Spring Load Research Articles

Identifying and assessing pond best management practice under future climate scenarios

Climate change can play important roles in the hydrological processes within watershed with ponds as the Best Management Practice (BMP). Unlike several other studies, this study integrated remote sensing technique with hydrological model to identify and simulate pond BMP. Limited studies have been carried out to evaluate pond BMP in relation to the climate change impacts on hydrology and water quality particularly in Mississippi watersheds. The objective of this study was to classify ponds on satellite imagery within the Big Sunflower River Watershed (BSRW) using Google Earth Engine (GEE) and incorporate this data with Soil and Water Assessment Tool (SWAT) model to evaluate future hydrological and water quality outputs. The SWAT model was calibrated and validated against streamflow (R2 and NSE values from 0.81 to 0.56) and sediment (R2 and NSE values from 0.91 to 0.40). Future climate data for the mid (2040–2060) and late (2079–2099) centuries were utilized to create climate change scenarios (e.g., RCP 4.5 and 8.5). Results of this study projected that the average annual flow and sediment load will increase by 26–46%, and 107-150% respectively by the late century compared to the baseline period (2002–2021). However, the projected sediment load with modified pond BMP data used in the SWAT model could decrease average annual sediment load by 44–46% under both RCP scenarios. Seasonal data analysis determined that spring, summer, and fall sediment loads were projected to decrease up to 42%, 52%, and 46% respectively under both RCP scenarios due to pond BMP. This study can be useful for the development of climate-smart management strategies in agricultural watersheds.

Evaluating the Effects of Climate Change on Spring Load Restrictions across Ontario, Canada

Evaluating the Effects of Climate Change on Spring Load Restrictions across Ontario, Canada

Development of Static Test Equipment and a System for Lever-Loaded Air Springs

In light of the heavy load applied by traditional air spring test equipment and its complex structural system design, a lever-type torque loading air spring test system is designed. It adopts the principle of Chinese scales to apply the load on the air spring in the form of proportional amplification, which can apply a simulated load on the air spring of 500–800 kg, using the vertical sliding shaft as the transverse limit to make the air spring elongate and compress by 280 mm in the longitudinal direction. The measurement and control system of the test equipment is then developed based on the LabVIEW platform, and the required sensors are selected and installed. The system can achieve real-time data acquisition of the air pressure, load, height and other parameters of the air spring and air spring charging and discharging control. Following the debugging of the function of the test equipment, the function of each subsystem is normal and able to meet the requirements of air spring characteristic and pressure tightness tests. For small spaces, such as laboratories, by reducing the installation of hydraulic and other oil source systems, avoiding the use of large mass blocks to simulate the loading of air spring loads and optimizing the complex installation and debugging process, this miniaturized design for air spring test equipment has benefits for practical applications.

Application of the Machine Learning Method to Determine Spring Load Limits and Winter Weight Premium

Freight transportation plays a crucial role in sustaining the Canadian economy. However, heavy truck transportation also puts enormous pressure on roadway networks. Spring Load Restrictions (SLR) are implemented to minimize road damage caused by heavy traffic during the thaw-weakening season, and Winter Weight Premium (WWP) is used to reduce the impact of SLR on trucking operations by allowing higher axle loads in winter. However, existing policies apply fixed dates each year for these restrictions, regardless of the actual structural capacity of the pavement. Different methods have been proposed to improve the application of SLR and WWP; however, they rely mainly on indirect indices, such as the cumulative thawing index and cumulative freezing index, which pose challenges in their calculation. This study explores the practical implementation of machine learning models for accurately determining the start and end dates of SLR and WWP. In a novel approach, machine learning models directly derive the start and end dates of SLR and WWP from frost and thaw depths in the pavement structure which are determined by pavement temperature and moisture content. In contrast to previous studies that neglected the influence of soil moisture content on determining the start and end dates of SLR and WWP, this study examines the variation in soil moisture content to evaluate the validity of existing theories. The findings reveal a high level of agreement between the machine learning model’s estimations of frost and thaw depths and the measured values, with R2 values exceeding 0.91.

In vivo measurement of strain in the periodontal space of pig (Sus scrofa) incisors using in-fiber Bragg sensors.

The incisor teeth in pigs, Sus scrofa, function in association with a disc-shaped snout to explore the environment for potential food. Understanding how mechanical loading applied to the tooth deforms the periodontal ligament (PDL) is important to determining the role of periodontal mechanoreceptors during food exploration and feeding. The objective of this study was to use fiber Bragg (FBG) sensors to measure strain in vivo within the PDL space of pig incisors. The central mandibular incisors of pigs underwent spring loaded lingual tipping during FBG strain recording within the labial periodontal space. FBG sensors were placed within the periodontal space of the central mandibular incisors of ~2-3-month-old farm pigs. The magnitude and orientation of spring loads are expected to mimic incisor contact with food. During incisor tipping with load calibrated springs, FBG strains in vitro (N = 6) and in vivo (N = 6) recorded at comparable load levels overlapped in range (-10-20 με). Linear regressions between peak FBG strains, that is, the highest recorded strain value, and baseline strains, that is, strain without applied spring load, were significant across all in vivo experiments (peak strain at 200 g vs. baseline, p = .04; peak strain at 2000 g vs. baseline p = .03; peak strain at 2000 g vs. 200 g, p = .004). These linear relationships indicate that on a per experiment basis, the maximum measured strain at different spring loads showed predictable differences. A Friedman test of the absolute value of peak strain confirmed the significant increase in strain between baseline, 200 g, and 2000 g spring activation (p = .02). Mainly compressive strains were recorded in the labial PDL space and increases in spring load applied in vivo generated increases in FBG strain measurements. These results demonstrate the capacity for FBG sensors to be used in vivo to assess transmission of occlusal loads through the periodontium. PDL strain is associated with mechanoreceptor stimulation and is expected to affect the functional morphology of the incisors. The overall low levels of strain observed may correspond with the robust functional morphology of pig incisors and the tendency for pigs to encounter diverse foods and substrates during food exploration.

Intertidal spring dynamics and coastal nutrient loading revealed through geophysics, drone surveys, and in‐situ monitoring

AbstractCoastal eutrophication poses an increasing risk to ecosystem health due to enhanced nutrient loading to the global coastline. Submarine groundwater discharge (SGD) represents a significant pathway for nitrate‐nitrogen (NO3‐N) transport to the coast, but diffusive SGD transport is difficult to monitor directly, given the low flux rates and expansive discharge areas. In contrast, focused SGD from intertidal springs can potentially be sampled and directly gauged, providing unique insight into SGD and associated contaminant transport. Basin Head is a coastal lagoon in Prince Edward Island, Canada that is a federally protected ecosystem. Nitrate‐nitrogen is conveyed from agricultural fields in the contributing watershed to the eutrophic lagoon via intertidal groundwater springs and groundwater‐dominated tributaries. We used several field methods to characterize groundwater discharge, nutrient loading, and in‐channel mixing associated with intertidal springs. The tributaries and intertidal springs were gauged and sampled to estimate a representative summer nitrate load to the lagoon. Our analysis revealed that NO3‐N export to the lagoon through tributaries and springs throughout summer 2023 was on average 401 kg N/month, with the combined spring loading comparable in magnitude to the combined tributary loading. We collected thermal infrared and visual imagery using drone surveys and found spatial overlap between cold‐water plumes from the spring discharge and macroalgae blooms, indicating the local thermal and ecosystem impacts of the focused SGD. We also mapped the electrical resistivity (salinity) distribution in the water column around one large spring with electromagnetic geophysics at different tidal stages to reveal the three‐dimensional spring plume dynamics. Results showed that the fresher spring water floated above the saline lagoon water with the brackish plume oriented in the direction of the tidal current. Collectively, our multi‐pronged field investigations help elucidate the hydrologic, thermal, and nutrient dynamics of intertidal springs and the cascading ecosystem impacts.

Investigation on the free vibration characteristics of vertical quasi-zero stiffness isolation system considering the disc spring's loading position

This study investigates a quasi-zero stiffness (QZS) isolation system designed for vertical vibration control in isolation structures. The QZS characteristics are achieved by connecting a negative stiffness disc spring in parallel with the linear positive stiffness spiral spring. Initially, the force-displacement relationship of the disc spring is established, taking into account the loading position, and validated through static experiments. Subsequently, the free vibration equations of the QZS system are derived. The Jacobi elliptic functions and the Newton-harmonic balance (NHB) method are then employed to study its free vibration characteristics without considering damping. The results demonstrate that the natural vibration period of the QZS system is related to the nondimensional amplitude and the height of the loaded inner cone. Furthermore, the second-order analytical approximate solution obtained via the NHB method exhibits remarkable accuracy. Finally, the critical damping ratio of the damped QZS isolation system is investigated using a numerical approach. The findings indicate that the critical damping ratio is only correlated with the nondimensional amplitude. This study addresses the gaps in existing research and provide recommendations for the parametric design of vertical QZS isolation system.

Experimental and numerical investigation on low‐velocity impact response of sandwich structure with functionally graded core

AbstractThe present research investigates optimizing the impact resistance of functionally graded sandwich structures using experimental and numerical approaches. The low‐velocity impact (LVI) responses of functionally graded sandwich composite (FGSC) with different configurations with skin material jute/rubber/jute (JRJ) and core material having epoxy and sea sand by volume fraction of sea sand at 0%, 10%, 20%, and 30%. Sandwich structures were impacted with LVI (5.89, 10.92, and 15.18 m/s), with the impactor dropped from heights of 0.5, 1, and 1.5 m with precompressed spring loads. FGSC samples are considered a deformable body, and the impactor is modeled as a rigid body using commercially accessible dynamic explicit software. The burn‐out test and weight method were used to test the core's gradience; both methods' results substantially matched, and the variance in gradation could be observed. The proposed sandwich structure characteristics are examined by energy absorption, peak force, energy loss percentage, and coefficient of restitution. Results showed that SC30S provides greater energy absorption and superior damage resistance when tested on LVI. To evaluate the accuracy of experimental findings in predicting the indentation behavior of the sandwich structure, the finite element analysis was used to compare with the experimental results. According to the examination of these proposed FGSC overall performance, they could potentially be employed as sacrificial materials for LVI applications like claddings to shield major structural components. The systematic approach used in this work serves as a standard for choosing and using FGSC effectively for LVI applications.Highlights Low‐velocity impact behavior of sandwich structures was investigated. Combining flexible skin and epoxy core enhances energy absorption. Based on impact energy levels, impact damage areas were determined. Examined sandwich structure advantages in structural and aerospace uses. In terms of time and cost, the numerical analysis method would be useful.

Nonlinear Dynamic Mechanical Characteristics of Air Springs Based on a Fluid–Solid Coupling Simulation Method

The use of air springs has become widespread in various industries due to their exceptional superelastic properties; however, their strong nonlinear characteristics have become a hindrance to numerical simulations of air springs and have garnered increasing attention. This paper examined the nonlinear dynamic mechanical characteristics of air springs from a fluid–structure interaction perspective and verified the accuracy of the simulation analysis model through quasistatic tension and compression experiments. The average relative errors for air spring load and gas pressure were found to be 8.1% and 7.7%, respectively, which supports the validity of the model. The impact of frequency and amplitude excitations on the axial load characteristics of air springs was investigated through tension and torsion testing. The results showed that increasing the excitation frequency improves the stability of the axial load, while increasing the excitation amplitude enhances the axial load value. The change in axial compression was found to be more significant than that in axial tension, as it was affected not only by the axial load but also by the radial load, which is a key factor affecting the dynamic characteristics of air springs. A radial load analysis model was established to study the influence of frequency and amplitude excitations on the axial load characteristics of air springs. The simulation results indicated that under different amplitudes, the radial load of air springs goes through four stages: a steady period, rising period, steady period, and falling period. Additionally, under the same amplitude, the radial load value increases with an increase in frequency. This research on the dynamic load characteristics of air springs under amplitude and frequency excitations is important for their application in low-frequency and low-amplitude vibration environments, and its findings can be utilized to improve the technical parameters of air springs for suspension damping.

The Static Adjustment Method of Secondary Suspension Load for Six-Axle Locomotives via Modified Mayfly Optimization Algorithm

The static distribution of the static secondary suspension load has a significant impact on the adhesion, rim wear and passenger comfort of the locomotive. At present, the locomotive secondary suspension load adjustment is mainly realized by artificial padding to adjust the height of the spring. This process is not predictable, so it is difficult to complete the adjustment at one time, and it is limited by the operating space, technician proficiency, weight and volume of equipment, etc., so the efficiency of the whole industrialization process needs to be improved. Therefore, it is necessary to study the relationship between the shimming quantity, shimming position and load variance, and establish an expert system to guide the spring adjustment process, so as to avoid the waste of time, space and human resources caused by repeated operation. Based on the analysis of the supporting structure and force balance relationship, the general theoretical model of the load adjustment of the secondary suspension design with flexicoil suspension springs of six-axle locomotive is established in this study. The mayfly algorithm (MOA), which is characterized by high efficiency and precision, is introduced into the field of the locomotive spring load adjustment process for the first time in this study. However, the standard MOA still faces the problems of low efficiency of late iteration, sample degradation and algorithm prematurity. Therefore, this study optimized the standard MOA from the parameter control, offspring mutation strategy and weighted optimization strategy of the position updating process. The experimental analysis of an HXD1C locomotive proves that the proposed method is a great improvement in terms of efficiency and accuracy.

Closed cervical traction techniques: moving into the 21st century.

Closed cervical traction for reducing dislocating cervical injuries, deformity correction, or discectomy distraction has been implemented in its modern form since the 1930s. Cervical traction state of the art has not changed significantly since the 1960s, with most reductions performed by using Gardner-Wells tongs or halo traction; however, there are many limitations of traditional weight-pulley traction, including limited reduction efficacy and patient safety shortcomings. In this paper, the authors review the history of cervical traction in the 20th century and the limitations of current traction techniques and describe a novel traction device developed at the University of Utah with robotic actuator load or position control and real-time force-sensing capabilities. Preliminary biomechanical testing results using the novel device in an extension spring loading model, with intact cadavers, and in iatrogenic facet injury cadaveric models demonstrated preliminary safety and efficacy of the device. The authors believe this and future research efforts aimed toward improving the efficacy and safety of cervical traction will help advance the field into the 21st century.

Analisis Pemeliharaan Spring Load Cell Pada Mesin Roto Packer Di PT. Semen Indonesia (Persero) Tbk

PT. Semen Indonesia (Persero) Tbk is a company that produces cement and runs the production process 24 hours a day. The company uses Roto Packer in its packaging process. In carrying out machine maintenance efforts, the company has implemented a maintenance system, namely preventive maintenance. However, even though preventive maintenance has been carried out, the company still experiences downtime on the spring load cell components. Therefore, it is necessary to carry out an analysis in order to determine the time between breakdowns, breakdown times, and arrange optimal maintenance schedules for spring load cell components. This study uses the Reliability Centered Maintenance (RCM) method with the Kolmogorov-Smirnov test to develop preventive maintenance activities that have been scheduled by the company. The results obtained, for the spring load cell component, component replacement must be carried out every 4 days with a replacement time of 49 minutes and a reliability of 39.87%.

Human motion energy harvesting backpack using quasi-zero stiffness mechanism

In this study, a quasi-zero stiffness energy harvesting backpack (QZS-EHB) is proposed. The bistable quasi-zero stiffness mechanism is constructed by paralleling a pair of positive stiffness springs and a pair of negative stiffness springs to make the vibration system more easily excited by low-frequency human motion and generate snap-through action. The bidirectional vibration is mechanically modulated into unidirectional higher speed rotation, thus improving the electromechanical conversion efficiency. The theoretical model of the bistable system is established by using the Lagrange functional method to verify the bistable mechanism. A prototype is manufactured to validate the advantages of the design. The effects of spring stiffness, load weight, and motion speed on the performance of the harvester are investigated through a series of experiments. The results show that the peak voltage is 5.11 V and the peak power is 2.37 W, and the average power of the harvester is 0.77 W, with the load of 5 kg and the speed of 10 km/h. The QZS-EHB can light up a combination of LED lamps with a total power of 6 W, charge the cell phone, and enable self-powered localization and wireless information transmission, when people are jogging with the load of 2 kg. These tests verify that the proposed design can be used for emergency power consumption of people's outdoor activities.

Optimization and evaluation of swing leg retraction for a hydraulic biped robot

AbstractTo improve the locomotion performance of legged robots, the swing leg retraction (SLR) technique is investigated in a hydraulic biped robot. First, the influence of SLR on the locomotion performance of the hydraulic biped robot is analyzed in theory and simulations based on an extended spring load inverted pendulum model. The influence contains three performance indicators: energy loss/effiency, friction/slipping, and impact/compliance. Second, by synthesizing three performance indicators, using unified objective method and particle swarm optimization algorithm, the optimal SLR rate for gait planning based on Bezier curve is addressed. Finally, experiments are implemented to validate the effectiveness and feasibility of proposed method. And, the results show that the SLR technique is useful to reduce the impact force, improve the robot's locomotion stability and make room for impedance performance improvement of compliance controller. This research provides an insight for locomotion control of hydraulic legged robots.

CAD Modelling of Automated Spring Load Testing Machine

Abstract: Mechanical engineering is useful and strong of diligence. The product and manufacturing process involves transforming raw devices into final details that meet the required parameters while applying slice edge technology. Stiffness, gravitational and compression modulus measurements are performed on our structures using spring force testers. Various inspection machines are used in the spring processing industry. As the sector diversifies and grows, different types of services are deployed in a colorful process. Our concept spring roller machine is very easy to use by using microcontroller and digital display. This machine is used to perform various types of spring load tests on springs of various compasses and lengths. It is a set with a wide operating range. This machine is easy to set up and operate.

Crowning Method on Bearing Supporting Large Wind Turbine Spindle Considering the Flexibility of Structure of Shaft System

To meet the precision design of bearings on large wind turbine spindles, a crowning method of bearing on wind turbine spindles considering the flexibility of the support structure is proposed. Firstly, a finite element (FE) model of the shaft system with a flexible structure is constructed by connecting the shaft and bearing through constraint equations (CE) and multi-point constraint (MPC) algorithms and replacing the bearing rollers with nonlinear spring elements and dampers. Then, the Newmark integration algorithm is used to solve the model and analyze the effect of the structure’s rigidity on the load distribution of bearings. Then, perform convergence analysis of the sequences of the spring load distribution using a high-pass filter based on fast Fourier transform (FFT) and root mean square error (RMSE) to obtain a suitable number of replacement springs. Finally, a sub-model of the upwind bearing is constructed with structured mesh. With the maximum Von Mises stress of the roller profile as the design target, the optimal logarithmic crowning of the roller and its tolerance zone under the given working conditions are obtained. The results show that the FE model of the shaft system proposed has good convergence. The FE model of the shaft system considering the flexibility of the support structure can obtain more accurate load distributions of bearings and can make the accurate crowning design of the bearing rollers based on the actual working conditions. This provides support for the precision design of bearings in large shaft systems.

Distributed Virtual Inertia Implementation of Multiple Electric Springs Based on Model Predictive Control in DC Microgrids

Smartload with series-connected dc electric spring (ES) and noncritical load (NCL) structure can compensate for load voltage and improve the power quality. In this article, a distributed virtual inertial control framework based on model predictive control is proposed for low inertia in the dc microgrid. First, the current prediction model of the ES bidirectional full-bridge dc/dc converter is presented. Based on the virtual inertia control, model predictive control is used to optimize virtual capacitance during the operation. When the system suffers from disturbance, virtual capacitance increases to slow down the change of the dc bus voltage. When disturbance happens, the system can be stabilized quickly by reducing the virtual capacitance. Then, the consensus algorithm based on distributed control is established after defining the NCL voltage deviation rate, which can achieve the balance of NCL voltage deviations, and effectively avoid the more significant voltage deviation in one of NCLs. Furthermore, the small-signal model of the proposed control method is developed, and the influence of the proposed controller on the small-signal stability is described by employing the eigenvalue analysis method. Finally, based on the RT-Lab hardware-in-the-loop simulation system, comparisons and analysis are made to verify the effectiveness of the proposed control method under power step conditions.

Implementation of boosting algorithms for prediction of punching shear strength of RC column footings

For more accurate and reliable prediction of punching shear strength of reinforced concrete (RC) column footings, machine learning (ML) was utilized. To develop ML-based strength prediction models, an experimental database was built by collecting test results (total 218 specimens without shear reinforcement and subjected to concentric loads) from the literature, and three boosting algorithms including the adaptive boosting (AdaBoost), gradient boosting (GBR), and extreme gradient boosting (XGBoost) were implemented. Input variables were carefully selected based on existing study results, and optimal hyperparameters of the ML-based models were determined based on the grid search with k-fold cross-validation. The ML-based models with the optimal hyperparameters showed highly accurate predictions without any significant and evident bias for all input variables, and outperformed current design codes and existing models. The relative feature importance of input variables showed that the effective footing depth is the most important variable for the prediction performance, and the boxplot analysis, ANOVA test, and t-test results revealed that spring or uniform load supports can be used instead of sand supports to avoid the complexity of the test setup. Finally, based on the study results, a simple design equation was proposed to widen the scope of application and to consider the effects of the shear span-to-depth ratio, steel yield strength, and test setup.

Advancing freshwater ecological forecasts: Harmful algal blooms in Lake Erie

Ecological models help provide forecasts of ecosystem responses to natural and anthropogenic stresses. However, their ability to create reliable predictions requires forecasts with track records sufficiently long to build confidence, skill assessments, and treating uncertainty quantitatively. We use Lake Erie harmful algal blooms as a case study to help formalize ecological forecasting. Key challenges for models include uncertainty in the deterministic structure of the load-bloom relationship and the need to assess alternative drivers (e.g., biologically available phosphorus load, spring load, longer term cumulative load) with a larger dataset. We enhanced a Bayesian model considering new information and an expanded data set, test it through cross validation and blind forecasts, quantify and discuss its uncertainties, and apply it for assessing historical and future scenarios. Allowing a segmented relationship between bloom size and spring load indicates that loading above 0.15 Gg/month will have a substantially higher marginal impact on bloom size. The new model explains 84 % of interannual variability (9.09 Gg RMSE) when calibrated to the 19-year data set and 66 % of variability in cross validation (12.58 Gg RMSE). Blind forecasts explain 84 % of HAB variability between 2014 and 2020, which is substantially better than the actual forecast track record (R2 = 0.32) over this same period. Because of internal phosphorus recycling, represented by the long-term cumulative load, it could take over a decade for HABs to fully respond to loading reductions, depending on the pace of those reductions. Thus, the desired speed and endpoint of the lake's recovery should be considered when updating and adaptively managing load reduction targets.Results are discussed in the context of ecological forecasting best pactices: incorporate new knowledge and data in model construction; account for multiple sources of uncertainty; evaluate predictive skill through validation and hindcasting; and answer management questions related to both short-term forecasts and long-term scenarios.

Deriving electricity consumption patterns using a decomposition approach

Studying the effectiveness of certain policies on load management requires detailed knowledge about the consumption pattern of end-use sectors. The consumption pattern includes both short-time scale variations and long-term trends in electricity consumption. This paper proposes a decomposition method to extract the seasonal (spring, summer cooling, autumn, and winter heating), day-of-week (working day, weekend), and time-of-day load curves of end-use sectors from aggregate load data of the electricity distribution network. The obtained data is used to study variations in the consumption patterns of end-use sectors such as households. The estimated load profiles represent the behavior of a large group of consumers for several years. The model is used to disaggregate the load data of Tehran province in Iran from 2006 to 2018. Resultsshow that during years, industrial power consumption has decreased during peak hours at night due to the shrinkage of two-shift industries. By contrast, the households’ load profiles in the autumn and spring load have shifted upward. This is the net effect of reduced lighting electricity consumption and increased access to electricity-consuming devices. Regression analysis on load decomposition results shows a mild effect of energy subsidy reform on lighting and cooling demand.