Optimal Load Distribution Research Articles

Osseointegrated Implants and Osseoperception - A Review

There are many studies suggest that a peripheral feedback pathway can be restored with osseointegrated implants even after tooth loss. This implant mediated sensory-motor control, known as osseoperception may have important clinical implications in improving masticatory function with implant supported prosthesis. To understand this psychophysical integration of implants, available literature was evaluated using various online resources such as Pubmed, Google scholar, etc. using keywords like “osseoperception”, tactile sensibility, implant mediated sensory-motor control and mechanoreceptors. The long-term integration of an implant-bone restoration depends in part on optimal load distribution on the bearing tissue. Because natural teeth and implants vary in their anchorage mechanisms so there is a fundamental difference in the perception and control of the loading. The current review of literature deals with these issues and has been summarized under following heads: • Mechanism of osseoperception • Neurophysiological and psychophysical methods of assessing phenomenon of osseoperception • Mechanoreceptors contributing to osseoperception • Different concepts of osseoperception Keywords: ­ Osseointegration, osseoperception, active threshold, passive threshold, dental implant

Comparison of Efficiency-Based Optimal Load Distribution for Modular SSTs with Biologically Inspired Optimization Algorithms

The battle of currents between AC and DC reignited as a result of the development in the field of power electronics. The efficiency of DC distribution systems is highly dependent on the efficiency of distribution converter, which calls for optimized schemes for the efficiency enhancement of distribution converters. Modular solid-state transformers (SSTs) play a vital role in DC distribution networks and renewable energy systems (RES). This paper deals with efficiency-based load distribution for solid-state transformers (SSTs) in DC distribution networks. The aim is to achieve a set of minimum inputs that are consistent with the output while considering the constraints and efficiency. As the main feature of modularity is associated with a three-stage structure of SSTs, this modular structure is optimized using ant lion optimizer (ALO) and validated by applying it to the EIA (Energy Information Agency) DC distribution network which contains SSTs. In the DC distribution grid, modular SSTs provide the promising conversion of DC power from medium voltage to lower DC range (400 V). The proposed algorithm is simulated in MATLAB and also compared with two other metaheuristic algorithms. The obtained results prove that the proposed method can significantly reduce the input requirements for producing the same output while satisfying the specified constraints.

Output Current Ripple and Efficiency Optimal Control Method for Multi-Phase Interleaved LLC Resonant Converter

The interleaving technique is required for LLC resonant converters to achieve high power levels, handle higher input/output currents, and reduce output current ripple. However, the high system efficiency and the low output current ripple for the multi-phase LLC resonant converter cannot be achieved at the same time at low and medium power levels. Most methods try to keep the multi-phase LLC resonant converter in load-sharing mode regardless of the power level, which will result in low system efficiency at low and medium power levels. In this paper, an output current ripple and efficiency optimal control method with finite load distribution schemes is proposed for the multi-phase LLC resonant converter to balance the efficiency and output current ripple. The optimal control method improves the efficiency as much as possible while keeping output current ripple low. According to the calculation of output current ripple and efficiency, the optimal load distribution scheme is selected. The pulse-frequency modulation (PFM) and phase-shift modulation (PSM) hybrid control is applied to regulate the load of each phase. An analysis is provided, and a 540-W three-phase interleaved LLC resonant converter is built to verify the superiority.

Finding the Optimal Surgical Incision Pattern-A Biomechanical Study.

The closure of wounds and subsequent optimal wound healing is essential to any successful surgical intervention. Especially on parts of the body with limited possibilities for local reconstruction, optimal distribution of load is essential. The aim of the present study was therefore to examine three different incision patterns, conventional straight, Lazy-S and Zigzag, with regard to their biomechanical stability and mode of failure on a porcine skin model. Our results demonstrate the superior biomechanical stability of Lazy-S and Zigzag incision patterns with perpendicular suture placement. This holds true, in particular, for Zigzag incisions, which showed the highest values for all parameters assessed. Moreover, the observed superior stability of Lazy-S and Zigzag incision patterns was diminished when sutures were placed in tensile direction. The conventional straight incision represents the standard access for a large number of surgical procedures. However, we were able to demonstrate the superior biomechanical stability of alternative incision patterns, in particular the Zigzag incision. This is most likely caused by an improved distribution of tensile force across the wound due to the perpendicular placement of sutures. Moreover, this technique offers additional advantages, such as a better overview of the operated area as well as several cosmetic improvements. We therefore advocate that the surgeon should consider the use of a Zigzag incision over a conventional straight incision pattern.

A new modal lateral load pattern for improving pushover analysis to estimate nonlinear responses of structures

ABSTRACT Pushover analysis is a nonlinear procedure that is widely used as the primary tool for the nonlinear analysis of structures. In the conventional pushover method, the fundamental mode of the structure is selected as the dominant response mode of the multi degree of freedom (MDOF) system while neglecting the influence of higher modes. It has been proved that for many structures, higher vibration mode effects should be considered to boost the outcomes of the pushover analysis. In this study, a new Modal Load Pattern (MLP) is developed to improve pushover analysis procedure in estimating nonlinear responses of structures. For this purpose, MLP is defined by the directed algebraic combination of the weighted vibration mode-shape vectors of the structure. The weights of modes are determined using an optimisation algorithm such that the difference between the nonlinear responses of the structure under MLP and1 those of nonlinear time-history analysis is reduced to the minimum possible value. Comparing the outcomes of MLP with some well-known lateral load distributions shows that the proposed method increases the accuracy of responses resulting from pushover analysis. It is illustrated that against elastic behaviour, in the inelastic range of structural behaviour, it is likely that higher modes have a significant effect on the optimal lateral load distribution.

Investigating and Optimizing the Operation of Microgrids with Intelligent Algorithms

Microgrids need optimization to reduce economic problems and human losses. Scattered resources in power systems and microgrids have led to many environmental, economic and human, and animal losses. The most important part of these problems is related to voltage and frequency fluctuations when possible occurrences such as extreme load changes or errors in microgrids. These problems lead to microgrid collapse. Therefore, providing optimal solutions that can solve these challenges is essential. For this purpose, the present study has tried to provide a high-performance control structure in the time of internal and external disturbances based on short-term planning. The proposed approach is the use of an evolutionary neuro-fuzzy network. Perhaps the main reason for using this approach can be due to uncertainty in the distribution and distribution of loads in microgrids and power systems. Simulation has been performed in MATLAB and Simulink environments, and the results show that the optimal load distribution has been done evolution in microgrids.

Multi-Objective Optimization for the Radial Bending and Twisting Law of Axial Fan Blades

The performance of low-pressure axial flow fans is directly affected by the three-dimensional bending and twisting of the blades. A new blade design method is adopted in this work, where the radial distribution of blade angle and blade bending angle is composed of standard-form rational quadratic Bézier curves. Dendrite Net is then trained to predict the pneumatic performance of the fan. A non dominated sorting genetic algorithm is employed to solve the global optimization problem of the total pressure coefficient and efficiency. The simulation results show that the optimal blade load distribution along the radial direction becomes uniform, and the suction surface separation vortex and passage vortex are restrained. On the other hand, the tip leakage vortex is enhanced and moves toward the blade leading edge. According to the experimental results, the maximum efficiency increases by 5.44%, and the maximum total pressure coefficient increases by 2.47% after optimization.

Optimal Distribution of the Disk Load: Validity of the Betz–Joukowsky Limit

The popular axial momentum theory relies on the steady, incompressible, axisymmetric, and inviscid flow through a so-called actuator disk. The most important result of this theory is the famous Betz–Joukowsky limit, stating that the maximum power coefficient achievable by an open disk is limited to 16/27. Generally, this value is obtained assuming a priori that the disk is radially uniformly-loaded and the flow is axially one-dimensional. This, however, does not prove that the uniform type is the optimal load, or else that it returns the maximum value of the extracted power. For this reason, this paper preliminary shows that 16/27 is the exact value of the maximum power coefficient of a uniformly loaded disk, even if the flow is not assumed as one-dimensional. Then, it proves, using a calculus of variation approach, that the radially uniform load is optimal. The proof refers to an approximate classical local form of the axial momentum equation. Finally, the paper points out that, since the proof of the Betz–Joukowsky limit relies on this simplifying assumption, the exact evaluation of the optimal radial distribution of the disk load, leading to the maximum value of the power coefficient, is still an open question.

An adaptive-based control law for accurate frequency restoration and economic load dispatch in microgrid with clock drifts

This paper focuses on frequency regulation and the economic dispatch problems of microgrid primarily composed of multi energy resources. Due to different problems such as intermittency of renewable energy, unbalanced demand and supply, frequency fluctuation and so on in microgrid. These problems alternative stability and disturbance rejection present challenges to the design of controllers and management algorithms for voltage, frequency and power in all possible execution scenarios. In this work, a novel adaptive generalized distributed averaging integral controller has been designed. Goals of the designed controller are to realize accurate steady-state frequency recovery in the circumstances of clock drifts, cope with the uncertainty by dynamically updating of the control parameters while maintaining system stability, exchange generating data of each generator by the communication channel and, realize the optimal power distribution among the generators. Moreover, we add an additional controller to the hierarchical control scheme to distribute the load economically, the algorithm based on consistency ensures the optimal load distribution. Finally, the effectiveness of the designed control scheme is verified by simulation comparing it with other related distributed control methods.

Optimal Load Distribution in DG Sources Using Model Predictive Control and the State Feedback Controller for Switching Control

The distributed energy management of interconnected microgrids, which is based on model predictive control (MPC), relies on the cooperation of all the agents (i.e., microgrids). Model predictive control or MPC is widely used in industrial applications as an effective tool for dealing with multivariable limited control problems. MPC uses an explicit system model to predict the future horizon of the system and its outputs. This predictability allows calculating the optimal order of inputs to minimize output errors over a limited horizon, which is effected by the limitations of the system. This study presents a distributed economic model predictive control method using the new state feedback controller to control the switching of interface converters and compensate for the unbalanced and nonlinear loads. In this model, the islanding mode and the reconnection of the grid are considered to improve the transient behavior of the system to achieve steady-state power distribution. It has been proposed that it could obtain better results in predictive control, utilizing similarity transform in the state matrix and its modification. First, this model is simulated on distributed generation sources with power-sharing and local loads using the state feedback controller in MATLAB Simpower. Then, the performance of the proposed method is evaluated, confirming that it is more reliable than the FS-MPC and DSVM-MPC methods.

Geopolymer Concrete Performance Study for High-Temperature Thermal Energy Storage (TES) Applications

Solar energy is an energy intermittent source that faces a substantial challenge for its power dispatchability. Hence, concentrating solar power (CSP) plants and solar process heat (SPH) applications employ thermal energy storage (TES) technologies as a link between power generation and optimal load distribution. Ordinary Portland cement (OPC)-based materials are widely used in sensible TES, but their use is limited to operation temperatures below 400 to 500 °C because of thermal degradation processes. This work proposes a geopolymer (GEO)-based concrete as a suitable alternative to OPC concrete for TES that withstands high running temperatures, higher than 500 °C. To this end, thermophysical properties of a geopolymer-based concrete sample were initially measured experimentally; later, energy storage capacity and thermal behavior of the GEO sample were modeled numerically. In fact, different thermal scenarios were modeled, revealing that GEO-based concrete can be a sound choice due to its thermal energy storage capacity, high thermal diffusivity and capability to work at high temperature regimes.

Research on Heating and Power Load Optimal Dispatch Based on the Particle Swarm Optimization Algorithm Combined with the Levy Flight

Optimal dispatch of heat and power loads for combined heat and power (CHP) power plants is an effective solution to further improve energy efficiency and reduce energy consumption. To obtain a better load dispatch effect, this paper used the Particle Swarm Optimization (PSO) algorithm based on Levy flight (Levy-PSO algorithm) to solve the optimal dispatch problem. Meanwhile, the constraint condition processing method which is based on the characteristics of the internal thermoelectric load and external total load of electricity and heat network of thermoelectric units is also proposed to combine with the Levy-PSO algorithm for solving the problem. The optimal results are obtained via using the PSO and Levy-PSO algorithms for a typical CHP plant of seven units. The results show that the power generation cost obtained from the Levy-PSO algorithm is the lowest (10121.84$), which is close to the results of the literature and is better than the optimization results of the traditional PSO algorithm. Moreover, the results show that the constraint processing method adopted in this paper can well solve the characteristics of multiple constraints of load optimal dispatch problem. Meanwhile, the comparison results between the PSO algorithm and Levy-PSO algorithm indicate that the Levy-PSO algorithm has more obvious advantages in the performance of the algorithm. When combining with the proposed constraint processing scheme, the Levy-PSO algorithm shows better convergence and robustness, which is more suitable for solving similar practical engineering problems of heating and power optimal load distribution.

A model for cascading failures with the probability of failure described as a logistic function

In most cascading failure models in networks, overloaded nodes are assumed to fail and are removed from the network. However, this is not always the case due to network mitigation measures. Considering the effects of these mitigating measures, we propose a new cascading failure model that describes the probability that an overloaded node fails as a logistic function. By performing numerical simulations of cascading failures on Barabási and Albert (BA) scale-free networks and a real airport network, we compare the results of our model and the established model describing the probability of failure as a linear function. The simulation results show that the difference in the robustness of the two models depends on the initial load distribution and the redistribution of load. We further investigate the conditions of our new model under which the network exhibits the strongest robustness in terms of the load distribution and the network topology. We find the optimal value for the parameter of the load distribution and demonstrate that the robustness of the network improves as the average degree increases. The results regarding the optimal load distribution are verified by theoretical analysis. This work can be used to develop effective mitigation measures and design networks that are robust to cascading failure phenomena.

Using a Computer Simulator to Create a Digital Model of a CCGT Power Unit

The report is devoted to the problem of creating a digital model of an energy facility in relation to the PGU-450T power unit in the absence of the possibility of conducting field experiments to obtain initial information. Under these conditions, it is proposed to use a computer simulator of the object under study. The main provisions of checking the adequacy and validity of the simulator to a real object for static and dynamic processes are outlined, examples of such verification are given. The application of a specific simulator of JSC "Simulators for power plants" for modeling the parameters of the equipment of the CCGT and the CCGT as a whole during the operation of the CCGT at reduced loads is justified. The results of the numerical experiments carried out on the simulator were used to create a digital model of the PGU-450T in the form of a data library and regression equations in the form of dependencies of the main indicators of gas turbines, waste heat boilers, steam turbines and CCGT as a whole on the power of the CCGT, outdoor air temperature, fuel consumption for various modes of gas and steam turbines. The effectiveness of the use of the digital model of the PGU-450T (Russian model of CCGT) is illustrated by the example of solving the practical problem of optimal distribution of the total load of the GTU between gas turbines when the CCGT operates at reduced loads.

The Effects of the Model Predictive Controller Compared to the LQR Controller on the Optimal Distribution of the Suitable Load and the Creation of Balance in Microgrids

Background and Objectives: Distributed generation (DG) sources are modeled using an ideal DC voltage source connected to the microgrid via voltage source converters (VSCs). Model predictive control presents a distinct method for energy processing.Methods: In this method, the electric power converter is considered a power amplifier with a discrete and nonlinear structure. Therefore, unlike linear control methods, the discrete and nonlinear nature of the converter is considered in this method. In this paper, the distributed model predictive controller was selected from among different methods of load allocation among DG sources due to its more advantages compared to the linear quadratic regulator (LQR) controller.Results: It has been Proposed that we could obtain better results in predictive control, utilizing similarity transform in the state matrix and its modification. In this research, all the simulations have been performed in the MATLABSimpower environment of MATLAB software.Conclusion: Moreover, to demonstrate the superior performance of the model predictive controller compared to the LQR controller, both performance modes of the microgrid, namely the grid-connected and islanding modes, have been considered.

Comparison of Clinical and Radiological Parameters around Microthread Implants in Patients with and without History of Treated Periodontitis

Introduction: Maintenance of crestal bone level is crucial for the success of implant-supported prosthetic rehabilitation. Implant neck design plays an important role in maintaining crestal bone levels. The microring neck design is known to counteract the marginal bone loss and improving bone-to-implant contact by providing optimal load distribution as reported by the finite element studies and animal studies. Aim: The current study aimed to evaluate dental implants’ short-term (12 months) clinical and radiographic parameters in periodontally healthy patients versus those with history of treated periodontitis. Materials and Methods: The current prospective interventional study was performed at AB Shetty Memorial Institute of Dental Sciences, Mangalore, Karnataka, India from 2016 to 2018. In the study, 24 microthreaded implants were placed in periodontally healthy patients (group A, n=12) and patients with a history of treated periodontitis (group B, n=12). Peri-implant radiographic crestal bone loss, clinical measurements like probing pocket depths, bleeding on probing, and soft tissue complications were assessed around implants at time of implant loading, and 3, 6, and 12 months postloading. Statistical Package for the Social Sciences (SPSS) software version 22 was used for statistical analysis. Statistical significance was set at p<0.05. Results: At the end of one-year postloading, peri-implant crestal mean bone loss of 2.317±0.914 mm (mesial), 2.37±1.276 mm (distal) and 2.673±1.178 (mesial), 2.87±1.075 (distal) mm were observed in groups A and B, respectively. The probing pocket depths were 3.729±0.95 mm and 4.017±0.67 mm in groups A and B, respectively at the end of the study period. However, there was no statistical significance for probing depths among both groups. At the end of the study period, soft tissue complications were 16.67% in group B, while no complications were noted in group A. None of the groups showed any technical or mechanical complications. Conclusion: The results of the study revealed that crestal bone loss and pocket depths around implants are similar in both groups at various follow up periods. However, the incidence of peri-implant soft tissue complications is higher in patients with history of treated periodontitis.

A roller taper modification method for load distribution optimization of planetary roller screw mechanism

A roller taper modification method for load distribution optimization of planetary roller screw mechanism

An analytical solution of the optimal chillers operation problems based on ASHRAE guideline 14

The heating, ventilation, and air conditioning (HVAC) systems consume about half the energy in office buildings, and chillers account for the largest share of energy consumption, specifically in tropical or subtropical zones. This study aims to establish a method to optimize chiller sequencing and load distribution to reduce energy consumption. A constrained optimization problem is formulated using the total power consumption of chillers as the objective function. Each chiller load is restricted to lie between its upper and lower bounds, and the total chiller load must meet the cooling demand. Unlike the conventional approaches that use quadratic or cubic models, the Gordon-Ng simplified model suggested by ASHRAE guideline 14 is adopted to estimate the chiller efficiency. An analytical solution is derived for optimal chiller loading as follows: as n chillers are turned on, the optimal load distribution occurs as the top (k−1) energy-efficient chillers run at maximum load, and the bottom (n−k) energy-efficient chillers run at minimum load, where k is selected to meet the cooling demand. To determine the optimal chiller combination, one can determine the optimal load distributions for all admissible combinations. The combination that requires minimal energy consumption is the optimal chiller sequence. The proposed method is demonstrated using the HVAC data of a city hall in Taiwan. As the chilled water supply temperatures are set the same, the active chiller combination dominates the optimization results. To reduce the chiller switching induced by the optimization of chiller sequencing, one could either increase the minimal uptime or downtime of chillers or adopt shift operation. The energy-saving is significant no matter which operation strategy is adopted.

Optimal Switching and Load Distribution Strategy for Energy Minimization and Performance Optimization of Cloud Data Center

Cloud computing is a promising computing technology utilized in every stage of the business. The cloud offers different services to cloud users from anytime to anywhere, and it is attained with different parameters, like load optimization, resource optimization. Due to the increase in data center, energy consumption has become a major issue in green data centers. The majority of data centers are function using peak load with huge scales. Thus, it is essential for carrying out energy saving in cloud data centers. This paper designed an energy-saving method using fat tree. The proposed techniques optimize the load at different zones of data center and user in the cloud platform. Here, the distribution of load in cloud data centers is performed using Taylor-based Manta Ray Foraging Optimization (Taylor-MRFO), which is an integration of Manta Ray Foraging Optimization (MRFO) and Taylor series. The method utilized different objectives that involve power, load, latency, and bandwidth. With the load distribution, the switching of cloud data center to the desired mode is performed using Actor critic neural network (ACNN). Thus, the dual strategy leads to performance optimization in cloud infrastructure and also in consolidating parallel workload in data centers more effectively. The proposed Taylor-MRFO+ACNN outperformed other methods with minimal energy of 0.553, minimal load of 0.363, and minimal fitness of 0.437, respectively.

Methodological Bases of Optimal Load Distribution at a Thermal Power Plant with a Complex Composition of Equipment, Taking into Account Market Requirements

The features of a mathematical model for optimizing the distribution of heat and electricity at a large thermal power plant with a complex composition of equipment as part of traditional heating units and a heating CCGT are considered. The selection and justification of optimization criteria at different stages of preparation and entry of the station to the electricity and capacity market is given. The disadvantages of the previously proposed optimal distribution algorithms are analyzed in relation to thermal power plants with a complex composition of equipment and with a complex scheme for the supply of electricity and heat. A method and algorithm for solving the problem are proposed based on the equivalence of the CHP equipment and the decomposition of the problem taking into account the schemes of electricity and heat output. The description of mathematical optimization methods is given, taking into account the peculiarities of the CCGT operating modes at reduced loads. The requirements for information support when integrating the developed algorithm into the application software of the automated process control system based on the PTC are given.