轴向板条对海洋立管涡激振动抑振的数值研究

涡激振动是一种在海洋工程领域普遍存在的流固耦合物理现象,易导致海洋立管等结构物发生疲劳损伤或失稳。本文为抑制涡激振动而提出一种附加轴向板条的方案,采用非定常流体数值计算方法求解分析不同分布形式下的轴向板条对海洋立管涡激振动特性的影响。首先定义轴向板条的分布形式;采用Newmark-β法求解立管双自由度振动方程并编写UDF,计算采用k-ω/SST湍流模型,并结合动网格技术模拟立管的振动过程。研究结果表明,轴向板条对海洋立管具有较好的抑振效果;当轴向板条数目n=20时,流向振动幅度减小78.57%,横向振动幅度减小58.10%。本文研究结果可为海洋结构物涡激振动的抑振提供参考。     

上游静止柱体对圆柱体结构尾激振动的影响

文章运用半隐式特征线分裂算子有限元算法对串列布置的静止上游柱体和下游圆柱的尾激振动问题进行了数值模拟研究。数值结果表明:上游柱体结构的形状、尺寸比(d/D)和折减速度(Ur)三个参数对下游圆柱体结构的动力响应、运动轨迹与涡脱落模态有着显著的影响,且与单圆柱工况相比存在明显区别;随着尺寸比的增大,上/下游柱体结构之间的互扰作用会由流致效应逐渐转变为尾流效应,使得频率特性发生变化,并会导致下游圆柱体结构的振动响应增强;当d/D=0.5和1.0时,下游圆柱体结构的运动轨迹主要为“8”字形;当d/D=1.5时,除了“8”字形外,运动轨迹还会呈现双弯刀形、“双8”字形和不规则形状。通过对流体力系数与位移时程曲线及位移PSD曲线特性进行分析,揭示了其相互作用的内在机理。另外,下游圆柱体结构的尾流场特性也会随参数的变化而变化,其涡脱落模态主要为2S、P+S和2P三种。     

地下交通转换平台射流通风特性及风流组织方式研究

为探究地下交通转换平台内通风系统的合理布局,采用比尺模型试验和CFD模拟相结合的方法,研究射流风机和通风组织对地下交通转换平台内气流运动的影响。结果表明： 1）当联络通道内风机射流朝向敞开段时,为使风机升压系数Kj最大,630 mm、900 mm、1 120 mm射流风机的布设位置应距离敞开段分别大于40、50、65 m; 2）大口径射流风机具有更大的Kj,但占用的断面空间更大,且射流诱导段更长,应根据联络通道长度和高度合理选择射流风机口径; 3）地下交通转换平台的通风组织不宜采用同侧开启方式,采用对角抽吸方式时,联络通道内的污染物混入比最低、通风效率最高。     

地下工程平衡稳定理论再研究

目前,地下工程结构在计算分析方面主要采用传统松弛荷载理论（类似荷载结构法）和数值分析法;在合理结构构造、施工工法等方面主要采用岩承理论（类似地层结构法）,这些已有理论或方法隐含“变形协调控制”假定,实际应用中往往将这些假定忽视,因而需进一步发展新的理论方法以适应实际应用的需求。在大量工程实践和试验的基础上,揭示地下工程有效承载结构层与围岩荷载转移规律之间的关联性,建立地下工程平衡稳定理论,此理论既能反映传统“松弛荷载理论”和现代“岩承理论”的基本内容,又能拓宽平衡稳定性等内容,是地下工程平衡稳定性的新认识、新理念; 同时提出能量控制技术、预支护技术、受力独立性综合技术、变形协调控制技术4项关键技术。将这些技术合理地运用到实际工程施工中,将便于利用已有或新成果分类预防和控制地下工程质量和安全,为解决新型地下工程问题提供新的依据。     

金门特长公路隧道通风方案比选研究

为解决目前特长公路隧道通风方案比选时局限于送排式通风的问题,从方案设计角度分析几种不同纵向通风模式的功能特点、适用条件,结合送排式通风与互补式通风的特点,提出单通道互补式通风的组合通风模式,并以金门特长公路隧道为例,从全寿命周期角度,对“通风井送排式”“单通道互补式”“双通道互补式”“吸尘式”4种通风方案进行比选研究。结果表明： 1）送排式通风受通风井位置影响较大,从运营通风角度,宜设置在上坡隧道中部偏下风方向; 2）互补式、吸尘式通风能够突破通风井选址的限制,但对于超过5 km的隧道,需要设置专用排烟井; 3）单通道互补式通风省去送风井,保留了送排式通风的排烟功能,具备比选价值。     

压气条件下泥膜进气值测量试验研究

在压气条件下,进气是泥膜透气失效的起始点。为测量并研究泥膜在气压下的进气压力值（进气值）,通过自制的试验装置,在不同压气条件下对3种泥膜进行进气值测量试验。试验结果表明： 1)由闭气排水与固结排水的差值可以辨别泥膜是否进气,得到泥膜进气值; 2)采用泥膜特征孔径D90/2代入推导公式可近似计算泥膜进气值; 3)泥膜在不同压气条件下进气值不同,增压速率越低,泥膜进气值越大; 4)在较快和较慢的增压速率下存在进气值变化趋于稳定的现象,最大进气值为最小值的2倍以上。     

Developing an enhanced weight-based topological map-matching algorithm for intelligent transport systems

Map-matching (MM) algorithms integrate positioning data from a Global Positioning System (or a number of other positioning sensors) with a spatial road map with the aim of identifying the road segment on which a user (or a vehicle) is travelling and the location on that segment. Amongst the family of MM algorithms consisting of geometric, topological, probabilistic and advanced, topological MM (tMM) algorithms are relatively simple, easy and quick, enabling them to be implemented in real-time. Therefore, a tMM algorithm is used in many navigation devices manufactured by industry. However, existing tMM algorithms have a number of limitations which affect their performance relative to advanced MM algorithms. This paper demonstrates that it is possible by addressing these issues to significantly improve the performance of a tMM algorithm. This paper describes the development of an enhanced weight-based tMM algorithm in which the weights are determined from real-world field data using an optimisation technique. Two new weights for turn-restriction at junctions and link connectivity are introduced to improve the performance of matching, especially at junctions. A new procedure is developed for the initial map-matching process. Two consistency checks are introduced to minimise mismatches. The enhanced map-matching algorithm was tested using field data from dense urban areas and suburban areas. The algorithm identified 96.8% and 95.93% of the links correctly for positioning data collected in urban areas of central London and Washington, DC, respectively. In case of suburban area, in the west of London, the algorithm succeeded with 96.71% correct link identification with a horizontal accuracy of 9.81 m (2σ). This is superior to most existing topological MM algorithms and has the potential to support the navigation modules of many Intelligent Transport System (ITS) services.     

You (expect to) get what you pay for: A system approach to delay, fare, and complaints

In this paper, an analytical framework integrating delay, fare, and complaints with passenger air travel has been laid out. Examining aggregate monthly data for US domestic air travel, we have identified causal relationships among fare, complaints, and levels of delay. An analytical framework is proposed that formalizes these relationships in an integrated manner. This integrated framework is then estimated in a set of simultaneous equations by using 118 months of data from January 1997 to October 2006. Results show that complaints are influenced by levels of delays. However, complaints are positively influenced by average yield. These findings lead us to support the central hypothesis that complaints are responsive to levels of delays, but they tend to vary according to fare. That is, air travelers are less likely to complain in return for lower fares, even when faced with the same or even higher levels of delays. These findings have important policy implications, including the passengers’ bill of rights and regulator’s choice between market and operational performances.     

基于VRML的虚拟现实技术在数字管道中的应用

介绍了虚拟现实建模语言(VRML),将基于VRML的虚拟现实技术和HTML、 JavaScript相结合,并应用到数字管道中,构建了真实感强、交互性强的可视化虚拟石油管道场景和管道泄漏模型,实现了石油管道虚拟场景的漫游,以及用户与虚拟场景的交互,并及时、直观地反映管道泄漏.应用结果表明:将基于VRML的网络交互式虚拟现实技术应用到数字管道,促进了数字管道的信息共享,提高了数字管道的决策管理效率.     

Using on-board logging devices to study the longer-term impact of an eco-driving course

In this paper the long-term impact of an eco-driving training course is evaluated by monitoring driving behavior and fuel consumption for several months before and after the course. Cars were equipped with an on-board logging device that records the position and speed of the vehicle using GPS tracking as well as real time as electronic engine data extracted from the controller area network. The data includes mileage, number of revolutions per minute, position of the accelerator pedal, and instantaneous fuel consumption. It was gathered over a period of 10 months for 10 drivers during real-life conditions thus enabling an individual drive style analysis. The average fuel consumption four months after the course fell by 5.8%. Most drivers showed an immediate improvement in fuel consumption that was stable over time, but some tended to fall back into their original driving habits.