, with peak fluctuations reaching approximately 10% of the mean value. This flow non-uniformity reflects a deterioration of inlet energy distribution and is detrimental to downstream compression efficiency. Particle ingestion behavior is strongly dependent on particle density and diameter. Low-density and small particles are more readily entrained into the vortex core and ingested, whereas particles with higher density or larger size exhibit increased inertia and reduced sensitivity to vortex-induced energy transport. The ingestion region is biased toward the lower portion of the intake, consistent with the vortex core location. These findings provide insight into vortex-induced energy distortion and FOI mechanisms, offering guidance for improving aeroengine intake design and energy-efficient operation during near-ground conditions. Full article

As a critical component of sustainable energy systems, enhancing the efficiency of Vertical Axis Wind Turbine (VAWT) is paramount. This study addresses the key Challenges of poor startup performance and low power output in VAWTs by investigating the aerodynamic performance of an optimized double Darrieus vertical axis wind turbine (DD-VAWT) via design of experiment (DOE) and response surface methodology (RSM). The numerical method was validated with experimental data and reported numerical work. Response surface statistical analysis was conducted to evaluate the effect of the designed variables on the objective function with 29 cases. The optimal parameters of four designed variables were determined after linear regression analysis to obtain the optimal DD-VAWT. The aerodynamic performance of the optimal DD-VAWT was numerically studied and compared with that of a one-blade VAWT and a pre-optimized DD-VAWT. The velocity contours of different azimuth angles reveal that the optimal blades significantly minimized flow disturbances at the interface of the primary and auxiliary blades, further enhancing their performance. The results demonstrate that the output power of the optimized double-layer blades increased by approximately 28.5% compared to the original ones. This study provides new insights for improving the aerodynamic performance of VAWT and has much potential beneficial to the application of the DD-VAWT technique, supporting the broader transition towards a sustainable energy future. Full article

This work addresses the numerical solution of fourth-order initial value problems of the form $y^{\left(4\right)}=f(x,y,y^{\prime })$, extending the capabilities of standard Runge–Kutta–Nyström (RKN) methods which are typically limited to $y^{\left(4\right)}=f(x,y)$. Problems of this type arise naturally in structural and vibroacoustic dynamics, where velocity-dependent damping and coupling effects are essential for realistic modeling. Despite their practical importance, efficient explicit schemes that preserve the fourth-order structure while allowing derivative dependence remain limited. We generally present an explicit s-stage method that incorporates the first derivative into the internal stage approximations, necessitating the introduction of a new matrix parameter D in the order conditions. We successfully derive the algebraic order conditions for this extended method up to the seventh algebraic order. A particular pair of orders 6(4) is constructed at an effective cost of only four stages per step in contrast to eight function evaluations required in conventional RK pairs. This reduction in effective stage cost, together with the direct treatment of derivative-dependent terms, constitutes a structural and computational distinction from existing Runge–Kutta and RKN approaches. To demonstrate the physical relevance of the proposed solvers, we examine coupled fourth-order models arising in structural and vibroacoustic dynamics, including viscoelastic beam systems with aerodynamic (velocity-proportional) damping and structure–acoustic interaction in a thin-walled duct. These examples illustrate the capability of the method to handle coupled dynamics with derivative-dependent damping and source terms that are central to realistic modeling of such systems. On these representative problems, the proposed pair clearly and decisively outperforms existing Runge–Kutta pairs from the current literature, achieving substantially higher accuracy for the same computational effort. The results indicate that explicit fourth-order Nyström-type schemes with derivative-aware internal stages provide both a theoretical extension of classical RKN theory and measurable efficiency gains, offering a competitive alternative to reduction-based first-order formulations for velocity-dependent fourth-order systems. Full article

Virtual impactors are widely used for particulate matter (PM) classification due to their advantages of small cut-off particle size, simple structural design, ease of operation, and high particle handling capability, enabling subsequent analysis based on the desired aerodynamic diameter. Existing studies have mainly focused on the effects of particle size and structural parameters on classification performance, whereas systematic investigations into the regulatory mechanisms of fluid medium properties and ambient temperature variations on cut-off particle size remain relatively limited. Particularly under low-temperature gas conditions, variations in gas dynamic viscosity may significantly influence the dynamics of inertial particle separation, thereby altering the classification performance of virtual impactors. In this study, a low-temperature carbon dioxide-driven virtual impactor is proposed. By regulating the physicochemical properties of low-temperature gas, effective control over the particle inertial separation process is achieved, thereby expanding the tunable range of classification performance in virtual impactors. Numerical simulation results indicate that under low-temperature CO₂ conditions, the virtual impactor can achieve a cut-off particle size classification capability of approximately 1.8 μm for fine particles. Under identical channel dimensions, a comparative analysis between conventional rectangular main channels and trapezoidal main channels was conducted, quantitatively showing that wall loss decreased from 44% to 24%. Based on the trapezoidal main channel configuration, further parametric studies on the horizontal inlet geometric dimensions were performed, revealing their influence on separation efficiency and wall loss. To validate the reliability of the numerical simulation results, particle separation experiments were conducted using polystyrene microspheres with particle sizes of 2 μm and 5 μm. Experimental results demonstrate that the virtual impactor can achieve stable particle separation and confirm the reliability of simulation-predicted particle classification trends. The results further show that, when driven by low-temperature CO₂ combined with trapezoidal main channel structural optimization, the cut-off particle size of the virtual impactor decreases by approximately 26%, from 2.5 μm to about 1.8 μm. The trapezoidal channel structure significantly reduces particle wall loss under specific cut-off particle size conditions, while the low dynamic viscosity characteristic of low-temperature CO₂ lowers the internal gas temperature environment of the microchannel, thereby improving inertial particle separation efficiency. Full article

Ice accretion along aircraft leading edges, particularly at stagnation line parting strips, remains difficult to remove using conventional electrothermal anti-icing systems. These systems require continuous high-power heating to maintain the stagnation region above the melting point, often exceeding 10–12 kW/m². This study introduces an Ice Cavitation Deicer (ICD) that removes ice through rapid, localized cavitation generated within a thin melt layer formed at the ice–surface interface. In the proposed approach, a short pulse of electric current melts a 1–10 µm interfacial layer and causes a cavitation impulse of approximately 1–10 MPa. This impulse ejects the stagnation-line ice in a direction normal to the surface, often against the external airflow, enabling the immediate aerodynamic removal of the remaining ice. Analytical modeling based on the energy conservation principle was used to determine the optimal foil geometry, thermal pulse parameters, thermal stress, and material selection. Experiments with various metallic foils and substrate materials validated the predicted ejection behavior. The impulses were sufficient to fracture and eject ice 1–10 mm thick. The observed ice fragment velocities varied from 1 m/s to 10 m/s. Compared with conventional thermal anti-icing, the ICD concept reduces power consumption by approximately two orders of magnitude while offering rapid and reliable leading-edge deicing. The low power requirements, rapid response, and compatibility with thin-foil heater architectures make ICD a promising technology for both conventional and electrified aircrafts, UAVs, rotorcrafts, and other platforms where power availability is limited. This manuscript presents the first theoretical and experimental research on the ICD method and is a concept-proof work. Further research and development are required before the ICD is ready to be tested in flight. Full article