Speed Operation Research Articles

Research on Low Speed Operation of PMSM Based on Improved Non-Singular Fast Terminal SMC

Research on Low Speed Operation of PMSM Based on Improved Non-Singular Fast Terminal SMC

Simulation and experimental analysis of traveling wave resonance of flexible spiral bevel gear system

Considering the traveling wave resonance (TWR) phenomenon of the spiral bevel gear system in the aero engine accessory casing, the flexible spiral bevel gear model is established by three-dimensional (3D) shell element and beam element. The dynamic model of flexible bevel gear system considering the change of meshing teeth pairs in the operating process is established by component mode synthesis (CMS) method and rotational modal projection method. The proposed model is verified by finite element (FE) solid model and experiment test results. On this basis, the TWR phenomenon of the bevel gear system is studied in combination with dynamic response and vibration stress experimental test. The results show that the bevel gear system has 3 nodal diameter resonance speeds within the actual operating speed range, which are one 1st nodal diameter resonance of pinion and two 1st nodal diameter resonance of gear. When the bevel gear system operates at TWR state, the amplitude corresponding to fm ± m·fr (fm is meshing frequency, m is nodal diameter number, fr is shaft rotational frequency) is larger than that corresponding to fm in the Fast Fourier transform (FFT) spectrum of the of the gear foundation vibration stress, and the vibration stress cloud map of the gear foundation shows the corresponding nodal diameter vibration shape. The research results provide a theoretical basis for feature recognition and influence law evaluation of TWR of bevel gear system.

Winding process of fibre-reinforced thermoplastic tubes with integrated tape production through in-situ roving impregnation and infrared consolidation

AbstractThis paper introduces a novel method for producing fibre-reinforced thermoplastic tubes by integrating tape production and consolidation into a single operation. This innovation diverges from conventional methods by combining processes to reduce costs by using raw materials instead of organotapes, allowing customised material combinations and utilising residual heat from tape production. The new process uses carbon roving and molten low-viscosity PA6 granulates, processed through a new direct impregnation setup in siphon design. Key advancements include a high-speed impregnation module capable of up to 1 m/s with high-performance extruders, cost-effective infrared emitters for winding, and a powered consolidation unit with adjustable winding angles between ± 65° and ± 90°. Experiments demonstrate operational speeds of approximately 471 mm/min, with an optimal cross-winding speed of 354 mm/min due to the technical limitations of the laboratory extruder and IR emitter used. Based on the technical limitations of the current system, future improvements and methodological changes will be discussed.

Research on the dynamic variation law of the discontinuous characteristics of the curvic coupling of aero-engine rotors under working conditions

For the design of advanced aero-engine rotor systems under ultra-high rotational speeds, this paper undertakes a comprehensive investigation into the contact stiffness of curvic couplings, which are the key connection mechanism of modern aero-engine rotors, and explores the dynamic variations in the contact stiffness of curvic couplings under working conditions. Firstly, the impact of dynamic loads of aero-engine rotors under working conditions on the curvic coupling is studied; Then, the contact stiffness analytical model of curvic couplings considering three-dimensional geometric features is proposed with the effects of dynamic loads; Finally, based on the established stiffness model, the dynamic loss laws of contact stiffness of curvic couplings under different dynamic loads are investigated, and the established model is experimentally validated. The results show that the dynamic loss law of contact stiffness varies with different dynamic loads. In comparison to the contact stiffness under the static condition, dynamic loads significantly reduce the contact stiffness of curvic couplings under working conditions, which in turn leads to changes in the dynamic characteristics of the rotor with curvic couplings. For the safe integration of the discontinuous rotor system under ultra-high rotational speeds, it is essential to carefully design and regulate the operating speed, transmission torque, unbalanced mass, and preload.

Performing Magnetic Boundary Modulation to Broaden the Operational Wind Speed Range of a Piezoelectric Cantilever-Type Wind Energy Harvester

Small piezoelectric wind-induced vibration energy harvesting systems have been widely studied to provide long-term sustainable green energy for a large number of wireless sensor network nodes. Piezoelectric materials are commonly utilized as transducers because of their ability to produce high output power density and their simple structure, but they are prone to material fracture under large deformation conditions. This paper proposes a magnetic boundary modulated stepped beam wind energy harvesting system. On the one hand, the design incorporates a composite stepped beam with both high- and low-stiffness components, allowing for efficient vibration and electrical energy output at low wind speeds. On the other hand, a magnetic boundary constraint mechanism is constructed to prevent the piezoelectric sheet from breaking due to excessive deformation. Experiments have confirmed that the effective operational wind speed range of the harvester with magnetic boundary constraints is doubled compared to that of the harvester without magnetic boundary constraints. Furthermore, by adjusting the magnetic pole spacing of the boundary, the harvesting system can generate sufficiently high output power under high-wind-speed conditions without damaging the piezoelectric sheet.

Semantic Segmentation Model-Based Boundary Line Recognition Method for Wheat Harvesting

The wheat harvesting boundary line is vital reference information for the path tracking of an autonomously driving combine harvester. However, unfavorable factors, such as a complex light environment, tree shade, weeds, and wheat stubble color interference in the field, make it challenging to identify the wheat harvest boundary line accurately and quickly. Therefore, this paper proposes a harvest boundary line recognition model for wheat harvesting based on the MV3_DeepLabV3+ network framework, which can quickly and accurately complete the identification in complex environments. The model uses the lightweight MobileNetV3_Large as the backbone network and the LeakyReLU activation function to avoid the neural death problem. Depth-separable convolution is introduced into Atrous Spatial Pyramid Pooling (ASPP) to reduce the complexity of network parameters. The cubic B-spline curve-fitting method extracts the wheat harvesting boundary line. A prototype harvester for wheat harvesting boundary recognition was built, and field tests were conducted. The test results show that the wheat harvest boundary line recognition model proposed in this paper achieves a segmentation accuracy of 98.04% for unharvested wheat regions in complex environments, with an IoU of 95.02%. When the combine harvester travels at 0~1.5 m/s, the normal speed for operation, the average processing time and pixel error for a single image are 0.15 s and 7.3 pixels, respectively. This method could achieve high recognition accuracy and fast recognition speed. This paper provides a practical reference for the autonomous harvesting operation of a combine harvester.

Near-field strong coupling and entanglement of quantum emitters for room-temperature quantum technologies

In recent years, quantum nanophotonics has forged a rich nexus of nanotechnology with photonic quantum information processing, offering remarkable prospects for advancing quantum technologies beyond their current technical limits in terms of physical compactness, energy efficiency, operation speed, temperature robustness and scalability. In this perspective, we highlight a number of recent studies that reveal the especially compelling potential of nanoplasmonic cavity quantum electrodynamics for driving quantum technologies down to nanoscale spatial and ultrafast temporal regimes, whilst elevating them to ambient temperatures. Our perspective encompasses innovative proposals for quantum plasmonic biosensing, driving ultrafast single-photon emission and achieving near-field multipartite entanglement in the strong coupling regime, with a notable emphasis on the use of industry-grade devices. We conclude with an outlook emphasizing how the bespoke characteristics and functionalities of plasmonic devices are shaping contemporary research directives in ultrafast and room-temperature quantum nanotechnologies.

Design and Testing of a Crawler Chassis for Brush-Roller Cotton Harvesters

In China’s Yangtze River and Yellow River basin cotton-growing regions, the complex terrain, scattered planting areas, and poor adaptability of the existing machinery have led to a mechanized cotton harvesting rate of less than 10%. To address this issue, we designed a crawler chassis for a brush-roller cotton harvester. It is specifically tailored to meet the 76 cm row spacing agronomic requirement. We also conducted a theoretical analysis of the power transmission system for the crawler chassis. Initially, we considered the terrain characteristics of China’s inland cotton-growing regions and the current cotton agronomy practices. Based on these, we selected and designed the power system and chassis; then, a finite element static analysis was carried out on the chassis frame to ensure safety during operation; finally, field tests on the harvester’s operability, stability, and speed were carried out. The results show that the inverted trapezoidal crawler walking device, combined with a hydraulic continuously variable transmission and rear-drive design, enhances the crawler’s passability. The crawler parameters included a ground contact length of 1650 mm, a maximum ground clearance of 270 mm, a maximum operating speed of 6.1 km/h, and an actual turning radius of 2300 mm. The maximum deformation of the frame was 2.198 mm, the deformation of the walking chassis was 1.0716 mm, the maximum equivalent stress was 216.96 MPa, and the average equivalent stress of the entire frame was 5.6356 MPa, which complies with the physical properties of the selected material, Q235. The designed cotton harvester crawler chassis features stable straight-line and steering performance. The vehicle’s speed can be adjusted based on the complexity of the terrain, with timely steering responses, minimal compaction on cotton, and reduced soil damage, meeting the requirements for mechanized harvesting in China’s inland small plots.

MFCA-MICNN: a convolutional neural network with multiscale fast channel attention and multibranch irregular convolution for noise removal in dMRI

Diffusion magnetic resonance imaging (dMRI) currently stands as the foremost noninvasive method for quantifying brain tissue microstructure and reconstructing white matter fiber pathways. However, the inherent free diffusion motion of water molecules in dMRI results in signal decay, diminishing the signal-to-noise ratio (SNR) and adversely affecting the accuracy and precision of microstructural data. In response to this challenge, we propose a novel method known as the Multiscale Fast Attention-Multibranch Irregular Convolutional Neural Network for dMRI image denoising. In this work, we introduce Multiscale Fast Channel Attention, a novel approach for efficient multiscale feature extraction with attention weight computation across feature channels. This enhances the model's capability to capture complex features and improves overall performance. Furthermore, we propose a multi-branch irregular convolutional architecture that effectively disrupts spatial noise correlation and captures noise features, thereby further enhancing the denoising performance of the model. Lastly, we design a novel loss function, which ensures excellent performance in both edge and flat regions. Experimental results demonstrate that the proposed method outperforms other state-of-the-art deep learning denoising methods in both quantitative and qualitative aspects for dMRI image denoising with fewer parameters and faster operational speed.

Laser assembly of CeO2 nanobrushes and their resistive switching performance

Memristors in a metal/oxide/metal configuration emerge as an advanced non-volatile information storage technology due to their high operating speed and low power consumption. Understanding the role of the microstructure of the active oxide layer in its resistive switching (RS) performance is scientifically important for revealing the conduction mechanism of oxides, and technically critical for the development of high-performance memristors. Here, self-assembly of vertically aligned CeO2 nanobrushes, a typical RS material, is demonstrated in pulsed laser epitaxy. The growth map of CeO2 is fully explored, and optimized growth conditions for CeO2 nanobrushes are established. Microstructure and crystallinity characterizations reveal the single-crystalline epitaxy nature of CeO2 nanobrushes. A nanobrush memristor exhibits a threshold switching feature with an On/Off ratio of 50, as 16 times high as a thin-film memristor, and excellent endurance of up to 500 cycles and retention time of up to 104 seconds. Theoretical fitting of the I-V curves of the thin-film and nanobrush memristors show interfacial switching in the thin-film memristor and filamentary switching in the nanobrush memristor. The distinct RS mechanisms between thin films and nanobrushes suggest the fundamental role of the structural design of active oxide layers in the RS performance of oxide-based memristors.

Exploring the Learning Curve of Dental Implant Placement Using a Task-Autonomous Robotic System Among Young Dentists From Different Specialties-A Pilot Module Study.

The learning curve effect of dynamic computer-assisted implant surgery (D-CAIS) was observed among inexperienced novice surgeons. The learning curves can provide valuable information for novice surgeons and valid comparisons between new and conventional techniques. Recently, robotic computer-assisted implant surgery (R-CAIS) has shown promise as a novel dental implant surgical technique for both partially and edentulous patients. However, its learning curve remains unknown. The aim of this study was to explore the learning curve of dental implant placement surgery with a task-autonomous robotic system among young dentists with different specialties. Four young dentists (mean age: 25.3 ± 1.5 years at the beginning of their first attempt) with equal representation of males and females and with different specialties participated in this study. None of the participants had prior experience in R-CAIS. Each operator placed eight implants over eight attempts using a semi-active task-autonomous robotic system. Among the eight implants, four were straight lateral incisor implants, and four were 30°-tilted premolar implants. The implants were placed in each dental quadrant of the maxillary and mandibular jaw modules. The operation time was recorded. Coronal, apical, and angular deviations between the planned and actual sites of implant placement were measured by merging preoperative and postoperative cone-beam computed tomography (CBCT) scans. The data were analyzed with repeated-measures ANOVA (α = 0.05). The mean time for implant placement was associated with the number of attempts (p < 0.01). The time taken for the second attempt was significantly shorter than that of the first attempt (33.26 vs. 30.47 min; p < 0.001) then it plateaued. Three-dimensional (3D) angular (p = 0.31), coronal deviation (p = 0.26), and apical deviation (p = 0.06) did not differ significantly among attempts. The mean values and standard deviations of 3D coronal deviation, 3D apical deviation, and 3D angular deviation were 0.71 ± 0.31 mm, 0.72 ± 0.30 mm, and 0.94 ± 0.58°, respectively. Neither the position of the jaw (p > 0.59) nor the tilt angle of the implant (straight or 30°-tilted, p > 0.85) was related to implant placement accuracy. Dentists quickly learned the basic workflow of R-CAIS and thus facilitated the clinicians in the mastery of implant placement on edentulous jaw modules, leading to a comparable operating speed and high precision. Moreover, the accuracy of placement of straight and tilted implants in both the maxilla and mandible with R-CAIS was satisfactory.

Evaluation of Selected Factors Affecting the Speed of Drivers at Signal-Controlled Intersections in Poland

In traffic engineering, vehicle speed is a critical determinant of both the risk and severity of road crashes, a fact that holds particularly important for signalized intersections. Accurately selecting vehicle speeds is crucial not only for minimizing accident risks but also for ensuring the proper calculation of intergreen times, which directly influences the efficiency and safety of traffic flow. Traditionally, the design of signal programs relies on fixed speed parameters, such as the posted speed limit or the operational speed, typically represented by the 85th percentile speed from speed distribution data. Furthermore, many design guidelines allow for the selection of these critical speed values based on the designer’s own experience. However, such practices may lead to discrepancies in intergreen time calculations, potentially compromising safety and efficiency at intersections. Our research underscores the substantial variability in the speeds of passenger vehicles traveling intersections under free-flow conditions. This study encompassed numerous intersections with the highest number of accidents, using unmanned aerial vehicles to conduct surveys in three Polish cities: Toruń, Bydgoszcz, and Warsaw. The captured video footage of vehicle movements at predetermined measurement sections was analyzed to find appropriate speeds for various travel maneuvers through these sections, encompassing straight-through, left-turn, and right-turn relations. Our analysis focused on how specific infrastructure-related factors influence driver behavior. The following were evaluated: intersection type, traffic organization, approach lane width, number of lanes, longitudinal road gradient, trams or pedestrian or bicycle crossing presence, and even roadside obstacles such as buildings, barriers or trees, and others. The results reveal that these factors significantly affect drivers’ speed choices, particularly in turning maneuvers. Furthermore, it was observed that the average speeds chosen by drivers at signalized intersections did not reach the permissible speed limit of 50 km/h as established in typical Polish urban areas. A key outcome of our analysis is the recommendation for a more precise speed model that contributes to the design of signal programs, enhancing road safety, and aligning with sustainable transport development policies. Based on our statistical analyses, we propose adopting a more sophisticated model to determine actual vehicle speeds more accurately. It was proved that, using the developed model, the results of calculating the intergreen times are statistically significantly higher. This recommendation is particularly pertinent to the design of signal programs. Furthermore, by improving speed accuracy values in intergreen calculation models with a clear impact on increasing road safety, we anticipate reductions in operational costs for the transportation system, which will contribute to both economic and environmental goals.

Efficient and cost-effective maximum power point tracking technique for solar photovoltaic systems with Li-ion battery charging

This paper presents an effective approach to achieve maximum power point tracking (MPPT) in photovoltaic (PV) systems for battery charging using a single-sensor incremental conductance (InC) method. The objective is to optimize the MPPT process while minimizing the number of sensors required. The suggested technique leverages the relationship between the PV module's output voltage and the duty cycle to automatically adjust and reach the MPP, resulting in optimal power generation. By eliminating the PV current sensor from the control circuit, the developed method reduces both the cost and size of the MPPT circuit. Compared to the conventional InC method, the developed approach demonstrates improved response speed and accuracy in steady-state operation, along with the ability to damp oscillations near the MPP. Extensive simulations using MATLAB/Simulink validate the performance of the developed technique across various environmental conditions. The results highlight the recommended method's realistic and effective MPP tracking capabilities, achieving higher efficiency (99.12 %) compared to the classical method (97.8 %) under high irradiance levels.

Adaptive threshold wavefront sensing for portable adaptive optics system driven by hybrid parallel technique

Abstract Our Portable Adaptive Optics (PAO) system designed for high-contrast imaging of exoplanets with current 2-4 meter class telescopes achieves a correction speed of nearly 1000Hz, utilizing a Shark-Hartmann Wavefront Sensor (S-H WFS) in a $9\times9$ sub-aperture configuration. As we look towards adapting the PAO system for larger telescopes, an increase in the number of sub-apertures in the WFS and enhanced precision in wavefront detection are imperative. Originally programmed in LabVIEW, our initial PAO software is based on a traditional centroid calculation module for nighttime wavefront sensing and lacks adaptive processing of background noise. To address these limitations and to boost the PAO system’s performance and accuracy in wavefront detection, we propose a compressive neural network(Th-Net) combined with a specialized hybrid parallel programming approach for wavefront detection. Our experimental results indicate that this hybrid parallel technique and Th-Net significantly enhance the PAO system’s operational speed and wavefront detection precision under uneven background noise. This work paves the way that a duplicable and low-cost PAO system can be used for direct imaging of exoplanet with large telescopes.

Research on Intelligent Disinfection Robot Based on STC89C52 Microcontroller Control System

An intelligent disinfection robot based on STC89C52 microcontroller as the control system is proposed to address the current problems of high cost, scene limitations, and complex control factors. With the assistance of software development, the peripheral circuit of the chip is designed, and the robot can dynamically adjust its path according to its own and surrounding companions' position and motion status. The research results indicate that the various modules of the system can work in coordination, with high accuracy in tracking routes, precise image recognition, timely obstacle avoidance, fast response speed, and safe and reliable system operation. The disinfection robot carried out disinfection and sterilization test on the most common staphylococcus aureus and mold, and the test results showed that the pathogenic bacteria decreased significantly after 2 min, and all bacteria were removed after 3 min, and the killing rate of bacteria was up to&gt;96.0%. This method improves the obstacle avoidance ability and collaborative efficiency of robot systems in complex environments, and can effectively respond to various unexpected situations.

Statistical modelling of a tractor tractive performance during ploughing operation on a tropical Alfisol

Abstract Tractor is the most prominent off-road agricultural machinery that is significant to the global food security. The tractive modelling of tyre–soil interaction and agricultural implement dynamics is a complex phenomenon that require holistic approach. Terramechanics techniques such as empirical, semi-empirical, analytical, and numerical methods such as finite element models and discrete element models have gained traction in tractive performance studies. Some of these approaches are premised on large arrays of variables for modelling tractive performance based on the soil–tyre and tools interactions. In this study, soft computing in R software domain was used to model the tractor tractive performance during ploughing operations on a tropical Alfisol. The research farm at the National Centre for Agricultural Mechanization was used for the field experiment. The experimental design was a nested-factorial under a Randomized Complete Block Design having three replications. The input factors were tractor power size, T, (60, 65, and 70 hp); tyre inflation pressure, P, (83, 124, and 165 kPa); implement configuration, I, (2 and 3 bottoms disc plough); and operational speed, S, (6.31, 7.90, 9.47, 11.05, and 12.63 km/h). Standard procedures were followed to obtain the measured parameters in the field, which were statistically analysed. Correlation analysis and analysis of variance of the measured parameters at 5% significance level were established. Multiple linear regression was used to develop the model, validated using the 10-fold cross-validation method. The results revealed that the evaluated variables have a range of 1.56–7.79 kN, 5.15–27.20%, 9.10–32.00 cm, 4.50–13.94%, 1.31–1.67 g/cm3, 95.89–207.78 kPa, and 98.67–295.56 for draught, wheel slip, depth of cut, moisture content, bulk density, cone index (CI), and shear stress, respectively. A positive correlation exists between the towing force (TF) and the measured variables except for the shear stress and CI. The final developed model has seven variables for predicting TF with a 6.5% error and an average of 0.4735 cross validation root mean square error. The model quality of fit achieved an R Adj 2 = 0.8754 {R}_{\text{Adj}}^{2}=0.8754 which satisfactorily described the response variable. The study provides insights into tractive dynamic systems modelling of machine, tractive medium (soil), and agricultural tools anchored on soft computing approach. Its adoption will assist in quality ploughing operation integrating the variables established in the model.

Numerical investigation on the heat dissipation of phase change materials used in the high-speed train brake system

The massive heat dissipation demands of the brake system in high-speed trains pose a significant obstacle to achieving higher operation speeds. Phase change material has attracted considerable attention in various fields due to their exceptional heat dissipation capabilities, yet their utilization in the brake system of high-speed trains remains unexplored. This study aims to investigate the feasibility of phase change material application in the brake system of high-speed train. Specifically, in Case A, the introduction of phase change material resulted in a notable 21% decrease in the average temperature and a remarkable 40% reduction in the maximum temperature difference within the brake system. The latent heat of the phase change material plays a crucial role in maintaining a substantial temperature differential between the cooling components and discs, thereby enhancing heat flux in the brake system. Phase change materials exhibit superior cooling performance compared to traditional air cooling methods in the brake system. To expedite the cooling process of phase change material and facilitate its transition from liquid to solid, an optimized brake system structure utilizing phase change material was proposed. This optimized design holds promise in enhancing the overall heat dissipation efficiency of the high-speed train brake system.

Simulation of pulse width modulation strategies: validation based on sound quality evaluations

Though the noise emitted by an electrical machine is less loud than the one from a combustion engine, the existing literature shows it is not always more pleasant. This is because of its strong tonal content, due to deterministic excitations (e.g. electromagnetic slotting and switching effects, mechanical gear mesh effects). The main source of magnetic noise is the radiation of the stator submitted to electromagnetic forces, which are harmonic by nature. Their highest frequency components are generated by the Pulse Width Modulation (PWM) technique designed to vary the supply frequency and operating speed of the electric machine. Several PWM strategies exist, leading to specific harmonic spectra. A temporal simulation model has been developed to auralize the corresponding electromagnetic noise emitted by the machine, considering its mechanical and electrical parameters. A testbench has been adapted to test the relevance of the model, both from an electrical and acoustic point of view. The simulation model and the testbench can be used to generate virtual and real sound stimuli to be assessed in a jury testing. The subjective evaluations allow to assess the relevance of the auralization model, but also to identify the least annoying strategies.

Computer Simulation and Simulation of Mechanical and Electrical Equipment based on Artificial Intelligence Algorithms

The computer in mechanical and electrical equipment can now detect equipment faults through simulation thanks to the advancement of artificial intelligence (AI) technology, which makes it convenient to monitor mechanical and electrical equipment. This paper begins with a quick introduction to Agent and the Agent system, explains how Agent is applied in the current context, then analyzes the hierarchical fault diagnostic model, identifies its flaws, and suggests improvement techniques. To increase the model’s accuracy and speed of operation, the contract net model and D-S (Dempster-Shafer) evidence theory are then incorporated. Finally, simulation experiments are used to confirm the accuracy and consistency of this model. According to the experimental findings, this optimization model runs at a pace that is noticeably faster than that of other models when subjected to the same workload, demonstrating the model’s efficacy. Models 1, 2, and 3 are put side by side to demonstrate how clearly multi-task processing may cut down on the model’s running time. In the second experiment, samples of mechanical and electrical equipment defect data are taken from two groups. The results of the comparative experiments demonstrate that the optimized model in this work can be reliable up to a maximum of 0.91 and a minimum of 0.63. It is demonstrated that the model in this work is rational by the fact that among the four types of fault prediction, the optimized model’s reliability is significantly greater than the traditional model’s. The research described in this publication therefore has some reference value for computer simulation of electromechanical equipment.

Influence of Plateau Environment on Operating Speed at Exit Ramps

Influence of Plateau Environment on Operating Speed at Exit Ramps