架悬机车驱动装置悬挂参数及结构的研究

采用多体动力学软件SIMPACK建立完整的动力学模型,分析架悬式机车驱动装置悬挂的纵向、横向刚度对其振动加速度和机车平稳性能的影响,发现在10 kN.m-1数量级选择纵、横向刚度,可以显著改善机车的横向性能和电动机工作条件,有效降低横向轮轨力,还可以提高机车的稳定性。认为采用摆杆悬挂是实现弹性架悬的重要结构方式。提出了等效刚度的概念,分析表明在相同的等效刚度下,传统架悬模型和弹性架悬模型的计算结果是吻合的。     

高速铁路接触网动力学响应实验及仿真

对接触网在受电弓/接触网垂向耦合振动过程中的瞬态位移和应力响应进行研究。对某试验接触网进行了动应力测试实验,并与有限元计算结果进行比较,误差在10%以内。利用有限元软件ANSYS建立Re250 0型简单链型悬挂接触网的二维模型,采用模态叠加法进行瞬态仿真分析,得到接触网的位移及应力时间历程。结果说明随着车速提高,接触网振动加剧,动态位移和应力显著增加,对接触网的疲劳和安全可靠性会造成严重影响。     

低速磁浮车辆曲线通过动态响应仿真分析

结合青城山磁浮列车示范线工程,开展低速磁浮车辆动态曲线通过性能研究。建立35个自由度的磁浮车辆空间模型,考虑主动电磁悬浮与导向,仿真分析低速磁浮车辆曲线通过动态响应。计算结果表明,低速磁浮车辆可以60km·h-1速度安全通过半径300m的曲线,以90km·h-1速度平顺通过半径1100m的无超高曲线。低速磁浮车辆小半径曲线通过能力主要受车/轨横向间隙的影响,大半径曲线可不设置超高,但最大通过速度主要受乘坐舒适性的制约。     

重载货车转向架非线性干摩擦力等效线性化及在车桥耦合中的应用

采用基于振动周期内平均功率相等的描述函数法,对重载货车转向架的非线性干摩擦力进行等效线性化,即将斜楔与摇枕间的垂向和横向非线性摩擦力线性等效为垂向和横向线性阻尼力,将旁承和心盘处的回转摩擦阻力矩线性等效为同转摩擦阻尼力,进而建立重载货车的等效线性动力学模型.以C80型重载货车为例,分别采用给出的等效线性动力学模型和非线性动力学模型进行重载货车动力学仿真计算.结果表明:等效线性动力学模型虽然小幅度增大了车体的垂向和横向平稳性指标、轮轨横向作用力、脱轨系数和轮重减载率,但是仍在GB/T 5599-1985标准范围内,满足其各项评价指标要求,验证了用描述函数法对非线性干摩擦力等效线性化的正确性和合理性;说明基于描述函数法的重载货车等效线性动力学模型可以替代复杂的非线性动力学模型进行重载货车通过既有桥梁的车桥耦合动力响应分析,为重载货车的非线性悬挂系统参数优化和车桥耦合动力学性能分析提供了新的有效途径.     

船舶机舱机械通风数值模拟分析和优化设计

某海监船在进行机舱机械通风效用试验时,发现存在舱内气流分布不均、风速梯度变化较大、部分船员操作和活动区域温度偏高等问题。为解决上述问题,利用CFD技术对该船机舱机械通风系统进行数值分析,并提出机舱气流改进方案。随后,选取优化前、后的典型截面进行分析,指出在保证舱内适当负压的情况下可将机械送风改为机械抽风,从而实现机舱进风量增加50%,并消除舱内气流大漩涡和改善气流组织。此外,还提出可在右舷开一个新增风口,以更有效消除气流漩涡,并降低局部温度。结果显示,该优化方案能够满足海船规范要求且施工方便,对于工程设计具有一定的参考价值。     

船-舵-桨的水动力干扰效应研究

基于计算流体力学方法,结合滑移网格技术与动网格技术,实现了带自由面的双桨双舵舰船的直航与斜拖的数值模拟试验,并根据数值模拟所得到的若干数据求取了桨的实效伴流系数ω_p、流入舵的纵向有效速度u_R与有效冲角α_R、操舵诱导横向力关于舵力的修正因子α_H及其距舰船重心的无量纲距离x"_H等船、舵、桨相互干扰系数。数值模拟结果与权威水池试验结果相比对,吻合良好。     

基于流固耦合的隧道遮光棚力学行为研究

结合具体工程,对敞开式拱形遮光棚进行了基于流固耦合的三维数值模拟,得出了遮光棚在风荷载作用下的力学行为,研究结果可为遮光棚结构抗风设计提供依据。     

地铁火灾人员逃生的模拟与分析

本文针对地铁火灾人员逃生这一问题,系统分析了地铁火灾与人员逃生过程与特点,在此基础上,提出了地铁火灾人员疏散时间计算模型。通过实验与软件,对地铁火灾的烟气的温度与扩散进行模拟,确定了地铁火灾人员疏散可用时间,总结了人员疏散所需时间的计算方法。结合了上海某地铁站的实例进行安全性分析,使得本文所提出地铁火灾疏散时间模型得以运用。     

A discrete choice framework for modeling and forecasting the adoption and diffusion of new transportation services

Major technological and infrastructural changes over the next decades, such as the introduction of autonomous vehicles, implementation of mileage-based fees, carsharing and ridesharing are expected to have a profound impact on lifestyles and travel behavior. Current travel demand models are unable to predict long-range trends in travel behavior as they do not entail a mechanism that projects membership and market share of new modes of transport (Uber, Lyft, etc.). We propose integrating discrete choice and technology adoption models to address the aforementioned issue. In order to do so, we build on the formulation of discrete mixture models and specifically Latent Class Choice Models (LCCMs), which were integrated with a network effect model. The network effect model quantifies the impact of the spatial/network effect of the new technology on the utility of adoption. We adopted a confirmatory approach to estimating our dynamic LCCM based on findings from the technology diffusion literature that focus on defining two distinct types of adopters: innovator/early adopters and imitators. LCCMs allow for heterogeneity in the utility of adoption for the various market segments i.e. innovators/early adopters, imitators and non-adopters. We make use of revealed preference (RP) time series data from a one-way carsharing system in a major city in the United States to estimate model parameters. The data entails a complete set of member enrollment for the carsharing service for a time period of 2.5 years after being launched. Consistent with the technology diffusion literature, our model identifies three latent classes whose utility of adoption have a well-defined set of preferences that are significant and behaviorally consistent. The technology adoption model predicts the probability that a certain individual will adopt the service at a certain time period, and is explained by social influences, network effect, socio-demographics and level-of-service attributes. Finally, the model was calibrated and then used to forecast adoption of the carsharing system for potential investment strategy scenarios. A couple of takeaways from the adoption forecasts were: (1) placing a new station/pod for the carsharing system outside a major technology firm induces the highest expected increase in the monthly number of adopters; and (2) no significant difference in the expected number of monthly adopters for the downtown region will exist between having a station or on-street parking.