Search Results (541)

This paper presents a comprehensive three-dimensional numerical investigation of a novel vertical-axis wind turbine (VAWT) characterised by a unique aerodynamic profile and a passive blade-pitch control mechanism. Unlike conventional fixed-geometry designs, the proposed turbine utilizes rectangular blades mounted on horizontal axes via articulated bearings, allowing them to rotate freely up to 90 degrees, constrained by a vertical pin-and-belt system. This configuration ensures that blades on the power-stroke side hit the vertical stopper to capture maximum wind energy, while blades on the return-stroke side open up to 90 degrees to significantly reduce aerodynamic drag. This dynamic adjustment enables the turbine to operate efficiently in low-wind conditions (3–5 m/s) while maintaining enhanced torque stability. To ensure numerical reliability, a rigorous grid independence study was performed, and the computational domain was configured to eliminate wall interference effects. The aerodynamic performance was analyzed using COMSOL Multiphysics v6.2 by solving the Reynolds-averaged Navier–Stokes (RANS) equations. Four turbulence models—SST, k–ε, k–ω, and RNG—were evaluated, with the SST model demonstrating the highest fidelity in capturing flow separation and wake structures under adverse pressure gradients. This study establishes the turbine’s performance benchmarks, including the power coefficient (C_p) versus tip speed ratio (TSR) curves. The numerical results were validated against laboratory experimental data, with excellent agreement (relative error < 5%). The findings identify the optimal geometric parameters and tangential velocity distributions that distinguish this configuration (Patent FAP 20240465) from traditional VAWTs. Finally, the successful implementation of a 2 kW prototype confirms the model’s accuracy and highlights the turbine’s potential as a stable and efficient solution for sustainable urban energy harvesting. Full article

This paper presents a novel decomposition method for split-film probes to improve pitch angle determination over a wide range of flow velocities. Conventional approaches often suffer from the velocity dependence of the directional response function, resulting in large angular errors. The proposed method introduces a new functional formulation based on effective cooling velocities and velocity-dependent reference parameters. These parameters are explicitly derived from calibration data and modeled using fourth-order polynomial regressions to suppress velocity-induced variance. Experimental verification conducted for velocities between 2.2 and 14.6 m/s demonstrates that the proposed method collapses the calibration data more effectively than previous models. The total angular estimation error does not exceed ±2° within the pitch angle range from −60° to 60°. The proposed approach is therefore suitable for reliable measurements in low-velocity regions of complex flows, such as wakes and recirculation zones. Full article

To address the high deployment cost, insufficient local coverage, and limited timeliness of conventional wave-observation methods in onboard real-time applications, this study conducts a comparative investigation of centre-of-gravity-equivalent instantaneous wave-height estimation models based on three-degree-of-freedom ship motion responses under the framework of the wave buoy analogy (WBA). The heave, roll, and pitch responses of a 1:2 scaled Series 62 4667-1 planing craft model in regular head seas are used as inputs, while the synchronous instantaneous wave-height signal measured by a wave probe near the centre of gravity is used as the label. A unified protocol is established with consistent inputs, labels, window construction, data partitioning, and evaluation metrics. Six models, namely SVR, TCN, LSTM, CNN-LSTM, Transformer, and LSTM-MHA, are compared and validated using STAR-CCM+ numerical simulation data and towing-tank experimental data. The results indicate that, in the simulated case of H = 0.10 m and T = 1.5 s, LSTM-MHA achieves the highest estimation accuracy, with RMSE and R² values of 0.001231 and 0.997848, respectively, but it also has the largest model size and computational cost. In comparison, TCN achieves near-optimal accuracy with a smaller parameter count and lower inference latency, and shows stable performance across multiple conditions. The towing-tank experimental results further show that both LSTM-MHA and TCN clearly outperform the SVR baseline. Overall, accuracy in the simulation domain, robustness in the towing-tank experimental domain, and cross-domain generalisation capability are not fully consistent. Therefore, the selection of onboard instantaneous wave-height estimation models should jointly consider estimation error, model complexity, computational latency, window length, and practical deployment requirements. Full article

This study consists of two main components. The first part establishes a seakeeping assessment method, while the second part focuses on hull-form optimization with seakeeping performance as the objective. For the seakeeping analysis, the Lewis conformal mapping method is used to calculate the sectional hydrodynamic coefficients. Strip theory is then applied to obtain the global hydrodynamic coefficients of the hull. The coupled heave and pitch motion responses are calculated and compared with nonlinear time-domain simulation results and experimental data, showing good agreement. A multivariate linear regression model is established to approximate the relationship between the principal hull-form parameters and the heave and pitch RAOs. The comparison between the regression model and strip theory results shows that the prediction error remains within 5%, indicating that the regression model can provide an efficient surrogate objective function for hull-form optimization. The particle swarm optimization (PSO) algorithm is then employed to optimize the hull form, with the ship length, breadth, draft, and block coefficient considered as design variables. To further evaluate the optimized hull, additional calculations are conducted under different Froude numbers and encounter angles. Under head sea conditions with varying Froude numbers, the optimized hull reduces the peak heave RAO by 11.6–31.1% and the peak pitch RAO by 8.6–17.9%. Under different encounter angles at F_r = 0.3, the reductions in peak heave and pitch RAOs are 31.1–33.9% and 16.5–18.8%, respectively. These results demonstrate that the proposed regression assisted PSO optimization framework can effectively reduce the heave and pitch responses of the Wigley hull under the investigated regular wave conditions. Full article

To improve the path-following performance of an underactuated autonomous underwater vehicle (AUV) under varying path geometries and desired velocities, this study proposes a direct X-rudder control method based on Task-Informed Inductive-Bias Conservative Soft Actor–Critic (TIB-CSAC). The proposed method directly learns the X-rudder control policy from the path-following information of the current and subsequent path segments in a data-driven way, thereby avoiding the complex design and manual tuning of guidance laws and attitude controllers for rudder command generation. To support such two-segment policy learning, a task-informed inductive-bias encoder is proposed to construct structured and conditioned state representations, thereby improving sample efficiency and overall training quality. In addition, given the long-tail characteristics of task difficulty in agent training, a multi-head conservative value evaluation mechanism is incorporated to mitigate return drawdowns induced by challenging tasks in the tail stage of training and to enhance tail-stage convergence stability. The path-following performance is validated in three representative scenarios with different path pitch, path heading variations, and desired surge velocity conditions. The results show that, compared with the baseline soft actor–critic (SAC) method, TIB-CSAC improves multiple vertical and horizontal error metrics, including maximum absolute error, mean absolute error, tail error, and error threshold exceedance ratio. These results indicate that TIB-CSAC not only improves overall adherence to the reference path, but also more effectively suppresses extreme errors and tail errors, thereby demonstrating stronger path-following robustness and reliability. Full article

To address the problems of state estimation bias, dynamic threat response lag, and insufficient safety margin in formation coordination caused by the mismatch between the three-dimensional continuous motion model and the discrete sampling characteristics of sensors in UAV multi-aircraft collaborative inspection missions under complex dynamic environments, this paper studies a trajectory planning method that integrates model predictive control and multi-constraint optimization. By constructing a three-dimensional continuous motion model of the UAV and discretizing it using the Euler integral method, the mapping deviation between the continuous motion characteristics and the discrete working mechanism of the airborne system is solved. Based on the model predictive control method, a patrol trajectory tracking planning model is designed, and state increment and integral augmentation strategies are introduced to transform global reference trajectory tracking into a constrained quadratic programming problem in the rolling time domain, achieving high-precision closed-loop tracking. Furthermore, a dynamic environment model coupling static terrain height field and sudden spherical threat is constructed to systematically characterize the static obstacles and random dynamic threats faced by the UAV in complex scenarios such as mountains and hills. On this basis, multiple constraints such as flight altitude, pitch angle, horizontal turning angle, terrain safety margin, and multi-aircraft collision avoidance are integrated to establish a comprehensive objective function that includes range cost, attitude penalty, and safety cost. Through a collaborative mechanism of global optimization and local online correction, a reference trajectory that meets the requirements of formation safety and flight efficiency is generated and used as the input command for the tracking planning model, forming a closed-loop architecture of global optimization generation, local closed-loop tracking, and dynamic real-time correction for trajectory planning. Experimental results show that the success rate of dynamic obstacle avoidance in complex dynamic environments is always higher than 99.9%, and the mean square error of trajectory tracking is stable in the range of 0.02–0.04 km, which verifies its significant advantages in dynamic adaptability, tracking accuracy and formation safety. Full article

To improve the operational efficiency of in situ soil remediation, this study investigated the operating parameters of the crushing–mixing working element of a novel tracked multi-axis in situ soil remediation device according to the soil contamination characteristics and process requirements. A DEM-based numerical simulation model was established, and response surface methodology (RSM) and machine learning algorithms were further integrated to model the response relationships, predict the evaluation indicators, and optimise the operating parameters. Single-factor experiments were conducted using the dispersion coefficient and soil fragmentation rate as the main evaluation indicators to determine the parameter range for the steepest ascent test. The steepest ascent test was used to rapidly approach the optimal parameter region, and RSM was then applied to establish the nonlinear mapping relationships between the operating parameters and response indicators. On this basis, machine learning models were introduced to further analyse and predict the experimental data, thereby improving the multi-objective optimisation process. A comparative analysis showed that, under the same dataset and evaluation metrics, the machine learning models achieved higher prediction accuracy than the RSM model. Among them, the decision tree model exhibited the best overall performance and provided a more stable optimisation result than the random forest and support vector regression models. The optimal parameter combination, identified by the decision tree model, was a rotational speed of 81 rpm, an average mixing pitch of 195 mm, a descent speed of 0.061 m/s, and an average mixing time of 1.1 s. Under this parameter combination, the dispersion coefficient was 0.171, and the residual bond count was 1511. The comparison of the RSM and machine learning models showed that the machine learning models achieved higher prediction accuracy. The relative errors between the optimal and actual simulation values were 2.56% and 4.92%, respectively. These results demonstrate that machine learning algorithms are applicable to the parameter optimisation of soil remediation working elements. The proposed DEM–RSM–machine learning framework can improve the efficiency and accuracy of equipment development and process optimisation, providing a scientific and technical basis for the development of intelligent agricultural equipment and sustainable agricultural engineering. Full article

This paper investigates altitude control of the Unmanned Aerial Vehicle (UAV) through the elevator. Elevators are flight control surfaces, which control lateral altitude by changing the pitch balance. The angle deflection along with the thrust from propulsion system is matched and guided by the system for the gain or loss of altitude over desired range of distance. A linear time-invariant elevator–altitude channel model is obtained by linearizing the six-degree-of-freedom equations of motion about a steady, level-flight trim condition. The resulting transfer function is analyzed using state-space representation and root-locus techniques, revealing that the uncompensated unity-feedback system is unstable. A proportional-integral (PI) controller is then designed and implemented in a unity-feedback configuration. The closed-loop dynamics are evaluated through time-domain simulations under step, ramp, and parabolic altitude commands, and key performance indices such as rise time, settling time, overshoot, and steady-state error are extracted. The Routh–Hurwitz criterion is used to derive an admissible gain range and to select a gain that balances response speed and robustness. The steady-state error is quantified analytically for step, ramp, and parabolic inputs, confirming a finite error for step inputs and infinite error for ramp and parabolic inputs, consistent with a type-0 system. The results demonstrate that a simple PI-based elevator controller can stabilize the linearized altitude channel and significantly improve transient performance, providing a useful baseline for more advanced nonlinear or adaptive designs in UAV flight-control applications. Full article

This paper presents a hybrid algorithmic framework for fault detection and isolation (FDI) in nonlinear quadrotor unmanned aerial vehicle (UAV) systems operating under closed-loop conditions. The proposed method integrates a Linear Quadratic Control (LQC) strategy, synthesized through Linear Matrix Inequalities (LMIs), with a Conditional Long Short-Term Memory Autoencoder (CLSTM-AE) and an adaptive residual-based decision mechanism. The LQC scheme provides robust trajectory tracking through regional pole-placement constraints, while the CLSTM-AE learns the nominal closed-loop input–output temporal behavior of the UAV using only fault-free data. In contrast to conventional symmetric autoencoder-based detectors, the proposed CLSTM-AE uses the control inputs together with the available attitude estimates, represented by the Euler angles yaw, pitch, and roll, as conditioning information, while reconstructing only the monitored attitude outputs. This asymmetric structure allows the residuals to capture inconsistencies between the commanded control effort and the observed attitude response, which is particularly relevant in closed-loop nonlinear systems where feedback compensation may attenuate fault signatures. Deviations from nominal behavior are detected through reconstruction residuals computed using a smoothed Mean Squared Error (MSE) criterion and evaluated against an adaptive $3\sigma$ threshold. The framework is validated in three-dimensional flight simulations considering abrupt, transient, and incipient actuator fault scenarios. The obtained results show that the proposed approach outperforms representative conventional machine-learning methods, achieving an average accuracy of 98.2%, an average recall of 97.8%, and an average false positive rate of 1.4%. These results suggest that the proposed hybrid algorithm provides an effective and interpretable solution for closed-loop fault diagnosis in nonlinear UAV systems under measurement noise and system variability. Full article

As robotics technology advances, quadruped robots have become capable of operating in complex environments with varying elevation, including ramps and level changes that are challenging for conventional wheeled platforms. While this terrain adaptability opens new opportunities for inspection, rescue, and exploration tasks, the repetitive impacts, frequent ground-contact transitions, and abrupt postural changes inherent to legged locomotion pose significant challenges for LiDAR odometry. High-frequency gait vibrations and abrupt attitude changes introduce intra-scan motion distortion that conventional single-twist deskewing cannot adequately suppress. In addition, sparse vertical geometric constraints in elevation-varying environments weaken Z-axis observability, allowing vertical drift to corrupt the horizontal pose estimate through Hessian coupling. To address these failure modes within a LiDAR-only framework, we propose a Piecewise-Constant Velocity deskewing scheme that partitions each scan into multiple temporal segments with safety clamping on vertical and attitude components, together with a two-stage ICP that decouples SE(3) optimization into horizontal (x, y, yaw) and vertical (z, roll, pitch) stages and applies observability-aware weighting in the vertical update. The proposed odometry front-end is evaluated on four real-world sequences collected with a Unitree Go2 quadruped robot equipped with a Velodyne VLP-16 LiDAR. Experimental results show consistently lower Absolute Pose Error (APE) than ICP, KISS-ICP, and F-LOAM across all sequences. Vertical drift suppression is most pronounced in the ramp-containing sequences, where baseline methods exhibit substantial Z-axis divergence. Full article

Background/Objectives: In this three-stage study, we aimed to adapt an Auditory-Visual Stroop test (AV-Stroop test) for tinnitus subjects, evaluate the correlation between performance in the conventional Stroop test (C-Stroop test) and the AV-Stroop test; assess the effect of cognitive screening test performance on the AV-Stroop test’s results; and apply the AV-Stroop test in participants with tinnitus and controls. Methods: At the First Stage, the AV-Stroop test was adapted using white noise (WN), pure tone (PT), and narrow band (NB) sound stimuli. At the Second Stage, results of the AV-Stroop test, the C-Stroop test, and the Montreal Cognitive Assessment (MOCA) were compared (n = 45). At the Third Stage, the AV-Stroop test was applied to participants with and without tinnitus (n = 70). The tinnitus group was assessed with an additional test track (stimuli matched to tinnitus spectral characteristics, Tinnitus Pitch). Results: We adapted 34 training and evaluation tracks for the AV-Stroop test. AV-Stroop test’s results were correlated with C-Stroop test’s total task time (WN, p-value = 0.002; NB and PT, p-value < 0.001 comparing C-Stroop word reading task; and WN, NB, and PT, p-value < 0.001 for C-Stroop color naming task), and number of errors (NB, p-value < 0.001 comparing C-Stroop word reading task, and p-value = 0.012 for C-Stroop color naming task). Participants’ MOCA scores were not associated with AV-Stroop test performance. Participants with tinnitus required more time and made more errors in the AV-Stroop test. Additionally, the tinnitus group made more errors in the Tinnitus Pitch track. Conclusions: The AV-Stroop test proved to be an accessible, easy-to-administer tool for evaluating attentional and inhibitory control in participants with tinnitus. The stimulus with spectral characteristics similar to tinnitus perception was more effective in assessing top-down executive control in participants with the symptom. Full article

Vertical axis wind turbines (VAWTs) have regained attention for distributed, urban, and floating offshore applications, yet the literature remains fragmented across competing rotor concepts and modelling traditions. This review consolidates the principal archetypes—Savonius, H-Darrieus, troposkein Darrieus, helical Darrieus, and Savonius–Darrieus hybrids—through five governing parameters: drag-versus-lift-driven operating principle, tip speed ratio $\mathsf{\lambda }=\mathsf{\omega }\mathrm{R}/{\mathrm{V}}_{\infty }$ (0.6–1.2 for Savonius; 2.5–5.0 for Darrieus), solidity $\mathsf{\sigma }=\mathrm{N}\mathrm{c}/\mathrm{R}$ (0.1–0.4), chord-based Reynolds number ${\mathrm{R}}_{\mathrm{e}\_\mathrm{c}}$ (${10}^5–{10}^6$), and peak power coefficient ${\mathrm{C}}_{\mathrm{p}\_\mathrm{m}\mathrm{a}\mathrm{x}}$ (0.15–0.25 for Savonius; 0.35–0.45 for optimized H-Darrieus). Off-design performance is dominated by unsteady mechanisms that quasi-steady streamtube models cannot resolve—leading edge vortex shedding, dynamic stall hysteresis, blade–wake interaction, and flow-curvature-induced virtual camber—each examined for its contribution to the instantaneous torque ${\mathrm{C}}_{\mathrm{T}}\left(\mathsf{\theta }\right)$ and the cycle-averaged ${\mathrm{C}}_{\mathrm{p}}$. Turbulence closures are benchmarked against phase-locked PIV and torque measurements: $\mathrm{k}–\mathsf{\omega }\mathrm{S}\mathrm{S}\mathrm{T}$ URANS captures peak-region ${\mathrm{C}}_{\mathrm{p}}$ to within $\pm 5–10\%$ but over-predicts torque below ${\mathsf{\lambda }}_{\mathrm{o}\mathrm{p}\mathrm{t}}$; the $\mathsf{\gamma }–{\mathrm{R}}_{\mathrm{e}\_\mathsf{\theta }}$ transition SST model reduces this error to $\pm 3–5\%$; DES, DDES, and LES reach $\pm 2–3\%$ at one to two orders of magnitude higher cost. Best practice computational fluid dynamics (CFD) guidelines are consolidated: domain extents of 15D upstream, 10D downstream, and 20D lateral; rotating sub-domain ${\mathrm{D}}_{\mathrm{r}\mathrm{o}\mathrm{t}}$ $\approx 1.5\mathrm{D}$; ${\mathrm{y}}^{+}\le 1$; $\Delta \mathsf{\theta }\le 0.1°$; and 20–30 revolutions before sampling. Performance enhancement strategies (variable pitch, guide vanes, helical twist, and hybridization) are reviewed quantitatively, with reported ${\mathrm{C}}_{\mathrm{p}}$ gains of $5–30\%$. Four research priorities are identified: (i) transition-sensitive turbulence closures validated below ${\mathrm{R}}_{\mathrm{e}\_\mathrm{c}}$ = $5\times {10}^5$; (ii) coupled aero-hydro-servo-elastic models for floating offshore VAWTs; (iii) machine-learning-augmented turbulence modelling—including physics-informed neural networks (PINNs) and neural-network-corrected RANS closures—to improve unsteady flow prediction at sub-LES cost; and (iv) integrated aeroacoustic–aeroelastic frameworks for urban and building-integrated deployment. Full article

Wireless power transfer (WPT), characterized by its excellent insulation properties and ease of maintenance, has recently emerged as a promising solution to the power supply challenges faced by online monitoring equipment on high-voltage transmission towers in complex environments. Existing research primarily relies on regular, closely wound solenoids to power these monitoring devices; however, this approach often makes it difficult to optimize the magnetic field distribution to maximize mutual inductance, thereby limiting transmission efficiency and power and hindering lightweight design. To address these issues, this paper proposes an optimized design scheme for variable-pitch (non-uniform) domino WPT coils based on insulator string structures. First, a parameter calculation model utilizing segmented current analysis is constructed to accurately determine the inductance of non-uniform solenoids, with simulations confirming an error rate below 5%. Subsequently, by integrating domino multi-coil theory into an elitist non-dominated sorting genetic algorithm (NSGA-II), dual-objective optimization is performed. Targeting maximum transmission efficiency and output power under spatial and insulation constraints, a set of Pareto optimal solutions is derived. Ultimately, a 113.7 W insulator domino coil WPT system prototype is constructed to validate the design’s stability. The proposed system achieves a maximum efficiency of 85.73%, with a single-stage load delivering up to 97.48 W. Full article

Large-aperture steerable reflector antennas are pivotal for deep-space exploration and satellite communication, but their high-frequency performance is often compromised by wind-induced structural deformations. This study employs a high-fidelity fluid–structure interaction (FSI) framework, coupling Computational Fluid Dynamics (CFD) and the Finite Element Method (FEM), to investigate the dynamic response of an 18 m Square Kilometre Array (SKA) antenna under transient wind loads. The structural FEM is validated against experimental modal data, ensuring the capture of essential vibration characteristics. We evaluate steady-state wind pressure coefficients ($C_p$) and transient responses under a simulated Davenport wind spectrum across the antenna’s full operational elevation range. Surface accuracy degradation is rigorously quantified using the Root Mean Square Error (RMSE) of the best-fit paraboloid. The results demonstrate a significant correlation between peak deformation and surface error, pinpointing 15° and 90° pitch angles as the most critical configurations for profile degradation due to the “air pocket effect” and asymmetric pressure distributions, respectively. These insights establish a robust theoretical basis for structural optimization and the development of active surface control strategies for next-generation aerospace signal acquisition infrastructure. Full article

As a key component of sugarcane harvesting machinery, the spiral sugarcane lifter (SSL) enhances harvesting quality by lifting lodged sugarcane (LSC) into a posture suitable for stalk-base cutting and feeding. To improve the SSL’s lifting performance for LSC, this study developed a rigid–flexible coupling (RFC) simulation model of the sugarcane–SSL interaction and conducted kinematic and force analyses to identify the main shortcomings of the original design. Critical structural and operational parameters affecting lifting performance–including the lifting roller pitch, roller diameter, roller inclination angle, and lifter shoe length—were redesigned using mechanism-based constraints and simulation-assisted evaluation. The optimized SSL exhibited increased lifting speed and stability under low–speed, severe–lodging conditions. Under side-forward lodging (side deflection angle = 30°), the average maximum vertical height of the centroid (VHC) increased by 40.36%, and paired comparisons across three simulated lodging-angle scenarios showed significant improvement. Field tests under severe lodging at 0.55 m/s (≈2 km/h) yielded an average absolute simulation–to–field error of 5.37%. These findings support the effectiveness of the proposed parameter redesign for the tested medium-size harvester, although further validation is required under higher forward speeds, greater biomass throughput, and more variable soil conditions. Full article