Optimum design of flow distribution in quenching tank for heat treatment of A357 aluminum alloy large complicated thin-wall workpieces by CFD simulation and ANN approach

Based on the computational fluid dynamics （CFD） method, a quenching tank with two agitator systems and two flow-equilibrating devices was selected to simulate flow distribution using Fluent software. A numerical example was used to testify the validity of the quenching tank model. In order to take tank parameters （agitation speed, position of directional flow baffle and coordinate position in quench zone） into account, an approach that combines the artificial neural network （ANN） with CFD method was developed to study the flow distribution in the quenching tank. The flow rate of the quenching medium shows a very good agreement between the ANN predicted results and the Fluent simulated data. Methods for the optimal design of the quenching tank can be used as technical support for industrial production.

Mean-field Theory for Some Bus Transport Networks with Random Overlapping Clique Structure

Transport networks, such as railway networks and airport networks, are a kind of random network with complex topology. Recently, more and more scholars paid attention to various kinds of transport networks and try to explore their inherent characteristics. Here we study the exponential properties of a recently introduced Bus Transport Networks （BTNs） evolution model with random overlapping clique structure, which gives a possible explanation for the observed exponential distribution of the connectivities of some BTNs of three major cities in China. Applying mean-field theory, we analyze the BTNs model and prove that this model has the character of exponential distribution of the connectivities, and develop a method to predict the growth dynamics of the individual vertices, and use this to calculate analytically the connectivity distribution and the exponents. By comparing mean-field based theoretic results with the statistical data of real BTNs, we observe that, as a whole, both of their data show similar character of exponential distribution of the connectivities, and their exponents have same order of magnitude, which show the availability of the analytical result of this paper.

Optimal Allocation of Public Transport Hub Based on Load Loss Value and the Economy of Distribution Network

The rapid development of electric buses has brought a surge in the number of bus hubs and their charging and discharging capacities.Therefore,the location and construction scale of bus hubs will greatly affect the operation costs and benefits of an urban distribution network in the future.Through the scientific and reasonable planning of public transport hubs on the premise of meeting the needs of basic public transport services,it can reduce the negative impact of electric bus charging loads upon the power grids.Furthermore,it can use its flexible operation characteristics to provide flexible support for the distribution network.In this paper,taking the impact of public transport hub on the reliability of distribution network as the starting point,a three-level programming optimization model based on the value and economy of distribution network load loss is proposed.Through the upper model,several planning schemes can be generated,which provides boundary conditions for the expansion of middle-level optimization.The normal operation dispatching scheme of public transport hub obtained from the middle-level optimization results provides boundary conditions for the development of lower level optimization.Through the lower level optimization,the expected load loss of the whole distribution system including bus hub under the planning scheme given by the upper level can be obtained.The effectiveness of the model is verified by an IEEE-33 bus example.

Empirical investigation of topological and weighted properties of a bus transport network from China

Many bus transport networks （BTNs） have evolved into directed networks. A new representation model for BTNs is proposed, called directed-space P. The bus transport network of Harbin （BTN-H） is described as a directed and weighted complex network by the proposed representation model and by giving each node weights. The topological and weighted properties are revealed in detail. In-degree and out-degree distributions, in-weight and out-weight distributions are presented as an exponential law, respectively. There is a strong relation between in-weight and in-degree （also between out-weight and out-degree）, which can be fitted by a power function. Degree-degree and weight-weight correlations are investigated to reveal that BTN-H has a disassortative behavior as the nodes have relatively high degree （or weight）. The disparity distributions of out-degree and in-degree follow an approximate power-law. Besides, the node degree shows a near linear increase with the number of routes that connect to the corresponding station. These properties revealed in this paper can help public transport planners to analyze the status quo of the BTN in nature.

IMPLEMENTING SECURITY FOR ACTIVE NETWORKS IN INTERNET

Active networks is primarily a Defense Advanced Research Projects Agency(DARPA)-funded project focusing on the research of mechanisms, applications, and operating systems to develop a reconfigurable network infrastructure. This letter proposes an Secure Active Tracing System (SATS) to implementing security for active networking in Internet. Unlike currently existing schemes, SATS reduces the computational overloads by executing the filtering operation on selected packet streams only when needed.

Coordinated Restoration Method for Electric Buses and Network Reconfigurations in Distribution Systems Under Extreme Events

Distribution systems are facing challenges in serving lifeline loads after extreme events.Network reconfiguration is a traditional and practical method for power supply restoration,which has strong but inflexible power transfer capabilities influenced by network topology.Multiple failures of utility power under extreme events will further limit the efficiency of network reconfiguration.Electric buses(EBs)can be utilized to achieve power supply considering their discharging capabilities as mobile storage devices.However,the mobility of EBs and the influences of transport systems must be carefully considered to enhance the resilience of distribution systems.Reconfiguration and EBs are complementary in terms of recovery capabilities and location flexibility,and more important loads can be recovered by the coordination between EBs and network reconfiguration.This paper proposes a coordinated restoration method for EBs and reconfigurations considering the influences of transport systems.The post-disaster restoration problem is formulated as a bi-level model,in which the network topology is optimized in the upperlevel aiming at maximizing restoration loads through the main grid and EBs,while the traffic paths of all EBs are optimized with the goal of maximizing the restoration loads by the EBs in the lower-level considering time consumption and energy consumption during movement.The PSO and a genetic algorithm are used to solve the proposed bi-level optimization problem.Simulation studies are performed to verify the superiority of the proposed method.

Optimal Coordination of Transportable Power Sources and Repair Crews for Service Restoration of Distribution Networks Considering Uncertainty of Traffic Congestion

This paper proposes a new method for service restoration of distribution network with the support of transportable power sources(TPSs)and repair crews(RCs).Firstly,a coupling model of distribution networks and vehicle routing of TPSs and RCs is proposed,where the TPSs serve as emergency power supply sources,and the RCs are used to repair the faulted lines.Considering the uncertainty of traffic congestion,the probability distribution of the travel time spent on each road is derived based on the Nesterov user equilibrium model,and a two-stage stochastic program is formulated to determine the optimal routings of TPSs and RCs.To efficiently solve the proposed stochastic mixed-integer linear program(MILP),a two-phase scenario reduction method is then developed to scale down the problem size,and an adaptive progressive hedging algorithm is used for an efficient solution.The effectiveness of the proposed methods and algorithms has been illustrated in a modified IEEE 33-bus system.

Multi-stage Dynamic Post-disaster Recovery Strategy for Distribution Networks Considering Integrated Energy and Transportation Networks

To improve the resilience of distribution networks(DNs),a multi-stage dynamic recovery strategy is proposed in this paper,which is designed for post-disaster DN considering an integrated energy system(IES)and transportation network(TN).First,the emergency response quickly increases the output of gas turbines(GTs)in the natural gas network(NGN),and responsively reconfigures the DN in microgrids,to maximize the amount of loads to be restored.The single-commodity flow model is adopted to construct spanning tree constraints.Then,in the second stage of energy storage recovery,mobile energy storage systems(MESSs)are deployed to cover the shortages of power demands,i.e.,to further restore the loads after evaluating the load recovery situation.The Floyd algorithm based dynamic traffic assignment(DTA)is selected to obtain the optimal path of the MESSs.In the third stage,the outputs of various post-disaster recovery measures are adjusted to achieve an economically optimized operation.Case studies demonstrate the effectiveness of the proposed dynamic post-disaster recovery strategy.

Particle dispersion modeling in ventilated room using artificial neural network

Due to insufficiency of a platform based on experimental results for numerical simulation validation using computational fluid dynamic method(CFD) for different geometries and conditions,in this paper we propose a modeling approach based on the artificial neural network(ANN) to describe spatial distribution of the particles concentration in an indoor environment.This study was performed for a stationary flow regime.The database used to build the ANN model was deducted from bibliography literature and composed by 261 points of experimental measurement.Multilayer perceptron-type neural network(MLP-ANN) model was developed to map the relation between the input variables and the outputs.Several training algorithms were tested to give a choice of the Fletcher conjugate gradient algorithm(TrainCgf).The predictive ability of the results determined by simulation of the ANN model was compared with the results simulated by the CFD approach.The developed neural network was beneficial and easy to predict the particle dispersion curves compared to CFD model.The average absolute error given by the ANN model does not reach 5%against 18%by the Lagrangian model and 28% by the Euler drift-flux model of the CFD approach.

自动驾驶环境下交通-配电网协同优化运行

为深入分析耦合电力-交通网在混行条件下产生的协同优化效果,提出自动驾驶环境下交通-配电网协同优化策略。建立智慧交通-配电系统协同运行框架,并分析自动驾驶与人工驾驶汽车的行驶特性,构建基于变分不等式的交通网动态混合均衡模型;结合基于二阶锥规划的配电网动态最优潮流模型,设计两阶段迭代求解算法实现耦合网络协同运行仿真。通过与现有单一车型场景及非协调运行情况对比分析,探究在需求高峰时段混行环境下的优化运行效果,最终结果证明了所提协同优化策略的优越性,使得网络综合运行成本相较现有场景有了显著降低,实现了自动驾驶汽车渗入这一新场景下的耦合网络优化运行。

冷链物流配送网络选址-路径-库存的优化

为了控制冷链配送成本,优化3级冷链物流网络节点空间布局,提出了一种基于需求存在模糊随机性的选址-路径-库存多目标优化模型。以京津冀城市群为例,提供了优化方案。针对由物流基地、配送中心、末端网点构成的冷链物流3级配送网络,考虑到冷链产品存在订货提前期及需求具有模糊随机性,引入模糊随机变量,建立了CLRIP集成优化模型。首先,通过穷举法计算出各备选配送中心点的最佳订货周期和最优库存管理方式,将客户需求点与备选配送中心点进行随机组合,确定需要开放的备选配送中心点的位置与数量。然后,基于客户模糊随机需求,综合考虑土地、库存和配送等成本,进行选址-路径动态规划;利用改进禁忌搜索算法修正模型,直至达到最终要求,从而确定需要开放的备选配送中心点及其最优巡回路线。最后,对京津冀城市群多组实例进行分析。结果表明:使用模糊随机变量的试验数据中,最优解目标函数值和偏差稳定度均优于使用固定需求量的对照组数据,验证了CLRIP模型和改进算法的有效性。研究结果可为京津冀城市群冷链物流配送网络构建提供最优选址位置、配送路径和库存管理,为相关冷链物流配送网络优化提供实用可靠的参考方案。

考虑电力-交通交互的配电网故障下电动汽车充电演化特性

由于配电网与交通网间的耦合交互作用,高渗透率电动汽车接入下的配电网故障可能对两网的运行产生扩大影响,厘清故障下电动汽车充电负荷对电网和交通网运行状态的影响以及充电站充电负荷分布是及时阻断故障影响的基础。针对此,提出了考虑电力-交通交互的配电网故障下充电特征演化分析方法。首先,考虑配电网故障下充电负荷变化对配电网的影响,提出了计及充电负荷变化灵敏度的变步长重复潮流模型以计算配电网最大供电能力。其次,结合用户有限理性决策和动态交通均衡,构建了配电网故障影响下的交通运行状态演化模型。进一步,以电动汽车出行和充电为耦合单元,基于改进的Davidson函数描述供电能力对充电行为的影响,建立了两网交互的作用关系。最后,仿真分析了考虑网络耦合的配电网故障下充电负荷演化规律及其影响。

交通网-电网耦合框架下制氢加氢站选址定容

制氢加氢站(HPRS)的合理规划对氢燃料电池车的发展具有重要意义,为此提出交通网-电网耦合框架下的HPRS规划方法,实现大规模分布式新能源并网电力系统下的HPRS选址定容。首先,基于城市路网阻抗信息模型对氢燃料车的出行进行模拟,得到交通网层面上加氢负荷时空分布。然后,以建设运营成本、交通网加氢负荷覆盖率与电力网络运行稳定性为约束,构建多目标综合评价选址定容模型,通过改进粒子群算法对HPRS的选址和设备容量进行求解。在33节点电网和25节点交通网上进行算例分析,结果表明所提出方法有效降低了HPRS在接入电网后引起的电网损耗与电压偏差,同时提升新能源自消纳率,提高了HPRS的交通网服务质量并减少了建设运营成本。

考虑交通网络不确定性及SESS与MESS空间配合的分布式储能防灾应急规划

为在大规模停电后提供紧急电力支持,提出一种考虑交通网络不确定性及固定储能系统(SESS)和移动储能系统(MESS)空间配合的分布式储能(DES)防灾应急规划方法。基于正态分布区间数,建立通行时间不确定的MESS调度交通网络模型,提出考虑SESS与MESS空间配合效应的联合应急供电策略。采用机会约束描述交通网络不确定性,构建考虑供电可靠性最优的DES选址定容模型。将机会约束转换为确定性等价类,针对DES防灾应急系统多个供电可靠性衡量指标间的冗余问题,提出基于规格化法平面约束法的多目标优化求解方法。采用改进的IEEE 30节点系统及交通网络进行验证,结果表明所提方法能够实现多区域最优配置,验证了SESS与MESS的空间配合效应,并反映了交通网络不确定性对规划结果的影响。

考虑移动电氢资源多区互济的负荷恢复方法

电-氢-交通协同可以提高灵活性并增强能源系统韧性。提出了一种考虑移动电氢资源多区互济的负荷恢复方法。针对分布在多个区域的电-氢耦合系统,提出在停电期间协同利用本地的分布式电源、储氢和移动电氢资源,快速恢复关键用户需求。通过基于交通地图系统的移动电氢资源调度,提出了负荷恢复和移动电氢资源调度集成的故障恢复决策框架。进而构建了移动电氢资源交通调度与负荷恢复的耦合约束,建立了移动电氢资源参与恢复的混合整数二阶锥规划模型,可以进行恢复与交通调度的统一决策。所提方法从时间和空间维度协调和分配电氢资源,最大限度地恢复关键用户的需求。通过构建的多区域电-氢耦合系统和相对应的交通系统,验证了所提协同恢复方法的有效性和优越性。

考虑多微网接入的气电-交通耦合系统分布式规划方法

电动汽车的激增加剧了交通网和气电配网的深度耦合;微网的广泛渗透,通过多种能源转换设备将地理位置毗邻的多能负荷聚合于气电配网的受端。由于电动汽车用户的出行策略和微网的多能需求将引起配电网潮流的波动,传统研究中采用的交通、微网独立规划方法可能造成电网运行电压越限或线路过载的问题。因此,能源-交通深度耦合的新型电力系统亟需更全面的规划模型来满足各主体安全性和经济性的需求。为解决上述问题,文中提出多微网接入的气电-交通耦合系统的协同规划模型。考虑到微网的多能互补特性,协同规划模型在微网中引入氢能系统,通过合理配置微网能源转换设备提高微网内部气转电的效率,进而降低微网的用电需求,提高气电配网运行安全性。在规划过程中,考虑到各主体对隐私保护的需求,建立了分布式求解框架实现各子系统的求解过程分散自治。最后,基于中国浙江省某地数据验证了所提协同规划模型和分布式求解策略的有效性。

基于蚁群算法的交通-配电网抢修恢复优化决策方法

极端事件导致大停电后,受损的交通道路会影响电网抢修车队前往故障线路区域的进度,从而延缓配电网恢复。为此,提出了一种结合损坏道路修复的配电网抢修恢复方法。首先,在分析不同受损道路对抢修车行驶影响的基础上,构建交通网抢修车约束。其次,考虑受损道路抢修对电网抢修的影响,以配电网失电量最小为目标,协调线路抢修和道路修复建立配电网抢修策略模型,并采用蚁群算法对所提模型进行求解。最后,以IEEE33节点配电网与一个12节点交通网相耦合的系统作为算例进行分析,仿真结果表明,所提方法切实有效提高了配电网抢修速度,减小了大停电后配电网负荷的失电量。所提方案更适用于灾害发生的实际情况,可为配电网灾后恢复提供参考。

基于A-RAFT模型的垂直管道输送测速方法

流速作为流动特性的重要参数之一,在垂直管道提升效率研究中有着重要地位.以管道固液两相流为研究对象,研究管道测速的方法.结合深度学习技术,提出了基于注意力机制的光流全场递归匹配模型(A-RAFT),提高了网络对速度场突变区域的估计能力;构建了一个虚实结合的数据集,用于训练神经网络模型.对新提出的模型与数据集进行评估,结果显示:该模型在合成的图像上实现了高精度的速度场计算,与现有其他模型相比,估计误差减小了15.6%;开展了垂直管道固体颗粒输送的模拟实验,本模型在实验中所采集的真实流场数据上同样展现了准确的估计性能,该模型对颗粒速度的测量平均相对误差低于5%.上述实验结果充分证明了该方法在速度场中有较高的估计精度,模型有较强的泛化能力.这一研究能够为能源开采、隧道掘进、污水处理以及长距离管道运输等领域中的固液两相流特性分析提供新思路.

考虑可再生能源协调出力的电动汽车充电定价方法

在一个配电网和城市交通网耦合框架中,提出一种电动汽车充电定价方法。建立以社会总成本最小为目标的电动汽车充电服务费的双层优化模型,模型上层为在含风电的配电网中求解充电服务费(Charging Service Fees, CSF)的二阶锥问题;下层为一个遵循用户均衡(User Equilibrium, UE)原则的交通分配问题。该模型考虑了风电输出和OD交通流的不确定性,建立基于深度强化学习的求解框架,对随机双层问题进行解耦和近似求解。以5节点系统和某城市交通-电力耦合网为例,验证了该模型的有效性。

公路运输碳排放空间关联性及影响因素研究

基于2010年,2015年和2019年中国30个省域物流公路运输碳排放数据和空间关系网,采用社会网络分析法(social network analysis,SNA)、地理信息系统(geographic information system,GIS)和地理探测器,从整体碳排放空间分布、整体网络结构演变、网络密度、凝聚子群、网络节点以及影响因素等角度分析我国30个省份公路运输碳排放空间分布和特征。结果表明:(1)从空间分布看,2010—2019年公路运输碳排放空间分布中部和南部排放量较多,西部和北部较少;(2)整体网络分析中,网络结构具有明显的核心-边缘结构,核心省份网络密度高且具有较发达的公路网络体系;(3)在不同的省份中,以广东省为例的中部和东部省份处于网络连接的核心,而甘肃省等较为偏远的省份处于网络的边缘;(4)产业结构、货运量和载货汽车拥有量对碳排放影响最大,而且交叉因子的影响作用大于单一因素的影响作用。