舰船电力系统中智能电网技术的应用浅析

  目的  由于海面传感器的耗能点分散、负荷波动幅度较大且节能降耗的需求迫切,为了优化能量供给模式,提出面向海面传感器的船载能量网络（SEN）。  方法  首先,基于车载能量网络、动态无线充电技术及电动船舶技术,分析SEN模型的可行性;然后,基于海面传感器的特点,建立SEN模型,提出能量传输最大化和能量损失最小化这2个问题,并定义问题模型和约束函数;最后,通过开源模型MaritimeSim选取基础实验数据集。  结果  实验结果表明,通过合理设置参数,SEN能够将海上可再生能量传输至海面传感器节点,从而补充电力网络并增强其电能传输能力。  结论  研究成果可为优化海面传感器的能量供给提供新的设计方法。     

小型船用PEM燃料电池仿真特性分析

随着能源的逐渐枯竭和环境污染的持续加重,燃料电池的发展越来越受到世界各国的重视,燃料电池具有高效、清洁和环保等特点,特别是质子交换膜(PEM)燃料电池已经得到了广泛的应用。文章基于Matlab/Simulink软件平台,建立PEM燃料电池动态模型,以输入信号源作为外加负载,分析了随负载电流变化时,电池工作温度变化和阴阳极进气压力对PEM燃料电池输出电压和输出功率的影响。仿真结果与实际燃料电池特性曲线基本一致,所搭建的动态模型也能较好的反映PEM燃料电池的动态特性。     

基于EHPN的A-SMGCS机场滑行道运行控制建模

针对先进机场场面引导与控制系统(A-SMGCS)中滑行道运行控制建模, 提出一种基于扩展层次Petri网(EHPN)的建模方法。将滑行道分为交叉口和直线段模块, 然后对模块内部的航空器行为进行封装, 形成基于扩展Petri网的过程层通用模块, 并进一步采用同步合成技术建立过程层模型。利用面向Agent的着色赋时Petri网建立决策层通用模块, 同时将场面运行控制知识与模块的库所和变迁集成, 增强模型的智能决策能力。采用某机场12 h滑行道运行数据进行了仿真试验。研究结果表明: 基于EHPN模型的仿真, 其直线段和交叉口冲突次数分别为6次和11次, 滑行道系统总延误为346 s, 与SIMMOD仿真模型效果相近, 因此, 该建模方法有效。     

动力定位系统舵桨组合推力分配研究

\t  目的   \t为提高不同海况下半潜式平台的定位能力,针对其动力定位系统提出3种基于遗传算法的最优推力分配模式。\t  方法   \t首先,通过分析目标函数中不同项对系统功率、推进器磨损及推力误差的影响,确定推力角和松弛矩阵的权值系数;然后,提出一种限选法来降低推力损失,并采用设置上游推进器禁止进入推力禁区的方法来避免推进器之间的水动力干涉;最后,针对复杂多变海况下的推力分配问题,使用提出的3种推力分配策略进行仿真模拟。\t  结果   \t结果表明,限选法能够有效降低系统的能耗、推进器磨损和推力误差;提出的推力分配策略在复杂海况下有效。\t  结论   \t所提方法及策略能在一定程度为动力定位系统的设计提供思路和参考。     

一种全平衡式往复压缩机稳定性优势分析

  目的  相对于其他类型的空压机,往复式空压机能够获取更高压力的压缩空气,因而曲柄连杆机构的受力较大。  方法  对比分析3种结构形式的星型往复式空压机惯性力和力矩的特点,得出该型空压机对惯性力有良好的自平衡能力,能在一定程度上减小惯性力矩。建立曲柄连杆机构虚拟样机,将推导的活塞受力构造SPLINE曲线加载到活塞质心,分别将曲轴和连杆柔性化,并将基于IMPACT函数的接触碰撞力模型嵌入到ADAMS虚拟样机中。  结果  动力学仿真结果表明：该型空压机能够自平衡二阶惯性力,曲轴相对于连杆更趋近于柔性体,间隙运动副的存在会抑制某些频率点的振动,但会大大拓宽激振频带。  结论  该分析结果可为空压机的设计提供参考。     

Energy savings in transport

A decade of increasing Federal attention to urban transportation needs has culminated in the 1970 Urban Mass Transportation Assistance Act. This Act is intended to provide 10 thousand million dollars over the next 12 years in Federal assistance money to urban public transportation systems. This paper examines the needs of selected U.S. cities as a basis for (1) understanding the vast, various and complex transportation needs of urban areas throughout the country, and (2) assessing the sufficiency of these funds. The sample cities have been placed into three broad categories based on the state of development of their transportation systems. In Category I cities, the essential need is to ensure the survival of bus systems for the use of non‐drivers, or to provide some other viable alternative to the automobile; in Category II cities, the primary needs are to relieve auto congestion and to improve public transportation components, while in Category III cities, the primary need is massive investment to improve and to extend public transportation facilities. It is concluded that the presently intended Federal funding level for transportation will not meet the financial requirements of the Category III cities.     

非均衡网络交通流动态演化规律研究

为描述非均衡网络交通流实际成本-流量状态,考虑置存成本和路段行程时间,建立行程时间动态函数,将其引入用户均衡模型,构建基于出行总成本动态、路径流量动态、路段行程时间动态的交通流演化模型。利用简单网络,采用四阶龙格库塔方法对建立的模型进行数值模拟。动态模型弹性需求下,出行成本调整范围由大到小,趋于平衡值;路径流量迅速增加后,以较小调整范围,趋于平衡值;路段行程时间迅速增加后,逐步趋向于平衡值。固定需求下的出行成本、路径流量、路段行程时间均是反复调整多次后趋近于平衡值,调整范围缩小,次数增加。算例模拟结果表明,模型能够描述网络交通流从一种非均衡状态到另一种非均衡状态的动态调整过程。     

动车低速脱轨的动态响应

在动车轴箱下方安装防护装置, 进行线路低速脱轨试验。车辆借助脱轨器完成脱轨, 利用应变片、加速度和位移传感器采集脱轨车辆的动态响应, 采用高速摄像仪和视频摄像仪分别记录了脱轨车辆的运动姿态。基于试验数据, 评估了脱轨条件下钢轨抗倾翻能力, 验证了脱轨安全防护装置的性能, 分析了动车脱轨后的动态响应和脱轨速度、车辆质量和线路对动态响应的影响。试验结果表明: 当动车低速脱轨时, 防护装置撞击钢轨的最大横向力为177.18kN, 小于钢轨横向抵抗力510.00kN, 因此, 脱轨安全防护装置可以扣住钢轨外侧, 有效限制脱轨车辆的横向移动。车辆的脱轨过程分为惰行、轨上运动、落地和路基滑行4个阶段, 各阶段的动态响应均随脱轨速度和车辆质量的增大而增大。当动车脱轨速度为22km·h^-1时, CRTSⅡ型双块式无砟轨道的脱轨距离约为15.80m, CRTSⅠ型板式无砟轨道的脱轨距离约为20.87m, 因此, CRTSⅡ型双块式无砟轨道的轨枕可以起到减速带的作用, 减小脱轨距离。     

拉格朗日松弛启发式算法求解时空网络下的弧路径问题

为减少车辆调度成本,优化车辆运输路径,在时空网络中研究路段作业车辆的弧路径问题;考虑道路出行的时变性,利用车辆运行的时间、空间特征,构建时间-空间网络,建立弧路径问题的时空网络流模型;设计了拉格朗日松弛启发式算法,引入拉格朗日乘子松弛耦合约束,构建拉格朗日松弛问题;进一步通过拉格朗日分解,把松弛问题分解为单车最短路问题;用次梯度算法更新乘子,求解拉格朗日对偶问题,并更新原问题最优解的下界;使用启发式算法获得可行解,并更新原问题最优解的上界;用六结点运输网络和Sioux-Falls网络下的算例对算法进行实证分析。计算结果表明：六结点运输网络中6个算例的上下界间隙值等于0或接近0,Sioux-Falls网络中算例2的间隙值为0.02%,其余5个算例的间隙值等于0,均可以得到质量较高的近似最优解;在最复杂的算例(15辆车,70个任务)中,算法在可接受的时间内也得到了间隙值为0的解,找出了最优的车辆路径;随着迭代次数的增加,拉格朗日乘子会逐步收敛到固定值;当车辆容量从50增加到100时,最优解从52下降到42,说明在任务数和车辆数一定时,适当增加车容量可以降低运营成本。可见,与商业求解器相比,拉格朗日松弛启发式算法的间隙值更小,求解质量更高,可以更有效地求解弧路径问题。     

Analysis of volatility in driving regimes extracted from basic safety messages transmitted between connected vehicles

Driving volatility captures the extent of speed variations when a vehicle is being driven. Extreme longitudinal variations signify hard acceleration or braking. Warnings and alerts given to drivers can reduce such volatility potentially improving safety, energy use, and emissions. This study develops a fundamental understanding of instantaneous driving decisions, needed for hazard anticipation and notification systems, and distinguishes normal from anomalous driving. In this study, driving task is divided into distinct yet unobserved regimes. The research issue is to characterize and quantify these regimes in typical driving cycles and the associated volatility of each regime, explore when the regimes change and the key correlates associated with each regime. Using Basic Safety Message (BSM) data from the Safety Pilot Model Deployment in Ann Arbor, Michigan, two- and three-regime Dynamic Markov switching models are estimated for several trips undertaken on various roadway types. While thousands of instrumented vehicles with vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication systems are being tested, nearly 1.4 million records of BSMs, from 184 trips undertaken by 71 instrumented vehicles are analyzed in this study. Then even more detailed analysis of 43 randomly chosen trips (N = 714,340 BSM records) that were undertaken on various roadway types is conducted. The results indicate that acceleration and deceleration are two distinct regimes, and as compared to acceleration, drivers decelerate at higher rates, and braking is significantly more volatile than acceleration. Different correlations of the two regimes with instantaneous driving contexts are explored. With a more generic three-regime model specification, the results reveal high-rate acceleration, high-rate deceleration, and cruise/constant as the three distinct regimes that characterize a typical driving cycle. Moreover, given in a high-rate regime, drivers’ on-average tend to decelerate at a higher rate than their rate of acceleration. Importantly, compared to cruise/constant regime, drivers’ instantaneous driving decisions are more volatile both in “high-rate” acceleration as well as “high-rate” deceleration regime. The study contributes to analyzing volatility in short-term driving decisions, and how changes in driving regimes can be mapped to a combination of local traffic states surrounding the vehicle.