Research on the End Surface Dent of the Main Shaft Forging

In the process of the stretching of the shaft forgings, if the process parameters are not properly selected, the end-face dent will take place. The end-face dent affects the performance of large forgings and leads to much material wasting. Finite element method was employed to perform numerical simulation of the stretching of a main shaft with an upper flat anvil and a lower V-shaped anvil. The orthogonal test table was designed by selecting the anvil width, the Reduction ratio and the feed as influencing factors. Accordingly, simulations were carried out to solve the end-face dent values under different parameter combinations. The analysis showed that the optimal parameter combination gave an anvil width ratio of 0.75, a Reduction ratio of 0.2, and an initial feed of 300 mm. Through extremum difference analysis, it was found that among the three factors are the feed, the reduction ratio, and the anvil width ratio in the decreasing order of the influence on the end- face dent. Comprehensive analysis showed that when the anvil is relatively narrow, increasing the relative feed can reduce the end-surface dent remarkably. It is advisable that during the stretching of shaft forgings with a flat upper anvil and a V-shaped lower anvil, the combination of the anvil width ratio of 0.75, the reduction ratio of 0.2, and increasing the feed can reduce the end-face dent, thereby reducing the end cutting and saving material costs

International Journal of Smart Grid and Clean Energy Research on battery storage system configuration in active distribution networks

Abstract In this paper, an optimizing model of battery energy storage system (BESS) capacity configuration for active distribution networks (ADNs) is proposed to mitigate the negative effect of distribution systems with distributed generations (DGs), in stability, economy and reliability perspectives. In details, this model involved the charging and discharging limitation and the operation constrains of BESS, as well as the power flow balance of distribution system. Moreover, three optimizing objectives have been presented for the optimal power profile of BESS, including minimizing the voltage fluctuation, reducing the feeder loss and maximizing the consecutive supplied power of ADN, respectively. After the optimization, the BESS capacity is calculated by estimation of the maximum charging and discharging energy. Finally, the simulation results are shown to illustrate the procedure of capacity configuration

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

Design, modelling, and test of a solid-state main breaker for hybrid DC circuit breaker

Driven by the requirements of reducing air pollutant gas emissions and fuel consumption, the concepts of more electric ship and electric aircraft are attracting increasing attention. Medium voltage DC (MVDC) distribution architectures have been proposed as potential candidates to transmit and distribute energy from generators to motors in these applications. However, the low impedance in MVDC systems results in extremely fast propagation speed of fault currents. Therefore, it is necessary to interrupt the DC fault in a very short period. This paper investigates a solid-state circuit breaker with an ultrafast interruption speed as a main breaker for a hybrid DC circuit breaker. A simulation model of the hybrid circuit breaker is established using PLECS software to evaluate the performance of the main breaker. A 1 kV solid-state main breaker prototype based on series and parallel connected insulated gate bipolar transistors (IGBTs) is built. Series and parallel connection of IGBTs are implemented to increase the voltage and current level. The maximum voltage across the solid-state circuit breaker is limited to 1.8 kV during current interruption. The solid-state main breaker prototype is experimentally tested under dynamic current conditions. The solid-state main breaker prototype successfully interrupts current of 400 A within 300 microseconds and presents good voltage balancing as well as current sharing performance. The experimental results show good agreement with the simulation results. <br/

Charging Guidance of Electric Taxis Based on Adaptive Particle Swarm Optimization

Electric taxis are playing an important role in the application of electric vehicles. The actual operational data of electric taxis in Shenzhen, China, is analyzed, and, in allusion to the unbalanced time availability of the charging station equipment, the electric taxis charging guidance system is proposed basing on the charging station information and vehicle information. An electric taxis charging guidance model is established and guides the charging based on the positions of taxis and charging stations with adaptive mutation particle swarm optimization. The simulation is based on the actual data of Shenzhen charging stations, and the results show that electric taxis can be evenly distributed to the appropriate charging stations according to the charging pile numbers in charging stations after the charging guidance. The even distribution among the charging stations in the area will be achieved and the utilization of charging equipment will be improved, so the proposed charging guidance method is verified to be feasible. The improved utilization of charging equipment can save public charging infrastructure resources greatly

Charging Guidance of Electric Taxis Based on Adaptive Particle Swarm Optimization

Electric taxis are playing an important role in the application of electric vehicles. The actual operational data of electric taxis in Shenzhen, China, is analyzed, and, in allusion to the unbalanced time availability of the charging station equipment, the electric taxis charging guidance system is proposed basing on the charging station information and vehicle information. An electric taxis charging guidance model is established and guides the charging based on the positions of taxis and charging stations with adaptive mutation particle swarm optimization. The simulation is based on the actual data of Shenzhen charging stations, and the results show that electric taxis can be evenly distributed to the appropriate charging stations according to the charging pile numbers in charging stations after the charging guidance. The even distribution among the charging stations in the area will be achieved and the utilization of charging equipment will be improved, so the proposed charging guidance method is verified to be feasible. The improved utilization of charging equipment can save public charging infrastructure resources greatly

Coordinated Charging Strategy for Electric Taxis in Temporal and Spatial Scale

Currently, electric taxis have been deployed in many cities of China. However, the charging unbalance in both temporal and spatial scale has become a rising problem, which leads to low charging efficiency or charging congestion in different stations or time periods. This paper presents a multi-objective coordinated charging strategy for electric taxis in the temporal and spatial scale. That is, the objectives are maximizing the utilization efficiency of charging facilities, minimizing the load unbalance of the regional power system and minimizing the customers’ cost. Besides, the basic configuration of a charging station and operation rules of electric taxis would be the constraints. To tackle this multi-objective optimizing problems, a fuzzy mathematical method has been utilized to transfer the multi-objective optimization to a single optimization issue, and furthermore, the Improved Particle Swarm Optimization (IPSO) Algorithm has been used to solve the optimization problem. Moreover, simulation cases are carried out, Case 1 is the original charging procedure, and Cases 2 and 3 are the temporal and spatial scale optimized separately, followed with Case 4, the combined coordinated charging. The simulation shows the significant improvement in charging facilities efficiency and users’ benefits, as well as the better dispatching of electric taxis’ charging loads

Optimal Scheduling of a Battery Energy Storage System with Electric Vehicles’ Auxiliary for a Distribution Network with Renewable Energy Integration

With global conventional energy depletion, as well as environmental pollution, utilizing renewable energy for power supply is the only way for human beings to survive. Currently, distributed generation incorporated into a distribution network has become the new trend, with the advantages of controllability, flexibility and tremendous potential. However, the fluctuation of distributed energy resources (DERs) is still the main concern for accurate deployment. Thus, a battery energy storage system (BESS) has to be involved to mitigate the bad effects of DERs’ integration. In this paper, optimal scheduling strategies for BESS operation have been proposed, to assist with consuming the renewable energy, reduce the active power loss, alleviate the voltage fluctuation and minimize the electricity cost. Besides, the electric vehicles (EVs) considered as the auxiliary technique are also introduced to attenuate the DERs’ influence. Moreover, both day-ahead and real-time operation scheduling strategies were presented under the consideration with the constraints of BESS and the EVs’ operation, and the optimization was tackled by a fuzzy mathematical method and an improved particle swarm optimization (IPSO) algorithm. Furthermore, the test system for the proposed strategies is a real distribution network with renewable energy integration. After simulation, the proposed scheduling strategies have been verified to be extremely effective for the enhancement of the distribution network characteristics

A Novel Temporal Network-Embedding Algorithm for Link Prediction in Dynamic Networks

Understanding the evolutionary patterns of real-world complex systems such as human interactions, biological interactions, transport networks, and computer networks is important for our daily lives. Predicting future links among the nodes in these dynamic networks has many practical implications. This research aims to enhance our understanding of the evolution of networks by formulating and solving the link-prediction problem for temporal networks using graph representation learning as an advanced machine learning approach. Learning useful representations of nodes in these networks provides greater predictive power with less computational complexity and facilitates the use of machine learning methods. Considering that existing models fail to consider the temporal dimensions of the networks, this research proposes a novel temporal network-embedding algorithm for graph representation learning. This algorithm generates low-dimensional features from large, high-dimensional networks to predict temporal patterns in dynamic networks. The proposed algorithm includes a new dynamic node-embedding algorithm that exploits the evolving nature of the networks by considering a simple three-layer graph neural network at each time step and extracting node orientation by using Given&rsquo;s angle method. Our proposed temporal network-embedding algorithm, TempNodeEmb, is validated by comparing it to seven state-of-the-art benchmark network-embedding models. These models are applied to eight dynamic protein&ndash;protein interaction networks and three other real-world networks, including dynamic email networks, online college text message networks, and human real contact datasets. To improve our model, we have considered time encoding and proposed another extension to our model, TempNodeEmb++. The results show that our proposed models outperform the state-of-the-art models in most cases based on two evaluation metrics