http://yetl.yabesh.ir/yetl1/handle/yetl/19056 Thu, 27 Aug 2026 11:37:21 GMT 2026-08-27T11:37:21Z https://localhost:443/yetl1/bitstream/id/184255/ http://yetl.yabesh.ir/yetl1/handle/yetl/19056 http://yetl.yabesh.ir/yetl1/handle/yetl/4316853 Numerical Assessment of Steady-State RANS Models for Predicting Boundary-Layer Transition, Separation, and Wake Characteristics of Horizontal-Axis Wind Turbines Bouhelal, Abdelhamid; Agagna, Belkacem; Hamlaoui, Mohammed Nadjib; Smaili, Arezki Abstract. Reliable prediction of aerodynamic loads and near-wake structure of horizontal-axis wind turbines remains challenging, particularly under transitional and separated-flow regimes where steady eddy-viscosity Reynolds-Averaged Navier–Stokes (RANS) closures are still widely used in engineering practice. Yet, systematic steady-state benchmarking of transition-sensitive eddy-viscosity models remains limited. In this work, seven turbulence closures—Spalart–Allmaras, standard/RNG/realizable k–ε, k–ω shear stress transport (SST), and the transition-sensitive k–kL–ω and γ–Reθ—are assessed against the Model EXperiments In COntrolled COnditions (MEXICO) rotor experiments using exact blade geometry in a rotating subdomain with controlled mesh and near-wall treatment. Simulations are validated at inflow velocities of 10, 15, and 24 m s−1 using blade pressure distributions, sectional loads, thrust and power, and three-component near-wake velocities. The transition-sensitive k–kL–ω model provides the best overall agreement, maintaining thrust errors of 2.4–5.1% and power errors of 2.1–4.8% across the three operating conditions, at a computational cost of ≈120–172 h/CPU. Among fully turbulent closures, Spalart–Allmaras and realizable k–ε provide a competitive accuracy–cost compromise, whereas k–ω SST and RNG k–ε are inexpensive (≈18–36 h/CPU) but lose reliability under stall, with power errors reaching ≈46% and ≈28%, respectively. The γ–Reθ model shows the largest inconsistency in separated regimes, with power errors exceeding 50% at 24 m s−1. These results quantify the accuracy–cost tradeoff of common eddy-viscosity RANS models and provide practical guidance for steady wind-turbine computational fluid dynamics (CFD) predictions. Thu, 01 Jan 2026 00:00:00 GMT http://yetl.yabesh.ir/yetl1/handle/yetl/4316853 2026-01-01T00:00:00Z http://yetl.yabesh.ir/yetl1/handle/yetl/4316763 Fleet Based Monitoring With Multi-Feature Hierarchical Clustering Achilleos, Achilleas; Peng, Dandan; Terzi, Ludovico; Desmet, Wim; Gryllias, Konstantinos Abstract. Fleet-wide condition monitoring is a well-known method for continuously assessing the operational health of an entire fleet, ensuring efficient and reliable performance. With the emergence of fleet-based monitoring, incorporating digital modeling, advanced diagnostics, and predictive maintenance has become possible. This approach compares physical machines to their digital counterparts, enabling more sophisticated fault detection. A critical challenge addressed by fleet monitoring is managing large data volumes efficiently, avoiding the need for excessive high-frequency data. The proposed approach leverages supervisory control and data acquisition (SCADA) data from similar wind turbines in the same region, assuming healthy operation for most turbines. Deviations in multiple measurements from normal conditions serve as fault indicators, enabling early detection and targeted interventions while reducing data processing demands. This study proposes an unsupervised learning method using statistical analysis of SCADA data, applied on a fleet of 22 wind turbines. After preprocessing to understand stochastic behaviors, a multifeature hierarchical clustering (MFHC) model identifies patterns and groups turbines based on operational characteristics. By analyzing extracted features, the model efficiently detects faults and optimizes performance without requiring labeled datasets. Thu, 01 Jan 2026 00:00:00 GMT http://yetl.yabesh.ir/yetl1/handle/yetl/4316763 2026-01-01T00:00:00Z http://yetl.yabesh.ir/yetl1/handle/yetl/4316752 Aerodynamic Enhancement of Airfoils for Wind Energy Applications Using Active Fluid Gurney Flaps Jerez, Mario Lucas; Saavedra, Jorge Abstract. Rising global energy demand underscores the need for more efficient renewable power generation, yet further expansion of wind farms is often constrained by geographical and environmental limitations. Enhancing the aerodynamic efficiency of individual turbine blades, therefore, represents a promising strategy to improve overall energy capture. This work investigates the aerodynamic performance of an active fluid Gurney flap (AFGF), a flow-control concept inspired by conventional Gurney flaps (GF) but employing trailing-edge jet injection to actively manipulate the pressure field. Unlike passive devices, the active fluid Gurney flap allows real-time control of aerodynamic loading through modulation of the injection pressure, enabling adaptive performance under different operating conditions. A two-dimensional computational fluid dynamics (CFD) framework was developed in ansysfluent to analyze the Active Fluid Gurney Flap on an S809 airfoil at a Reynolds number of Re=1×106. Unsteady Reynolds-averaged Navier–Stokes (URANS) simulations were conducted for three configurations: a clean airfoil, a conventional Gurney flap, and the proposed active fluid Gurney flap. The results show that the active fluid Gurney flap substantially modifies the pressure distribution by enhancing suction on the suction side and increasing diffusion on the pressure side. This redistribution leads to higher circulation and, consequently, a significant lift augmentation while maintaining controllable aerodynamic behavior. The findings demonstrate that the active fluid Gurney flap provides a flexible and efficient mechanism for aerodynamic performance enhancement, outperforming traditional passive high-lift devices. Due to its controllability and geometric reversibility, the active fluid Gurney flap represents a promising active flow-control strategy with potential applications in wind turbine blade design to improve aerodynamic efficiency and, by extension, their power output potential. Thu, 01 Jan 2026 00:00:00 GMT http://yetl.yabesh.ir/yetl1/handle/yetl/4316752 2026-01-01T00:00:00Z http://yetl.yabesh.ir/yetl1/handle/yetl/4316736 Investigation of the Synergetic Effect of Using J-Shaped Profile and Winglets in Single and Multiple Profile Blades of Small-Scale Horizontal Axis Wind Turbines Hamad, Saif Al; Abousabae, Mohamed; Shaker, Omar; Amano, Ryoichi S. Abstract. The design of wind turbine blades plays a significant role in determining the aerodynamic performance of the rotor. In this study, the effect of using different blade design modifications for small-scale horizontal axis wind turbines (HAWTs) was investigated. Experimentally generated wind power was used for the computational fluid dynamics (CFD) validation on the lab scale, and experimental laboratory wind speeds and tip speed ratios (TSRs) were used to run CFD simulations. The synergetic effect of combining two blade design modifications (winglets and pressure-side J-shaped truncation) was investigated. This study focuses on the average power coefficient enhancement for different wind speeds. Furthermore, the effect of using the same design modifications was studied for multiple-profile HAWT blades using profiles with high thickness-to-chord ratios near the hub. Combining the two design modifications and their effects on single- and multiprofile HAWT blades has not been addressed in the literature. This study aims to fill these gaps. It was found that adding the one-third pressure-side J-shaped truncation (J(1/3)) to the winglet cases enhanced the power coefficient and produced a synergetic effect on the overall power coefficient. Furthermore, the J(1/3) pressure-side truncation was found to be more effective for multiple-profile HAWTs. At the same time, the negative 30 deg cant angle downstream winglet (N30) and the combination of winglet N30 with J(1/3) provided better performance for the single-profile HAWTs. Thu, 01 Jan 2026 00:00:00 GMT http://yetl.yabesh.ir/yetl1/handle/yetl/4316736 2026-01-01T00:00:00Z