Variable Speed Research Articles

Design of Freewheel Differential

In a tricycle, achieving stability during turns and optimizing traction are critical. A conventional differential may not always be ideal due to its complexity and cost, especially in lightweight vehicles like tricycles. The sprag-type freewheel differential provides a simplified yet highly effective solution. It allows each wheel to rotate independently, accommodating speed variations during turns, and improving handling without the need for complex gears or electronic control systems. In this paper, sprag-type freewheel differential is an innovative mechanism designed to enhance the performance and manoeuvrability of tricycles. Unlike conventional fixed axle systems, this differential utilizes sprag clutches to facilitate independent wheel movement while maintaining efficient torque transfer.

Graded Walking Energetics under Cold Strain.

Although research supports selecting steeper routes to minimize metabolic rate (Ṁ) in mountain racers, the "steeper is cheaper" strategy has yet to be confirmed for slower, more typical graded walking speeds under cold stress. Confirm whether "steeper is cheaper" is true for typical graded walking speeds in individuals exposed to incremental cold stress. Fourteen healthy, military-age adults (age, 24 ± 6 years; height, 1.72 ± 0.08 m; body mass, 72 ± 16 kg) completed four 20-min treadmill walks in three ambient temperatures (20, 10, and 0 °C) in light clothing (i.e., shorts, t-shirt, light gloves). Each walk involved five stages at incremental vertical speeds (0.00, 1.93, 3.86, 5.79, 7.79 m·min-1) but variable treadmill speeds (0.54, 0.72, 1.07 m·s-1). To verify the "steeper is cheaper" strategy, we compared Ṁ between treadmill speeds at matched vertical speeds in each temperature. We also tested if the 90% confidence interval around the mean percent paired difference between Load Carriage Decision Aid (LCDA) metabolic model predictions and measured Ṁ was within ±10%. Ṁ was significantly higher for the faster treadmill speed at matched vertical speeds in all but two comparisons at 20 °C, all but two comparisons at 10 °C, and all comparisons at 0 °C (p < 0.05). LCDA metabolic model predictions were statistically equivalent to measured Ṁ during graded walking at 20 °C (90% CI, -3.1, 0.6%) and 10 °C (-7.8, -2.6%) but not 0 °C (-16.2, -9.5%). Route planners should recommend steeper but shorter routes to minimize Ṁ in individuals that walk in temperate-to-cold environments. The LCDA metabolic model provides accurate Ṁ predictions in lightly dressed individuals in temperatures down to 10 °C, but users should expect underestimated Ṁ in 0 °C or colder.

Generalized Multivariate Symplectic Sparsest United Decomposition for Rolling Bearing Fault Diagnosis

The non-stationary characteristics of the vibration signals of rolling bearings will be aggravated under variable speed conditions. Meanwhile, multichannel signals can provide a more comprehensive characterization of state information, providing multiple sources of information that facilitate information fusion and enhancement. However, traditional adaptive signal decomposition methods generally assume that the frequency information is constant and stationary, and it is difficult to achieve a unified decomposition when dealing with multichannel time-varying signals. Therefore, the intention of this paper is to propose a multichannel signal adaptive decomposition method applicable to variable speed conditions. Specifically, this paper takes advantage of the strong adaptability and robustness of symplectic geometric mode decomposition (SGMD). To improve its applicability to multichannel time-varying signals at variable rotational speeds, a generalized multivariate symplectic sparsest united decomposition (GMSSUD) method is proposed. In GMSSUD, firstly, the completely adaptive projection (CAP) method is employed to achieve a unified representation of the multichannel signals. Then, the generalized demodulation method is introduced to stabilize the signal and subsequently reduce the noise through component screening and reconstruction. Finally, with the new proposed operator as the optimization objective, the constructed sparse filter parameters are optimized to achieve the frequency band segmentation. The analysis results demonstrate that the GMSSUD method possesses higher decomposition precision for multichannel signals with variable speeds and also has a stronger diagnosis ability for variable-speed bearing faults.

Control of a Doubly Fed Induction Generator for Variable Speed Wind Energy Conversion Systems using Fuzzy Controllers optimized with a Genetic Algorithm

This paper presents a comprehensive study of a wind turbine system operating under variable wind conditions, utilizing a Doubly Fed Induction Generator (DFIG) connected to the grid. The DFIG is controlled via a rotor-side transducer, allowing for independent regulation of the conductors to manage both active and reactive power flows effectively. The control strategy focuses on generating reference voltages for the rotor to ensure that active and reactive power align with the desired targets, optimizing the tracking of the maximum power point to maximize electrical output. The research analyzes the system's dynamic performance under fluctuating wind conditions, emphasizing control strategies for managing active and reactive energy. A notable innovation is the integration of fuzzy logic and genetic algorithm into the control strategy for the wind turbine's switching mechanism, which enhances system performance and efficiency. Simulation results demonstrate that this approach provides higher efficiency, improved performance, and greater stability compared to the traditional Proportional-Integral (PI) controllers. Advanced artificial intelligence methods, such as fuzzy genetic algorithm control, were employed and the proposed system's effectiveness was validated with Matlab/Simulink simulations.

Impact of Variable Speed Limits on Crash Frequency and Crash Rate: Stimulation by Flow Rate and Percentage of Heavy Vehicles

The current transportation system has faces a high number of crashes on road networks, often resulting in damages and human losses. According to WHO, these incidents cost most countries approximately 3% of their GDP. This underscores the need to address the weaknesses in road networks and provide efficient countermeasures to reduce these incidents and consequent losses. This study focuses on Variable Speed Limits (VSL) as a crucial factor that could influence road safety. To model the impact of VSL on the Crash Frequency (CF) and Crash Rate (CR), the researchers also considered other relevant traffic characteristics, such as the Percentage of Heavy Vehicles (PHV) and Average Daily Traffic (ADT). For the purposes of this research, the Mandali-Baqubah highway in Iraq was selected as a case study for the potential implementation of VSL. The statistical regression approach was utilized to model the Safety Performance Functions (SPF) for the road crash count. The findings of the current study revealed that the VSL are positively and significantly associated with CF and CR.

A parameter-interactive digital twin model of bearings under variable speeds

The digital twin model plays a crucial role in the Prognostics and Health Management of mechanical systems. However, current digital twin models for bearings lack comprehensive research on the interaction between the virtual entity and the physical entity under variable speeds. Accordingly, a parameter-interactive digital twin model has been proposed to interact the information of bearings between virtual entity and physical entity in real-time under variable speeds. Firstly, a bearing dynamics model considering speed, stiffness and fault size was established under variable speeds. Secondly, speed parameter was updated in accordance with the ridgeline of the power spectrum. Stiffness and fault size parameters were updated through an iterative extended Kalman filtering process. Condition of the bearing was monitored through various parameters. The accuracy of the dynamic model and the feasibility of parameter interaction were validated through experimental studies. These findings demonstrate that the proposed method can effectively ensure real-time parameter interaction and condition monitoring under variable speeds within the digital twin model.

A Sparse Representation Method Based on Multiobjective Optimization for the Extraction of Nonperiodic Fault Features of Rolling Bearing Under Variable Speed

A Sparse Representation Method Based on Multiobjective Optimization for the Extraction of Nonperiodic Fault Features of Rolling Bearing Under Variable Speed

Propagation of Slow Slip Events on Rough Faults: Clustering, Back Propagation, and Re‐Rupturing

AbstractSeismic and geodetic observations show that slow slip events (SSEs) in subduction zones can happen at all temporal and spatial scales and propagate at various velocities. Observation of rapid tremor reversals indicates back‐propagating fronts traveling much faster than the main rupture front. Heterogeneity of fault properties, such as fault roughness, is a ubiquitous feature often invoked to explain this complex behavior, but how roughness affects SSEs is poorly understood. Here we use quasi‐dynamic seismic cycle simulations to model SSEs on a rough fault, using normal stress perturbations as a proxy for roughness and assuming rate‐and‐state friction, with velocity‐weakening friction at low slip rate and velocity‐strengthening at high slip rate. SSEs exhibit temporal clustering, large variations in rupture length and propagation speed, and back‐propagating fronts at different scales. We identify a mechanism for back propagation: as ruptures propagate through low‐normal stress regions, a rapid increase in slip velocity combined with rate‐strengthening friction induces stress oscillations at the rupture tip, and the subsequent “delayed stress drop” induces secondary back‐propagating fronts. Moreover, on rough faults with fractal elevation profiles, the transition from pulse to crack can also lead to the re‐rupture of SSEs due to local variations in the level of heterogeneity. Our study provides a possible mechanism for the complex evolution of SSEs inferred from geophysical observations and its link to fault roughness.

Characterizing Model Uncertainties in Simulated Coast-to-Offshore Wind over the Northeast U.S. Using Multi-platform Measurements from the TCAP Field Campaign

Numerical weather prediction models, such as the Weather Research and Forecasting model, are widely used to provide estimates of the offshore wind energy resource owing to their large spatial coverage compared to available observations. Nevertheless, spatiotemporal distribution of model biases is highly dependent on factors including model configuration, location, and the interplay of multiscale physical processes. Here we focus on the characterization of model uncertainties in simulated coast-to-offshore winds over the northeast United States by varying sea surface temperature (SST) forcings and surface layer (SL) and planetary boundary layer (PBL) parameterizations, as well as identifying biases that may be directly passed from initial and boundary conditions. Multiple measurements, including aircraft data collected during the U.S. Department of Energy's Two-Column Aerosol Project experiment, are used to constrain the model results, and facilitate quantitative comparisons. Our analysis indicates that while SST forcing has notable impacts on simulated air temperature and moisture within PBL, the modeled winds are in general more sensitive to the choices of SL and PBL physics than to SST. The model’s forcing data not only controls the vertical dependence of wind speed errors, but also alters regional variability in the wind speed’s spatial correlation, which underscores the impact of initial and boundary conditions on simulated winds. Coastal and offshore near-surface wind speed biases tend to exhibit much higher similarity in winter than in summer due to the presence of much stronger and more persistent synoptic wind conditions. This study highlights the importance of accurate atmospheric forcing and parameterization choices in improving wind forecasts and suggests the potential for extrapolating coastal wind biases to offshore locations, aiding wind energy forecasting and informing the third Wind Forecast Improvement Project.

On the MJO Phase Speed among Different Background Moisture and Zonal Wind Base States

Abstract The variability of the phase speed of the Madden–Julian oscillation (MJO) is poorly understood. The authors assess how the phase speed of the convective signal of the MJO associates with the background states over eastern Africa and the Indian Ocean. Relaxation of the coupling between tropical modes and their circulation has been previously linked to faster propagation; for example, the MJO speeds up over the eastern Pacific where its convective signal decouples from the circulation. In contrast, our results show that fast MJO events happen to exist during periods of wetter background states (&gt;90 days) from East Africa across the Indian Ocean, whereas slow MJO is associated with dry background states. We found that fast MJO exhibits strong active and inactive phases with a structure suggesting more hierarchical convection. Results indicate that the association of the phase speed of the MJO as seen in the integrated filtered moist static energy with its tendency is stronger than the association of the phase speed as observed in the dry static energy with its tendency which is consistent with the acceleration of the MJO during wet background states. Also, our results indicate that the MJO may be faster during periods of enhanced low-level moisture because these periods have anomalously weak upper-tropospheric easterly background winds, which reduce the westward advection of the MJO by the background easterly wind, resulting in higher eastward phase speed of the MJO. The acceleration of the MJO by the background zonal wind overwhelms the deceleration associated with the moist-wave dynamics. Significance Statement This study shows that the Madden–Julian oscillation (MJO), which is the dominant subseasonal weather signal in the tropics, moves eastward more quickly across eastern Africa and the Indian Ocean when the region is abnormally moist. The faster propagation does not appear to result from the higher moisture but instead from encountering weaker-than-normal upper-air winds from the east that tend to occur during moist periods.

Maximizing Wind Turbine Power Generation Through Adaptive Fuzzy Logic Control for Optimal Efficiency and Performance

Wind power output fluctuations, driven by variable wind speeds, create significant challenges for grid stability and the efficient use of wind turbines, particularly in high-wind-penetration areas. This study proposes a combined approach utilizing an ultra-capacitor energy storage system and fuzzy-control-based pitch angle adjustment to address these challenges. The fuzzy control system dynamically responds to wind speed variations, optimizing energy capture while minimizing mechanical stress on turbine components, and the ultra-capacitor provides instantaneous buffering of power surpluses and deficits. Simulations conducted on a 50 kW DFIG wind turbine powering a 23 kW load demonstrated a substantial reduction in power fluctuations by a factor of 3.747, decreasing the power fluctuation reduction scale from 13.04% to 3.48%. These results highlight the effectiveness of the proposed system in improving the stability, reliability, and quality of wind energy, thereby advancing the broader adoption of renewable energy and contributing to sustainable energy solutions.

Mode recognition in a kerosene-fueled scramjet combustor by a Swin Transformer neural network

Recognizing the combustion mode in scramjet engines is critical for suppressing oscillations and stabilizing the combustion process in hypersonic aircrafts. Current accesses mainly depend on mechanical measurement and dominant frequencies based on image analysis methods, such as proper orthogonal decomposition and dynamic mode decomposition. However, these traditional methods either lack of precision or fall short of the need for prior knowledge, poor generalization, and low efficiency, posing challenges in practical implementations, especially when online controlling is highlighted in the scramjet combustions. Recently, machine learning (ML) has been introduced to the combustion community due to its superiority in high flexibility and efficiency in addressing complex problems. The classical convolutional neural network (CNN) architectures have been reported to achieve efficient combustion mode recognition in furnace combustion, swirling combustor, and rotating detonation engines. However, those CNN-based models are incapable of utilizing the global flame features and the coherences of local areas, resulting in insufficient accuracy and robustness in scramjet combustions with high inflow speed and distinct mode variations. To address this problem, this paper reports a Swin (shifted window) Transformer model, an advanced ML structure outstanding in capturing both global and local features by its self-attention mechanism with high computational efficiency, to identify combustion modes in scramjet engines. The Swin-T was trained and validated in a kerosene-fueled cavity-based scramjet combustor, and results show that it can achieve a considerable accuracy of 95.28%. Comparisons with CNN-based models further indicate that Swin-T outperforms in accuracy, efficiency, and robustness by around 0.7%, 80%, and 3%, respectively.

Vibration-based gear wear area monitoring for quantitative assessment of wear severity under variable speed conditions

Vibration-based gear wear area monitoring for quantitative assessment of wear severity under variable speed conditions

Spatial Layout Optimization Approach for Highway Variable Speed Limit Zones Based on Improved Q-Learning Algorithm

Spatial Layout Optimization Approach for Highway Variable Speed Limit Zones Based on Improved Q-Learning Algorithm

Hierarchical Speed Planner for Automated Vehicles: A Framework for Lagrangian Variable Speed Limit in Mixed-Autonomy Traffic

Hierarchical Speed Planner for Automated Vehicles: A Framework for Lagrangian Variable Speed Limit in Mixed-Autonomy Traffic

Study on the influence of working condition variation on lubrication characteristics of EHA internal pump

The operational characteristics of Electro-Hydraulic Actuator(EHA) result in the EHA internal pump facing complex conditions of variable speed, pressure and even direction, resulting in poor lubrication state of friction pairs. To study the evolution of lubrication states under varying working conditions, a joint simulation model using Simerics-MP+(Version: 5.2.13, https://www.simerics.com) and MATLAB(Version: R2020a, https://www.mathworks.com/products/matlab.html) was established, focusing on the gear ring/shell friction pair as the subject of study. The model was used to predict the micro-motion trajectory of the gear ring, and the results were validated through experimental measurements of dynamic changes. Results indicate that the simulation model can accurately predict oil film variations, with a calculation error of less than 7.6% for oil film thickness. The reduction in speed and the increase in load pressure will deteriorate the lubrication characteristics of the oil film in the friction pair. Under pump operating conditions, when the direction reversing rate increases from 20,000 rpm/s to 40,000 rpm/s, the average oil film thickness increases by 23.64%, while the fluctuation rate decrease by 2.57%. Similarly, when the direction reversing pressure decreases from 10 MPa to 5 MPa, the average oil film thickness increases by 22.39%, and the fluctuation rate decreases by 8.17%. During pump-motor working condition switching, an increase in the switching rate from 10,000 rpm/s to 40,000 rpm/s results in a 10.33% increase in average oil film thickness and a 20.9% decrease in fluctuation rate. This study significantly contributes to enhancing our comprehension of the evolutionary patterns of friction pairs in EHA internal pumps under varying operational conditions.

Analysis of Print Speed Variations Effect and Nozzle Temperature on the Tensile Strength of 3D Printed TPU-95A Products

Analysis of Print Speed Variations Effect and Nozzle Temperature on the Tensile Strength of 3D Printed TPU-95A Products

Analyzing the Impact of Alcohol on Driving Performance and Road Traffic Accidents: Evidence from Simulations and Limited-Time Data from Bangladesh

This study evaluated the effects of alcohol consumption on driving performance and its contribution to road traffic accidents using simulated experiments and real-world data from 2016 to 2018. The simulated experiments involved 25 drivers, revealing significant impairments in reaction time, vigilance, perception, and control at varying blood alcohol concentration (BAC) levels. Higher BAC levels were associated with increased accident rates, with statistical analysis demonstrating a linear trend between BAC and performance metrics such as average speed (p &lt; 0.05), speed variability (p &lt; 0.01), and lane position deviation (p &lt; 0.01). Real-world data indicated a total of 6,114 accidents in 2016, 5,345 in 2017, and 5,367 in 2018, with a notable decline in serious and fatal accidents by 2018 (fatal accidents decreased from 2,197 in 2016 to 219 in 2018). However, slight accidents rose sharply from 2,662 in 2017 to 4,673 in 2018. Alcohol-related cases peaked at 163 in 2017 before dropping to 125 in 2018. Chi-square analysis found no statistically significant relationship between fatalities and exceeding the legal BAC limit (p = 0.1013 for 2016, p = 0.0981 for 2017, p = 0.1923 for 2018). The study emphasizes the persistent role of alcohol in increasing road safety risks, particularly in accident severity. These findings highlight the importance of stricter enforcement of BAC regulations, public awareness campaigns, and targeted interventions to mitigate high-risk behaviors, such as speeding and poor vehicle control, to enhance road safety outcomes.

Research on sliding mode control for path following in variable speed of unmanned vehicle

Research on sliding mode control for path following in variable speed of unmanned vehicle

Damping-regulated GSR array method for multi-harmonic fault diagnosis under variable speed conditions

Abstract Bearing fault diagnosis under variable speed conditions is essential due to the complexities introduced by speed fluctuations. The accurate detection of multi-harmonic faults is critical for ensuring reliability in intricate operating environments. From the perspective of the beneficial effects of noise, in this study we propose a novel damping-regulated generalized stochastic resonance (GSR) array method designed for multi-harmonic fault diagnosis under variable speed conditions. First, we employ computed order tracking (COT) to transform non-stationary time-domain signals into stationary signals in the angular domain. A damping-regulated GSR oscillator is then introduced within this domain, forming the basis of our GSR array. By analyzing the system stationary response, we reveal the diagnostic performance in theory to assess the array’s capacity for enhancing multi-harmonic fault characteristics. Through simulations and experimental validation, our method demonstrates superior diagnostic accuracy, particularly in variable speed scenarios. It excels in preserving and enhancing weak multi-harmonic fault characteristics while offering significant advantages in high diagnostic robustness. These findings provide significant potential for practical applications in fault diagnostics across various engineering systems.&amp;#xD;