Vertical Axis Research Articles

Comprehensive study of vortices interaction and blades height effect in a Darrieus vertical axis wind turbine with J‐type blades

AbstractThere is a growing demand to improve the performance of vertical axis wind turbines to facilitate their commercialization for application in urban areas. This study utilizes a 3D numerical analysis to examine the influence of different vortices generated on turbine efficiency with straight and J‐type blades. The numerical simulation of this study employs the Reynolds‐Averaged Navier–Stokes equations and ‎sliding ‎mesh techniques ‎to more accurately model the rotational motion of blades about the turbine axis in relation to the ‎wind. Comparing the output ‎torque and the flow field at different span‐wise sections, the J‐type blades achieve better ‎performance at mid‐spans where the effect of stall vortices is dominant. Conversely, the lower ‎performance of J‐type blades is seen at tip spans due to stronger tip vortices. Investigations also ‎reveal the criticality of the downwind region on the overall performance at high tip speed ratios. It is observed that by ‎increasing the height, the tip vortices are limited to the tip sections, and stall vortices expand further ‎along the blade. At TSR = 1, the improvement by J‐type blades rises from 10% at a height of 0.8 m to 44% ‎at 3 m. The growth in height at lower wind speeds becomes more beneficial. Compared to the straight blades, the self‐starting ‎generated torque by J‐type blades for heights of 0.8, 1.2, and 1.6 m, are improved by 15.6%, ‎‎26.9%, and 34.7%, respectively. Overall, it is concluded that by increasing the blade height, the superiority ‎of the J‐type blade becomes more noticeable as the blade mainly contributes to suppressing the stall ‎vortices effect where the tip vortices effect is not presented. ‎

Optimization of Turbine Blade Pitch Angle of a Home-built Wind Turbine for Maximum Power Output

Wind energy greatly reduces the world’s dependence on fossil fuels (oil and natural gas), and is environmentally friendly. One of the most cost-effective alternatives of energy sources is wind power. This study used a horizontal axis wind turbine to investigate the optimal blade pitch angle that can give maximum electrical output. A proportional integral derivative pitch controller was used to establish blade pitch angles. Wind speeds of 3, 4, 5 and 6 m/s were run at every pitch angle respectively, and a maximum electrical power output was 1124.7W at a blade pitch angle of 16.80 when a wind speed of 6 m/s was used. Every wind speed was run for 100 seconds. A simulation, using Visual Basic 6 software, was done at the respective blade pitch angles, and a wind speed of 6 m/s. The electricity produced was recorded. The simulated electrical power produced yielded a relationship that predicted the electrical power output with a coefficient of determination (R2) of 0.9752; this shows a very close agreement with actual electric output values.

LIDAR3DNETV2: Smart Approach to Classify 3D Objects Based on Real Point Clouds

Point clouds generated in simulators avoid collection time costs and provide a high and organized amount of point clouds, an ideal scenario for deep learning networks. However, these networks have limitations when applied to real point clouds. This work proposes a multilayer perceptron-based method to classify 3D objects based on real point clouds obtained using LiDAR sensors. The method includes a pre-processing step that normalizes and adjusts the point clouds in the 3D Cartesian plane to overcome discrepancies in the point distribution. Furthermore, we created a dataset and used the ModelNet dataset for comparison purposes. The proposed neural network, Lidar3DNetV2, achieved 98.47% and 125 μs in accuracy and test time with real data, respectively. The pre-processing step provided a significant increase in the classifier’s performance. Finally, the proposed method performs better than other state-of-the-art networks considering real point clouds.

Multi-channel depth segmentation network based on 3D graph convolution algorithm and its application in point cloud segmentation

At the current stage, 3D graph convolutional algorithms face the following issues: (1) The problem of determining the neighborhood in graph convolution. (2) The issue of fusing features at different depths in deep graph convolutional algorithms. (3) The challenge of feature fusion and recognition in multi-path deep graph convolutional algorithms. Based on these, the paper "Multi-Path Deep Segmentation Network Based on 3D Graph Convolutional Algorithm and Its Application in Point Cloud Segmentation" is proposed. Inspired by visual computing theory, the neighborhood for 3D graph convolution is determined based on the receptive field theory, and a 3D graph convolutional algorithm based on visual selectivity is proposed; A single-link deep feature extraction algorithm for 3D graph convolution based on visual selectivity is proposed, it achieve the fusion of features at different depths learned by the graph convolutional algorithm by a pyramid learning method; A multi-link feature extraction algorithm for 3D graph convolution based on visual selectivity is proposed, and achieve multi-link feature fusion and segmentation to utilize the RPN calculation method. compared with algorithms such as PointNet, PointNet++, KPConv deform, 3D GCN, and My Algorithm, The segmentation performance and geometric invariance of the proposed algorithm are validated on the ShapeNetPart dataset and the custom Mortise_and_Tenon_DB dataset, and Experiments demonstrate that the proposed algorithm is correct and feasible, offers superior 3D point cloud segmentation performance, and exhibits strong geometric invariance.

Heat and mass transfer in tri‐layer ependymal ciliary transport with diverse viscosity

AbstractSpecialized cilia known as ependymal cilia help the cerebrospinal fluid, which is important for nutrient delivery and waste elimination, to circulate in the brain. Many species rely on cilia and flagella for fluid movement and motility. Cilia and flagella, for instance, produce propulsive forces that allow unicellular creatures like Paramecium and Euglena to move through their surroundings. Ciliary transport plays a role in the flow of egg cells, or oocytes, via the fallopian tubes in the female reproductive system. The transfer of the egg towards the uterus is facilitated by the synchronized wave‐like motion produced by the cilia lining the fallopian tubes. Like this, ciliary transport in men aids in the passage of sperm cells via the epididymis. Many scientists and researchers are studying the mechanics of cilia motion and the properties of the viscoelastic fluid layer to develop new treatments and applications in biomedical engineering and for some diseases. Motivated by a wide range of biological applications, the objective of this study is to investigate the heat and mass transfer flow of tri‐layered Newtonian fluids flowing with the help of ciliary beating in a Cartesian coordinate. The fluid is incompressible, and fluid layers are not intermixed. The fluid flow with heat and mass transfer is first modelled in wave and then transmuted into fixed. Solutions for temperature profile, velocity distribution, stresses and concentration distribution are obtained. The influence of incipient parameters is displayed with graphs and plotted with the computational software MATHEMATICA 13.0 in the results section. The key findings obtained from graphs show that the maximum magnitude for temperature and velocity is achieved in the middle layer of fluid whereas in the outer layer concentration profile is maximum. It is predicted that this approach will make an essential contribution to the progress and enhancement of various types of drug delivery systems in the biomedical industry and biomechanics.

Enhancing Robot End‐Effector Trajectory Tracking Using Virtual Force‐Tracking Impedance Control

This article presents an extended Cartesian space robot control framework that features a virtual force tracking impedance control to enhance the end‐effector trajectory tracking performance. Initially, the concept of a virtual surface is introduced, which is assumed to be at some constant distance from the desired end‐effector trajectory. This virtual surface generates a virtual contact force when interacting with the torque‐controlled robot end‐effector. The interaction is then manipulated using an impedance control model to track a constant desired force. If the robot end‐effector deviates from the desired trajectory, the constant force‐tracking impedance control generates a compliance trajectory that regulates the end‐effector movements, constraining it to the desired trajectory. For robust force tracking, impedance parameters are optimally tuned using a closed‐loop dynamic model incorporating both robot and impedance dynamics. Additionally, super twisting sliding mode control (STSMC) is integrated to overcome uncertainties and the impact of robot dynamics on force‐tracking performance. Experimental validation confirms the theoretical claims of the proposed approach. It demonstrates that force‐tracking impedance control improves the end‐effector trajectory tracking by quickly reacting to the dynamic trajectories compared to position control only and effectively maintains it on the desired trajectories.

Eyes and hand are both reliable at localizing somatosensory targets.

Body representations (BR) for action are critical to perform accurate movements. Yet, behavioral measures suggest that BR are distorted even in healthy people. However, the upper limb has mostly been used as a probe so far, making difficult to decide whether BR are truly distorted or whether this depends on the effector used as a readout. Here, we aimed to assess in healthy humans the accuracy of the eye and hand effectors in localizing somatosensory targets, to determine whether they may probe BR similarly. Twenty-six participants completed two localization tasks in which they had to localize an unseen target (proprioceptive or tactile) with either their eyes or hand. Linear mixed model revealed in both tasks a larger horizontal (but not vertical) localization error for the ocular than for the manual localization performance. However, despite better hand mean accuracy, manual and ocular localization performance positively correlated to each other in both tasks. Moreover, target position also affected localization performance for both eye and hand responses: accuracy was higher for the more flexed position of the elbow in the proprioceptive task and for the thumb than for the index finger in the tactile task, thus confirming previous results of better performance for the thumb. These findings indicate that the hand seems to beat the eyes along the horizontal axis when localizing somatosensory targets, but the localization patterns revealed by the two effectors seemed to be related and characterized by the same target effect, opening the way to assess BR with the eyes when upper limb motor control is disturbed.

New results in stereopsis and Listing's law.

Human eyes' optical components are misaligned. This study presents comprehensive geometric constructions in the binocular system, with the eye model incorporating the fovea that is displaced from the posterior pole and the lens that is tilted away from the eye's optical axis. It extends their previously considered horizontal misalignment with the vertical components. When the eyes' binocular posture changes, 3D spatial coordinates of the retinal disparity (iso-disparity curves), the subjective vertical horopter, and the eye's torsional orientation transformations are visualized in GeoGebra's simulations. The consequences and functional roles of vertical misalignment of the eye's optical components are explained in the following findings: (1) The classic Helmholtz theory, which states that the subjective vertical retinal meridian inclination to the retinal horizon explains the backward tilt of the perceived vertical horopter, is less relevant when the eye's optical components are misaligned. Instead, the lens vertical tilt provides the retinal vertical criterion that explains the experimentally measured vertical horopter inclination. (2) Listing's law, which originally restricts single-eye torsional positions and has imprecise binocular extensions, is formulated for binocular fixations using Euler's rotation theorem. It, however, replaces Listing's plane, which is defined for eyes looking at infinity, with the eyes muscles' natural tonus resting position corresponding to the abathic distance fixation of empirical straight frontal horopter. This new meaning of Listing's plane provides neurophysiological significance that has remained elusive.

Aspect ratio-dependent volume resistivity in unidirectional composites: Insights from electrical conduction behavior

The electrical properties of carbon fibers serve as the foundation for the multifunctional applications of carbon fiber-reinforced composite structures. In scenarios that exploit the electrical characteristics of materials, accurate estimation of electrical resistivity stands as a critical factor. This study endeavors to elucidate the electrical conduction behaviors in unidirectional composites with different fiber orientation angles (0°, 15°, 30°, 45°, 60°, 75°, and 90°) and aspect ratios, thereby deriving the volume resistivity within an arbitrary Cartesian coordinate system. Employing thermal infrared imaging technology and finite element analysis, we identified distinctive electrical conduction behaviors associated with aspect ratios in carbon fiber composite plates. Notably, a critical aspect ratio exists wherein the diagonal yarn is the only conductive path between two electrodes. Below this critical threshold, no direct conductive path exists, and current flows through the shortest distance between parallel yarns. Conversely, beyond the critical aspect ratio value, multiple yarns form conductive paths between the two electrodes. Based on the electrical conduction behavior of unidirectional composites under different angles and aspect ratios, the volume resistivity with finite boundaries was derived and examined under an arbitrary Cartesian coordinate basis.

Aerodynamic Study of a Horizontal Axis Wind Turbine in Surge Motion Under Angular Speed and Blade Pitch Controls

Aerodynamic Study of a Horizontal Axis Wind Turbine in Surge Motion Under Angular Speed and Blade Pitch Controls

Stochastic analysis and soliton solutions of the Chaffee–Infante equation in nonlinear optical media

The Chaffee–Infante (CI) equation is a nonlinear equation that may be used to predict the complex dynamics of soliton propagation in a nonlinear optical medium. In this study, we elucidate soliton solutions of the CI equation by stochastic differential equations (SDEs) with the Wiener process. In excess of the modified auxiliary equation (MAE) method, we obtain new exact soliton solutions. By combining stochastic differential equations with the Wiener process, we explain the stochastic processes directing the magnitude of solitons within the basis of the CI equation, providing dynamic views on their behavior. The acquired solitary waves are depicted via 3D and 2D graphs to show their dynamics with and without Brownian motion.

Highly Stable Lattice Boltzmann Method with a 2-D Actuator Line Model for Vertical Axis Wind Turbines

Highly Stable Lattice Boltzmann Method with a 2-D Actuator Line Model for Vertical Axis Wind Turbines

Ellipsometry of surface acoustic waves using 3D vibrometry for viscoelastic material characterization by the estimation of complex Lamé coefficients versus the frequency

Surface acoustic waves (SAW) are adequate regarding material characterization because they have low geometric attenuation compared to bulk waves. SAW can be generated easily by normal excitation using contact transducers or power lasers and have also a unique elliptic polarization, characterized by two parameters: the ellipticity (H/V) ratio between the horizontal and the vertical components of the elliptic motions and the orientation angle (θ) between the horizontal axis of the ellipse and the surface. In the case of a viscoelastic isotropic material, a complete characterization is achieved by the association of the quantitative measurement of the polarization and the propagative characteristics, the complex wavenumber, of the SAW. In practice, this operation is performed using 3D lased vibrometry for propagation monitoring in space and time. The post-processing is carried out by Quaternion Fourier Transform, the Prony algorithm and the complex Lamé coefficients identification for the theoretical model of propagation on the material. Good agreement is observed between the obtained results and the ones of the pulse-echo method.

Enhancing postural balance assessment through neural network-based lower-limb muscle strength evaluation with reduced markers

Aiming to simplify the data acquisition process for balance diagnosis and focused on muscle, a direct factor affecting balance, to assess and judge postural stability. Utilizing a publicly available kinematic dataset, the research retained 3D coordinates and mechanical data for 8 markers on the lower limbs. By integrating this data with the musculoskeletal model in OpenSim, inverse kinematic calculations were performed to derive muscle forces. These forces, alongside the coordinates, were split into an 8:2 training and test set ratio. A neural network was then developed to predict muscle forces using normalized coordinate data from the training set as input, with corresponding muscle force data as training labels. The model’s accuracy was confirmed on the test set, achieving coefficients of determination ( R 2 ) above 0.99 for 276 muscle forces. Furthermore, the Force Maximum Percentage Difference (FMPD) was introduced as a novel criterion to evaluate and visualize lower limb balance, revealing significant discrepancies between the patient and control groups. This study successfully demonstrates that the neural network model can precisely predict lower limb muscle forces using reduced markers and introduces FMPD as an effective tool for assessing limb balance, providing a robust framework for future diagnostic and rehabilitative applications.

Analysis of the mechanical behavior of asphalt pavement under multiple coupling effects

To reveal the damage mechanism of asphalt pavement caused by the spatial rotation of the stress principal axis, and the influence range of excess pore water pressure on the pavement under the coupling action of multiple fields, the finite element software was used to build the three-dimensional highway asphalt pavement models. Under the consideration of gravity's impact on dynamic computation results, the study investigated the variation patterns of the direction cosines of the principal stress axes of asphalt structural pavement elements under the action of moving loads, as well as the distribution laws of excess pore water pressure. The results indicate that the influence of gravity on dynamic computation results increases gradually with the depth of the pavement, reaching an error of first principal stress of 40.1 % at the bottom of the lower layer. During the movement of loads, the pavement elements directly under the load undergo rotation of the principal stress axes in the xy, xz, and yz planes under different physical fields. With increasing pavement depth, the angle between the first principal stress and the z-axis primarily fluctuates between −90° to 0° and 90° to 180°. The cosine of the angle between the first principal stress and the x-axis and z-axis undergoes instantaneous positive and negative conversion, which can easily lead to transverse and longitudinal cracks on the road surface. The influence widths of excess pore water pressure along the longitudinal and transverse directions of the pavement increase with the pavement depth, with maximum widths of 5.426 m and 2.075 m, respectively. These findings offer new insights into the mechanisms behind the formation of transverse and longitudinal cracks on asphalt pavement and hold significant implications for the improvement of asphalt design methods.

Blade Pitch Angle Regulation for H-Type Darrieus Vertical Axis Wind Turbine: A Review

Blade Pitch Angle Regulation for H-Type Darrieus Vertical Axis Wind Turbine: A Review

Correlation between sagittal balance and thoracolumbar elastic energy parameters in 42 spines subject to spondylolisthesis or spinal stenosis and 21 normal spines

The curvature of the lumbar spine plays a critical role in maintaining spinal function, stability, weight distribution, and load transfer. We have developed a mathematical model of the lumbar spine curve by introducing a novel mechanism: minimization of the elastic bending energy of the spine with respect to two biomechanical parameters: dimensionless lumbosacral spinal curvature cLS and dimensionless curvature increment along the spine CI. While most of the biomechanical studies focus on a particular segment of the spine, the distinction of the presented model is that it describes the shape of the thoracolumbar spine by considering it as a whole (non-locally) and thus includes interactions between the different spinal levels in a holistic approach. From radiographs, we have assessed standard geometrical parameters: lumbar lordosis LL, pelvic incidence PI, pelvic tilt PT, sacral slope ψ0 and sagittal balance parameter SB = sagittal vertical axis (SVA)/sacrum-bicoxofemoral distance (SFD) of 42 patients with lumbar spinal stenosis (SS) or degenerative spondylolisthesis (SL) and 21 radiologically normal subjects. SB statistically significantly correlated with model parameters cL5 (r = −0.34, p = 0.009) and −CI (r = 0.33, p = 0.012) but not with standard geometrical parameters. A statistically significant difference with sufficient statistical power between the patients and the normal groups was obtained for cLS, CI, and SB but not for standard geometrical parameters. The model provides a possibility to predict changes in the thoracolumbar spine shape in surgery planning and in assessment of different spine pathologies.

Exploring the use of deep learning models for accurate tracking of 3D zebrafish trajectories.

Zebrafish are ideal model organisms for various fields of biological research, including genetics, neural transmission patterns, disease and drug testing, and heart disease studies, because of their unique ability to regenerate cardiac muscle. Tracking zebrafish trajectories is essential for understanding their behavior, physiological states, and disease associations. While 2D tracking methods are limited, 3D tracking provides more accurate descriptions of their movements, leading to a comprehensive understanding of their behavior. In this study, we used deep learning models to track the 3D movements of zebrafish. Videos were captured by two custom-made cameras, and 21,360 images were labeled for the dataset. The YOLOv7 model was trained using hyperparameter tuning, with the top- and side-view camera models trained using the v7x.pt and v7.pt weights, respectively, over 300 iterations with 10,680 data points each. The models achieved impressive results, with an accuracy of 98.7% and a recall of 98.1% based on the test set. The collected data were also used to generate dynamic 3D trajectories. Based on a test set with 3,632 3D coordinates, the final model detected 173.11% more coordinates than the initial model. Compared to the ground truth, the maximum and minimum errors decreased by 97.39% and 86.36%, respectively, and the average error decreased by 90.5%.This study presents a feasible 3D tracking method for zebrafish trajectories. The results can be used for further analysis of movement-related behavioral data, contributing to experimental research utilizing zebrafish.

Robot Manipulator Minimum Jerk Trajectory Planning Based on the Improved Dung Beetle Optimizer Algorithm

Trajectory planning of robotic manipulators in complex environments involves generating smooth and collision-free paths, and key aspects to consider include dynamic environment perception, path planning, trajectory smoothing and optimization, and obstacle avoidance. This paper discusses different algorithms and finally proposes a fusion of the improved Dung Beetle Optimizer (IDBO) algorithm using chaotic mapping, sinusoidal random mutation, and nonlinear convergence strategies with the bidirectional Rapidly exploring Random Tree (RRT) algorithm to achieve manipulator trajectory planning and minimum jerk optimization trajectory. In the experiment, the IDBO algorithm was compared with other heuristic algorithms. The path obtained was shortened by 9.18% on average, and the calculation time was shortened by 33.81% on average. Then, the paper explored Cartesian coordinate space trajectory planning and joint space trajectory planning when the robot was working in 3D space, and verified the superiority of the latter. In controlling the movement of the robot, by combining the minimum angle jerk planning and the bidirectional RRT obstacle avoidance algorithm to drive the joint angle parameters obtained by the spatial trajectory planning, the trajectory of the robot manipulator can be smoother, more efficient, and avoid obstacles in complex 3D space. This research helps improve the performance, safety, and technical support of robots, and is of great research significance.

Differences in cervical sagittal parameters and muscular function among subjects with different cervical spine alignments: a surface electromyography-based cross-sectional study.

We analyzed cervical sagittal parameters and muscular function in different cervical kyphosis types. This cross-sectional study enrolled subjects with cervical spine lordosis (cervical curvature < -4°) or degenerative cervical kyphosis (cervical curvature > 4°), including C-, S-, and R-type kyphosis. We recorded patients' general information (gender, age, body mass index), visual analog scale (VAS) scores, and the Neck Disability Index (NDI). Cervical sagittal parameters including C2-C7 Cobb angle (Cobb), T1 slope (T1S), C2-C7 sagittal vertical axis (SVA), spino-cranial angle (SCA), range of motion (ROM), and muscular function (flexion-relaxation ratio (FRR) and co-contraction ratio (CCR) of neck/shoulder muscles on surface electromyography). Differences in cervical sagittal parameters and muscular function in subjects with different cervical spine alignments, and correlations between VAS scores, NDI, cervical sagittal parameters, and muscular function indices were statistically analyzed. The FRR of the splenius capitis (SPL), upper trapezius (UTr), and sternocleidomastoid (SCM) were higher in subjects with cervical lordosis than in subjects with cervical kyphosis. FRRSPL was higher in subjects with C-type kyphosis than in subjects with R- and S-type kyphosis (P < 0.05), and was correlated with VAS scores, Cobb angle, T1S, and SVA. FRRUTr was correlated with NDI, SCA, T1S, and SVA. FRRSCM was correlated with VAS scores and Cobb angle. CCR was correlated with SCA and SVA. Cervical sagittal parameters differed among different cervical kyphosis types. FRRs and CCRs were significantly worse in R-type kyphosis than other kyphosis types. Cervical muscular functions were correlated with cervical sagittal parameters and morphological alignment.