船舶消防预案研究

文章对船舶火灾特点及灭火原理进行了分析说明,针对船上不同典型部位所发生的火灾,提出了消防预案,可用来指导船员的消防救援。     

大水位差港口客运缆车控制系统方案设计

针对大水位差港口的水文特征和地理特征 ,提出了其客运缆车控制系统的技术方案。该方案运用先进的控制技术和网络通信技术 ,大大加强了对缆车的运行管理和监控力度 ,提高了安全性和可靠性     

寒区公路隧道消防工程设计探讨

分析寒区公路隧道消防工程的现状,提出全新的消防设计方案,以解决寒冷地区冬季消防不可靠、费用高的问题.该设计方案可为类似工程提供一定借鉴.     

考虑交通小区相关性的小汽车拥有行为研究

为分析建成环境对家庭小汽车拥有的影响，考虑交通小区间的空间相关性，分别基于0-1 邻接矩阵、共同边界邻接矩阵及质心空间距离矩阵构建了层次Bayesian 模型，并与不考虑空间相关性的模型结果进行了对比.基于长春居民出行调查数据对模型参数进行估计，结果表明：交通小区间的空间相关性显著存在；以不考虑空间相关性的模型作为对比，基于公共边界邻接矩阵的层次Bayesian 模型拟合效果最优；在控制家庭层面社会经济变量后，居住密度、土地利用混合度、交叉口密度及公共交通站点密度均对家庭小汽车拥有具有显著的负向效应，表明通过优化城市建成环境策略可有效抑制小汽车拥有量的增长.     

An insight into linear quarter car model accuracy

The linear quarter car model is the most widely used suspension system model. A number of authors expressed doubts about the accuracy of the linear quarter car model in predicting the movement of a complex nonlinear suspension system. In this investigation, a quarter car rig, designed to mimic the popular MacPherson strut suspension system, is subject to narrowband excitation at a range of frequencies using a motor driven cam. Linear and nonlinear quarter car simulations of the rig are developed. Both isolated and operational testing techniques are used to characterise the individual suspension system components. Simulations carried out using the linear and nonlinear models are compared to measured data from the suspension test rig at selected excitation frequencies. Results show that the linear quarter car model provides a reasonable approximation of unsprung mass acceleration but significantly overpredicts sprung mass acceleration magnitude. The nonlinear simulation, featuring a trilinear shock absorber model and nonlinear tyre, produces results which are significantly more accurate than linear simulation results. The effect of tyre damping on the nonlinear model is also investigated for narrowband excitation. It is found to reduce the magnitude of unsprung mass acceleration peaks and contribute to an overall improvement in simulation accuracy.     

Indirect method for wheel–rail force measurement and derailment evaluation

Wheel set flange derailment criteria for railway vehicles are derived and the influence of wheel–rail contact parameters is studied. An indirect method for wheel–rail force measurement based on these derailment evaluation criteria is proposed. Laboratory tests for the calibration of strain–force devices on the bearing box are carried out to determine the relationship between the applied force and the measured strain. The simulation package, SIMPACK, is used to develop a passenger car model to generate wheel–rail forces and vibration signals. Different cases are considered in this model to provide an accurate validation of the identified wheel–rail forces. A feasibility test is conducted in the Beijing Loop test line using a passenger car equipped with a set of strain gauges on the wheel set. The comparison of the force time history applied to the instrumented wheel set and that obtained using the indirect method is presented.     

某出口米轨动车组(Tc车)车体设计及试验对比研究

介绍了使用大型通用计算机辅助工程软件ANSYS和有限元方法,对某出口米轨动车组(Tc车)进行了分析计算的过程,并通过实际物理样机试验数据与计算数据的比对,分析车身强度的可靠性。     

地铁车辆段综合管线设计研究

研究目的:综合管线设计是地铁车辆段设计的一大难点,也是地铁车辆设计成功与否的关键点之一。通常综合管线是在各专业完成施工图后才能正式开展工作,因此每次综合管线设计的工期都非常短。目前传统的综合管线设计思路及方法已不能满足设计需要,为了较好地解决综合管线设计周期短、质量要求高的难题,推出新的综合管线设计方法尤为迫切。研究结论:通过地面车辆段、双层车辆段综合管线设计的归纳总结,提出采用反推法设计综合管线。采用反推法设计综合管线,将缩短地铁车辆段综合管线设计周期50%,提高了设计质量的同时还提高了设计人员的综合素质。     

Vertical random vibration analysis of vehicle–track coupled system using Green's function method

A vertical vehicle–track coupled dynamic model, consisting of a high-speed train on a continuously supported rail, is established in the frequency-domain. The solution is obtained efficiently by use of the Green's function method, which can determine the vibration response over a wide range of frequency without any limitations due to modal truncation. Moreover, real track irregularity spectra can be used conveniently as input. The effect of the flexibility of both track and car body on the entire vehicle–track coupled dynamic response is investigated. A multi-body model of a vehicle with either rigid or flexible car body is defined running on three kinds of track: a rigid rail, a track stiffness model and a Timoshenko beam model. The results show that neglecting the track flexibility leads to an overestimation of both the contact force and the whole vehicle vibration response. The car body flexibility affects the ride quality of the vehicle and the coupling through the track and can be significant in certain frequency ranges. Finally, the effect of railpad and ballast stiffness on the vehicle–track coupled vibration is analysed, indicating that the stiffness of the railpad has an influence on the system in a higher frequency range than the ballast.     

Short-term speed predictions exploiting big data on large urban road networks

Big data from floating cars supply a frequent, ubiquitous sampling of traffic conditions on the road network and provide great opportunities for enhanced short-term traffic predictions based on real-time information on the whole network. Two network-based machine learning models, a Bayesian network and a neural network, are formulated with a double star framework that reflects time and space correlation among traffic variables and because of its modular structure is suitable for an automatic implementation on large road networks. Among different mono-dimensional time-series models, a seasonal autoregressive moving average model (SARMA) is selected for comparison. The time-series model is also used in a hybrid modeling framework to provide the Bayesian network with an a priori estimation of the predicted speed, which is then corrected exploiting the information collected on other links. A large floating car data set on a sub-area of the road network of Rome is used for validation. To account for the variable accuracy of the speed estimated from floating car data, a new error indicator is introduced that relates accuracy of prediction to accuracy of measure. Validation results highlighted that the spatial architecture of the Bayesian network is advantageous in standard conditions, where a priori knowledge is more significant, while mono-dimensional time series revealed to be more valuable in the few cases of non-recurrent congestion conditions observed in the data set. The results obtained suggested introducing a supervisor framework that selects the most suitable prediction depending on the detected traffic regimes.