Running Conditions Research Articles

Influence of foot strike patterns and cadences on patellofemoral joint stress in male runners with patellofemoral pain

ObjectivesThis study aimed to determine the effect of foot strike patterns and cadences in male runners with patellofemoral pain (PFP). DesignCross-sectional study. SettingBiomechanics lab. Methods20 male runners with PFP were instructed to randomly complete six running conditions (three cadence conditions in rearfoot strike pattern (RFS) or forefoot strike (FFS)) under a preferred running speed. Main outcome measuresThe primary outcomes were peak knee joint and moment, and secondary outcomes were patellofemoral joint stress. ResultsRunning with increased cadence has a lower flexion angle (P = 0.027, η2 = 0.45), lower extension moment (P = 0.011, η2 = 0.29), lower internal rotation moment (P = 0.040, η2 = 0.17), lower patellofemoral stress (P = 0.029, η2 = 0.52) than preferred cadence. FFS running performed significantly lower flexion angle (P = 0.003, η2 = 0.39), lower extension moment (P < 0.001, η2 = 0.91), lower adduction moment (P = 0.020, η2 = 0.25) lower patellofemoral stress (P < 0.001, η2 = 0.81) than RFS running for all cadence. ConclusionsPreliminary findings provide new perspectives for male runners with PFP to unload patellofemoral joint stress in managing PFP through the combination of the FFS pattern and increased cadence.

Bidimensional local characteristic-scale decomposition and its application in gear surface defect detection

Visual image-based inspection methods can directly reflect the type of defects on the surface of gears. However, these methods have many problems: firstly, as a two-dimensional signal, the data volume of images is large and the processing is relatively time-consuming. Although some existing image signal processing methods (e.g. bidimensional empirical mode decomposition (BEMD)) have good decomposition results, their decomposition speed is slow. The bidimensional local characteristic-scale decomposition (BLCD) method is proposed in this paper, which adaptively decomposes an image from high to low frequencies into several bidimensional intrinsic scale components. It is demonstrated that the BLCD method maintains the advantages of the BEMD method in terms of good decomposition ability and adaptive capability while significantly reducing the processing time and improving the computational efficiency. Secondly, in the running state of the gears, the obtained images sometimes contain noise, which is not convenient for detecting surface defect types. A gear surface defect detection method based on BLCD image denoising is proposed in this paper. Firstly, it uses the BLCD denoising module for preprocessing to provide high signal-to-noise ratio images for the subsequent detection module, and then uses the detection module for defect identification and classification. Experiments prove that the BLCD denoising module has excellent performance and it is well coupled with the detection module, giving the whole method higher accuracy and stability than other classification methods.

Experimental performance analysis of evacuated tube type direct-expansion solar-assisted heat pump system

ABSTRACT The direct-expansion solar-assisted heat pump (DX-SAHP) have proven to be one of the strategies in reducing energy consuming for domestic hot water supply. The poor pressure resistance of the collector, high heat loss from radiation for the traditional bare-plate type DX-SAHP limits its popularization and application. To solve this problem, a DX-SAHP system using evacuated tube type collector-evaporator was proposed in this post. System optimization can not only reduce operating costs, but also results in energy savings. Thus, a DX-SAHP prototype was established and tested under various running conditions to obtain the optimal running parameters. Based on experimental data, the impacts of refrigerant charge, solar radiation intensity, and ambient temperature on the performances of evacuated tube type DX-SAHP system are investigated and evaluated. The outcome indicates that this system coefficient of performance (COP) increases by 73.4% with the decrease of charge amount of refrigerant in the range of 5.66–6.67 kg. And, the average collector efficiency is 0.71 with a refrigerant charge of 5.66 kg. And, the optimal COP of this presented system is 5.27 when an ambient temperature, solar radiation intensity, and refrigerant charge are 26.3°C, 954.37 , and 5.56 kg, respectively.

Spring-like Triboelectric Nanogenerator for Monitoring Body Vibration State of the Ship Power Equipment

In the navigation process, monitoring the running state of ship power plant equipment is crucial. In bad weather, when the critical equipment is abnormal, it is especially necessary to find out the root cause of the failure as soon as possible. In this case, it is required to use rapid detection equipment to detect and judge the key parameters. This paper proposes a vibration sensor (VS-TENG) of triboelectric nanogenerators based on spring vibration. The sensor adopts the spring structure inside and vibrates with the ship power equipment to collect the low-frequency vibration energy. This paper uses the VS-TENG sensors of two different spring parameters to study the electrical signal output under the excitation conditions of varying vibration frequencies. The results show that in the frequency range of 3–500 Hz, the efficient processing of different vibration excitation frequency signals can be realized, and the vibration frequency can be accurately identified. The error of medium-high frequency identification in VS-TENG is less than 1%. Especially at the resonant frequency, the maximum voltage output value can be achieved. On the PT500 Mini test bench, VS-TENG can reasonably identify the motor frequency and shutdown state. Therefore, VS-TENG can be applied to the condition monitoring of the vibration of the ship’s power plant and has a broad application prospect.

Effect of Running-In Conditions on the Super Low EHD Sliding Friction of Si3N4 Ball and WC Plate in Glycerol–Water Solution

Effect of Running-In Conditions on the Super Low EHD Sliding Friction of Si3N4 Ball and WC Plate in Glycerol–Water Solution

Vehicle State Joint Estimation Based on Lateral Stiffness.

In this study, a vehicle state joint estimation method based on lateral stiffness was applied to estimate the running states of electric vehicles driven by rear-drive, in-wheel motors. Different from the estimation methods used in other research, the joint estimator designed in this study uses the least-squares (LS) algorithm to estimate the lateral stiffness of the front and rear axles of the vehicle, deploying the high-degree cubature Kalman filter algorithm to estimate the vehicle state. We establish a three-degree-of-freedom nonlinear vehicle model with longitudinal velocity, lateral velocity, and yaw rate, and the lateral stiffness of the front and rear axles as the principal parameters. For the low-speed running state of the vehicle, a linearized magic tire model with high fitting accuracy was used to calculate the lateral force of the entire vehicle. The LS algorithm with a forgetting factor was used to design a lateral stiffness estimator to assess the front-axle and rear-axle lateral stiffness of the entire vehicle. The generalized high-degree cubature Kalman filter (GHCKF) algorithm was used to design the vehicle state estimator and further improve the GHCKF algorithm. A vehicle state estimator, using the square root generalized high-degree cubature Kalman filter (SRGHCKF), was designed. Therefore, the joint estimator, comprising a lateral stiffness estimator and a vehicle state estimator, adopts the LS-GHCKF/SRGHCKF algorithm and enables the estimation of the lateral stiffness, the longitudinal velocity, the lateral velocity, and the yaw rate of the entire vehicle during the driving process. A double lane change and slalom simulation were performed to analyze the feasibility and accuracy of the joint estimation algorithm and verify the results of the LS-GHCKF algorithm and the LS-SRGHCKF algorithm. Further, a low-speed driving experiment was carried out for electric vehicles driven by rear in-wheel motors. The inertial navigation system (INS), the global positioning system (GPS), the real-time kinematic (RTK), and an angle sensor were used to collect real-time vehicle data. The results were compared to verify the feasibility of the joint estimator and the progressiveness of the algorithm. The experimental verification and simulation both show that the vehicle state joint estimator, designed based on the LS-GHCKF/SRGHCKF algorithm, can accurately estimate the real-time state of the vehicle. Additionally, the LS-SRGHCKF algorithm shows better effectiveness and robustness than the LS-GHCKF algorithm.

Thermal characterization of an interior permanent magnet electric motor

An engineering approach for thermal characterization of an interior permanent magnet electric motor (EM) with a specific aim to identify the interdependency between its mean coil temperature and the maximum component temperatures at the conditions with constant and variable loads/speeds is proposed. For specifying the convective thermal boundary condition between stator and rotor that spins at variable speeds/directions, an experimental study aimed at devising the empirical heat transfer correlation for the Taylor-Couette airflow with varying rotating speed/direction is performed. It is firstly revealed that the responsive air-gap heat convection to angular acceleration follows the characteristic of a single-degree-of-freedom (SDOF) dynamic system. Along with the heat transfer correlation for the unsteady Taylor-Couette airflow, the nonhomogeneous thermal conductivity model that considers the orthogonality effect of the bent coiled winding at its two axial ends is integrated with an iteration scheme for calculating the air bulk temperature in an entrapped air chamber. The favorable experimental validation with the maximum discrepancies between predicted and measured component temperatures less than 7 % in steady and unsteady running conditions affirms the accuracy of the proposed simulation model. At a rotating speed of 4800 rev/min and power output of 1688 W, the maximum-to-mean temperature differences of stator winding, magnetic stripe, and steel sheet of stator reach respectively 6.75 °C, 3.21 °C, and 6.94 °C. Particularly, all the maximum component temperatures are well correlative with the mean temperature of the stator winding at the various steady/unsteady operating conditions. This finding extends the application of lumped thermal network analysis that predicts the averaged components’ temperatures; and enables the on-line monitoring for the maximum component temperatures using a mean stator-winding temperature as the controlling factor.

Favorable performance of WSe2 monolayer modified by chromium catalyst for sensing thermal runaway gases H2, CO and C2H4 using first-principles theory

Detections of thermal runaway gases in the Li-ion batteries (LIBs) is a workable method to evaluate its running status. In this work, first-principles theory is adopted to simulate adsorption and sensing performances of Cr-decorated WSe2 (Cr-WSe2) monolayer upon three typical gases, namely H2, CO and C2H4, in the thermal runaway LIBs. Results indicate that Cr-WSe2 monolayer performs physisorption upon H2 with Ead of −0.37 eV and chemisorption upon CO and C2H4 with Ead of −1.62 and −1.41 eV. The Cr adatom causes strong magnetic behavior to Cr-WSe2 monolayer, which impact the magnetic property of the adsorbed gas molecule as well. The analysis of electronic property suggests the strong potential of Cr-WSe2 monolayer as a high-sensitivity CO and C2H4 sensor, and can be applied as a reusable C2H4 sensor after gas desorption for 70.38 s at 398 K. The analysis of work function (WF) reveal its potential as a potential WF-based gas sensor upon H2 and CO as well. The findings in this work uncover the sensing potential of Cr-WSe2 monolayer upon three typical gases of thermal runaway in the LIBs, which can be explored as a nano gas sensor to perform thermal runaway monitoring using the nano-sensing method.

Low bandgap GaAsNBi solar cells

The development of low bandgap GaAsNBi solar cells grown using MBE is reported. The devices include a pin heterostructure with GaAsNBi as the i-layer. The substrate rotation is stopped during epitaxy of the GaAsNBi layer (stationary growth), resulting in a continuous variation in the source fluxes over the wafer, enabling the investigation of a wide range of growth conditions in a single growth run. In particular, we investigate the photovoltaic properties as a function of the As/Ga flux ratio around the critical stoichiometric value. For slightly below stoichiometric As/Ga, the lattice mismatch and bandgap energy are minimized to 8 × 10-4 and 0.86 eV, respectively, while the crystal quality remains good. Increasing As/Ga over the stoichiometric ratio leads to a rapid increase in the tensile mismatch and bandgap energy, likely due to reduction in Bi incorporation. Despite a slightly higher open-circuit voltage offset at high As/Ga, this also leads to an clear increase in the external quantum efficiency and short-circuit current, suggesting significant differences in the carrier collection efficiencies. The results demonstrate the importance of the As/Ga flux ratio in controlling the solar cell performance, and the viability of GaAsNBi as a candidate for a low bandgap subjunction in multijunction solar cells.

Research on a Speed Guidance Strategy for Mine Vehicles in Three-Fork Roadways Based on Vehicle–Road Coordination

Aiming at the traffic congestion problem of mining vehicles in the intersection area of three-fork roadways in coal mines, this paper proposes a speed guidance strategy based on a vehicle–road cooperative environment to adjust the running state of mining vehicles in the three-fork roadway system. The proposed speed guidance strategy can realize the safe and effective passage of underground mining vehicles to the greatest extent. Taking a three-fork roadway in a coal mine as an actual case, the operation of mining vehicles in the three-fork roadway is optimized and scheduled. Through the joint simulation of PTV VISSIM 4.3 traffic simulation software and PYTHON 3.7, the travel time, number of queuing vehicles, and delay time of mining vehicles passing through the three-fork roadway entrance under the two conditions of no speed and speed guidance in the coal mine are simulated and compared. The results show that after using the proposed speed guidance strategy for scheduling, mining vehicles can quickly pass through the three-fork roadway intersection. The travel time is reduced by 18.4%, the number of queuing vehicles is reduced by 41.5%, and the delay time is reduced by 24.1%, which effectively improves the transportation efficiency of underground mining vehicles.

Reliability analysis of floating offshore wind turbine generator based on failure prediction and preventive maintenance

Aiming at the failure problems of a floating offshore wind turbine (FOWT) generator, a preventive maintenance model based on failure prediction is established. Firstly, the fault tree method is adopted to calculate the generator reliability value under run or failure states. Secondly, quantitative analysis of the generator in a maintainable state is carried out by the Markov method. Finally, a system optimization model is established based on the failure prediction and preventive maintenance (FPPM) strategy. The single-objective teaching-learning-based optimization (TLBO) algorithm and multi-objective genetic algorithm (MOGA) are respectively used to solve the optimization problem. The results show that FPPM strategy can greatly improve the availability and mean time between failure (MTBF) value, which increase the system working time and efficiency. Multi-objective analysis can get the best combination of the system maintenance cost, availability, and risk than single-objective. The time interval of failure prediction is calculated as 19788 h and the system is in a safe state if the remaining life is predicted to be more than 101 h. Sensitivity analysis of the model is carried out to determine that the optimization objectives are less sensitive to variables, which verifies the rationality and accuracy of the proposed model.

Application of the Residue Method in Steam Superheater Fault Detection

Steam superheaters experience various defects that can affect the system’s operation differently. Maintaining a constant temperature and pressure of the steam at the turbine input is crucial for optimal steam turbine running conditions. This is achieved by regulating the steam temperature in the superheater, which is divided into three parts with devices mounted at each connection point to allow for the injection of condensates to cool the steam. The steam pressure is controlled by adjusting the fuel flow command. However, controlling the output temperature can be challenging because of the transfer time delay between the points where the water is sprayed and the points where the steam temperature is measured. To address this challenge, a temperature control system was developed and tested in three different environments using an electronic simulator, the block-oriented simulation hardware BORIS, data acquisition board dSpace, and a MATLAB R2020a Simulink with a PI controller model. These simulations allowed for the study of the superheater’s function in both normal and fault conditions, with the possibility of controlling the system structure and detecting faults through the proper implementation of weighting matrices.

Self-reconfigurable partial mesh using ZigBee control board towards fail-proof and reliable home area networking

This paper deals with the development of customized home area networking (HAN) with a higher degree of reliability so that a robust communication network can be provided to smart home owners. The proposed HAN is built with the multi-gateway-based partial mesh networking topology using state-of-the-art ZigBee control boards (ZCBs) where each individual appliance within the home can be monitored and controlled remotely. The gateway is actually used to coordinate amongst the In-home display (IHD), ZCBs, smart meter, and remote server-based home energy management software or even with the mobile Apps in such a way that the best possible energy usage can be made. In order to enhance the reliability of this proposed HAN, the active and pseudo gateway concept is introduced in which under the failure of the active gateway (AG), pseudo gateway (PG) takes control, and the HAN gets reconfigured automatically. Malfunctioning or failure of any ZCB or even gateway node is detected during route discovery with ad-hoc on-demand distance vector (AODV) routing. The reliability of the proposed system is estimated using possible path models and region models considering all of its running conditions. The proposed system is simulated and a prototype laboratory setup with 15 and 10 numbers of ZCBs is built for experimental verification. Various failure conditions are experimented with to demonstrate the alternate routes and activation of PG as AG. The availability of an alternate route and/or gateway makes this proposed HAN a fail-proof and reliable one and this is reflected from the relative values obtained from the simulated and experimental results.

Exergy Audit of Thermodynamic Parameters and Performance Analysis of OgorodeThermal Power Plant in Delta State, Nigeria.

The exergy analysis of the Ogorode steam plant is presented. The aim was to determine and identify the magnitudes and locations of real exergy losses in order to improve plant efficiency. The exergy losses occurred in the various components of the steam plant, such as the boiler, economizer, turbine, super heater, condenser, pump, feed water heater, reheater, and air pre-heater, and these have been calculated using the concepts of availability or available exergy and irreversibility. The temperature and pressure of the plant's running conditions were combined to determine the process irreversibility (exergy losses) and the exergy efficiency of the plant. The exergy losses of the individual components of the plant show that the maximum exergy losses are in steam turbine operation one (STO ) and steam turbine operation two (STO ). STO and STO are 813 kJ/kg, 1 2 1 2 820 kJ/kg, 822 kJ/kg, and 815 kJ/kg that occurred in the turbine between 2019 and 2021, while the minimum exergy losses in STO and STO are 68 kJ/kg, 150 kJ/kg, 1 2 280 kJ/kg, and 276 kJ/kg that occurred in the boiler system between 2019 and 2021. There was a high energy efficiency of 82% and 80% in the superheater between 2019 and 2021, while 89% in the feed water heater and 73% in the superheater had the same duration. The efficiency of 15% and 21% were the minimum in the pump system between 2019 and 2021, while the turbine had a minimum of 20% and 27% from 2019 to 2021. The highest losses of exergy occurred in the turbine as a result of the irreversibility inherent in the turbine process, corroded turbine blades as a result of wet steam, and low energy delivered to the superheater tubes. It was concluded that the turbine inlet temperature should be increased, more energy delivered to the superheater should be increased, boiler pressure should be increased, and weak feed water heaters should be replaced for better performance of the thermal plant.

Durable hydrogel-based lubricated composite coating with remarkable underwater performances

HypothesisHydrogel coatings have received great attention in the field of such as medical devices, water treatment membranes, flexible electronics, and marine antifouling. However, when it comes to lubrication of hydrogel materials, though it has great potential applications in the field of industrial and medical drag reduction, some restrained properties are urgently needed to overcome for releasing the practical potential. ExperimentsDurability of high lubrication was revealed from the sliding test during the long-term storage, as well as the long-distance sliding. Some variables which possibly affect the lubrication performance were examined to demonstrate that excellent lubricity of the coating would not be easily influenced by load, frequency, friction pair and temperature. The microstructure and mechanical characterization of the lubricative coating indicate that the resistance to harsh running conditions is premised on enough hydration extent and robustness. The formulae of Possion ratio and ball-on-disk contact stress which apply to soft matter were used for calculating contact stress values in tribology tests. Anti-swelling and bio-compatibility are also verified. FindingsThis work found a route of achieving superior lubrication and coexisting with stability in lubrication, which can be used for drag reduction in medical devices and shipbuilding industry.

Research on the Service Condition Monitoring Method of Rolling Bearings Based on Isomorphic Data Fusion

In order to solve the problem that it is difficult for a single sensor to accurately characterize the running state of rotating bearings under complex working conditions, this paper proposes a data-level fusion method based on multi-source isomorphic sensors to monitor spindle bearings. First, new vibration signals in the X,Y,Z direction were obtained through the process of decomposing, de-noise, and reconstructing. Second, the PCA algorithm was used to select the time-domain and frequency-domain features of the vibration signals, construct the feature matrix, and perform dimensionality reduction in the feature matrix. Finally, the entropy weight method was introduced to obtain the initial weights of the three directions as the inputs of the adaptive function. The chaotic particle swarm optimization algorithm proposed in this paper helps particles jump out of the local optimum. Chaotic mapping is used to initialize the velocity and position of the particles, which calculates globally optimal weights in three directions. In order to extract bearing signal features more accurately and efficiently, a DenseNet and Transformer (DAT) feature extraction model is proposed to deal with the complex changes and noise interference of bearing signals. Through the open data set of Jiangnan University and the data collected by our own experimental platform, the maximum accuracy of the DAT model was verified to be 100%.

Optimization of train timetables and placement of charging facilities for battery‐powered LRT

AbstractThe energy consumption of a battery‐powered train in an interstation depends on the running time and state of energy (SOE) at departure. In this paper, we develop an optimization method of train timetables to minimize energy consumption in line with several stations. The variables in this proposed optimization model are running, dwell, and charging times as real numbers and places of charging facilities as binaries. Additionally, we conducted a case study using the real‐world light rail transit (LRT) route, vehicle, and onboard battery model to confirm the effectiveness. In the case study, the proposed method can optimize the timetable and placement of charging facilities by considering the track gradient and battery SOE.

Intelligent framework for degradation monitoring, defect identification and estimation of remaining useful life (RUL) of bearing

The proposed intelligent framework seamlessly integrates degradation monitoring, defect identification, and remaining useful life (RUL) estimation for a comprehensive and holistic solution to bearing health assessment. It leverages a novel directed divergence (DD) measure to compare probability distributions, generating a Health Indicator (HI) that captures the health status of the bearings. This HI serves as the cornerstone for the framework's cohesive approach. An alert system, utilizing the modified Pauta criterion and iterative window scanning, enhances anomaly detection, while a double-check mechanism minimizes false alarms. For defect identification, dynamic analysis-assisted wavelet filtering harnesses a customized wavelet filter generated from simulated interactions between defective components and rolling elements. In parallel, the framework employs a graph convolution network (GCN) with a 1D health indicator, derived from the proposed directed divergence measure between the reference data and running conditions, to estimate RUL. By unifying these components, proposed framework offers an efficient, integrated, and proactive solution for monitoring, defect identification, and RUL estimation in bearing health assessment, ultimately enhancing system reliability and maintenance strategies.

Fundamental study for vehicle abnormal sound detection and the direction estimation methods using machine learning with noise reduction

Detecting abnormal sounds during vehicle running condition is important to find out the vehicle’s problems at the early stage. In the mass production process, the detection test is generally carried out by the inspector. However, continuous inspection by a human for long time is hard and miss false inspection may occur. In addition, normal running noise, such as road noise, deteriorates the inspection accuracy. Therefore, an automatic accurate abnormal sound detection method under the normal noise is necessary to improve the detection accuracy with small man-hour. Furthermore, if the method has an ability to localize the abnormal sound direction, the countermeasure also becomes easy. In this study, we considered the noise detection method using machine learning with a noise reduction method and attempted the noise direction using two microphones. As a fundamental study, we applied these methods in an open room and the result showed that the method could detect the abnormal sound under normal noise condition and indicate the direction well in the simple verification test.

Biomechanics and Injury Prevention for Barefoot/Minimalist Running

Category: Sports; Midfoot/Forefoot Introduction/Purpose: Running-related injuries are common despite technological advances in running shoes. Recently, there has been a movement towards barefoot/minimalist running. Proponents of barefoot running state that this style of running provides beneficial modifications to the gait cycle and prevents running-related injuries. The purpose of this review is to analyze the biomechanical consequences of barefoot/minimalist running and determine if barefoot/minimal style running prevents running- related injuries. Methods: A literature search was done on PUBMED using the keywords: Barefoot running, minimalist running, injury prevention, injury rate, foot-strike pattern, vertical loading rate, biomechanics, and clinical outcomes. Studies were then grouped into biomechanical studies and clinical outcome studies. Biomechanical studies were included if they analyzed outcomes such as: foot strike index/pattern, vertical loading rates, vertical ground reaction forces, joint angles, cadence, step length, or joint energy absorption. Clinical outcome studies were included if they analyzed injury rates and pain. Results: Biomechanical studies indicated that barefoot/minimalist running coincided with a transition to an anterior foot strike, lower vertical loading rates, higher cadences, less energy absorption at the knee joint, and higher energy absorption at the ankle joint. The clinical outcome studies indicated improvement of prior injuries and equivocal injury rates in the barefoot/minimal style running groups. Conclusion: Foot strike pattern is more important than footwear in regard to injury prevention and vertical loading rate. Switching to minimalist footwear improves running biomechanics with a more anterior foot strike, higher cadence, lower vertical loading rate, less knee energy absorption, and a shorter stride length. However, minimalist footwear places higher degrees of stress on the ankle joints and an increased injury rate when abruptly transitioning to barefoot running. Runners must weigh the risks and benefits and take caution to properly strengthen their feet and safely transition to minimalist running.