短波顶浪中KVLCC2船模阻力增加CFD计算研究

波浪中航行船舶阻力增加,特别是短波中的阻力增加,是船舶界关注的焦点之一,也是船舶水动力学界研究的热点之一。论文采用基于RANSE的数值波浪水池技术,针对KVLCC2船型,开展了短波顶浪中船舶阻力增加的数值计算研究。与模型试验结果的比较表明,文中的CFD方法能够相当准确地计算短波顶浪中航行船舶的阻力增加;对船体各部分波浪增阻的分析表明,船体艏段产生的波浪增阻占主导地位,艉段的波浪增阻很小,而平行中体段对波浪增阻几乎无贡献。     

短波顶浪中KVLCC2船模阻力增加CFD计算研究

波浪中航行船舶阻力增加,特别是短波中的阻力增加,是船舶界关注的焦点之一,也是船舶水动力学界研究的热点之一。论文采用基于RANSE的数值波浪水池技术,针对KVLCC2船型,开展了短波顶浪中船舶阻力增加的数值计算研究。与模型试验结果的比较表明,文中的CFD方法能够相当准确地计算短波顶浪中航行船舶的阻力增加;对船体各部分波浪增阻的分析表明,船体艏段产生的波浪增阻占主导地位,艉段的波浪增阻很小,而平行中体段对波浪增阻几乎无贡献。     

基于短脉冲信号设计的导流罩模型宽带透声性能测试方法

通过分析传统水声材料透声性能测试方法基本原理,明确声波绕射和模型边缘声衍射引起的测试数据时域混叠,是导致获取的透声系数存在异常值的主要原因。为了消除测试数据时域混叠,本文利用宽频短脉冲设计技术和时域数据截取技术,形成了一种应用声波传递路径中有无模型时,水听器接收到的宽频信号衰减量得到模型透声系数方法,有效解决了导流罩透声性能低频测试数据有效性问题,为导流罩透声设计效果评估提供实验手段。     

A network‐based dynamic air traffic flow model for short‐term en route traffic prediction

This paper presents a dynamic network‐based approach for short‐term air traffic flow prediction in en route airspace. A dynamic network characterizing both the topological structure of airspace and the dynamics of air traffic flow is developed, based on which the continuity equation in fluid mechanics is adopted to describe the continuous behaviour of the en route traffic. Building on the network‐based continuity equation, the space division concept in cell transmission model is introduced to discretize the proposed model both in space and time. The model parameters are sequentially updated based on the statistical properties of the recent radar data and the new predicting results. The proposed method is applied to a real data set from Shanghai Area Control Center for the short‐term air traffic flow prediction both at flight path and en route sector level. The analysis of the case study shows that the developed method can characterize well the dynamics of the en route traffic flow, thereby providing satisfactory prediction results with appropriate uncertainty limits. The mean relative prediction errors are less than 0.10 and 0.14, and the absolute errors fall in the range of 0 to 1 and 0 to 3 in more than 95% time intervals respectively, for the flight path and en route sector level. Copyright © 2017 John Wiley & Sons, Ltd.     

Travel mode identification using bluetooth technology

Bluetooth technology has been widely used in transportation studies to collect traffic data. Bluetooth media access control (MAC) readers can be installed along roadways to collect Bluetooth-based data. This data is commonly used to measure traffic performance. One of the advantages of using Bluetooth technology to measure traffic performance is that travel time can be measured directly with a certain level of error instead of by estimation. However, travel time outliers can commonly be observed due to different travel mode on arterials. Since travel mode information cannot be directly obtained from the raw Bluetooth-based data, a mathematical methodology is in need to identify travel mode. In this study, a genetic algorithm and neural network (GANN)-based model was developed to identify travel mode. GPS-enabled devices were used to collect ground truth travel time. In order to additionally compare the model performance, K nearest neighbor (KNN) and support vector machine (SVM) were also implemented. N-fold cross validation was applied to statistically assess the models’ results. Since the model performances depend on the model inputs, seven collections of model inputs were tested in order to achieve the best travel mode identification performance. An arterial segment with four consecutive links and three intersections was selected to be the study segment. The results suggested that correctly identifying the three travel modes successfully every time was not possible, although the GANN based model had low misidentification rates. In our study, 6.12% of autos were misidentified as bikes and 10.53% of bikes were misidentified as autos using three links.     

短时交通流预测的多层递阶方法

短时交通流预测在城市交通控制和管理中起着十分重要的作用。然而,目前很多预测模型均假定模型的参数是不随时间变化的,这与实际不符,从而影响了预测的精度。本文提出采用多层建模与灰色建模的综合方法预测短时交通流。该方法把预测问题分为两部分:一是预测模型参数的预测;二是根据参数预测值的交通流预测。其中,对模型参数运用灰色理论预测方法。实例分析表明,本方法有较好的预测精度和实用价值。     

预制节段梁模板系统结构设计与系统运作

结合沪闵路高架二期节段梁的预制施工,介绍短线匹配法预制节段梁的模板系统结构设计原则、设计要求和模板系统的构成以及相关操作设备,并介绍了在预制施工过程中该模板系统中固定模板、匹配节段、底模、侧模等的运行方式和所采取精度控制方法。     

牵引电压损失与电力系统短路容量关系的研究

从分析高速客运专线牵引负荷特点入手,结合京津城际工程具体实践,分析了从高速牵引负荷引起的电压损失与系统短路容量的关系、电力系统的电压损失与系统短路容量的关系、高速牵引负荷引起的初步电压不平衡与系统短路容量的关系,得出电力系统短路容量与客运专线牵引变压器的电压损失无关、与电力系统电压损失成反比、与电压不平衡值成反比,从理论和远期规划上提出电力系统短路容量应满足4000MVA以上.     

五河口斜拉桥索塔环向预应力筋张拉试验研究

宿淮高速公路五河口斜拉桥工程,进行了索塔足尺模型试验。针对环向预应力筋张拉伸长值比理论伸长值偏大这一现象,对环向预应力筋的张拉控制进行了研究,测试了6束预应力筋的摩阻系数。同时,还对初始张拉控制应力进行了试验分析,提出了相应的张拉控制程序。最后,对索塔锚固区共290束环向预应力筋实测张拉伸长值结果进行了概率统计分析,证实了理论伸长值修正算法中各项修正的必要性。     

基于长短期记忆神经网络与注意力机制的智能汽车分车型跟驰模型

考虑到跟驰车流中前车车型对智能汽车跟车行为的影响，采用长短期记忆 （Long Short Term Memory，LSTM）神经网络，基于 NGSIM 数据集，通过 One-Hot方法编码车型特征，并引入注意力机制 （Attention Mechanism） 生成输入特征的注意力权重，训练并建立了一种可根据前车车型产生不同跟驰行为的智能车辆跟驰模型 （Identifiable Vehicle Type Car-Following Model，IVT-CF）。在不同前车车型的跟车场景中仿真发现，IVT-CF 模型仿真车辆的速度和位移的均方误差 （Mean Square Error，MSE） 比不分车型的 LSTM 模型分别降低了 23.8%、31.7%，比 IDM 模型分别降低了 15.8%、18.7%，仿真精度更高。在混入大型车辆的车队跟驰场景仿真中发现，交通流速度和车头间距的收敛时间为 92 s，该模型能较快收敛，具有较好的稳定性和抗干扰能力。