Search Results (380)

The intentional yaw offset of wind turbines has shown potential to redirect wakes, enhancing overall plant power production, but it may increase fatigue loading on turbine components. This study analyzed fatigue loads on the NREL 5 MW reference wind turbine under varying yaw offsets using blade element momentum theory, dynamic blade element momentum, and the converging Lagrange filaments vortex method, all implemented in OpenFAST. Simulations employed yaw angles from −40° to 40°, with turbulent inflow generated by TurbSim, an OpenFAST tool for realistic wind conditions. Fatigue loads were calculated according to IEC 61400-1 design load case 1.2 standards, using thirty simulations per yaw angle across five wind speed bins. Damage equivalent load was evaluated via rainflow counting, Miner’s rule, and Goodman correction. Results showed that the free vortex method, by modeling unsteady aerodynamic forces, yielded distinct differences in damage equivalent load compared to the blade element method in yawed conditions. The free vortex method predicted lower damage equivalent load for the low-speed shaft bending moment at negative yaw offsets, attributed to its improved handling of unsteady effects that reduce load variations. Conversely, for yaw offsets above 20°, the free vortex method indicated higher damage equivalent for low-speed shaft torque, reflecting its accurate capture of dynamic inflow and unsteady loading. These findings highlight the critical role of unsteady aerodynamics in fatigue load predictions and demonstrate the free vortex method’s value within OpenFAST for realistic damage equivalent load estimates in yawed turbines. The results emphasize the need to incorporate unsteady aerodynamic models like the free vortex method to accurately assess yaw offset impacts on wind turbine component fatigue. Full article

Background/Objectives: Amelogenesis imperfecta (AI) represents a heterogeneous group of inherited disorders affecting the quality and quantity of dental enamel, making clinical diagnosis challenging. This study aimed to identify genetic variants in Slovenian patients with non-syndromic AI and to evaluate enamel morphology using radiographic parameters. Methods: Whole exome sequencing (WES) was performed on 24 AI patients and their families. Panoramic radiographs (OPTs) were analyzed using Fiji ImageJ to assess crown dimensions, enamel angle (EA), dentine angle (DA), and enamel–dentine mineralization ratio (EDMR) in lower second molar buds, compared to matched controls (n = 24). Two observers independently assessed measurements, and non-parametric tests compared EA, DA, and EDMR in patients with and without disease-causing variants (DCVs). Statistical models, including bootstrap-validated random forest and logistic regression, assessed variable influences. Results: DCVs were identified in ENAM (40% of families), AMELX (15%), and MMP20 (10%), including four novel variants. AI patients showed significant enamel deviations with high reproducibility, particularly in hypomineralized and hypoplastic regions. DA and EDMR showed significant correlations with DCVs (p < 0.01). A bootstrap-validated random forest model yielded a 90% (84.0–98.0%) AUC-estimated predictive power. Conclusions: These findings highlight a novel and reproducible radiographic approach for detecting developmental enamel defects in AI and support its diagnostic potential. Full article

This article represents a full estimation of helicopter rotor aerodynamic characteristics in ground effect conditions through the application of a coupled empirical blade element–momentum theory algorithm. The main focus of this research includes the evaluation of the required weighted power coefficients $\left({\displaystyle \raisebox{1ex}{$C_P$}\!\left/ \!\raisebox{-1ex}{$\sigma $}\right.}\right)$ for a hovering state in close proximity to obstacles and their relation to the weighted thrust force coefficients’ values $\left({\displaystyle \raisebox{1ex}{$C_T$}\!\left/ \!\raisebox{-1ex}{$\sigma $}\right.}\right),$ varying the relative distance from the helicopter rotational plane to the ground surface $\left({\displaystyle \raisebox{1ex}{$H$}\!\left/ \!\raisebox{-1ex}{$R$}\right.}\right)$ and the rotor’s collective pitch angle (θ). The represented numerical and experimental results show that an increase in the collective pitch angles (θ) leads to a rise in the generated weighted thrust force coefficients $\left({\displaystyle \raisebox{1ex}{$C_T$}\!\left/ \!\raisebox{-1ex}{$\sigma $}\right.}\right)$ and in the weighted power coefficients $\left({\displaystyle \raisebox{1ex}{$C_P$}\!\left/ \!\raisebox{-1ex}{$\sigma $}\right.}\right)$ for every individual fixed normalized distance from the ground surface $\left({\displaystyle \raisebox{1ex}{$H$}\!\left/ \!\raisebox{-1ex}{$R$}\right.}\right)$. Moreover, a decline in the relative distance from the ground $\left({\displaystyle \raisebox{1ex}{$H$}\!\left/ \!\raisebox{-1ex}{$R$}\right.}\right)$ requires less power to keep the rotation going in hover. The dependencies indicate that the ground effect zone covers a distance of up to 2R from the rotational plane to the ground surface. Full article

Unmanned Aerial Vehicles (UAVs) have become powerful tools for high-resolution, quantitative remote sensing in ecological and environmental studies. In this study, we present a novel approach to accurately mapping and estimating the biomass of Suaeda salsa using UAV-based visible-light imagery combined with hue angle inversion modeling. By integrating diffuse reflectance standard plates into the flight protocol, we converted RGB pixel values into reflectance and derived hue angle metrics with enhanced radiometric accuracy. A hue angle cutoff threshold of 249.01° was identified as the optimal cutoff to distinguish Suaeda salsa from the surrounding land cover types with high confidence. To estimate biomass, we developed an exponential inversion model based on hue angle data calibrated through extensive field measurements. The resulting model—Biomass = 3.57639 × 10⁻¹⁵ × e^0.12201×α—achieved exceptional performance (R² = 0.99696; MAPE = 3.616%; RMSE = 0.02183 kg/m²), indicating strong predictive accuracy and robustness. This study highlights a cost-effective, non-destructive, and scalable method for the real-time monitoring of coastal vegetation, offering a significant advancement in remote sensing applications for wetland ecosystem management. Full article

Background/Objectives: This study aimed to assess the potential of sternal morphometric parameters derived from multidetector computed tomography (MDCT) for sex estimation in a contemporary Greek population. A secondary objective was to develop and evaluate statistical and machine learning models based on these measurements for forensic identification. Methods: Sternal measurements were obtained from chest MDCT scans of 100 Greek adults (50 males, 50 females). Morphometric variables included total sternum length, surface area, angle, and index (SL, SSA, SA, and SI); manubrium length, width, thickness, and index (MBL, MBW, MBT, and MBI); sternal body length, width, thickness, and index (SBL, SBW, SBT, and SBI); and xiphoid process length and thickness (XPL and XPT). Logistic regression and a Random Forest classifier were applied to assess the predictive accuracy of these parameters. Results: Both models showed high classification performance. Logistic regression identified MBL and SBL as the most predictive variables, yielding 91% overall accuracy, with 92% sensitivity and 90% specificity. The Random Forest model achieved comparable results (91% accuracy, 88% sensitivity, 93% specificity), ranking SSA as the most influential feature. Conclusions: MDCT-derived sternal morphometry provides a reliable, non-invasive method for sex estimation. Parameters such as MBL, SBL, and SSA demonstrate strong discriminatory power and support the development of population-specific standards for forensic applications. Full article

Coordinating vehicle handling stability and energy consumption remains a key challenge for distributed driving electric vehicles (DDEVs). In this paper, a hierarchical torque vectoring control strategy is proposed to address this issue. First, a tire road friction coefficient (TRFC) estimator based on the fusion of vision and dynamic is developed to accurately and promptly obtain the TRFC in the upper layer. Second, a direct yaw moment control (DYC) strategy based on the adaptive model predictive control (MPC) is designed to ensure vehicle stability in the middle layer, where tire cornering stiffness is updated dynamically based on the estimated TRFC. Then, the lower layer develops the torque vectoring allocation controller, which comprehensively considers handling stability and energy consumption and distributes the driving torques among the wheels. The weight between stability and economy is coordinated according to the stability boundaries derived from an extended phase-plane correlated with the TRFC. Finally, Hardware-in-the-Loop (HIL) simulations are conducted to validate the effectiveness of the proposed strategy. The results demonstrate that compared with the conventional stability torque distribution strategy, the proposed control strategy not only reduces the RMSE of sideslip angle by 44.88% but also decreases the motor power consumption by 24.45% under DLC conditions, which indicates that the proposed method can significantly enhance vehicle handling stability while reducing energy consumption. Full article

To address the interception problem against maneuvering targets, this paper proposes a multi-agent cooperative guidance law based on a multi-directional interception formation. A three-dimensional agent–target engagement kinematics model is established, and a fixed-time observer is designed to estimate the target acceleration. By utilizing the agent-to-agent communication network, real-time exchange of motion state information among the agents is realized. Based on this, a control input along the line-of-sight (LOS) direction is designed to directly regulate the agent–target relative velocity, effectively driving the agent swarm to achieve time-to-go consensus within a fixed-time boundary. Furthermore, adaptive variable-power sliding mode control inputs are designed for both elevation and azimuth angles. By adjusting the power of the control inputs according to a preset sliding threshold, the proposed method achieves fast convergence in the early phase and smooth tracking in the latter phase under varying engagement conditions. This ensures that the elevation and azimuth angles of each agent–target pair converge to the desired values within a fixed-time boundary, forming a multi-directional interception formation and significantly improving the interception performance against maneuvering targets. Simulation results demonstrate that the proposed cooperative guidance law exhibits fast convergence, strong robustness, and high accuracy. Full article

Building-integrated photovoltaics (BIPV) requires tools that improve and facilitate simulating and predicting the system’s output energy. The efficiency of a photovoltaic (PV) system can be determined by the performance ratio (PR), which relates the actual system’s output energy to the theoretical output according to the installed power and the solar irradiation, thus accounting for the power losses the PV system undergoes. Among the different parameters affecting PR, module temperature and the angle of incidence of irradiance are the most dependent on the BIPV application due to the varied module positioning. This paper assesses the suitability of several BIPV temperature models and determines the angular losses for any possible module positioning. The proposed methodology is easy to replicate and results in polar heatmap graphs to estimate PR at the desired location as a function of the tilt and azimuth angles of the modules. The calculations require irradiance, ambient temperature, and wind speed data, which can easily be obtained worldwide. Dynamic sky conditions are addressed through filters that smooth out quickly changing input data to avoid high and low peaks. The developed graphs are helpful in the decision-making process for BIPV designs by allowing the designer to estimate the expected PR of the BIPV system for any possible position of the modules on the building envelope, reducing the effect of uncertainties and resulting in more accurate and better predictions of the system’s output energy. The method applied to a BIPV façade in Madrid showed a deviation of less than 3% between the estimated and monitored PRs; the PR values ranged between 0.74 and 0.82, depending on the BIPV application and module position. Full article

Tendon-driven robots offer advantages in terms of their compliance, lightweight design, and remote actuation, making them ideal for applications requiring dexterity and safety. However, existing tendon-driven actuators often suffer from low integration and inaccurate proprioceptive sensing due to their complex pulley-based tension sensors and bulky angle sensors. This paper presents the design and control of a compact and proprioceptive modular tendon-driven actuator. The actuator features a simplified single-pulley tension sensing mechanism and a novel maze-slot fixation method, minimizing friction and maximizing the structural integrity. A 3D Hall effect sensor is employed for accurate estimation of the tendon length with minimal space usage. A feedforward PID controller and a model-based tendon length observer are proposed to enhance the dynamic performance and sensing accuracy. Bench tests demonstrate that the actuator achieves a high power density (0.441 W/g), accurate closed-loop tension control, and reliable tendon length estimations. The proposed design provides a practical and high-performance solution for tendon-driven robots, enabling more agile, compact, and robust robotic systems. Full article

This study introduces a novel and versatile application of neural networks (NNs) to enhance two distinct aspects of Wireless Power Transfer (WPT) systems. First, a compact NN architecture is presented for accurate distance estimation and automated impedance matching in a WPT system. Trained on either impedance measurements or scattering parameters acquired from the transmitter side, this NN effectively predicts the inter-coil distance and identifies optimal capacitance values for maximizing power transfer. Validation using both simulated and experimental data demonstrates consistently low prediction error rates. Second, a separate NN is employed to predict the optimal transmission frequency for minimizing the phase angle between voltage and current, thereby maximizing the power factor. This NN, validated on experimental data spanning various load conditions and inter-coil distances, achieves performance comparable to traditional PI control, but with significantly faster prediction speeds. This speed advantage is crucial for real-time applications and directly contributes to improved power efficiency. The results presented in this study, including the high accuracy of distance and capacitance prediction and the rapid determination of optimal frequencies for power factor maximization, showcase the significant potential of NNs for optimizing WPT systems. These findings open the way for more efficient, adaptable, and intelligent wireless energy transfer solutions, with potential applications ranging from dynamic charging of electric vehicles to real-time optimization of implantable medical devices. Full article

To study the variation law of the penetration power of energy-concentrated jets on target plates with different yield strengths under lateral disturbance, a finite element model of the dynamic penetration of energy-concentrated jets was established. Targets with different yield strengths (355 MPa–1275 MPa) were analyzed under conditions from low speed (100 m/s) to high speed (400 m/s). The dynamic penetration morphology of the jet, the dynamic failure mode of the target plate and the dynamic penetration depth of the jet were analyzed. The influence law of the target plate strength on the dynamic penetration of the jet was analyzed by introducing the offset angle as a parameter and combining it with the dynamic penetration depth of the jet. Based on dimensional analysis, a prediction model for the dynamic penetration performance of the jet that considered both the lateral disturbance velocity and the strength of the target plate was obtained. A test of the dynamic penetration of the jet based on the rocket trolley was designed and carried out. Experiments were conducted to determine the dynamic penetration of the jet through target plates with different yield strengths under different lateral disturbance velocities, and the corresponding data were obtained. The reliability of the numerical simulation and of the prediction models was verified. The research results show that the jet offset angle under different yield strengths increases with the increase of the lateral disturbance velocity. When the lateral disturbance velocity is held constant, the size of the offset angle is negatively correlated with the yield strength of the target plate. The results of the prediction model, numerical simulation and dynamic penetration test were compared and verified. It was found that the three showed good consistency and that the prediction model could estimate the dynamic penetration depth of the jet with respect to the strength of the target plate. Full article

This work presents the initial experimentation of a real-time 5G mmWave downlink positioning testbed deployed at Airbus premises. This experimentation is part of a first-of-a-kind testbed for hybrid Global Navigation Satellite Systems (GNSS), fifth-generation (5G) new radio (NR) and sensor positioning, called the Hybrid Overlay Positioning with 5G and GNSS (HOP-5G) testbed. The mmWave 5G base station (BS) exploits the 5G standard positioning reference signal (PRS) to support positioning capabilities within the 5G NR downlink transmissions. Outdoor field results are used to characterize the received power levels and beam-based angle-of-arrival (AoA) estimation accuracy of this 5G mmWave PRS platform. The goal is to assess the suitability of this platform to enhance the positioning performance thanks to the 5G downlink mmWave transmissions. To the best of the authors’ knowledge, this paper presents the first AoA results using OpenAirInterface (OAI) PRS mmWave signal transmissions at 27 GHz for positioning. These initial field results indicate a maximum coverage of 30 m and an AoA accuracy limited by the reduced array size. The limitations and potential enhancements of this platform are provided as future recommendations. Full article

The global need to transition towards sustainable energy sources has increased the exploration of efficient methods to harness solar energy. Traditional solar panels, being stationary, often fail to capture the rays of the sun optimally across the day. This paper presents a SunPath navigator system that dynamically adjusts the solar panel’s angle, ensuring maximum exposure to the sun. The developed SunPath navigator system achieves a 27.67% average energy gain. This work has utilised the applications of various machine learning models, such as decision trees, AdaBoost, and K-nearest neighbour, for predicting energy generation. The relevance of these models is analysed based on multiple types of error such as MAE, MSE, RMSE, and R². The decision tree outperforms the other two models with a minimum error rate. It is paving the way for a future where solar energy is a primary, economical, and user-friendly power source in urban and rural areas. The dual-axis tracking system not only enhances energy generation but also estimates future energy generation. Full article

Low-cost MEMS sensors are widely utilized in UAV platforms to address attitude estimation problems due to their compact size, low power consumption, and cost-effectiveness. Diverse UAV payloads pose new challenges for attitude estimation, such as magnetic interference environments and high dynamic environments. In this paper, we propose a hierarchical decoupled attitude estimation algorithm, termed HDAEA. Initially, a novel hierarchical decoupling approach is introduced for the attitude and angle representation of the direction cosine matrix, enabling the representation of angles in a new manner. This method reduces the data dimensionality and nonlinearity of observation equations. Furthermore, a magnetic interference identification algorithm is proposed to compute the magnetic interference intensity accurately and quantitatively. Combining the quantified errors of estimated state variables, an error model for magnetic interference and attitude angles in high-dynamic environments is constructed. Subsequently, the proposed error model is employed to calibrate the hierarchical decoupled angles using accelerometer and magnetometer measurements, effectively mitigating the impact of magnetic interference on the calculation of pitch angles and roll angles. Moreover, the integration of the proposed hierarchical decoupled attitude estimation algorithm with the error-state extended Kalman filter reduces system nonlinearity and minimizes linearization errors. Experimental results demonstrate that HDAEA exhibits significantly improved attitude estimation accuracy of UAV payloads. Full article

With the increasing penetration of renewable energy sources, reduced system inertia and weakened frequency regulation capability have emerged as critical issues in power systems. As a result, wind turbines are now required to provide frequency support functions. To enable accurate analysis of the operational characteristics of wind turbines equipped with such control functions, this study proposes a virtual wind turbine model that estimates the operating point of a wind turbine in real-time under the assumption that frequency support functions are not performed. The proposed model is based on a turbine state observer that estimates wind speed and the power coefficient, and subsequently estimates generator power, generator speed, and blade pitch angle across various operating modes. Simulations were conducted under conditions with fluctuating wind speed and grid frequency, including MPPT, speed control, and pitch control operating regions. The accuracy of the proposed estimation model was evaluated, and the results demonstrated low estimation errors for key variables such as generator speed, power output, pitch angle, and wind speed across all conditions. These results quantitatively validate the robustness and applicability of the proposed model. Full article