新型竖直轴海洋能发电设备叶片的优化与仿真

为深入了解新型竖直轴海洋能发电设备的性能,对其叶片结构的流场特性和设备在流场中的动力收集特性进行仿真研究.建立不同结构叶片和不同数量叶片的二维模型,通过仿真计算,得到不同结构叶片的流场状态,以及叶片数分别为3、4和6时设备的合力和转矩大小.研究结果表明:在矩形叶片上开长方形孔能明显改善叶片后方的涡流,提高叶片的性能;当入口速度不变时,设备所受合力和转矩会随叶片数量的增加而增大;当叶片数量不变时,设备所受合力和转矩会随入口速度的增加而增大.研究结果可为新型竖直轴海洋能发电设备的结构设计与优化提供参考.     

面向实际交通状态的混合动力汽车能耗和CO2排放模型

由于混合动力汽车与传统燃油车的能耗排放因子具有差异性，导致机动车交通路网能耗排放的量化评估存在不确定性。本文建立混合动力汽车在实际交通状态中的能耗和CO2排放因子测算模型，基于车辆比功率VSP(Vehicle Specific Power)作为车辆行驶状态与能耗排放之间耦合关系的表征参数。通过引入内燃机转速区分内燃机开启和关闭工作状态，并计算内燃机开启状态下VSP对应的平均能耗率，同时，建立能够解析混合动力汽车能耗排放产生机理的VSP分布。通过收集典型行驶工况下车辆测试油耗数据和北京市车辆实际行驶轨迹数据，验证了模型的准确性，并应用模型测算混合动力汽车不同速度区间下的油耗和CO2排放因子。研究结果表明：在城市行驶工况(UDDS)和高速行驶工况(HWY)中，模型测算能耗排放因子与真实值的平均相对误差分别为3.7%和-1.7%，与不考虑内燃机开启状态相比，测算误差减少5.6%和4.3%；在实际交通状态下，采用传统燃油车的测算方法会导致混合动力汽车行驶平均速度为高速区间时油耗和CO2排放量被低估，当行驶平均速度为低速区间时油耗和CO2排放量会被高估。     

CRH3与CRH5型动车组中压供电方式对比分析

介绍了CRH3与CRH5型动车组车辆在各种情况下的中压分配情况,分别分析了CRH3、CRH5型动车组的中压供电方式及其控制条件和逻辑关系,对比分析了CRH3、CRH5型动车组中压供电的优缺点。供电技术的不断提升,使CRH3、CRH5型动车组的供电更加可靠、安全和稳定,同时也推动了高速列车技术的发展,使旅客乘坐更加舒适。     

基于遗传算法的道路车辆自组织网络源路由机制

在当前基于交叉路口的城市车辆自组织网络(VANETs)路由协议中，道路上数据包传输大多采用基于地理位置的贪婪转发策略，当数据量较大时，个别节点负载较重，极易引起传输延迟增大乃至丢包的情况.本文提出了一种基于遗传算法的源路由机制，通过记录单体车辆的驾驶信息而非传统方法中的车流均值数据，来预测道路上车辆网络的连通情况，并借助遗传算法，首次基于道路连通性、节点负载和连接跳数这3 点综合考虑，计算得出道路上最佳的源路由节点序列.仿真实验结果表明，在传输率与延迟时间上，性能均优于传统的贪婪路由机制，尤其在车流量为250 veh· lane-1· h-1时，传输率提升约13%.该研究可为智能交通信息通讯提供可靠助力.     

SiC功率器件在轨道交通行业中的应用

为解决轨道交通牵引及辅助系统的轻量化等问题,宽禁带半导体器件如SiC器件的应用需求越来越大。SiC材料具有禁带宽度大、击穿场强高、电子饱和漂移速率快等特性,能满足在中大功率、高温、高频条件下工作的应用要求。简述了SiC器件相比传统硅基器件的突出优势及商业化和实验室研发情况,介绍了SiC功率器件在轨道交通行业中的发展现状,同时对其在未来轨道交通装备业的发展提出了建议和展望。     

隧道内列车振动对近接建筑物的影响分析北大核心

针对运营列车通过隧道时引起近接建筑物地面振动进行了现场测试,并对测试数据进行了功率谱、Z振级及1/3倍频程分析。在此基础上,利用有限元软件建立了围岩-隧道-轨道结构振动模型,对运营列车引起的建筑物振动进行了计算分析。结合实测与计算结果,对近接建筑物的振动特性进行了评价。结果表明:列车以速度300 km/h通过隧道时,地面振动功率谱主频白天集中在33.0 Hz左右,夜间集中在42.7 Hz左右,夜间的主频比白天大;地面各测点处Z振级的总体趋势是先波动式上升,再平缓波动,后逐渐波动式下降,地面Z振级主要集中在20~80 dB;1/3倍频程分频最大振级白天位于48.4~60.8 dB,夜间位于47.4~59.4 dB;列车通过隧道时基础处振动速度峰值整体呈波浪形分布,引起的地面振动速度小于0.045 mm/s,小于规范限值要求,对建筑物基础以及人体舒适度的影响较小;在缺乏大量实测结果的条件下,结合小样本实测结果,采用有限元计算结果进行振动响应评价具有一定的可行性。     

Analyzing passenger train arrival delays with support vector regression

We propose machine learning models that capture the relation between passenger train arrival delays and various characteristics of a railway system. Such models can be used at the tactical level to evaluate effects of various changes in a railway system on train delays. We present the first application of support vector regression in the analysis of train delays and compare its performance with the artificial neural networks which have been commonly used for such problems. Statistical comparison of the two models indicates that the support vector regression outperforms the artificial neural networks. Data for this analysis are collected from Serbian Railways and include expert opinions about the influence of infrastructure along different routes on train arrival delays.     

Transport resilience and vulnerability: The role of connectivity

This paper aims to adopt a critical stance on the relevance and interpretation of the recently emerging concepts of resilience and vulnerability in transportation studies. It makes a clear distinction between engineering and ecological interpretations of these concepts and offers a systematic typology of various studies in this field. A core element in the study is the linkage between the aforementioned concepts and connectivity/accessibility in transport networks. The methodological findings in the study are put in perspective by addressing also such concepts as robustness, reliability and friability of transport systems.     

徐盐铁路引入徐州东枢纽牵引供电方案研究

徐盐铁路位于苏北地区,连接京沪、陇海、连镇、沿海四大铁路通道,是长三角城际网重要组成部分,也是京沪通道徐州-上海段的辅助通道,其引入徐州东枢纽以实现京沪高铁、郑徐高铁以及连徐高铁等4条线路的换乘,而枢纽地区牵引供电方案的合理性至关重要。因此,本文对徐盐铁路引入徐州东枢纽供电方案进行论证比较,并根据电分相检算分析提出推荐方案,以保证京沪高铁运行可靠性及工程实施的便捷性。     

Analysis of air traffic control operational impact on aircraft vertical profiles supported by machine learning

The Air Traffic Management system is under a paradigm shift led by NextGen and SESAR. The new trajectory-based Concept of Operations is supported by performance-based trajectory predictors as major enablers. Currently, the performance of ground-based trajectory predictors is affected by diverse factors such as weather, lack of integration of operational information or aircraft performance uncertainty.Trajectory predictors could be enhanced by learning from historical data. Nowadays, data from the Air Traffic Management system may be exploited to understand to what extent Air Traffic Control actions impact on the vertical profile of flight trajectories.This paper analyses the impact of diverse operational factors on the vertical profile of flight trajectories. Firstly, Multilevel Linear Models are adopted to conduct a prior identification of these factors. Then, the information is exploited by trajectory predictors, where two types are used: point-mass trajectory predictors enhanced by learning the thrust law depending on those factors; and trajectory predictors based on Artificial Neural Networks.Air Traffic Control vertical operational procedures do not constitute a main factor impacting on the vertical profile of flight trajectories, once the top of descent is established. Additionally, airspace flows and the flight level at the trajectory top of descent are relevant features to be considered when learning from historical data, enhancing the overall performance of the trajectory predictors for the descent phase.