Motor Performance Research Articles

Numerical study of aluminum combustion with agglomerate size distribution in solid rocket motor

The aluminum agglomerate size distribution plays an important role in influencing the particle distributed combustion in motor, which subsequently affects the performance of solid rocket motor significantly. In this work, a three-phase model to describe distributed combustion of aluminum agglomerates is established based on the Eulerian-Lagrangian method. Then, the agglomerate size, including mono size and distributed size, is studied to reveal its effect on aluminum combustion and motor flow field. The simulated results indicate that the increasing agglomerate mono size observes the obvious decrease of the average temperature inside the motor combustion chamber, implying the low combustion efficiency of large agglomerate size. When considering the agglomerate size distribution, the size distribution mode and the mean size D43 determine the combustion efficiency together. In particular, even the mean size is similar, with different distribution mode, like skewed distribution, bimodal or trimodal distribution, the combustion efficiency and flow field parameters are nonnegligible different. However, when the size distribution mode is the same and the peak range is similar, the mean size D43 becomes the only and predominant factor as the mono size.

Research on trajectory tracking control method for agricultural robot permanent magnet synchronous motor servo system based on improved EMD threshold wavelet filtering

In agricultural robots, trajectory tracking can be affected by disturbances such as road slopes or bumps, leading to sudden changes in path curvature and reduced control accuracy. To address this issue, we propose a servo motor drive control method based on an improved Empirical Mode Decomposition (EMD) threshold. A mathematical model integrating the drive and control of the servo motor is established, and the driving performance of the servo motor is analyzed. By detecting the speed and position of the servo motor, the specified three-phase current values for motor drive control are derived. The actual current of the motor is collected using Hall current sensors and denoised with an improved EMD threshold wavelet filtering method. Within the servo motor drive control model, the system calculates the difference between the given three-phase current values and the actual motor current, and this difference is then used to adjust the motor control via an input linear amplifier, ensuring integrated drive control. Simulation and experimental results show that the proposed trajectory tracking control method for the agricultural robot’s permanent magnet synchronous motor servo system exhibits high control accuracy, strong anti-interference ability, and good performance, making it more suitable for practical applications.

Self-powered intelligent badminton racket for machine learning-enhanced real-time training monitoring

Intelligent sensing technology exerts a crucial role in badminton training: by capturing the behavior of athletes, the technology can effectively promote the enhancement of motor skills and performance. However, sports sensors that are multifunctional, real-time, and convenient remain an ongoing challenge. This study designs a self-powered intelligent badminton racket (SIBR) with machine learning-based triboelectric/piezoelectric effects. The silver paste coating method is employed for constructing customized electrodes, thereby forming triboelectric sensing array on the badminton strings, which enables hitting position monitoring. Meanwhile, flexible piezoelectric films with a specific shape are embedded in the hand glue; thus, the grip posture is identified. These sensing arrays can directly convert mechanical signals into electrical signals for achieving zero power consumption. In addition, the study integrates a wireless module for signal acquisition and transmission at the bottom of the racket handle, which ensures real-time sensor monitoring based on normal usage. The collected multi-channel data obtained from the SIBR is utilized for machine learning, achieving an accuracy of hitting position that can reach 95.0%. SIBR provides a powerful reference for badminton training and unfolds a new path and direction for badminton sports monitoring.

Prediction of Solid Rocket Motor Performance Based on Deep Learning and Ignition Experimental Data

Prediction of Solid Rocket Motor Performance Based on Deep Learning and Ignition Experimental Data

The tolerable upper intake level of manganese alleviates Parkinson-like motor performance and neuronal loss by activating mitophagy

Manganese (Mn2+) is among the indispensable trace elements required by the human body, but high-dose Mn2+ exposure can lead to Mn poisoning. Therefore, the tolerable upper intake level (UL) for Mn2+ has been established for normal individuals in different countries. However, whether the UL of Mn2+ is suitable for the patients of Parkinson's disease (PD) is unclear.Here, we found unexpectedly that the dietary UL of Mn2+ supplement enhanced mitophagy through the PINK1/parkin-mediated ubiquitin-dependent pathway in MPTP- induced mice and cells. Mn2+ promoted mitochondrial biogenesis and dynamics, thereby increased the activity of the mitochondrial respiratory chain with restored mitochondrial function. Additionally, Mn2+ directly elevated the activity of mitochondrial superoxide dismutase (MnSOD), which contributed to the clearance of reactive oxygen species (ROS), restored dopaminergic and motor functions in the MPTP-induced PD mouse model. Similar results were also observed in SH-SY5Y cells, whereas knockdown parkin using siRNA or application of mitophagy inhibitors (Mdivi-1 or cyclosporine A), abolished the neuroprotective effects of Mn2+.These findings demonstrate that the dietary UL of Mn2+ is protective for the MPTP-induced Parkinson-like lesions with the mechanisms involving the activation of mitophagy, suggesting potential intervention of PD by moderately increasing dietary Mn2+ intake.

Discovery of a novel KV7.2/7.3 channels agonist for the treatment of neuropathic pain

Here, we designed, synthesized and evaluated a series of compounds as KV7.2/7.3 channels (or KCNQ2/3) agonists. The new compounds were assayed in vitro for KCNQ2/3 and other receptors binding affinity. The desired compound 16 showed high activity for KCNQ2/3 (EC50 = 1.03 ± 0.07 μM) without acute liver injury compared to flupirtine. It demonstrated powerful dose-dependent effects in multiple analgesic models, such as chronic constriction injury (CCI, ED50 = 12.02 mg/kg) and streptozotocin-induced diabetic peripheral neuropathic pain (DPNP, ED50 = 9.63 mg/kg) models. Additionally, compound 16 showed low affinity for human ether-a-go-go-related gene (hERG), high thresholds for acute toxicity, good motor performance in the rotarod test and acceptable pharmacokinetic properties. These results suggest the potentiality of compound 16 for the treatment of neuropathic pain.

Enhancing temperature and torque prediction in permanent magnet synchronous motors using deep learning neural networks and BiLSTM RNNs

This study aims to develop an effective method for predicting the temperature and torque of Permanent Magnet Synchronous Motors (PMSMs) using deep learning techniques, which is crucial for optimizing motor performance and ensuring longevity, particularly in the automotive industry. Various Neural Network (NN) architectures, including a Recurrent Neural Network (RNN) with a Bidirectional Long Short-Term Memory (BiLSTM) unit, were employed to model the complex relationships between motor parameters, such as stator winding, current, torque, and permanent magnet temperature. The findings demonstrate that an NN with two hidden layers (64 and 32 neurons) achieved an R2 score of 0.99 for both torque and temperature prediction, while the BiLSTM network effectively modeled temporal dynamics, leading to high-fidelity rotor temperature predictions. This research provides a novel application of BiLSTM RNNs in accurately predicting PMSM temperatures, offering valuable insights for industries reliant on these motors. Integrating these models into motor control systems can enhance operational efficiency, reduce overheating risks, and extend motor lifespan, contributing to energy savings and environmental sustainability by lowering energy consumption and reducing waste.

Assessment Of Sport-specific Motor Performance Profile In High-performance Athletes After A Multi-modal Neurologic Training Program

Assessment Of Sport-specific Motor Performance Profile In High-performance Athletes After A Multi-modal Neurologic Training Program

A variant of the Hspa8 synaptic chaperone modifies disease in a SOD1G86R mouse model of amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a relatively common and invariably fatal, paralyzing motor neuron disease for which there are few treatment options. ALS is frequently associated with ubiquitin-positive motor neuronal aggregates, a pathology suggestive of perturbed proteostasis. Indeed, cellular chaperones, which are involved in protein trafficking and degradation often underlie familial ALS. Spinal muscular atrophy (SMA) is a second, common paralytic condition resulting from motor neuron loss and muscle atrophy. While SMA is now effectively treated, mechanisms underlying motor neuron degeneration in the disease remain far from clear. To address mechanistic questions about SMA, we recently identified a genetic modifier of the disease. The factor, a G470R variant in the constitutively expressed cellular chaperone, Hspa8, arrested motor neuron loss, prevented the abnormal accumulation of neurofilament aggregates at nerve terminals and suppressed disease. Hspa8 is best known for its role in autophagy. Amongst its many clients is the ALS-associated superoxide dismutase 1 (SOD1) protein. Given its suppression of the SMA phenotype, we tested potential disease-mitigating effects of Hspa8G470R in a mutant SOD1 mouse model of ALS. Unexpectedly, disease in mutant SOD1 mice expressing the G470R variant was aggravated. Motor performance of the mice deteriorated, muscle atrophy worsened, and lifespan shrunk even further. Paradoxically, SOD1 protein in spinal cord tissue of the mice was dramatically reduced. Our results suggest that Hspa8 modulates the ALS phenotype. However, rather than mitigating disease, the G470R variant exacerbates it.

Clemastine enhances exercise-induced motor improvement in hypoxic ischemic rats

Neonatal hypoxic ischemia (HI) occurs owing to reduced cerebral oxygen levels and perfusion during the perinatal period. Brain injury after HI triggers neurological manifestations such as motor impairment, and the improvement of impaired brain function remains challenging. Recent studies suggest that cortical myelination plays a role in motor learning, but its involvement in motor improvement after HI injury is not well understood. This study aimed to investigate the impact of myelination on motor improvement following neonatal HI injury. We employed a modified Rice-Vannucci model; the right common carotid artery of postnatal day 7 (P7) Wistar rats was isolated and divided, and the rats were then exposed to hypoxic condition (90 min, 8 % O2). A total of 101 rats (66 males) were divided into four groups: trained-HI (n = 38), trained-Sham (n = 16), untrained-HI (n = 31), and untrained-Sham (n = 16). The trained groups underwent rotarod-based exercise training from P22 to P41 (3 days per week). Structural analysis using magnetic resonance imaging and immunohistochemistry (n = 6 per group) revealed increased fractional anisotropy and myelin density in the primary somatosensory cortex of the trained-HI group. We further evaluated the effect of myelination promotion on rotarod performance by administering clemastine, a myelination-promoting drug, via daily intraperitoneal injections. Clemastine did not enhance motor improvement in untrained-HI rats. However, clemastine-administered trained-HI rats (n = 7) exhibited significantly improved motor performance compared to both saline-administered trained-HI rats (n = 11) and clemastine-administered untrained-HI rats (n = 7). These findings suggest that myelination may be a key mechanism in motor improvement after HI injury and that combining exercise training with clemastine administration could be an effective therapeutic strategy for motor improvement following HI injury.

IoT Enabled Motor Drive Vehicle for the Early Fault Detection in New EnergyConservation

An IoT-enabled motor drive vehicle integrates Internet of Things (IoT) technology with traditional vehicle systems to enhance control, monitoring, and automation. Sensors and connected devices within the vehicle collect real-time data on parameters such as speed, battery status, motor performance, and environmental conditions. This data is transmitted to cloud-based platforms for analysis, enabling remote diagnostics, predictive maintenance, and optimization of vehicle performance. IoT integration also facilitates features like vehicle tracking, smart navigation, and user-specific adjustments, improving overall efficiency, safety, and user experience. This paper investigates the utilization of IoT enabled Programmable Logic Controller (PLC) technology to enhance fault detection in new energy vehicle (NEV) motor drive systems. With the increasing adoption of electric vehicles, ensuring the safety and reliability of motor drive systems becomes paramount. The study begins with a comprehensive review of existing literature, examining various fault detection methodologies and technologies. Subsequently, simulation analyses are conducted to evaluate the performance of IOT enabled PLC-based fault detection algorithms under different operating conditions. This paper presents the numerical results of fault detection in new energy vehicle (NEV) motor drive systems using Programmable Logic Controller (IOT enabled PLC) technology. Through comprehensive simulations and experimental validations, the IOT enabled PLC-based fault detection algorithms achieved an average detection accuracy of 93% across various fault scenarios. The response time of the fault detection system.

Battery Condition Monitoring of Quadrotor UAV Using Machine Learning Classification Algorithm

Unmanned aerial vehicle flight performance and efficiency rely on various factors. Flight instabilities can happen due to malfunctions inside the system and disturbances from the external environment. Battery status plays a significant role in healthy flight conditions. A weak battery will affect the performance of propellers and motors, and the presence of wind disturbance can contribute towards inefficient flying capabilities. Therefore, investigation of fault at the early stage is crucial to maintain the great performance of the UAV. This paper aims to investigate the best prediction system from the existing machine learning algorithm such as Decision Tree (DT), Linear Discriminant (LD), Naïve Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Neural Network (NN) to classify the battery condition of the quadrotor by extracting the features from the displacement time series dataset. By using recorded flight data, it will be statistically analyzed to extract the flying condition features. The extracted features are the Euclidian distance (ED), speed, acceleration, Periodogram Power spectral density (PSD) and Fast Fourier Transform (FFT) of the signal. The result shows that the two best classifier algorithms are the Decision Tree and Neural Network models with training accuracy of 98% and 93% in Set A and B, respectively.

Finite Element Model Calibration with Surrogate Model-Based Bayesian Updating: A Case Study of Motor FEM Model

This paper introduces an advanced methodology for the calibration of Finite Element Models (FEM) utilizing a surrogate model-based Bayesian updating framework. The approach is exemplified through a case study on motor design, where precise FEM calibration is essential for predicting and optimizing motor performance. Traditional calibration techniques are often computationally expensive due to the iterative nature of the simulation process. To mitigate this, the proposed method integrates surrogate models to approximate FEM simulations, significantly reducing the computational burden without sacrificing accuracy. Bayesian updating is then employed to iteratively refine the surrogate model by incorporating new data, thereby enhancing prediction accuracy. This dual approach not only accelerates the calibration process but also ensures a high level of precision, making it highly suitable for complex engineering applications requiring both efficiency and reliability. The case study underscores the effectiveness of this methodology, demonstrating its potential to streamline the design process in motor development and other FEM-dependent engineering fields. The findings suggest that the surrogate model-based Bayesian updating approach achieves robust calibration with significantly fewer simulations, thereby optimizing both time and computational resources.

PASS Theory and Movement Disorders: Methodology for Assessment and Intervention.

Executive dysfunction appears to be a significant secondary characteristic frequently linked with movement disorders. Planning is a high-level cognitive process integral to our executive functions. Children who show deficits in their planning ability usually have difficulties in making decisions or problem-solving, such as initiating tasks or mentally organizing a project, and monitoring and controlling their behavior effectively. These difficulties can significantly impact their academic performance and daily functioning even in adulthood. However, the existing research on the relationships between motor performance and planning abilities is limited and inconsistent. This study aimed to explore the potential associations between motor and planning skills in 30 preschool-aged children with Developmental Coordination Disorder (DCD) and 30 healthy controls matched for age, including utilizing the PASS theory and Cognitive Assessment System (CAS). The findings highlighted balance, total motor score, and planned codes as the primary factors distinguishing the two groups. A significant Pearson's correlation was observed between the overall MABC-2 scores and the subdomain scores, along with the Planning Scale indices for both groups, suggesting a substantial relationship between these assessments. Among the Planned codes items, the most notable predictor of overall motor performance in children was identified. Furthermore, the total score for Planned Connections emerged as the most robust predictor for tasks associated with manual dexterity. The relationship between motor skills and executive functions in early childhood plays a vital role in the development of early intervention strategies that utilize cognitive-motor tools.

Design of Solar Electric Bicycle Based on Pulse Width Modulation on Throttle as DC Motor Speed Regulator

Electric bicycles are designed to use renewable energy to support the government's efforts to reduce carbon emission consumption. Problems in every region or city regarding fueled vehicles certainly increase air pollution. Generally, electric bicycles are designed to be a solution and are used in daily life to carry out daily activities by requiring a bicycle gas throttle control that can assist the driver or person in controlling the speed of the electric bicycle. In this study, designing a solar electric bicycle as environmentally friendly energy based on PWM signals to determine the duty cycle value or duty cycle with an optimized PWM value can affect the speed system (km / h) in Revolutions Per Minute (RPM) to determine the performance of the DC motor. The electric bicycle uses 30Wp solar power, two 12V 7.5 Ah batteries assembled in series, and a 24V 250W DC brushless motor. Based on the design results with a total weight of 37kg electric bicycle plus the weight of 55kg, 65kg, 75kg, and 85kg driver variations, the total controller power value is 163.44 watts. The results of duty cycle testing (duty cycle) were 25%, 50%, 75%, and 98%, with 249.9 as the maximum value of PWM and 9.8 Vout generated against the maximum speed of the electric bicycle of 2648 RPM or the equivalent of an electric bicycle speed of 18-28 km/hour. In this case, the electric bicycle is in a condition suitable for use to carry out daily activities by utilizing clean, cheap and appropriate energy

Effect of the PROFIT-BR exercise program on physical fitness of children: a protocol study

BackgroundConsidering that school environments are important and have the potential to promote an active lifestyle with direct impacts on physical, motor, cognitive, affective, and psychological aspects, the objective of this study was to evaluate the effect of a physical exercise program in the school context.MethodsA parallel, two-arm experimental research project carried out over 19 weeks, with two non-consecutive weekly classes of 60 min, with the inclusion of PROFIT-BR in the initial 15 min of the class followed by 40 min based on specific physical education skills on indicators of physical fitness in children. Estimates of excess weight and visceral fat will be measured and assessed by body mass index and waist-to-height ratio, respectively, and cardiorespiratory fitness by performance in the six-minute run/walk test. Flexibility will be assessed by the sit and reach test; and muscle strength measured by the number of sit-ups in one minute. The 2 kg medicine ball throw and the horizontal jump will be used to measure upper and lower limb power, respectively, the square test used as a measure of agility, and the 20-meter run test used to measure speed. To analyze the differences between baseline and post-intervention values, repeated measures ANOVA adjusted for somatic maturation will be used. The magnitude of the effect size will be estimated by “partial eta squared”. Statistical analyses will be carried out using SPSS version 24.0 software and a 5% probability of error in the analyses will be accepted.DiscussionThe main theoretical assumption of the proposal is the cause-and-effect relationship between the regular practice of physical exercise in childhood and adolescence and several parameters of health and motor performance. The elaborated program has a lot of applicability because it was wondering from the perspective of the school, in terms of materials, space, and objectives of physical education.

Modeling of genetic algorithm tuned adaptive fuzzy fractional order PID speed control of permanent magnet synchronous motor for electric vehicle

This study presents a novel Genetic Algorithm-optimized Adaptive Fuzzy Fractional Order Proportional Integral Derivative (GA-AFFFOPID) controller for enhancing the speed control performance of permanent magnet synchronous motor (PMSM) drives in Electric Vehicles. The proposed GA-AFFFOPID controller, which combines the advantages of genetic algorithm optimization and adaptive fuzzy fractional-order PID control, represents a unique and innovative approach to address the control challenges associated with PMSM drives. Permanent magnet synchronous motor technology, known for its efficiency, compactness, reliability, and versatility in motion control applications, is increasingly adopted in electric vehicle drive systems. However, the inherent non-linearity, dynamics, and uncertainties of permanent magnet synchronous motors pose significant control challenges. The exceptional performance of the GA-AFFFOPID controller, demonstrated through its superior system dynamics, precise speed tracking, and robustness against parameter variations and sudden load disturbances, underscores the significant advancements enabled by the genetic algorithm optimization technique in improving the control performance of PMSM drives for electric vehicle applications. Comparative analysis with traditional control methods demonstrates the exceptional performance of the Genetic Algorithm-optimized Adaptive Fuzzy Fractional Order Proportional Integral Derivative controller. These findings highlight the significant performance improvements facilitated by the genetic algorithm optimization technique in enhancing the control performance of the adaptive fuzzy fractional order PID controller in PMSM drives for electric vehicle applications.

3D CFD modelling and experimental validation of conjugate heat transfer in wheel hub motors in micro-mobility vehicles

An experimental and 3D CFD (computational fluid dynamics) based numerical investigation of the in-wheel hub internal permanent magnet synchronous machine (IPMSM) has been carried out to understand the thermal dynamics associated with the power loss within the motor. This study can serve as a clear case baseline for the future development of thermal management systems in micro-powertrain systems. Experimental results show that the maximum winding temperature of 65.9 °C has been observed on the end-windings (EW) for an operating condition of 500 rpm (base speed), 4.5 Nm. Stretching beyond this operating point would increase the winding temperature beyond 65.9 °C, potentially damaging the winding insulation’s capabilities and magnetic properties of the permanent magnets (PMs) and adversely affecting the performance of the wheel motors. The hysteresis and eddy current loss coefficients for the stator core have been identified as 14.5×10-2 and 1.6×10-4 respectively. The experimentally identified losses have been distributed among the motor components for the development of the numerical thermal model in commercial CFD software Star-CCM+™. The CHT (conjugate heat transfer) thermal paths show that 68.4 % and 31.4 % of the total power loss from the stator components undergo convection and conduction, respectively. This indicates that convection heat transfer dominates on the in-wheel hub IPMSM motor topology. Almost 34.5 % of the heat loss is transferred radially through the air gap to the Permanent Magnets. When the air gap size decreased by 80 %, the amount of convection heat transfer through the air gap increased by a factor of 2 (69 %) and reduced the winding temperature by 13 % (57.3 °C).

Diosmin and Hesperidin Have a Protective Effect in Diabetic Neuropathy via the FGF21 and Galectin-3 Pathway.

Background and Objectives: This study aimed to investigate the protective effect of diosmin and hesperidin in diabetic neuropathy using a rat model, focusing on their impact on nerve regeneration through the fibroblast growth factor 21 (FGF21) and galectin-3 (gal3) pathway. Materials and Methods: Forty adult male Wistar rats were used in this study. Diabetes was induced using streptozotocin (STZ), and the rats were divided into control, diabetes and saline-treated, diabetes and diosmin + hesperidin (150 mg/kg) treated, and diabetes and diosmin + hesperidin (300 mg/kg) treated groups. Electromyography (EMG) and inclined plane testing were performed to assess nerve function and motor performance. Sciatic nerve sections were examined histopathologically. Plasma levels of FGF21, galectin-3, and malondialdehyde (MDA) were measured as markers of oxidative stress and inflammation. Results: Diabetic rats treated with saline displayed reduced nerve conduction parameters and impaired motor performance compared to controls. Treatment with diosmin and hesperidin significantly improved compound muscle action potential (CMAP) amplitude, distal latency, and motor performance in a dose-dependent manner. Histopathological examination revealed decreased perineural thickness in treated groups. Additionally, treatment with diosmin and hesperidin resulted in increased plasma FGF21 levels and reduced plasma levels of galectin-3 and MDA, indicating decreased oxidative stress and inflammation. Conclusions: Diosmin and hesperidin exhibited protective effects in diabetic neuropathy by promoting nerve regeneration, enhancing nerve conduction, and improving motor performance. These effects were associated with modulation of the FGF21 and galectin-3 pathway. These findings suggest that diosmin and hesperidin may hold potential as adjunctive therapies for diabetic neuropathy.

Relationship between Kinesthesia and Motor Performance in Young Adults with Generalized Joint Hypermobility: A Systematic Review

Relationship between Kinesthesia and Motor Performance in Young Adults with Generalized Joint Hypermobility: A Systematic Review