Load Distribution Research Articles

An Optimization Strategy for EV-Integrated Microgrids Considering Peer-to-Peer Transactions

The scale of electric vehicles (EVs) in microgrids is growing prominently. However, the stochasticity of EV charging behavior poses formidable obstacles to exploring their dispatch potential. To solve this issue, an optimization strategy for EV-integrated microgrids considering peer-to-peer (P2P) transactions has been proposed in this paper. This research strategy contributes to the sustainable development of microgrids under large-scale EV integration. Firstly, a novel cooperative operation framework considering P2P transactions is established, in which the impact factors of EV charging are regarded to simulate its stochasticity and the energy trading process of the EV-integrated microgrid participating in P2P transactions is defined. Secondly, cost models for the EV-integrated microgrid are established. Thirdly, a three-stage optimization strategy is proposed to simplify the solving process. It transforms the scheduling problem into three solvable subproblems and restructures them with Lagrangian relaxation. Finally, case studies demonstrate that the proposed strategy optimizes EV load distribution, reduces the overall operational cost of the EV-integrated microgrid, and enhances the economic efficiency of each microgrid participating in P2P transactions.

Optimizing Web-Based Survey Applications with Laravel and Cloud Computing

This study aims to optimize the performance of a web-based survey application through the implementation of the Laravel framework and cloud computing technology. Survey applications often face challenges regarding response speed and scalability as the number of users increases. In this research, performance testing of the application was conducted both before and after optimization. The test results show that the application's response time significantly decreased after the implementation of Laravel and cloud technology. Before optimization, the average response time reached 4.5 seconds at 1,000 users, while after optimization, it dropped to 2.2 seconds. This performance improvement was achieved through efficient caching implementation, database query optimization using Eloquent ORM, and balanced load distribution via load balancers. Additionally, the application's availability increased to 99.9% thanks to cloud features such as auto-scaling and data replication. The conclusion of this study indicates that the combination of Laravel and cloud computing technology is effective in enhancing the performance and reliability of web-based survey applications, providing practical guidance for other web application developers facing similar challenges.

Enhancing Software‐Defined Networking With Dynamic Load Balancing and Fault Tolerance Using a Q‐Learning Approach

ABSTRACTThe Software‐Defined Networking (SDN) paradigm represents a fundamental shift in networking by decoupling the control plane from the data plane in network devices. This architectural change offers numerous advantages, including network programmability and centralized management capabilities, which improve scalability and efficiency compared to conventional network architectures. However, the dynamic nature of network traffic presents overload challenges, both temporally and spatially, especially in multi‐controller SDN settings. To address these challenges, this paper presents an approach leveraging network traffic patterns for dynamic load balancing. The proposed framework optimizes migration strategies to reduce costs and enhance in‐packet request‐response rates. By exploiting load ratio variance across controllers, the architecture identifies optimal migration triplets, encompassing migration‐in and migration‐out domains by selecting a subset of switches. The architecture utilizes online Q‐learning technology to achieve optimal controller load balancing while minimizing associated expenses. The proposed approach ensures stability and scalability by imposing limits to maintain maximum efficiency and reduce migration conflicts. It iteratively converges to an optimal policy through a comprehensive set of simulations performed on switches under a wide range of load distribution situations. These results highlight the effectiveness and adaptability of the proposed methodology in addressing the intricacies present in dynamic network settings, encouraging further progress in the field of SDN technologies and their real‐world applications.

Nonlinear forced vibration of the FGM piezoelectric microbeam with flexoelectric effect

In this paper, based on the extended dielectric theory, Euler beams theory and von Karman’s geometric nonlinearity, a nonlinear FGM piezoelectric microbeam model is established with flexoelectric effect. The governing equations, initial conditions and boundary conditions are obtained by applying Hamilton’s principle and then solved by combining the differential quadrature method (DQM) and iteration method. The innovation of this paper is to construct a nonlinear forced vibration model of piezoelectric microbeams. The coupling response between the inverse flexoelectric effect and the inverse piezoelectric effect is investigated. Various effects are examined, including the functional gradient index m and transverse distributed load q affecting the distribution of electric potential. Results indicated that the functional gradient index m, beam thickness h, and span-length ratio L/h have a significant impact on the dimensionless deflection of the FGM microbeam. The influence of the flexoelectric effect on dimensionless deflection increases with the decrease of scale. In addition, transverse load q and the functional gradient index m also have a significant impact on the distribution of electric potential. This paper will provide useful theoretical guidance for the design of micro-sensors and micro-actuators.

Changing the Position of the Vehicle’s Center of Gravity as a Result of Different Load Distribution

Changing the Position of the Vehicle’s Center of Gravity as a Result of Different Load Distribution

Experimental and numerical investigation on progressive collapse resistance of three-dimensional RC structures

An investigation on progressive collapse for three-dimensional RC structures was presented in this paper, based on a series of experimental tests and numerical analysis. The crack pattern and load–displacement curves were compared and discussed according to three single-story specimens under the loss of corner column and a two-story specimen under the loss of edge column. Experimental results show that flexural action is the main resistance mechanism for specimens whether the removal of corner or edge column. Moreover, the effect of the slab and secondary beam on the loading transfer mechanism was studied based on finite element model with various scenarios established by ABAQUS. The numerical results had a well agreement with the experimental results. FEA results indicated that the phenomenon of uneven distribution of loads can be improved due to the existence of slab and secondary beam. Finally, the corresponding analytical models considering the effect of secondary beam and torsion of primary beam with various scenarios were proposed to predict progressive collapse resistance.

Impact sensing, localization and damage assessment in Fiber-Reinforced composites with ZnO Nanowires-Based sensor array

The structural integrity and monitoring of load distributions in composites are critical for safety and economic efficiency but still challenging. Herein, zinc oxide nanowires (ZnO NWs) were embedded into a carbon fiber-reinforced composite serving as mechanical reinforcement and sensing components. The presence of ZnO NWs in the composite material increased the flexural strength, interlaminar, and interfacial shear strength by respectively 4.9 %, 8.8 %, and 19.9 % due to the strong bonding at the fiber/resin interface and the mechanical interlocking effect. Additionally, the piezoelectric nature of ZnO NWs with an asymmetric crystal structure generated piezoelectric charges under stress, thereby enhancing the sensitivity of capacitive monitoring. A self-developed algorithm was then designed to analyze the array capacitance changes collected from the prepared composite laminate to determine the impact load with high precision with an error margin of 3 mm and not exceeding 0.25 MPa. Furthermore, damage was also able to be detected by monitoring capacitance changes. Overall, the proposed high-precision and minimally aggressive approach for load localization and quantification provides a promising direction and strategic pathway for the development of smart self-monitoring composites.

A Contact Mechanics Model for Surface Wear Prediction of Parallel-Axis Polymer Gears.

As surface wear is one of the major failure mechanisms in many applications that include polymer gears, lifetime prediction of polymer gears often requires time-consuming and expensive experimental testing. This study introduces a contact mechanics model for the surface wear prediction of polymer gears. The developed model, which is based on an iterative numerical procedure, employs a boundary element method (BEM) in conjunction with Archard's wear equation to predict wear depth on contacting tooth surfaces. The wear coefficients, necessary for the model development, have been determined experimentally for Polyoxymethylene (POM) and Polyvinylidene fluoride (PVDF) polymer gear samples by employing an abrasive wear model by the VDI 2736 guidelines for polymer gear design. To fully describe the complex changes in contact topography as the gears wear, the prediction model employs Winkler's surface formulation used for the computation of the contact pressure distribution and Weber's model for the computation of wear-induced changes in stiffness components as well as the alterations in the load-sharing factors with corresponding effects on the normal load distribution. The developed contact mechanics model has been validated through experimental testing of steel/polymer engagements after an arbitrary number of load cycles. Based on the comparison of the simulated and experimental results, it can be concluded that the developed model can be used to predict the surface wear of polymer gears, therefore reducing the need to perform experimental testing. One of the major benefits of the developed model is the possibility of assessing and visualizing the numerous contact parameters that simultaneously affect the wear behavior, which can be used to determine the wear patterns of contacting tooth surfaces after a certain number of load cycles, i.e., different lifetime stages of polymer gears.

АНАЛІЗ ВПЛИВУ ІНДИВІДУАЛЬНИХ ХАРАКТЕРИСТИК КОМІРОК LiFePO4 НА ПАРАМЕТРИ РОБОТИ АКУМУЛЯТОРНИХ ЗБІРОК

The article analyzes the influence of the individual characteristics of lithium-iron-phosphate (LiFePO4) cells on the overall efficiency, stability and duration of operation of battery assemblies. The influence of such parameters as internal resistance, polarization resistance and capacity on the operation of battery assemblies under different load regimes and changes in temperature conditions was studied. It was found that these parameters can vary significantly between cells within the same assembly, which leads to an imbalance during the operation of the battery system. Such an imbalance causes an uneven distribution of charge between the cells, which reduces the overall performance of the system, accelerates the degradation of the cells and shortens their service life. To overcome these problems, the use of active balancing methods based on Unscented Kalman Filter (UKF) algorithms was proposed. The application of UKF allows for dynamic evaluation of the state of charge (SOC) of each cell and adjustment of system parameters in real time, which provides more accurate management of battery assemblies in conditions of variable external factors such as temperature and current loads. Special attention is paid to online identification of battery parameters, such as polarization resistance and capacity, which change over time due to cell aging. The conducted studies confirmed that constant monitoring of temperature regimes is critically important for maintaining stable operation of battery cells, preventing overheating and ensuring uniform load distribution. The performed simulation confirmed the effectiveness of the application of adaptive methods of balancing and temperature control, which allows to increase the reliability and overall performance of battery assemblies.

The influence of anomalies in supporting structures on the validation of finite-element blade bearing models

Finite-element analysis is the only means to determine the load distribution of large slewing bearings considering flexible bearing rings and supporting structures. For reliable results, the plausibility of the models need to be validated. Previous attempts on validating a finite-element model of a slewing bearing against measurement results have indicated a huge dependence of the deformation on tolerances in the supporting structures. This dependence has not yet been explored in research in favor of a focus on tolerances of the bearing itself. The present work explores different irregularities of the flange that connects to the outer ring of the bearing and their effects on bearing deformation. The results show that single dents or bulges on the flange and inclined flanges of the adapter ring significantly change the load distribution and contact angles of the bearing. They also aggravate the risk of truncation. For the calculated fatigue life however, the bearings seem to be robust to these uncertainties for the shown load cases. The dimensions of the investigated tolerances are verified by comparing the resulting deformations of the bearing outer ring against experimental data.

Optimization of Low-Pressure Turbine Blade by Means of Fine Inspection of Loss Production Mechanisms

Abstract In this study, Proper Orthogonal Decomposition (POD) was applied to Large Eddy Simulations (LES) of two high-loaded low-pressure turbine cascades under unsteady inflow conditions to investigate entropy production within different parts of the blade passage. The terms for Turbulent Kinetic Energy (TKE) production, diffusion, and dissipation from the stagnation pressure transport equation were integrated across the computational domain. The POD-based method enables the decomposition of contributions from various coherent flow dynamics to TKE production and dissipation, such as the migration, bowing, tilting, and reorientation of incoming wake filaments, as well as the breakdown of streaky structures in the blade boundary layer and the formation of Von Karman vortices in the blade wake. This approach helps designers identify the dominant POD modes (i.e., turbulent flow structures) responsible for loss generation, understand their dynamics and locate where they primarily act. Using this optimization strategy, a new low-pressure turbine profile was designed. LES calculations on the optimized geometry showed that it is possible to design a higher-loaded profile with a lower global loss coefficient. The new loading distribution, characterized by stronger acceleration in the early part of the blade passage, results in reduced upstream wake migration losses and lower TKE production in the trailing edge wake zone due to early suction side boundary layer transition.

EV charging load forecasting and optimal scheduling based on travel characteristics

Accurate prediction of EV charging load distribution is not only the basis of evaluating the capacity of distribution network to receive EV, but also the necessary premise to promote the research of charging station planning and vehicle-network interaction. Based on this, firstly, according to the travel characteristics of electric private car and taxi users, this paper constructs the models of speed-flow and EV power consumption per mileage, thus, the real-time changes of vehicle speed and power consumption per mileage are simulated. Secondly, consider factors such as charging price, time, and power consumption along the way, as well as multiple sources of information such as traffic networks, vehicles, public quick charging stations, and distribution networks, the OD matrix analysis method is introduced to simulate EV moving characteristics for EV trip assignment matrix, and the EV charging load forecasting system with real-time interaction of multi-source information is established. Finally, the second-order cone optimization method is used to optimize the space-time distribution of regional charging load. The model and method proposed in this paper are more consistent with the actual situation and can improve the accuracy of prediction.

A Distributed Non-Intrusive Load Monitoring Method Using Karhunen–Loeve Feature Extraction and an Improved Deep Dictionary

In recent years, the non-invasive load monitoring (NILM) method based on sparse coding has shown promising research prospects. This type of method learns a sparse dictionary for each monitoring target device, and it expresses load decomposition as a problem of signal reconstruction using dictionaries and sparse vectors. The existing NILM methods based on sparse coding have problems such as inability to be applied to multi-state and time-varying devices, single-load characteristics, and poor recognition ability for similar devices in distributed manners. Using the analysis above, this paper focuses on devices with similar features in households and proposes a distributed non-invasive load monitoring method using Karhunen–Loeve (KL) feature extraction and an improved deep dictionary. Firstly, Karhunen–Loeve expansion (KLE) is used to perform subspace expansion on the power waveform of the target device, and a new load feature is extracted by combining singular value decomposition (SVD) dimensionality reduction. Afterwards, the states of all the target devices are modeled as super states, and an improved deep dictionary based on the distance separability measure function (DSM-DDL) is learned for each super state. Among them, the state transition probability matrix and observation probability matrix in the hidden Markov model (HMM) are introduced as the basis for selecting the dictionary order during load decomposition. The KL feature matrix of power observation values and improved depth dictionary are used to discriminate the current super state based on the minimum reconstruction error criterion. The test results based on the UK-DALE dataset show that the KL feature matrix can effectively reduce the load similarity of devices. Combined with DSM-DDL, KL has a certain information acquisition ability and acceptable computational complexity, which can effectively improve the load decomposition accuracy of similar devices, quickly and accurately estimating the working status and power demand of household appliances.

A size‐dependent model of strain gradient elastic laminated micro‐beams with a weak adhesive layer

AbstractA size‐dependent model for a laminated micro‐beam with a soft adhesive is developed in the framework of strain gradient elasticity theory. The layered beam is constituted by two Euler–Bernoulli strain gradient elastic isotropic beams, joined through a strain gradient spring‐type contact law at the adhesive level. The governing bending and extensional equations and boundary conditions are obtained by using the variational principle. The differential system shows a coupling between the flexural and axial behaviors of the upper and lower beams, due to the presence of interface terms related to shear and peeling stresses. Two benchmark problems have been presented through their closed‐form solutions, namely a simply‐supported laminated beam subjected to a uniform distributed load and a mode 2‐type loading configuration of a layered axially deformable beam. Size effects and non‐local phenomena, due to high strain concentrations, are highlighted. Though simple in their features, the examples prove the efficiency of the proposed approach in designing micro‐scale layered beams.

Computational Models of Dynamic Load Sources for Modeling of Construction Structures Operation Used in Monitoring of Technical Condition of Buildings and Structures

This research aims to develop principles for assessing the impact of mega-cyclic vibrodynamic loads on the reliability of construction structures. The relevance of the reasons for the development and improvement of algorithms for numerical modeling of multicycle dynamic loads on building structures is due to the steadily increasing intensity of such loads on buildings and structures in megacities, as well as the acute practical problem of significant differences in the dynamic characteristics of buildings and structures obtained as a result of mathematical modeling and determined by experimental methods. The article presents research materials on computational equivalent models of dynamic load sources for numerical modeling of the behavior of construction structures under their influence, using the method of vibroacoustic analogies. The article examines models of sources of dynamic impact on construction sites. Algorithms and final formulas for computational modeling of the simplest sources of dynamic load are developed using the method of vibroacoustic analogies. The dynamic properties of the simplest dynamic load sources were analyzed. A significant difference between the computational models of real and ideal dynamic load sources. The article presents research and development results intended for calculating the distribution of dynamic loads on elements of construction structures in industrial and civil engineering projects located in areas with high levels of transport vibrodynamic impacts. An important property of the proposed computational equivalent models of sources of dynamic impact on building structures is the possibility of computational verification of critical elements, points, or nodes of load-bearing structures of buildings and structures under dynamic overloads. The position of these critical elements, points, and nodes of load-bearing structures under dynamic loads can differ significantly from their position determined using static and quasistatic computational modeling methods.

Extraction of mandelic acid with tri-octyl-phosphine oxide (TOPO) in different solvents: Equilibrium and neural network analysis

Mandelic acid is an important carboxylic acid used in pharmaceutical industries. It is also important to use it as a purified form. In this study, selective extraction of mandelic acid was done by tri-octyl-phosphine oxide (TOPO) diluted in different solvents such as methyl isobutyl ketone, 1-octanol, octyl acetate, dimethyl phthalate, 2-octanone, cyclohexane and toluene. The high selectivity of mandelic acid from aqueous solution was supported by thermodynamic parameters (loading factor, distribution coefficient, and extraction efficiency). The obtained values for each solvent were applied to the Neural Network Analysis to predict phase equilibrium behaviour in ternary systems. The results showed the highest mandelic acid extraction efficiency (93.65 %) and distribution coefficient (14.74) were attained with the organic phase mixture prepared with MIBK and TOPO. Also, it was found that extraction efficiencies increased with increasing TOPO amount in the medium for all studied solvents.

Optimization of Warehouse Operations for Upstream Service Companies in the Oil & Gas Industry: A Case Study of XYZ Company

The oil and gas (O&G) sector drives various parts of Malaysia's economy and accounts for 19% of government revenue in 2021. The volatility of oil prices and the COVID-19 pandemic problem in recent years has taught O&G enterprises, especially upstream players, to focus on business needs and employ cost optimization techniques to compete and survive uncertain circumstances. This study seeks to analyze and describe the warehouse operations setup of an onshore supply base service organization that manages and maintains offshore O&G facilities. This study examines space utilization variables from several supply chain perspectives, particularly operations performance. Semi-structured interviews, site observations, and documentation review are used to collect data for the single-case study. The study findings suggest the organization under study has a basic warehouse setup with lean operations staff to complete business functions. In general, the warehouse arrangement for the organization under investigation is comparable to a distribution center, where 70% of warehouse activities comprise, material receiving, handling, and distribution (loading) to several offshore locations. Findings also showed that two factors, namely: material handling flow and movement, and inventory movement affect warehouse space utilization. The volume of material handled by the warehouse based on business demand, as well as the seasonal trend from offshore activities contribute to the frequency of material loading and impact the material throughput and transit time, which were found to have an impact on space utilization too.

Spur gear tooth root stress analysis by a 3D flexible multibody approach and a full-FE contact-based formulation

This paper proposes an original method to determine the gear tooth root stresses from a 3D finite element (FE) flexible multibody approach and a full-FE contact-based formulation. The contact problem is dealt with an augmented Lagrangian formulation whereas the analysis is performed by a preconditioned gradient solver (PCG). Tooth flank modifications are directly introduced within the 3D model. This one is thus able to take into account straightforwardly tooth bending and Hertzian-like deformations as well as the micro-geometry effect. Simulations are performed for several mesh periods, without making any assumptions about load distribution, tooth and gear blank flexibilities, and possible premature or delayed contacts between tooth pairs in quasi-static conditions. A precise distribution of tooth root stresses associated with instantaneous contacts conditions is then computed. For this study, a single stage spur gear with micro-geometry modifications corresponding to an arc-shaped profile crowning is modeled. Several output torques are considered. The obtained results are compared to those obtained using a 2D FE ISO-based model, where external forces are applied along the theoretical line of action.

Static and stability analyses of multi-strut type aluminium alloy suspen-dome structure with large opening

By combining the advantages of the multi-strut concept in cable dome structures with aluminum alloy material, a new type of prestressed spatial structure—the multi-strut aluminum alloy suspen-dome with a large opening—has been developed by integrating a lower prestressed support system into an aluminum alloy reticulated shell. The optimal structural configuration was determined by comparing the static, stability, and economic performance of three types of upper chord mesh topologies for this structure. Subsequently, an analysis of the static performance and large-scale parameters affecting the structural stability of the optimal grid topology was conducted. The study explored the influence of structural prestressing, overall shape, and support stiffness—encompassing three categories and seven parameters—on structural stability and material consumption. Additionally, the effects of initial imperfections and load distribution forms on structural stiffness and stability were further examined. Finally, the static properties, stability, and economic indicators of the structure were compared with two other structural systems: the steel suspen-dome and the cable dome. The results indicate that the pentagonal grid topology provides the best structural performance and that the structure is sensitive to cooling effects. Optimal values for key design parameters, such as the rise-span ratio and thickness-span ratio, were identified. Moreover, the economic advantages of the aluminum alloy suspen-dome compensate for its mechanical performance shortcomings compared to the steel suspen-dome. These findings offer innovative solutions and a scientific basis for the selection and stability design of aluminum alloy suspen-dome structures.

Dynamic Load Balancing of Multi-GPU Parallelization for VULCANO VE-U7 Corium Spreading Analysis Using SOPHIA

The SOPHIA code is a graphical processing unit (GPU)–based smoothed particle hydrodynamics (SPH) framework for fundamental nuclear thermal-hydraulic and safety applications developed by Seoul National University. Focusing on particle-based SPH methods, the SOPHIA code provides capabilities for computational fluid dynamics–level analysis in addressing complex multiphase and multiphysics issues related to severe accidents and reactor safety. Since the SPH method requires high computational cost due to the nature of the particle-based method, the SOPHIA code was parallelized with multiple GPUs. However, using multiple GPUs may cause load imbalance as particles migrate between GPUs, which degrades performance. In this study, we developed a dynamic load-balancing algorithm to increase computational efficiency by reducing the load imbalance. Additionally, a novel cumulative time comparison approach is proposed as a criterion for adjusting boundaries between GPUs, ensuring low load imbalance. To evaluate the performance of the algorithm, three-dimensional dam-break simulations were conducted. The results demonstrated enhanced computational efficiency and equitable load distribution among GPUs compared to using fixed subdomains. Additionally, the developed algorithm was utilized to analyze the VULCANO VE-U7 experiment, and the results were well aligned with the experimental data. Therefore, the practical applicability of the proposed dynamic load-balancing algorithm in nuclear safety analyses has been confirmed.