深厚砂卵石地层超深竖井施工降水模型试验研究北大核心CSCD

富水砂卵石地层中基坑降水始终是工程界较大的难题,文章针对兰州强透水深厚砂卵石地层超深竖井降水进行了理论计算和模型试验研究,研制了一套有效的模型试验系统,配置了砂卵石土体相似材料,并重点研究了基底注浆加固对基坑降水的影响。研究结果表明:(1)黄河对基坑降水有重要的影响,靠近黄河一侧降水漏斗曲线陡直,远离黄河一侧降水漏斗曲线平缓;(2)基底注浆降低了水力梯度,提高基坑施工安全性,可有效减少基坑内抽水量,但对于坑外抽水量作用不大;(3)基底加固区渗透系数存在最优值,对于该工程,建议取1×10^(-6)m/s为加固区目标渗透系数。     

黄土地区地铁隧道不同湿陷变形方式模型试验系统的研制及应用研究

为研究不同湿陷变形方式下黄土地区地铁隧道周围土体的沉降量变化规律,研制出一种测量黄土在均匀湿陷、不均匀湿陷时沉降量的模型试验装置。该装置由模型箱、流速控制系统和沉降量测量系统组成。通过室内模型试验对黄土在遇水后的湿陷变形规律进行总结,并对黄土地层在两种不同浸水方式下隧道围岩土体的变形规律进行分析。试验结果表明:不均匀湿陷最终累计沉降量大于均匀湿陷最终累计沉降量,说明不均匀湿陷对地铁结构物的危害更大;两种湿陷变形方式均为隧道中线附近沉降最大,因此,施工时应该重点加强隧道中线的位移监测。通过室内模型试验,分析了均匀与不均匀湿陷对黄土地铁隧道变形影响,研究结果对湿陷性黄土地区地铁隧道建设中施工把控及灾害规避具有重要意义。     

BIM技术在铁路机务段设计中的应用

铁路机务段设计具有涉及专业多、专业接口复杂的特点,传统二维设计容易出现管线碰撞及设计缺陷。为提高设计质量,以迁建西安机务段项目为依托,对BIM技术在大型铁路工程中的应用进行探索。本项目运用Inventor、Revit、Navisworks等设计软件作为BIM设计工具,将所涉及的14个专业工程内容全部进行BIM设计。通过建立族库、各专业BIM设计、模型整合的流程,构建一个覆盖全专业的数字化仿真模型,并总结出一套机务段BIM设计流程。实现了模型可视化展示、二维出图、属性信息添加、工程量统计、碰撞检测等应用。机务段工程设计复杂,专业接口众多,运用BIM技术可直观展示设计意图,并能轻易发现各专业设计中的错、漏、碰、缺问题,为提高设计质量提供有力保障。     

基于模型预测控制的主动四轮转向车辆稳定性研究

为提高汽车在高速、低附着系数路面下的操纵稳定性,论文设计模型预测控制器跟踪线性二自由度理想模型,得到横摆角速度与质心侧偏角偏差,然后由二次规划算法计算实际的四个车轮转角,并在Carsim软件中建立开环角阶跃工况进行联合仿真。结果表明:文章所设计的基于模型预测控制的主动四轮转向汽车相对于前轮转向,能够有效降低整车角阶跃输入下的横摆角速度与质心侧偏角,更好地跟踪理想控制目标,主动四轮转向汽车提高了整车的操纵稳定性和路径跟随精度。     

Coupled analytical-numerical approach for determining hydrodynamic responses of breakwater with multiple OWCs

A coupled analytical-numerical approach has been developed that allows one to study the hydrodynamic responses of breakwaters of arbitrary geometry that have multiple OWCs with horizontal openings. The approach incorporates the eigenfunction of the OWC fluid domain into the boundary element method, which automatically satisfy the boundary condition at the OWC opening. As a result, the exact geometry of the OWCs no longer needs to be modelled and the number of elements will be largely reduced especially when dealing with 3D breakwaters. With the present approach, a class of 2D OWC-integrated breakwater concepts in existing literature was simulated for validation and parametric study. The approach was then applied to the 3D breakwater with multiple OWCs under slender OWC assumptions, and the numerical results obtained are compared to data from a reduced scale model test. The 2D case studies show that the present approach is able to generate accurate results on reflection and transmission, wave loads, and energy extraction efficiencies with a higher efficiency than conventional boundary element method. Comparison of 3D results with the model test results shows that the prediction of the natural frequency and the wave reflection and transmission coefficients are in good agreement.     

非埋式桩板结构路基水平承载试验研究

非埋式桩板结构广泛应用于我国高速铁路的建设工程中,尤其是山区陡坡段,但对该结构的水平承载特性缺乏相应的研究。以杭黄高铁陡坡段非埋式桩板结构路基为原型,通过水平承载模型试验,研究结构内力弯矩、土压力分布形式,分析结构变形规律以及路基破坏形式。研究表明:当非埋式桩板结构路基承受水平荷载时,结构内力变化以桩基为主,并且随着水平荷载的增加,桩基弯矩呈基本不变至稳步增长最后剧烈增大的趋势;结构变形以水平位移为主,最大水平位移达7.25 mm,承载板发生内高外低的翘曲变形;当水平荷载达到一定时,路基边坡发生浅层破坏与局部压溃破坏,桩板结构的桩基于桩顶处与滑动面附近产生裂缝。试验过程中,桩板结构整体稳定性良好,但在水平荷载超过一定范围时结构变形过大,因此在水平推力较大的地段应谨慎选用非埋式桩板结构路基。     

Monitoring blade loads for a floating wind turbine in wave basin model tests using Fiber Bragg Grating sensors: A feasibility study

The blade loading contains sufficient information about the unsteady aerodynamics of Floating Wind Turbines (FWTs), and it serves as the basis for advanced controller developments. Wave basin model test is among the most reliable and economical methods for FWT investigations. However, few FWTs were reported being able to monitor the blade loads during wave basin tests. The main obstacles include strict space/mass limitations in model manufacturing and problems associated with signal transmission between rotating blades and stationary signal processing unit. In this paper, the feasibility of detecting blade loads for model FWT in wave basin tests is investigated. An on-line monitoring system is developed based on Fiber Bragg Grating (FBG) sensors and a Fiber Optical Rotary Joint (FORJ). Extensive validation tests are conducted under different environmental and operational conditions. Results show that the proposed FBG-FORJ sensing system presents impressive feasibility and reliability. As the authors know, it is among the first attempts to monitor the blade loads in real-time for model FWTs in wave basin tests. The present study will serve to enrich the knowledge about unsteady aerodynamics of FWTs and lay the foundation for experimental studies on advanced FWT controllers.     

自适应期望跟车间距和行为习惯的驾驶人跟驰模型

为满足智能车辆的个性化需求，提高智能车辆人-机交互协同的满意度和接受度，构筑双层驾驶人跟驰模型框架，提出自适应驾驶人期望跟车间距和行为习惯的个性化驾驶人跟驰模型。首先，提取个体驾驶人跟驰均衡状态的数据，采用高斯混合和概率密度函数(Gaussian Mixture Model and Probability Density Function, GMM-PDF)建立第 1 层模型，即驾驶人期望跟车距离模型。然后，将期望跟车距离参数引入模型，基于高斯混合-隐马尔可夫方法(Gaussian Mixture Model and Hidden Markov Model, GMM-HMM)学习驾驶习性，建立第2层模型预测加速度，即个性化驾驶人跟驰模型。其次，研究不同高斯分量个数对模型效果的影响，对比双层模型与 Gipps 模型、最优间距模型(Optimal Distance Model, ODM)、单层模型及通用模型的性能。最后，8位被试驾驶人的自然驾驶行为数据验证结果表明：高斯分量数量与模型性能存在一定的正相关性；在最优高斯分量数量下，8位被试驾驶人在训练集上预测误差均值为0.101 m·s-2，在测试 集上为0.123 m·s-2；随机选取其中1位驾驶人的2个跟车片段数据进行模型计算，结果显示，加速度的平均误差绝对值分别为0.087 m·s-2和0.096 m·s-2，预测效果优于Gipps模型、ODM模型、单层 模型及通用模型30%以上，与驾驶人实际跟驰行为的吻合度更高。     

考虑交通运行条件影响的驾驶员特征聚类

为提升驾驶员特征聚类方法的适用性与可靠性，本文基于机动车运行轨迹分析提出考虑交通运行条件影响的驾驶员特征聚类改进方法。首先，经过对车辆运行轨迹数据的分析发现，不同道路类型和平均速度条件会显著影响驾驶行为的集计特征；其次，提出改进的驾驶员特征聚类方法，第1步设计考虑道路类型与平均速度因素的车辆轨迹的切片和分类方法，从而稳定提取典型交通条件下的驾驶行为特征参数，第2步选用高斯混合模型聚类驾驶员特征。聚类案例表明， 在相同的道路类型和平均速度条件下，驾驶员类型越激进，其速度变异系数、加速度标准差和平均减速度等参数均值越高。不同聚类方法的对比表明，改进方法在驾驶员聚类的类内聚集度和类间分离度方面均表现更好，能有效提升驾驶员聚类的适用性与可靠性。     

Closing the loop in real-time railway control: Framework design and impacts on operations

Railway traffic is heavily affected by disturbances and/or disruptions, which are often cause of delays and low performance of train services. The impact and the propagation of such delays can be mitigated by relying on automatic tools for rescheduling traffic in real-time. These tools predict future track conflict based on current train information and provide suitable control measures (e.g. reordering, retiming and/or rerouting) by using advanced mathematical models. A growing literature is available on these tools, but their effects on real operations are blurry and not yet well known, due to the very scarce implementation of such systems in practice.In this paper we widen the knowledge on how automatic real-time rescheduling tools can influence train performance when interfaced with railway operations. To this purpose we build up a novel traffic control framework that couples the state-of-the art automatic rescheduling tool ROMA, with the realistic railway traffic simulation environment EGTRAIN, used as a surrogate of the real field. At regular times ROMA is fed with current traffic information measured from the field (i.e. EGTRAIN) in order to predict possible conflicts and compute (sub) optimal control measures that minimize the max consecutive delay on the network. We test the impact of the traffic control framework based on different types of interaction (i.e. open loop, multiple open loop, closed loop) between the rescheduling tool and the simulation environment as well as different combinations of parameter values (such as the rescheduling interval and prediction horizon). The influence of different traffic prediction models (assuming e.g. aggressive versus conservative driving behaviour) is also investigated together with the effects on traffic due to control delays of the dispatcher in implementing the control measures computed by the rescheduling tool.Results obtained for the Dutch railway corridor Utrecht–Den Bosch show that a closed loop interaction outperforms both the multiple open loop and the open loop approaches, especially with large control delays and limited information on train entrance delays and dwell times. A slow rescheduling frequency and a large prediction horizon improve the quality of the control measure. A limited control delay and a conservative prediction of train speed help filtering out uncertain traffic dynamics thereby increasing the effectiveness of the implemented measures.