Dynamic Load Research Articles

Analytical solution for simplifying the pile-soil interaction to a spring-damping system under horizontal vibration

An analytical solution is developed to investigate the model of simplifying the pile-soil interaction to a spring-damping system when the horizontal earthquake input or the top of the model is subjected to horizontal dynamic load. Based on Euler-Bernoulli theory, the dynamic equilibrium equations of pile in soil and air are established, respectively. Then, utilizing the corresponding boundary conditions, potential function decomposition, and transfer matrix method, the analytical expression of impedance at the pile head whether the horizontal earthquake input or the top of the system is subjected to horizontal dynamic load can be presented. Furthermore, the model of the pile-soil complex interaction can be simplified as a spring-damping system according to the stress equilibrium at the pile bottom in the air. Moreover, the rationality of the present solution (without simplification) is verified by comparison with existing methods, and then the difference between the present solution (without simplification) and the spring-damping system (simplification) is also compared. Finally, the influence of the parameter variation in the pile buried in the soil and soil free-field on the simplified model are discussed. The research results will be instructive for the simplification of numerical model for pile-soil interaction.

Comprehensive review of load balancing in cloud computing system

Load balancing plays a critical role in optimizing resource utilization and enhancing performance in cloud computing systems. As cloud environments grow in scale and complexity, efficient load balancing mechanisms become increasingly vital. This paper presents a comprehensive review of load balancing techniques in cloud computing systems, with a focus on their applicability, advantages, and limitations. The review encompasses both static and dynamic load balancing approaches, evaluating their effectiveness in addressing the challenges posed by cloud infrastructure, such as heterogeneity, scalability, and variability in workload demands. Furthermore, the review examines load balancing algorithms considering factors such as resource utilization, response time, fault tolerance, and energy efficiency. Additionally, the impact of load balancing on cloud performance metrics, including throughput, latency, and scalability, is analyzed. This review aims to provide insights into the state-of-the-art load balancing strategies and serve as a valuable resource for researchers, practitioners, and system designers involved in the development and optimization of cloud computing systems.

Utilizing dynamic load balancing to improve private cloud paradigm

Utilizing dynamic load balancing to improve private cloud paradigm

An Experimental Study on Vibration Induced by Dynamic Pile Load in an Adjacent Building

An Experimental Study on Vibration Induced by Dynamic Pile Load in an Adjacent Building

Impact of Dynamic Wheel Loading on Flexible Pavement Responses for Non-Free Rolling Conditions

Since 2000, pavement design methodologies have transitioned from empirical to mechanistic-empirical procedures. However, the complexities of loading patterns, contact stress distribution, material characterization, vehicle maneuvering, and dynamic load amplification are still not fully considered, despite their significant effect on pavement performance. In this study, state-of-the-art numerical models were used to investigate the changes in critical pavement responses derived from the combined effect of roughness-induced dynamic loading and vehicle maneuvering. A decoupled vehicle–tire–pavement interaction approach composed of a random process to generate artificial road roughness profiles, a mechanical full truck model, and three-dimensional finite element tire and flexible pavement models allow the prediction of the impact of road roughness on vehicle dynamics. In addition, the study quantifies the effect of dynamic loading on contact stresses, and consequently, on pavement critical responses. Because of the expected increase in axle weight from truck electrification, overweight scenarios were also considered. Changes in load history and distribution of strain fields were assessed for two typical pavement structures (thin and thick). The combined effect of roughness-induced dynamic loading and vehicle maneuvering greatly altered the critical pavement responses associated with bottom-up fatigue cracking, near-surface cracking, and rutting. Under the most adverse conditions, the critical tensile strains at the bottom of the asphalt concrete increased up to 125%, the shear strain increased up to 100%, and the compressive strain escalated to 120% when compared with the reference cases. Higher temperatures exacerbated the impact of dynamic wheel loading and vehicle maneuvering.

A Multiple Controlled Toffoli Driven Adaptive Quantum Neural Network Model for Dynamic Workload Prediction in Cloud Environments.

The key challenges in cloud computing encompass dynamic resource scaling, load balancing, and power consumption. Accurate workload prediction is identified as a crucial strategy to address these challenges. Despite numerous methods proposed to tackle this issue, existing approaches fall short of capturing the high-variance nature of volatile and dynamic cloud workloads. Consequently, this paper introduces a novel model aimed at addressing this limitation. This paper presents a novel Multiple Controlled Toffoli-driven Adaptive Quantum Neural Network (MCT-AQNN) model to establish an empirical solution to complex, elastic as well as challenging workload prediction problems by optimizing the exploration, adaption, and exploitation proficiencies through quantum learning. The computational adaptability of quantum computing is ingrained with machine learning algorithms to derive more precise correlations from dynamic and complex workloads. The furnished input data point and hatched neural weights are refitted in the form of qubits while the controlling effects of Multiple Controlled Toffoli (MCT) gates are operated at the hidden and output layers of Quantum Neural Network (QNN) for enhancing learning capabilities. Complimentarily, a Uniformly Adaptive Quantum Machine Learning (UAQL) algorithm has evolved to functionally and effectually train the QNN. The extensive experiments are conducted and the comparisons are performed with state-of-the-art methods using four real-world benchmark datasets. Experimental results evince that MCT-AQNN has up to 32%-96% higher accuracy than the existing approaches.

The significance of frontal plane static alignment in anticipating dynamic knee moment among transtibial prosthesis users: A cross-sectional study

BackgroundIn the process of transtibial prosthetic fitting, alignment is the process of positioning the prosthetic foot relative to the residual limb. Changes in frontal plane alignment can impact knee moments during walking, which can either cause or, when aligned properly, prevent injuries. However, clinical evaluation of dynamic knee moments is challenging, limiting prosthetists' insights into dynamic joint loading. Typically, knee joint loading is assessed in static stance using the knee moment arm as a proxy for subsequent dynamic alignment. It remains uncertain if static alignment accurately represents actual dynamics during walking. Research questionIs the frontal knee moment arm in stance predictive for the knee moment arm and external knee adduction moment during gait in transtibial bone-anchored prosthesis users? MethodsIn this cross-sectional study, twenty-seven unilateral transtibial bone-anchored prosthesis users underwent data acquisition on the M-Gait instrumented treadmill. Static and dynamic measurements were conducted, and knee moment arm and external knee adduction moment were calculated. Pearson's correlation and linear regression analyses were performed to examine relationships between static and dynamic knee moment arms and external knee adduction moments. ResultsThe static knee moment arm showed significant associations with dynamic knee moment arm at the ground reaction force peaks (First: r=0.60, r2=35%, p<0.001; Second: r=0.62, r2=38%, p=0.001) and knee adduction moment (First: r=0.42, r2=17%, p=0.030; Second: r=0.59, r2=35%, p=0.001). A 1 mm between-subject difference in static knee moment arm corresponded, on average, with a 0.9% difference in knee adduction moment at the first peak and a 1.5% difference at the second peak of the ground reaction force. SignificanceWhile static alignment is important to optimize adduction moments during stance it may only partly mitigate excessive moments during gait. The fair correlation and limited percentage of explained variance underscores the importance of dynamic alignment in optimizing the body's dynamic load during walking.

Improving Cargo Transportation Planning Based on Digital Technologies

From the beginning of 2023, JSC Russian Railways has been gradually connecting logical controls to limit the acceptance of applications for the transportation of goods of the GU-12 form beyond the infrastructural capabilities of JSC Russian Railways. The presence of restrictions will be determined based on the results of calculations of the Dynamic Infrastructure Load Model (DILM) of JSC Russian Railways. This control limits the possibility of coordinating requests for transportation and the daily client loading plan (DCPP) beyond the calculated capabilities of the infrastructure. In addition, the automated system “Electronic Consignment Note” (AS ETRAN) implements automatic consideration of requests for transportation in all types of traffic with destination to limiting directions. The operation of the system is based on forecasting the occupancy of infrastructure elements for the entire period of transportation planning using data on operations with trains/cars in motion, loaded but not yet dispatched cars, as well as previously agreed upon requests for transportation. Data on the parameters of public infrastructure facilities are taken into account, including taking into account the implementation of planned repairs and construction and installation work, as well as non-public railway tracks. The system implements the determination of possible alternative schedules in the absence of the possibility of acceptance for transportation in accordance with the schedule declared by the shipper.

Effect of SiC particle content on the mechanical behavior and deformation mechanism of SiCp/Al composite under high-frequency dynamic loading

SiCp/Al composites exhibit excellent structural properties but suffer from inherent brittleness, which has brought rigorous challenges on the materials processing. Ultrasonic-vibration-assisted approach offers a promising solution to improve the processability. However, the underlying mechanisms of mechanical response and defect suppression under ultrasonic action have been rarely studied for SiCp/Al composites, hindering the development of high-efficiency processing. This work aims to investigate the mechanical behavior and microstructural evolution in this class of composites with varying SiC volume fractions up to 45% under high-frequency dynamic load induced by ultrasonic vibration. The results showed that the deformation stress of SiCp/Al composites under ultrasonic compression was significantly reduced to only about 1/10 of that under conventional quasi-static loading. Moreover, as the SiC fractions increasing, this ultrasonic softening effect of SiCp/Al became more pronounced. The plastic limit of SiCp/Al with 45% SiC fraction nearly doubled under ultrasonic compression without any macroscopic cracks. The subsequent nanoindentation tests revealed a transition in work-hardening behavior of SiCp/Al with different SiC fractions under ultrasonic loading. Microstructure characterization was conducted to analyze the grain refinement, recrystallization, and deformation textures within the deformed areas, revealing the deformation mechanism of SiCp/Al under different loading mode. TEM observations confirmed the distribution of dislocations in Al matrix and dense stacking faults in SiC particles after ultrasonic compression, indicating that the plastic behaviors were enhanced in both Al matrix and SiC phase under the high-frequency cyclic stress. This research revealed the distinct deformation mechanisms of SiCp/Al composites under high-frequency dynamic loading, exhibiting extremely low deformation stress accompanied with significantly enhanced plasticity. The results provide theoretical insights for the high-efficiency processing and rapid property regulation of particle-reinforced-composites based on ultrasonic-vibration-assisted technologies.

Static and dynamic stress analysis of different crown materials on a titanium base abutment in an implant-supported single crown: a 3D finite element analysis

BackgroundThis Finite Element Analysis was conducted to analyze the biomechanical behaviors of titanium base abutments and several crown materials with respect to fatigue lifetime and stress distribution in implants and prosthetic components.MethodsFive distinct designs of implant-supported single crowns were modeled, including a polyetheretherketone (PEEK), polymer-infiltrated ceramic network, monolithic lithium disilicate, and precrystallized and crystallized zirconia-reinforced lithium silicates supported by a titanium base abutment. For the static load, a 100 N oblique load was applied to the buccal incline of the palatal cusp of the maxillary right first premolar. The dynamic load was applied in the same way as in static loading with a frequency of 1 Hz. The principal stresses in the peripheral bone as well as the von Mises stresses and fatigue strength of the implants, abutments, prosthetic screws, and crowns were assessed.ResultsAll of the models had comparable von Mises stress values from the implants and abutments, as well as comparable maximum and minimum principal stress values from the cortical and trabecular bones. The PEEK crown showed the lowest stress (46.89 MPa) in the cervical region. The prosthetic screws and implants exhibited the highest von Mises stress among the models. The lithium disilicate crown model had approximately 9.5 times more cycles to fatique values for implants and 1.7 times more cycles to fatique values for abutments than for the lowest ones.ConclusionsWith the promise of at least ten years of clinical success and favorable stress distributions in implants and prosthetic components, clinicians can suggest using an implant-supported lithium disilicate crown with a titanium base abutment.

An Implementation of Fuzzy to Minimize Electrical Losses in Induction Machine Vector Control

An induction motor is a motor that is difficult to control so it requires synchronous speed due to dynamic load changes which are controlled by the torque current while the field current is kept constant. Thus, in this paper, we implement an adaptive PID controller method with an induction motor using MATLAB which coordinates the speed of two decoupled loads with a three-phase induction motor, where an adaptive Self Tuning Regulator (STR) PID controller is designed and realized to regulate the speed of a three-phase induction motor. using MATLAB software and AVR Atmega16 microcontroller as input and output from the plant with communication using serial RS232. The results show that after coordinating the system with each motor which is given a neural controller and FOC, the motor speed results are obtained which gradually provide synchronous speed so it can be concluded that this system can work well to slow down the plant response. Keywords: Induction Motor, Motor Speed, neural method, PID controller

Effective characterization for the dynamic indentation and plastic parameters acquisition of metals

As a fundamental test technique for the mechanical properties of materials, indentation shows broad application prospects. Compared to the well-studied quasi-static indentation, relatively few and incomplete studies on the dynamic indentation test and characterization have been performed. In this work, a dynamic indentation test technique based on the split Hopkinson pressure bar system is developed, and the corresponding “three-wave method” is proposed to effectively acquire the time-resolved dynamic indentation load and depth. Systematic indentation tests under different loading rates and indenter cone angles are conducted on typical metal materials such as Cu, α-Fe, and α-Ti. Based on the dynamic indentation contact stiffness analysis and indentation theory the indentation hardness under different loading conditions are determined. A procedure for plastic parameter inversion of metal materials, including strain hardening and strain rate strengthening terms based on dynamic indentation, is provided. The effectiveness is verified by the traditional compression test results, providing a new method for characterizing the dynamic mechanical properties of metal materials.

Optimum urban runoff pollution control based on dynamic load calculation and effective control units identification – a case study in a highly urbanized basin in China

In the context of urban environmental challenges, urban runoff pollution, originating as a fundamental source of water pollution, remains a significant issue due to its widespread occurrence and sporadic release, which complicates precise monitoring and effective mitigation efforts. This study is dedicated to investigating prominent urban runoff pollution challenges in a highly urbanized basin located within the Guangdong-Hong Kong-Macao Greater Bay Area. Through a combination of field monitoring, statistical analyses, and modeling techniques, we present a comprehensive framework tailored to optimize control strategies for urban runoff pollution. The findings reveal variations in Event Mean Concentrations (EMCs) of ammonia nitrogen, ranging from 0.28 to 7.65 mg L-1, with distinct disparities observed across different rain events and land use categories. We delineated 431 drainage districts and organized them into 12 clusters based on similar discharge characteristics, which facilitates the establishment of a prioritized pollution control hierarchy. Subsequently, both the spatial and temporal load distributions were analyzed using EMC calculations, allowing us to determine a prioritized pollution control scheme that ranks units from level 1 to level 12. A 1-d mathematical model of target urbanized basin linking distributively discharged runoff pollution and dynamic river quality improvement has been established, and the dynamic simulation results ultimately demonstrate the importance sequence of managing runoff pollution discharges from 63 identified drainage districts, which accounts for 46.8% of the total urban runoff pollution load. This investigation provides a powerful research tool for urban runoff pollution control within extensive urban areas, offering valuable insights for the practical implementation of pollution control initiatives.

Thermal-Mechanical Coupling Analysis of Permeable Asphalt Pavements

In order to clarify the mechanical response of permeable asphalt pavements under a temperature effect, the mechanical responses of different types of permeable asphalt pavements, which were based on a self-developed drainage SBS-modified asphalt mixture with fiber, were simulated using ANSYS finite element software (APDL 19.2). The influence of temperature and temperature change on the mechanical behavior of the permeable asphalt pavements was studied, and the mechanical responses of the pavements at different driving speeds was analyzed. The results show that the extreme values of surface deflection, compressive strain of the soil foundation top surface, and the shear stress and tensile stress of the upper-layer bottom of the three kinds of pavements under dynamic load were about 10% smaller than those under static loads, and the extreme values under different temperature conditions were 28%~50% larger than the values obtained without different temperature conditions. During the 12 h heating process, the mechanical indexes of the three types of pavements with axle loads were consistent with the change law of temperature, and the peak values of the mechanical indexes under dynamic loads were smaller than those under static loads. In addition, the mechanical indexes of the three types of pavements under dynamic loads had the same law of change with speed under the same conditions, and the values were less than the extreme values under static loads, but the degree of influence was different.

Experimental and numerical settlement analysis of railway track over geogrid reinforced ballast

PurposeThe main objective of the present research is to investigate the benefits of using geogrid reinforcement in minimizing the rate of deterioration of ballasted rail track geometry resting on soft clay and to explore the effect of load amplitude, load frequency, presence of geogrid layer in ballast layer and ballast layer thickness on the behavior of track system. These variables are studied both experimentally and numerically. This paper examines the effect of geogrid reinforced ballast laying on a layer of clayey soil as a subgrade layer, where a half full scale railway tests are conducted as well as a theoretical analysis is performed.Design/methodology/approachThe experimental tests work consists of laboratory model tests to investigate the reduction in the compressibility and stress distribution induced in soft clay under a ballast railway reinforced by geogrid reinforcement subjected to dynamic load. Experimental model based on an approximate half scale for general rail track engineering practice is adopted in this study which is used in Iraqi railways. The investigated parameters are load amplitude, load frequency and presence of geogrid reinforcement layer. A half full-scale railway was constructed for carrying out the tests, which consists of two rails 800 mm in length with three wooden sleepers (900 mm × 90 mm × 90 mm). The ballast was overlying 500 mm thick clay layer. The tests were carried out with and without geogrid reinforcement, the tests were carried out in a well tied steel box of 1.5 m length × 1 m width × 1 m height. A series of laboratory tests were conducted to investigate the response of the ballast and the clay layers where the ballast was reinforced by a geogrid. Settlement in ballast and clay, was measured in reinforced and unreinforced ballast cases. In addition to the laboratory tests, the application of numerical analysis was made by using the finite element program PLAXIS 3D 2013.FindingsIt was concluded that the settlement increased with increasing the simulated train load amplitude, there is a sharp increase in settlement up to the cycle 500 and after that, there is a gradual increase to level out between, 2,500 and 4,500 cycles depending on the load frequency. There is a little increase in the induced settlement when the load amplitude increased from 0.5 to 1 ton, but it is higher when the load amplitude increased to 2 ton, the increase in settlement depends on the geogrid existence and the other studied parameters. Both experimental and numerical results showed the same behavior. The effect of load frequency on the settlement ratio is almost constant after 500 cycles. In general, for reinforced cases, the effect of load frequency on the settlement ratio is very small ranging between 0.5 and 2% compared with the unreinforced case.Originality/valueIncreasing the ballast layer thickness from 20 cm to 30 cm leads to decrease the settlement by about 50%. This ascertains the efficiency of ballast in spreading the waves induced by the track.

Effectiveness of a passive military exoskeleton in off-loading weight during static and dynamic load carriage: A randomised cross-over study

BackgroundLoad carriage imposes high physical stresses on the human body, increasing the risk of injuries. This study assessed the effectiveness of a passive military exoskeleton in off-loading the weight placed on the body during heavy load carriage under static standing and dynamic walking conditions. MethodsEight full-time regular personnel of the Singapore Armed Forces enrolled in the study. Static loading tests included nine trials of 10-s quiet standing while carrying different loads (0–55 kg) with and without the exoskeleton. For dynamic loading, participants walked on a treadmill on flat, inclined, and declined surfaces while carrying two different loads (25 kg, 35 kg) with and without the exoskeleton. In-shoe normal ground reaction forces (GRF) were recorded during quiet standing and treadmill walking. Differences in total force with and without the exoskeleton during static loading were compared using Wilcoxon one-sample signed ranked tests against zero (no weight off-load) as a reference. Statistical parametric mapping test was used to compare the walking in-shoe GRF-time series with and without exoskeleton use for each load and surface condition. ResultsExoskeleton use was effective in off-loading loads of 2.3–13.5 kg during static quiet standing but the response varied substantially across loads and among the participants. Statistical analysis revealed no meaningful differences in the walking in-shoe GRF with and without exoskeleton use. The results were largely consistent across flat, inclined, and declined surfaces, and both 25-kg and 35-kg loads. ConclusionsThe passive military exoskeleton was effective in off-loading some load from the human body during static quiet standing but not dynamic walking on flat and sloped surfaces. The varied response across loads and participants calls for better design and fitting of the military exoskeleton to individual users.

Fuzzy sliding mode control of automotive semi-active suspension based on optimization convergence law

Vehicle suspension smoothness and stability are important indicators for evaluating vehicles. This paper firstly establishes a 1/4 automobile semi-active suspension model, which is used as a reference model for comparison by optimizing the canopy model, and secondly designs the sliding mode controller, including the tracking model error design and the control system design, and then incorporates the optimized saturation function convergence law to control the convergence speed, and then combines it with the fuzzy control to improve the accuracy and robustness of the control system. The optimized semi-active suspension control system will enhance the vehicle’s vibration, smoothness, and stability. The final simulation test results show that the optimized sliding mode control suspension body vertical acceleration is reduced by 26.9%, suspension dynamic deflection by 36.7%, and tire dynamic load dynamic load is reduced by 28.4%.

Design of an Iterative Method for Optimizing Solar Power Systems using Quad LSTM with IoT Integration Operations

The growing necessity for sustainable energy solutions underscores the critical need for optimizing solar power systems. Existing methodologies predominantly focus on static strategies with limited adaptability to dynamic environmental and operational conditions, often resulting in suboptimal performance and inefficiency. This research introduces a comprehensive, integrated framework employing Internet of Things (IoT) technologies alongside advanced machine learning and deep learning methodologies to enhance solar power system efficiency and reliability levels. The proposed model integrates four key components: predictive maintenance using Support Vector Machines (SVM) for enhanced anomaly detection, solar power forecasting via Quad Long Short-Term Memory (QLSTM) neural networks, dynamic load balancing through Reinforcement Learning with Deep Q-learning, and the integration with smart grids employing Decentralized Multi-Agent Systems (MAS) with Auction-Based Mechanisms. Each method is selected based on its suitability to address specific challenges within the solar power domain: SVMs for their effectiveness in high-dimensional anomaly detection, QLSTMs for their superior temporal pattern recognition in forecasting, Deep Q-learning for its adaptability in dynamic load management, and MAS for efficient decentralized energy resource coordination operations. The implementation of these methodologies demonstrates significant advancements over traditional approaches. Predictive maintenance facilitated by SVMs leads to a 20% reduction in maintenance costs, while QLSTM-based forecasting achieves a 95% accuracy rate, thereby enhancing grid management and reducing revenue losses. Moreover, reinforcement learning optimizes energy utilization, decreasing wastage and system downtime by 10% and 5% respectively. Lastly, the MAS framework promotes a 20% increase in energy trading efficiency, yielding a 10% reduction in transaction costs and bolstering grid resilience levels. This work represents a significant leap forward in solar power optimization, offering a scalable, efficient, and intelligent framework that paves the way for more sustainable and reliable energy systems. The integration of IoT with machine learning and deep learning presents a paradigm shift in renewable energy management, marking a critical step toward achieving global sustainability objectives..

Comparative analysis of dynamic load characteristics of artillery steering gear based on different MBD methods

The multi-body dynamics analysis model of the steering gear of a certain type of artillery was established. The dynamic load characteristics of the output gear of the steering gear motor and its driven gear under the given speed and load conditions were calculated based on the three different MBD analysis methods, such as the speed, meshing force, and contact stress. Through the comparative analysis, it was concluded that the calculation results of rigid-flexible coupling analysis were closer to the theoretical value and the fluctuation range was smaller, which provides reference and basis for the prediction of gear fatigue life and the optimization design under dynamic load conditions.

In-situ reinforced Ti6Al4V-TiB2 titanium matrix composite fabricated by selective laser melting: Microstructure, mechanical properties and heat treatment

Ti6Al4V-x wt%TiB2 titanium matrix composites (x = 0, 0.01, 1, 3.4, 4.8) were prepared by selective laser melting (SLM), followed by solution-aging treatment. The microstructure and mechanical properties of the SLM-formed samples and the heat-treated ones were systematically investigated. In the SLM-formed samples, in-situ TiB whiskers clustered around the original position of the TiB2 particles, which reacted with the matrix through dynamic dissolution. The phase transformation of α' → α + β and the coarsening of TiB whiskers occurred during solution-aging treatment. The microhardness of the samples was enhanced with the increase of TiB2, while the heat treatment was harmful to microhardness. The fine grain strengthening and the dispersion strengthening caused by TiB whiskers enhanced the quasi-static compressive strength of the samples. The samples were modified successfully by solution-aging, and the compressive strength and strain of the Ti6Al4V-0.01wt.%TiB2 composite reached 1957 MPa and 23.7% respectively. The samples had higher ductility but lower strength under dynamic load, and the heat treatment had little effect on the dynamic properties.