基于压阻数的城轨交通地面储能系统选址方法

传统选址方法存在工作量大、步骤繁琐、易用性差的不足。基于城轨交通地面储能系统功能定位提出压阻数的概念,即全运营周期内所有列车运行至某站点时,该站点出现低电压的次数与制动电阻开启且持续一定时间的次数之和;提出基于压阻数的城轨交通地面储能系统选址方法。首先通过检测站点附近的低电压发生次数及制动电阻开启一定时间的次数计算压阻数;然后采用将平峰时段发车间隔作为选址仿真时的输入,在压阻数大于2 MP (M为全线列车数、P为每列车运行周期)的站点安装地面储能系统的选址策略;最后给出选址流程。算例研究表明:压阻数具有表征站点再生制动能量利用率和电能质量的能力;提出基于压阻数的选址方法是有效的,且受工况及运营图的影响小,具有普适性。     

Experimental study of a suspended subsea module at different positions in the splash zone

During the lifting of submerged marine modules, the highest dynamic loads occur in the splash zone. These forces are highly dynamic and cannot easily be estimated with sufficient accuracy using simple calculations or high-fidelity computational fluid dynamics because of their irregular and complex geometries. We conducted an experimental study in a wave tank to investigate the dynamics of a large subsea module in the splash zone subject to irregular waves. The experiments were conducted using a 1:8 scale model of a subsea module, similar to that designed by Offshore Oil Engineering Co., Ltd., for the subsea pipeline from the Wenchang gas fields to the Yacheng pipeline. The model was examined at five different positions relative to the free surface, from completely above the surface to fully submerged. The resulting data are analyzed in the time and frequency domains. Spectral analysis and a dynamic effect parameter are used to analyze the dynamic loads on the subsea module. Several time-domain numerical analyses are performed with different peak periods, and the tension of the sub-slings is obtained. The experimental results for the relative forces and motions of the subsea module are presented, providing a reference for the design of other similar modules and their installation and validation data for computational modeling.     

时速160 km城市轨道交通内置式泵房板式道床动力特性分析

北京轨道交通新机场线在部分区间隧道的最低点设置了内置式泵房板式道床。为了保证时速160 km市域列车在动荷载作用下轨道结构的安全性和可靠性,建立了车辆-轨道-隧道三维耦合动力分析模型,计算分析100~220 km/h共7种行车速度下内置式泵房板式道床的动力特性。提取道床振动评价指标(垂向位移、垂向加速度)、道床强度评价指标(纵横向拉应力)、行车平稳性评价指标(车体垂向、横向加速度)和行车安全性评价指标(轮轨垂向力、轮轨横向力、脱轨系数、轮重减载率)与规范允许值进行对比。计算结果表明,各动力学评价指标均随行车速度的增大而增加,各动力学评价指标均在规范允许值之内,并且有较大的安全余量。     

基于多智能体深度强化学习的停车系统智能延时匹配方法

“互联网＋”模式下区域停车“用户-资源”优化匹配是解决找车位难问题的有效途径，传统研究主要关注动态匹配机制设计，缺乏对用户匹配时机的考虑。在随机动态环境下，用户到达目的地附近后进行适当的延时等待，往往可以获得更优质的泊位资源，但取决于当前的停车供需模式。据此首次提出智能延时匹配策略，将每个停车用户抽象为智能体，构建多智能体深度Q学习模型(M-DQN)。结合系统的停车供需状态学习，用户自主决策延时等待时间，进入分配池后，系统利用匈牙利算法进行泊位匹配。在智能体总数量可变的环境下，利用集中式训练与分布式执行的框架，实现多智能体协同优化。为对比智能延时策略的效果，设计等待零时长策略(Greedy)和等待最大时长策略(Max Delay)。在算例中，结合同济大学四平路校区实测停车数据，设计3种不同的停车供需模式场景。在工作日早高峰时段，Greedy是最优的匹配策略，M-DQN和Max Delay的平均停车过程总用时会增加，匹配成功率下降；在工作日非高峰时段，M-DQN的平均停车过程总用时相较于Greedy和Max Delay分别减少23.8%和22.4%，效果提升明显；在工作日晚高峰时段，M-DQN的平均停车过程总用时相较于Greedy和Max Delay分别减少了12.8%和14.5%，M-DQN可以结合供需状态学习到最优的匹配策略。研究结果表明：在停车供需相对平衡的环境下，所提出的延时匹配策略和多智能体深度强化学习方法可以有效减少用户停车的平均行驶时间和步行距离，且停车周转率越高效果越好；但延时策略在应用方面仍有一定的局限性，不适用于停车供给紧张，停车周转率较低的场景。     

Real time prediction of operational safety limits for dynamic positioning of an FPSO in a Deepwater Artificial Seabed system

Aiming to overcome the limitations of conventional offshore field development concepts (dry tree or subsea tree) for petroleum production in ultra-deep water, a new alternative offshore field development solution, termed as Deepwater Artificial Seabed (DAS) system, is proposed. The DAS system works in concert with dynamic positioning (DP) floaters, such as dynamically positioned Floating Production, Storage and Offloading (FPSO) vessels. Rather than relying on the passive mooring system, the DP maintains the reliable position of the FPSO with steering and propulsion units. Nonetheless, critical DP failures, which has potential to cause the drift-off scenario for the FPSO, poses a serious threat to the structural safety of the DAS system. Therefore, it is crucial to establish operational limits for the DP FPSO to prevent such accidents. In this study, a 3-phase probabilistic modelling methodology is proposed to predict safety limits for the operation of the DP FPSO. A surrogate model is established by the Support Vector Machine (SVM) algorithm so as to decrease the computational cost due to the generation of large statistical samples. The statistical distribution of the operational safety limits of FPSO is simulated by the successive approximations through the fully-coupled drift-off analysis. The accuracy of the proposed methodology is verified by a series of mathematical tests. In order to validate the effectiveness of the methodology, the safety limit prediction of the FPSO for the DAS system is taken as a case study. The critical positions of the FPSO are predicted in real time and provides ample time and information for operators’ decision-making by the visualization of the safe moving range of the FPSO. The study contributes to the safety control of DP operations on floating production units in an efficient manner.     

大兴国际机场线列车运行安全性及平稳性分析

北京大兴国际机场线的设计速度高达160 km/h,采用CRH6型市域列车,相较于传统的地铁线路,对轨道结构的力学稳定性要求更高。介绍了新机场线轨道设计方案及特点,认为应通过轨道结构动力学、车体动力学及行车安全性三大类指标对列车运行的安全性及平稳性进行预测;提出了相关车辆和轨道结构动力学参数的选取原则及具体限值要求,并通过建立车辆、轨道及下部基础的动力学耦合有限元模型,分别计算桥梁和隧道及路基地段(列车速度在120~180 km/h情况下)钢轨与道床的纵横向加速度、位移,轮轨力、车辆的水平及垂向加速度,以及脱轨系数、轮重减载率等数据。研究表明,采用了特殊设计后,轨道结构各动力学指标均位于安全限值之内且安全余量较大。     

预约出行模式展望

城市交通系统中，由于供需时空不匹配，在一些瓶颈点前会产生较严重的拥堵排队现象。 虽然出行者最终都能完成出行，但是在瓶颈点前的拥堵和排队给出行者带来了额外的等待时 间。理想情况下，如果每个出行者都按照其实际通过的时刻到达瓶颈点，就可以减免在瓶颈点前 拥堵和排队产生的等待时间，即把在途拥挤排队时间转变为在家等待时间，而这两类时间的价值 在出行广义费用函数中有较大差异。在传统交通系统中，由于缺乏信息指引，出行者只能依靠在 路上排队获得瓶颈点的通行权。随着即时通讯、人工智能等新兴技术发展，可通过技术手段事先 为出行者排好通过瓶颈点的顺序，并将推算出的通过时刻告知出行者，出行者按照该时刻到达便 可无等待通过瓶颈点。基于此，本文提出预约出行的交通组织模式，即借助于创新科技手段，在 城市交通系统中利用预约将现场的拥堵排队转变为虚拟的线上排队，引导出行者按照实际能够 通过的时刻到达瓶颈点。研究认为：预约出行可以在一定程度上缓解出行供需时空不匹配问题， 在有限资源条件下减少在途排队，提高未来城市交通系统运营组织和管理的水平。     

服役转臂节点刚度对运营车辆动力学影响研究

运用SIMPACK软件建立CRH3型高速动车组模型,结合试验获得服役120万km转臂节点刚度变化范围,在考虑2种抗蛇行减振器、3种磨耗踏面与3种钢轨廓形匹配的实际运营工况下,仿真分析服役转臂节点刚度变化对车辆动力学性能的影响。结果表明:120万km服役转臂节点纵向刚度分布范围为90~150 MN/m,主要变化率在-10%~0%之间。在服役转臂节点刚度变化范围内,增大转臂节点纵向刚度可以降低车辆非线性临界速度及运行平稳性,并增大轮轨磨耗,其影响程度与轮轨匹配关系以及抗蛇行减振器种类关系紧密。为保证车辆运行稳定性及平稳性,建议避免使用60N(min-wear)钢轨,适当减小转臂节点刚度以及使用T60抗蛇行减振器。     

A dynamic Bayesian network model for real-time crash prediction using traffic speed conditions data

Traffic crashes occurring on freeways/expressways are considered to relate closely to previous traffic conditions, which are time-varying. Meanwhile, most studies use volume/occupancy/speed parameters to predict the likelihood of crashes, which are invalid for roads where the traffic conditions are estimated using speed data extracted from sampled floating cars or smart phones. Therefore, a dynamic Bayesian network (DBN) model of time sequence traffic data has been proposed to investigate the relationship between crash occurrence and dynamic speed condition data. Moreover, the traffic conditions near the crash site were identified as several state combinations according to the level of congestion and included in the DBN model. Based on 551 crashes and corresponding speed information collected on expressways in Shanghai, China, DBN models were built with time series speed condition data and different state combinations. A comparative analysis of the DBN model using flow detector data and a static Bayesian network model was also conducted. The results show that, with only speed condition data and nine traffic state combinations, the DBN model can achieve a crash prediction accuracy of 76.4% with a false alarm rate of 23.7%. In addition, the results of transferability testing imply that the DBN models are applicable to other similar expressways with 67.0% crash prediction accuracy.