Search Results (183)

Wind turbines play an important role for renewable energy generation related to sustainable development. Selection of a suitable blade shape is a key factor in wind turbine design, especially in low wind speed conditions such as urban areas. In addition, two airfoil models of the S-series, S4110 and S1012, are often selected based on their suitable aerodynamic properties with low Reynolds numbers, high applicability, and stable performance. However, there is no research design for wind turbine blades based on S4110 and S1012 under low wind conditions in countries around the world. The angle of attack was adjusted to observe variations in the key aerodynamic parameters while applying appropriate boundary conditions for different regions. The study results show that the overall performance of the optimized S4110 is better than that of the optimized S1012, particularly at larger angles of attack. The performance of the airfoil S4110 shows a strong improvement after optimization, with the aerodynamic performance from 17.35 at 3 m/s to 50.78 at 5 m/s. This paper proposed the airfoil combination usage of S4110 at the blade tip and S1012 at the blade root to form an optimal hybrid airfoil configuration for wind turbine blade, which can both take advantage of high aerodynamic efficiency in low wind conditions and ensure the necessary mechanical strength and stability for the entire wind turbine blade. The performance of the proposed small wind turbine blade model based on the optimal S4110 and S1012 airfoils was analyzed using the Qblade program. Its purpose is to create a new blade model for small wind turbines that moves beyond conventional applications to explore novel and integrated solutions for a sustainable energy future. Full article

A new approach for optimizing the blade profile of a horizontal axis wind turbine is proposed in this paper, based on the combination of the Blade Element Momentum (BEM) method and Proper Generalized Decomposition (PGD). The resulting algorithm was implemented in MATLAB (R2010A). To investigate the applicability of the proposed BEM-PGD method, simulations were conducted using the NREL phase VI turbine. By focusing on the tangential force coefficient as a parametrized solution, the study demonstrated a 21.7% improvement in the power coefficient relative to the baseline blade corresponding to a 20 kW turbine, while the tip speed ratio (TSR) ranged from 1 to 12, as assessed through a quantitative metric comparing the optimized and reference curves. These advancements are attributed to the algorithm’s capability to parameterize the solution and to select the appropriate airfoil type, thickness, chord, and twist, allowing for an optimized and realistic blade design. Full article

Vertical-Axis Wind Turbines (VAWTs) are efficient solutions for renewable energy generation, especially in regions with variable wind conditions. This study presents an optimized design of a small-scale H-type VAWT through the integration of Design of Experiments (DOE) and Computational Fluid Dynamics (CFD), using a fractional factorial 2^k−p approach to evaluate the influence of geometric and operational parameters on power output and power coefficient (Cp), which ranged from 0.15 to 0.35. The research began with a comprehensive assessment of renewable resources in Isla Fuerte, Colombia. Solar analysis revealed an average of 5.13 Peak Sun Hours (PSHs), supporting the existing 175 kWp photovoltaic system. Wind modeling, based on meteorological data and Weibull distribution, showed speeds between 2.79 m/s and 5.36 m/s, predominantly from northeast to northwest. Under these conditions, the NACA S1046 airfoil was selected for its aerodynamic suitability. The turbine achieved power outputs from 0.46 W to 37.59 W, with stabilization times analyzed to assess dynamic performance. This initiative promotes environmental sustainability by reducing reliance on Diesel Generators (DGs) and empowering local communities through participatory design and technical training. The DOE-CFD methodology offers a replicable model for energy transition in insular regions of developing countries, linking technical innovation with social development and education. Full article

Experimental optimization with surrogate models has received much attention for its efficiency recently in predicting the responses of the experimental optimum. However, with the development of multi-fidelity experiments with surrogate models such as Kriging, the traditional expected improvement (EI) in efficient global optimization (EGO) has suffered from limitations due to low efficiency. Only high-fidelity samples to be used in optimizing Kriging surrogate models are infilled, misleading the sequential sampling method in low-fidelity data sets. This recent theory based on multi-fidelity sequential infill sampling methods has gained much attention for balancing the selection of high- or low-fidelity data sets, but ignores the efficiency of sampling in experiments. This article proposes an Adaptive Sequential Infill Sampling (ASIS) method based on Bayesian inference for a multi-fidelity Hamilton Kriging model in the use of experimental optimization, aiming to address the efficiency of sequential sampling. The proposed method is demonstrated by two numerical simulations and one practical aero-engineering problem. The results verify the efficiency of the proposed method over other popular EGO methods in surrogate models, and ASIS can be useful for any other reliability engineering problems due to its efficiency. Full article

Optimal airfoil design remains a critical challenge in aerodynamic engineering, with traditional methods requiring extensive computational resources and iterative processes. This paper presents GEO-DSGA, a novel framework integrating hybrid geometric neural networks with deep symbiotic genetic algorithms for enhanced airfoil optimization. The methodology employs graph-based representations of airfoil geometries through a hybrid architecture combining graph convolutional networks with traditional deep learning, enabling precise capture of spatial geometric relationships. The parametric modeling stage utilizes CST, Bézier curves, and PARSEC methods to generate mathematically robust airfoil representations, subsequently transformed into graph structures preserving local and global shape characteristics. The optimization framework incorporates a deep symbiotic genetic algorithm enhanced with dominant feature phenotyping, applying biological symbiotic principles where design parameters achieve superior performance through mutual enhancement rather than independent optimization. This systematic exploration maintains geometric feasibility and aerodynamic validity throughout the design space. Experimental results demonstrate an 88.6% reduction in computational time while maintaining prediction accuracy within 1.5% error margin for aerodynamic coefficients across diverse operating conditions. The methodology successfully identifies airfoil geometries outperforming baseline NACA profiles by up to 12% in lift-to-drag ratio while satisfying manufacturing and structural constraints, establishing GEO-DSGA as a significant advancement in computational aerodynamic design optimization. Full article

The high computational cost of high-fidelity CFD simulations forms a major bottleneck in aerodynamic design. This paper introduces a multi-fidelity transfer learning framework to rapidly predict airfoil aerodynamics with high accuracy. Our approach involves pre-training a deep fully connected neural network on a large dataset of low-fidelity Euler simulations. The pre-trained model is then fine-tuned using a limited set of high-fidelity RANS data, enabling efficient knowledge transfer from low- to high-fidelity domains. A specialized logarithmic-exponential normalization method is developed to handle the scale differences between aerodynamic coefficients. The framework demonstrates exceptional performance: after fine-tuning with only 700 high-fidelity samples, the model accurately predicts pressure distributions (lowest RMSE = 0.053) and force coefficients (R² > 0.947 for lift and drag). This method successfully bridges the gap between computational efficiency and high accuracy, providing a powerful data-driven surrogate model that can significantly accelerate the aerodynamic design and optimization process. Full article

Unmanned Aerial Vehicles (UAVs) have revolutionized various industries due to their adaptability, efficiency, and capability to operate in diverse environments. However, conventional UAV designs face trade-offs between flight endurance and maneuverability. This study explores the design, analysis, and optimization of a biplane quadrotor UAV, integrating the vertical takeoff and landing (VTOL) capabilities of multirotors with the aerodynamic efficiency of fixed-wing aircraft to enhance flight endurance while maintaining high maneuverability. The UAV’s structural design incorporates biplane wings with different NACA airfoil configurations (NACA4415, NACA0015, and NACA0012) to assess their impact on drag reduction, stress distribution, and flight efficiency. Computational Fluid Dynamics (CFD) simulations in ANSYS Fluent 2023 R2 (Canonsburg, PA, USA).reveal that the NACA0012 airfoil achieves the highest drag reduction (75.29%), making it the most aerodynamically efficient option. Finite Element Analysis (FEA) further demonstrates that NACA4415 exhibits the lowest structural stress (95.45% reduction), ensuring greater durability and load distribution. Additionally, a hybrid flight control system, combining Backstepping Control (BSC) and Integral Terminal Sliding Mode Control (ITSMC), is implemented to optimize transition stability and trajectory tracking. The results confirm that the biplane quadrotor UAV significantly outperforms conventional quadcopters in terms of aerodynamic efficiency, structural integrity, and energy consumption, making it a promising solution for surveillance, cargo transport, and long-endurance missions. Future research will focus on material enhancements, real-world flight testing, and adaptive control strategies to further refine UAV performance in practical applications. Full article

This paper proposes a solution for the design of high-performance propellers optimized for various flight conditions. Considering both propulsion and electric motor efficiencies, a new design optimization methodology is proposed. The optimization of the electric propulsive system is directly achieved by simultaneously analyzing the aerodynamic performance of the propeller and the motor. This study is focused on small, low-speed Remotely Piloted Aircraft Systems, addressing the design of fixed pitch propellers that operate efficiently over the entire speed range. The aerodynamic methodology uses combined blade element and momentum theory, which is adequate for a preliminary design phase with low computational time. For the aerodynamic coefficients of the airfoils used in these applications, at low Reynolds numbers, a new database was developed that incorporates airfoil experimental data and analytical methods to cover a wide range of angles of attack, beyond stall. For the modelling of the motor behavior, an idealization of the circuit was carried out, which considers its basic electric parameters. The results show significant improvements with respect to the information available for a current commercial propeller. Full article

Low Reynolds number flows are central to the performance of airfoils used in small unmanned aerial vehicles (UAVs), micro air vehicles (MAVs), and aerodynamic platforms operating in rarefied atmospheres. Consequently, a deep understanding of airfoil behavior and accurate prediction of aerodynamic performance are essential for the optimal design of such systems. The present study employs Computational Fluid Dynamics (CFD) simulations to analyze the aerodynamic performance of a cambered plate at a Reynolds number of 10,000. Two Reynolds-Averaged Navier–Stokes (RANS) turbulence models, $\gamma$–$R{e}_{\theta }$ and $k\text{-}{k}_L\text{-}\omega$, are utilized, along with the Unsteady Navier–Stokes (UNS) equations. The simulation results are compared against experimental data, with a focus on lift, drag, and pressure coefficients. The models studied perform moderately well at small angles of attack. The $\gamma$–$R{e}_{\theta }$ model yields the lowest lift and drag errors (below 0.17 and 0.04, respectively), while the other models show significantly higher discrepancies, particularly in lift prediction. The $\gamma$–$R{e}_{\theta }$ model demonstrates good overall accuracy, with notable deviation only in the prediction of the stall angle. In contrast, the $k\text{-}{k}_L\text{-}\omega$ model and the UNS equations capture the general flow trend up to stall but fail to provide reliable predictions beyond that point. These findings indicate that the $\gamma$–$R{e}_{\theta }$ model is the most suitable among those tested for low Reynolds number transitional flow simulations. Full article

This work introduces a novel variable camber mechanism that combines the high-load capacity, structural stability, and mechanical efficiency of rigid-body mechanisms with the adaptability, lightweight design, and continuous and smooth motion of compliant mechanisms. The proposed mechanism, featuring an articulated airfoil structure with revolute joints and a cantilever beam that models and controls airfoil camber morphing, employs both standard and higher kinematic pairs to constrain mobility and facilitate camber adjustments through beam deflection and coordinated kinematic interactions. Through multidisciplinary optimization, this study determined the optimal mechanism configuration and airfoil shapes for a small fixed-wing UAV (Unmanned Aerial Vehicle), meeting its morphing and mission requirements, showing the potential for drag reduction by up to 13% across various cruise conditions, thus lowering overall mission drag and energy usage. 2D (airfoil) and 3D (wing) prototypes were built to demonstrate the working principle of the proposed mechanism and to highlight its morphing capabilities. It can morph into multiple airfoil configurations, producing continuous, smooth and efficient airfoil shapes. Moreover, the mechanism is robust, simple, and easy to manufacture, effectively harnessing the strengths of both rigid-body and compliant mechanisms. Full article

Gliders are engineless aircraft capable of maintaining altitude for extended periods and achieving long ranges. This paper presents an optimal aerodynamic design method for flying wing gliders, leveraging a combination of artificial neural networks (ANNs) as a surrogate model and genetic algorithms (GAs) for optimization. Data for training the ANN is generated using the vortex-lattice method (VLM). The study identifies optimal aerodynamic shapes for two objectives: maximum flight endurance and maximum range. A key finding is the inherent conflict between aerodynamic performance and static stability in tailless designs. By introducing a stability constraint via a penalty function, we successfully generate stable and high-performance configurations. For instance, the stabilized RG15 airfoil design achieves a maximum glide ratio of 24.1 with a robust 5.1% static margin. This represents a calculated 11.5% performance reduction compared to its unstable theoretical optimum, quantitatively demonstrating the crucial trade-off between stability and performance. The methodology provides a computationally efficient path to designing practical, high-performance, and inherently stable flying wing gliders. Full article

This paper presents the design of a permanent-magnet-assisted synchronous reluctance motor (PMa-SynRM) for compressor applications using Sm-series injection-molded magnets that eliminate heavy rare-earth elements. The high shape flexibility of the injection-molded magnets enables the formation of a curved multi-layer flux-barrier rotor geometry based on the Joukowski airfoil potential, optimizing magnetic flux flow under typical compressor operating conditions. Furthermore, electromagnetic performance, irreversible demagnetization behavior, and rotor stress sensitivity were analyzed with respect to key design variables to derive a model that satisfies the target performance requirements. The validity of the proposed design was confirmed through finite element method (FEM) comparisons with a conventional IPMSM using sintered NdFeB magnets, demonstrating the feasibility of HRE-free PMa-SynRM for high-performance compressor drives. Full article

The self-starting capability of straight-bladed H-type Darrieus Vertical Axis Wind Turbines (VAWTs) remains a major constraint for deployment, particularly in urban, low speed, and turbulent environments. We conducted a systematic review of technological strategies to improve self-starting, grouped into five categories: (1) aerodynamic airfoil design, (2) rotor configuration, (3) passive flow control, (4) active flow control, and (5) incident flow augmentation. Searches in Scopus and IEEE Xplore (last search 20 August 2025) covered the period from 2019 to 2026 and included peer-reviewed journal articles in English reporting experimental or numerical interventions on H-type Darrieus VAWTs with at least one start-up metric. From 1212 records, 53 studies met the eligibility after title/abstract screening and full-text assessment. Data were synthesized qualitatively using a comparative thematic approach, highlighting design parameters, operating conditions, and performance metrics (torque and power coefficients) during start-up. Quantitatively, studies reported typical start-up torque gains of 20–30% for airfoil optimization and passive devices, about 25% for incident-flow augmentation, and larger but less certain improvements (around 30%) for active control. Among the strategies, airfoil optimization and passive devices consistently improved start-up torque at low TSR with minimal added systems; rotor-configuration tuning and incident-flow devices further reduced start-up time where structural or siting constraints allowed; and active control showed the largest laboratory gains but with uncertain regarding energy and durability. However, limitations included heterogeneity in designs and metrics, predominance of 2D-Computational Fluid Dynamics (CFDs), and limited 3D/field validation restricted quantitative pooling. Risk of bias was assessed using an ad hoc matrix; overall certainty was rated as low to moderate due to limited validation and inconsistent uncertainty reporting. In conclusions, no single solution is universally optimal; hybrid strategies, combining optimized airfoils with targeted passive or active control, appear most promising. Future work should standardize start-up metrics, adopt validated 3D Fluid–Structure Interaction (FSI) models, and expand wind-tunnel/field trials. Full article

This study explores the critical role of the flow deflector in suppressing boundary layer separation and enhancing aerodynamic efficiency through systematic geometric parameterization and computational analysis. By defining eight key design variables, this research identifies optimal configurations that significantly delay flow separation at high angles of attack. Computational Fluid Dynamics (CFD) simulations reveal that optimized deflector geometries enhance suction peaks near the airfoil leading edge, redirect separated flow toward the upper surface, and inject momentum into the boundary layer to generate a more positive lift coefficient. The numerical results demonstrate that the optimized design achieves a 58.4% increase in lift coefficient and an 83.3% improvement in the lift–drag ratio by effectively mitigating large-scale vortical structures inherent in baseline configurations. Sensitivity analyses further highlight threshold-dependent “sudden-jump” behaviors in lift coefficients for parameters such as element spacing and deflection angles, while thickness exhibits minimal influence. Additionally, pre-stall optimizations show that strategically aligned deflectors preserve baseline performance with a 0.4% lift gain, whereas misaligned configurations degrade aerodynamic efficiency by up to 9.1%. These findings establish a direct correlation between deflector-induced flow redirection and separation suppression, offering actionable insights for passive flow control in stalled regimes. This research advances fundamental understanding of flow deflector-based separation management and provides practical guidelines for enhancing aerodynamic performance in aerospace applications. Full article

Unmanned Aerial Vehicles (UAVs) are widely used in meteorology and logistics due to their unique advantages nowadays. During their lifecycle, uncertainties—such as flight condition variations—can significantly affect both design and performance, making Robust Aerodynamic Design Optimization (RADO) essential. However, existing RADO methodologies face high computational cost of uncertainty analysis and inefficiency of conventional optimization algorithms. To address these challenges, this paper proposed a novel RADO methodology integrating a Stochastic Kriging (SK) surrogate model with the PPO-Clip reinforcement learning algorithm, targeting atmospheric uncertainties encountered by turbojet-powered UAVs in transonic cruise. The SK surrogate model, constructed via Maximin Latin Hypercube Sampling and refined using the Expected Improvement infill criterion, enabled efficient uncertainty quantification. Based on the trained surrogate model, a PPO-Clip-based RADO framework with tailored reward and state transition functions was established. Applied to the RAE2822 airfoil under Mach number perturbations, the methodology demonstrated superior reliability and efficiency compared with L-BFGS-B and PSO algorithms. Full article